Microswimmer

A microswimmer is a microscopic object with the ability to move in a fluid environment. ^[1] Natural microswimmers are found everywhere in the natural world as biological microorganisms, such as bacteria, archaea, protists, sperm and microanimals. Since the turn of the millennium there has been increasing interest in manufacturing synthetic and biohybrid microswimmers. Although only two decades have passed since their emergence, they have already shown promise for various biomedical and environmental applications. ^[1]

Given the recent nature of the field, there is yet no consensus in the literature for the nomenclature of the microscopic objects this article refers to as "microswimmers". Among the many alternative names such objects are given in the literature, microswimmers, microscale swimmers, micro/nanorobots and micro/nanomotors are likely the most frequently encountered. Other common terms may be more descriptive, including information about the object shape, e.g., microtube or microhelix, its components, e.g., biohybrid, spermbot, ^[2] bacteriabot, ^[3] or micro-bio-robot, ^[4] or behavior, e.g., microrocket, microbullet, microtool or microroller. Researchers have also named their specific microswimmers e.g., medibots, ^[5] hairbots, ^[6] iMushbots, ^[7] IRONSperm, ^[8] teabots, ^[9] biobots, ^[10] T-budbots, ^[11] or MOFBOTS. ^{[12] [1]}

Background

In 1828, the British biologist Robert Brown discovered the incessant jiggling motion of pollen in water and described his finding in his article "A Brief Account of Microscopical Observations…", ^[13] leading to extended scientific discussion about the origin of this motion. This enigma was resolved only in 1905, when Albert Einstein published his celebrated essay Über die von der molekularkinetischen Theorie der Wärme geforderte Bewegung von in ruhenden Flüssigkeiten suspendierten Teilchen . ^[14] Einstein not only deduced the diffusion of suspended particles in quiescent liquids, but also suggested these findings could be used to determine particle size — in a sense, he was the world's first microrheologist. ^[15]

Ever since Newton established his equations of motion, the mystery of motion on the microscale has emerged frequently in scientific history, as famously demonstrated by a couple of articles that should be discussed briefly. First, an essential concept, popularized by Osborne Reynolds, is that the relative importance of inertia and viscosity for the motion of a fluid depends on certain details of the system under consideration. ^[15] The Reynolds number Re, named in his honor, quantifies this comparison as a dimensionless ratio of characteristic inertial and viscous forces:

E. M. Purcell

Purcell’s swimming scallop
"Fast or slow, it exactly retraces its trajectory and it's back where it started".

${\displaystyle \mathrm {Re} ={\frac {\rho ul}{\mu }}}$

Here, ρ represents the density of the fluid; u is a characteristic velocity of the system (for instance, the velocity of a swimming particle); l is a characteristic length scale (e.g., the swimmer size); and μ is the viscosity of the fluid. Taking the suspending fluid to be water, and using experimentally observed values for u, one can determine that inertia is important for macroscopic swimmers like fish (Re = 100), while viscosity dominates the motion of microscale swimmers like bacteria (Re = 10⁻⁴). ^[15]

The overwhelming importance of viscosity for swimming at the micrometer scale has profound implications for swimming strategy. This has been discussed memorably by E. M. Purcell, who invited the reader into the world of microorganisms and theoretically studied the conditions of their motion. ^[16] In the first place, propulsion strategies of large scale swimmers often involve imparting momentum to the surrounding fluid in periodic discrete events, such as vortex shedding, and coasting between these events through inertia. This cannot be effective for microscale swimmers like bacteria: due to the large viscous damping, the inertial coasting time of a micron-sized object is on the order of 1 μs. The coasting distance of a microorganism moving at a typical speed is about 0.1 angstroms (Å). Purcell concluded that only forces that are exerted in the present moment on a microscale body contribute to its propulsion, so a constant energy conversion method is essential. ^{[16] [15]}

Microorganisms have optimized their metabolism for continuous energy production, while purely artificial microswimmers (microrobots) must obtain energy from the environment, since their on-board-storage-capacity is very limited. As a further consequence of the continuous dissipation of energy, biological and artificial microswimmers do not obey the laws of equilibrium statistical physics, and need to be described by non-equilibrium dynamics. ^[15] Mathematically, Purcell explored the implications of low Reynolds number by taking the Navier-Stokes equation and eliminating the inertial terms:

${\displaystyle {\begin{aligned}\mu \nabla ^{2}\mathbf {u} -{\boldsymbol {\nabla }}p&={\boldsymbol {0}}\\\end{aligned}}}$

where ${\displaystyle \mathbf {u} }$ is the velocity of the fluid and ${\displaystyle {\boldsymbol {\nabla }}p}$ is the gradient of the pressure. As Purcell noted, the resulting equation — the Stokes equation — contains no explicit time dependence. ^[16] This has some important consequences for how a suspended body (e.g., a bacterium) can swim through periodic mechanical motions or deformations (e.g., of a flagellum). First, the rate of motion is practically irrelevant for the motion of the microswimmer and of the surrounding fluid: changing the rate of motion will change the scale of the velocities of the fluid and of the microswimmer, but it will not change the pattern of fluid flow. Secondly, reversing the direction of mechanical motion will simply reverse all velocities in the system. These properties of the Stokes equation severely restrict the range of feasible swimming strategies. ^{[16] [15]}

As a concrete illustration, consider a mathematical scallop that consists of two rigid pieces connected by a hinge. Can the "scallop" swim by periodically opening and closing the hinge? No: regardless of how the cycle of opening and closing depends on time, the scallop will always return to its starting point at the end of the cycle. Here originated the striking quote: "Fast or slow, it exactly retraces its trajectory and it's back where it started". ^[16] In light of this scallop theorem, Purcell developed approaches concerning how artificial motion at the micro scale can be generated. ^[15] This paper continues to inspire ongoing scientific discussion; for example, recent work by the Fischer group from the Max Planck Institute for Intelligent Systems experimentally confirmed that the scallop principle is only valid for Newtonian fluids. ^{[17] [15]}

Physics

As discussed in the previous section, the motion of microswimmers is controlled by viscosity, meaning the motion is drag-dominant. In addition, the scallop theorem demonstrates microswimmers are unable to rely on time-dependence for movement, requiring them to have more than one degree of freedom. Derivations for the parallel and normal components of drag on simple geometries in creeping flow can be found in literature, ^{[18] [19] [20] [21] [22]} and recorded media, ^[22] notably in spheres:

${\displaystyle F_{sphere}=6\pi \mu ur}$

and spheroids with major and minor axis a, b:

${\displaystyle F_{parallel}=6\pi \left({\frac {4+a/b}{5}}\right)}$

${\displaystyle F_{perpendicular}=6\pi \left({\frac {3+2a/b}{5}}\right)}$

Due to the linear nature of the governing fluid equations, the superposition principle may be used to model more complex geometries, such as corkscrews, following the analysis of Purcell and others. For example, the drag and torque on the helical coil is as follows: ^[18]

${\displaystyle {\begin{bmatrix}F\\T\end{bmatrix}}={\begin{bmatrix}a&b\\b&c\end{bmatrix}}{\begin{bmatrix}u\\\omega \end{bmatrix}}}$

${\displaystyle a=2\pi n\sigma \left({\frac {\xi _{\parallel }\cos ^{2}(\theta )+\xi _{\perp }\sin ^{2}(\theta )}{\sin(\theta )}}\right)}$

${\displaystyle b=2\pi n\sigma ^{2}(\xi _{\parallel }-\xi _{\perp })\cos(\alpha )}$

${\displaystyle c=2\pi n\sigma ^{3}\left({\frac {\xi _{\parallel }\sin ^{2}(\theta )+\xi _{\perp }\cos ^{2}(\theta )}{\sin(\theta )}}\right)}$

${\displaystyle \xi _{\parallel }={\frac {2\pi \mu }{\ln \left({\frac {0.36\pi \sigma }{r\sin(\theta )}}\right)}},\xi _{\perp }={\frac {4\pi \mu }{\ln \left({\frac {0.36\pi \sigma }{r\sin(\theta )+0.5}}\right)}}}$

Where ${\displaystyle \theta =\arctan({\frac {2\pi \sigma }{\lambda }})}$. It is important to note that while the scallop theorem requires more than one degree of freedom, external forcing (e.g., magnetic) allows for the motion of a simple corkscrew.

Types

Different types of microswimmers are powered and actuated in different ways. Swimming strategies for individual microswimmers  ^{[3] [23] [24] [25] [26] [27]} as well as swarms of microswimmers  ^{[28] [29] [30] [31] [32] [33]} have been examined down through the years. Typically, microswimmers rely either on external power sources, as it is the case for magnetic, ^[34] optic, ^[10] or acoustic control, ^[35] or employ the fuel available in their surroundings, as is the case with biohybrid or catalytic microswimmers. Magnetic and acoustic actuation are typically compatible with in vivo microswimmer manipulation and catalytic microswimmers can be specifically engineered to employ in vivo fuels. The use of optical forces in biological fluids or in vivo is more challenging, but interesting examples have nevertheless been demonstrated. ^[10]

Often, researchers choose to take inspiration from nature, either for the entire microswimmer design, or for achieving a desired propulsion type. For example, one of the first bioinspired microswimmers consisted of human red blood cells modified with a flagellum-like artificial component made of filaments of magnetic particles bonded via biotin–streptavidin interactions. ^[36] More recently, biomimetic swimming inspired by worm-like travelling wave features, ^[37] shrimp locomotion, ^[38] and bacterial run-and-tumble motion, ^[39] was demonstrated by using shaped light. ^[10]

A different nature-inspired approach is the use of biohybrid microswimmers. These comprise a living component and a synthetic one. Biohybrids most often take advantage of the microscale motion of various biological systems and can also make use of other behaviours characterising the living component. ^[40] For magnetic bioinspired and biohybrid microswimmers, typical model organisms are bacteria, sperm cells and magnetotactic cells. ^[41] In addition to the use of magnetic forces, actuation of bioinspired microswimmers was also demonstrated using e.g., acoustic excitation  ^[42] or optical forces. ^[43] Another nature-inspired behavior related to optical forces is that of phototaxis, which can be exploited by e.g., cargo-carrying microorganisms, ^[44] synthetic microswimmers  ^{[45] [46] [47]} or biohybrid microswimmers. ^[48] A number of recent review papers are focused on explaining or comparing existing propulsion and control strategies used in microswimmer actuation. ^{[49] [50] [51] [52] [53]} Magnetic actuation is most often included for controlled in vivo guiding, even for microswimmers which rely on a different type of propulsion. In 2020, Koleoso et al. reviewed the use of magnetic small scale robots for biomedical applications and provide details about the various magnetic fields and actuation systems developed for such purposes. ^{[34] [1]}

Strategies for the fabrication of microswimmers include two-photon polymerisation 3D printing, photolithography, template-assisted electrodeposition, or bonding of a living component to an inanimate one by exploiting different strategies. More recent approaches exploit 4D printing, which is the 3D printing of stimuli-responsive materials. ^{[54] [55] [56] [57]} Further functionalization is often required, either to enable a certain type of actuation, e.g., metal coating for magnetic control or thermoplasmonic responses, or as part of the application, if certain characteristics are required for e.g., sensing, cargo transport, controlled interactions with the environment, or biodegradation. ^{[58] [59] [60] [61] [1]}

Microswimmers can also be categorized by their propulsion methods, ^[62] and two primary methods are used: self-propulsion and external-field propulsion. In self-propulsion, a chemical fuel is coated over the robot that reacts with the liquid environment to create bubbles that propel the robot. External-field propulsion offers more variety, using optical, magnetic, acoustic, or electric fields. External field is better suited for biological applications as it will not need chemical fuels that produce pollutants that may be harmful to the host that the microswimmers are servicing including films and chemicals that may be biocompatible. This propulsive method also provides higher spatial resolution and more controllability, with recent advancements enabling three-dimensional movement enhancing the flexibility and functionality of microswimmers.

Natural microswimmers

Further information: Bacterial motility and Protist locomotion

Changes in speed and Reynolds number with length of swimmer 

Natural microswimmers
Drawing of Chlamydomonas reinhardtii alga in a co-culture with Escherichia coli bacteria 

Motile systems have developed in the natural world over time and length scales spanning several orders of magnitude, and have evolved anatomically and physiologically to attain optimal strategies for self-propulsion and overcome the implications of high viscosity forces and Brownian motion, as shown in the diagram on the right. ^{[64] [15]}

Some of the smallest known natural motile systems are motor proteins, i.e., proteins and protein complexes present in cells that carry out a variety of physiological functions by transducing chemical energy into mechanical energy. These motor proteins are classified as myosins, kinesins, or dyneins. Myosin motors are responsible for muscle contractions and the transport of cargousing actin filaments as tracks. Dynein motors and kinesin motors, on the other hand, use microtubules to transport vesicles across the cell. ^{[65] [66]} The mechanism these protein motors use to convert chemical energy into movement depends on ATP hydrolysis, which leads to a conformation modification in the globular motor domain, leading to directed motion. ^{[67] [68] [15]}

Apart from motor proteins, enzymes, traditionally recognized for their catalytic functions in biochemical processes, can function as nanoscale machines that convert chemical energy into mechanical action at the molecular dimension. Diffusion of various enzymes (e.g. urease, and catalase), measured by fluorescent correlated spectroscopy (FCS), increases in a substrate-dependent manner. ^{[69] [70]} Moreover, when enzymes are membrane-bound, their catalytic actions can drive lipid vesicle movement. For instance, lipid vesicles integrated with enzymes such as transmembrane adenosine 5’-triphosphatase, membrane-bound acid phosphatase, or urease exhibit enhanced mobility correlating with the enzymatic turnover rate. ^[71]

Bacteria can be roughly divided into two fundamentally different groups, gram-positive and gram-negative bacteria, distinguished by the architecture of their cell envelope. In each case the cell envelope is a complex multi-layered structure that protects the cell from its environment. In gram-positive bacteria, the cytoplasmic membrane is only surrounded by a thick cell wall of peptidoglycan. By contrast, the envelope of gram-negative bacteria is more complex and consists (from inside to outside) of the cytoplasmic membrane, a thin layer of peptidoglycan, and an additional outer membrane, also called the lipopolysaccharide layer. Other bacterial cell surface structures range from disorganised slime layers to highly structured capsules. These are made from secreted slimy or sticky polysaccharides or proteins that provide protection for the cells and are in direct contact with the environment. They have other functions, including attachment to solid surfaces. Additionally, protein appendages can be present on the surface: fimbriae and pili can have different lengths and diameters and their functions include adhesion and twitching motility. ^{[72] [73] [15]}

Specifically, for microorganisms that live in aqueous environments, locomotion refers to swimming, and hence the world is full of different classes of swimming microorganisms, such as bacteria, spermatozoa, protozoa, and algae. Bacteria move due to rotation of hair-like filaments called flagella, which are anchored to a protein motor complex on the bacteria cell wall. ^[15]

The following table, based on Schwarz et al., 2017, ^[74] lists some examples of natural or biological microswimmers.

Motile microorganisms

	Name	Image	Size (μm2)a	Speed (μm/s)b	Propulsion mechanism	Natural swimming habitat	Sources
bacterial swimmers (prokaryotes)	Escherichia coli		0.5 × 2	30	Peritrichous bundles	Intestinal flora	[75]
	Serratia marcescens		1 × 2	50	Peritrichous bundles	Respiratory and urinary tracts (parasitic)	[76]
	Salmonella typhimurium		0.5 × 2	30	Peritrichous bundles	Intestines (parasitic)	[77]
	Bacillus subtilis		1 × 3	20	Peritrichous bundles	Intestinal flora	[78]
	Aliivibrio fischeri		1 × 2	50	Lophotrichous flagella	Mucus (symbiotic)	[79]
	Vibrio alginolyticus		2 × 3	40	Monotrichous flagellum	Blood (parasitic)	[80] [81]
	Listeria monocytogenes		0.5 × 1.5	<1	Peritrichous or amphitrichous bundles	Inter- and intracellular (parasitic)	[82] [83]
	Magnetococcus marinus		2 × 2	200	Two lophotrichous bundles	Marine water	[84] [85]
	Magnetospirillum gryphiswaldense		0.5 × 2	60	Two amphitrichous flagella	Freshwater sediments	[86]
	Mycoplasma mobile		0.5 × 0.5	5	Gliding via protrusions	Fish gills (parasitic)	[87]
protist swimmers (unicellular) eukaryotes)	Chlamydomonas		10 × 10	150	Two lophotrichous flagella	Freshwater, soil	[88]
	Tetrahymena		25 × 50	>500	Holotrichous cilia	Freshwater	[89]
	Trypanosome		3 × 20	30	Monotrichous flagellum	Blood (parasitic)	[90] [91]
sperm cells	Human		3 × 5	50	Monotrichous flagellum	Reproductive tract	[92] [93]
	Bovine		5 × 10	100	Monotrichous flagellum	Reproductive tract	[93] [94] [95]
	Murine		3 × 8	120	Monotrichous flagellum	Reproductive tract	[92] [94]

Synthetic microswimmers

"An artificial microswimmer is a cutting-edge technology with engineering and medical applications. A natural microswimmer, such as bacteria and sperm cells, also play important roles in wide varieties of engineering, medical and biological phenomena. Due to the small size of the microswimmer, the inertial effect of the surrounding flow field may be negligible. In such a case, reciprocal body deformation cannot induce migration of a swimmer, which is known as the scallop theorem. To overcome the implications of the scallop theorem, the microswimmer needs to undergo a nonreciprocal body deformation to achieve migration. The swimming strategy is thus completely different from macro-scale swimmers...". ^[96]

Under light fields, polystyrene/gold Janus particles are set to swim and rotate alternatively such that they follow a predefined path 

One of the current engineering challenges is to create miniaturized functional vehicles that can carry out complex tasks at a small scale that would be otherwise impractical, inefficient, or outright impossible by conventional means. These vehicles are termed nano/micromotors or nano/microrobots, and should be distinguished from even smaller molecular machines for energy, computing, or other applications on the one side and static microelectromechanical systems (MEMS) on the other side of this size scale. Rather than being electronic devices on a chip, micromotors are able to move freely through a liquid medium while being steered or directed externally or by intrinsic design, which can be achieved by various mechanisms, most importantly catalytic reactions, ^{[98] [99] [100] [101]} magnetic fields, ^{[102] [103]} or ultrasonic waves. ^{[104] [105] [106] [107] [108]}

There are a variety of sensing, actuating, or pickup-and-delivery applications that scientists are currently aiming for, with local drug targeting for cancer treatment being one of the more prominent examples. ^{[109] [5]} For applications like this, a micromotor needs to be able to move, i.e., to swim, freely in three dimensions efficiently controlled and directed with a reliable mechanism. ^[74]

It is a direct consequence of the small size scale of microswimmers that they have a low Reynolds number. This means the physics of how microswimmers swim is dominated by viscous drag forces, a problem which has been discussed extensively by physicists in the field. ^{[106] [110] [64]} This kind of swimming has challenged engineers as it is not commonly experienced in everyday life, but can nonetheless be observed in nature for motile microorganisms like sperm or certain bacteria. Naturally, these microorganisms served as inspiration from the very beginning to create artificial micromotors, as they were able to tackle the challenges that an active, self-sufficient microswimmer vehicle has to face. ^[111] With biomimetic approaches, researchers were able to imitate the flagella-based motion strategy of sperm and Escherichia coli bacteria by reproducing their respective flagellum shape and actuating it with magnetic fields. ^{[36] [112] [74] [15]}

Synthetic microswimmers are designed in a wide variety of shapes depending on the applications that they are used for.  Similar to natural microswimmers there is an energy cost associated with the movement and control of a microswimmer.  In nature, it is observed that micron-sized bacteria expend very little energy while larger microorganisms expend more energy. ^{[113] [114]}   This principle can be translated to synthetic microswimmers where the connection between the sizing and shape of a microswimmer and the energy spent has been studied by researchers. ^[114]   Piro et al. argue that needle-like microswimmers are more energy efficient than other shapes, while disk-like microswimmers are carried by flow gradients in the liquid and will be naturally predisposed to follow a time-optimal trajectory. ^[114]   Helical microswimmers have also gained interest as a geometrical shape for microswimmers due to being bioinspired by microstructures within many different types of plants that serve as water vessels. ^[115]

At the microscale, surface wettability is an important consideration in the material selection due to affecting the locomotion of the microswimmer.  Hydrophobic surfaces produce a large contact angle with the liquid which has the effect of exerting less frictional drag torque on the microswimmer body resulting in a lower step-out frequency required for movement. ^[116]

Microorganisms have adapted their locomotion to the harsh environment of low Reynolds number regime by invoking different swimming strategy. ^[117] For example, the E. coli moves by rotating its helical flagellum, ^{[118] [119]} Chlamydomonas flagella have a breaststroke kind of motion. ^[120] African trypanosome has a helical flagellum attached to the cell body with a planar wave passing through it. ^{[121] [122]} Swimming of these kind of natural swimmers have been investigated for the last half-century. ^[123] As a result of these studies, artificial swimmers have also been proposed, like Taylor sheet, ^[124] Purcell's two-hinge swimmer, ^{[16] [125]} three-linked spheres swimmer, ^{[126] [127] [128]} elastic two-sphere swimmer  ^[129] and three-sphere with a passive elastic arm, ^[130] which have further enhanced understanding about low Reynolds number swimmers. One of the challenges in proposing an artificial swimmer lies in the fact that the proposed movement stroke should not be reciprocal otherwise it cannot propel itself due to the Scallop theorem. In Scallop theorem, Purcell had argued that a swimmer with one-hinge or one degree of freedom is bound to perform reciprocal motion and thus will not be able to swim in the Stokes regime. ^{[117] [16] [123]}

Purcell proposed two possible ways to elude from Scallop theorem, one is 'corkscrew' motion  ^{[118] [111]} and the other is 'flexible oar' motion. ^{[131] [132]} Using the concept of flexible oar, Dreyfus et al reported a micro swimmer that exploit elastic property of a slender filament made up of paramagnetic beads. ^[36] To break the time inversion symmetry, a passive head was attached to the flexible arm. The passive head reduces the velocity of the flexible swimmer, bigger the head, higher is the drag force experienced by the swimmer. The head is essential for swimming because without it the tail performs a reciprocal motion and the velocity of the swimmer reduces to zero. ^{[133] [123]}

In a study by Huang et al. ^[134] microswimmers were placed in a sucrose solution to represent a viscosity that is similar to blood and tested different microswimmers and their ability to propel within the fluid using variants of the corkscrew and flexible oar techniques at different angles of alignment with an external magnetic field.  Due to the misalignment a helical motion was produced for the flexible oar and corkscrew case.  Under this test, the propulsion method that performed the fastest was the microswimmer with the tubular body and flexible planar tail due to taking advantage of the helical and corkscrew motion generated at an angle of 30-degree misalignment from the external magnetic field.  Microswimmers relying on the corkscrew motion had a reduced speed due to an increase in the drag experienced by the microswimmer due to the wobbling motion on the body.  However, when the microswimmer’s body is perpendicular to the external magnetic field the mobility of the flexible oar microswimmer was reduced due to a lack of the body’s helical motion while the wobbling effect on the corkscrew microswimmer was reduced so it was able to achieve better motion.

The corkscrew and flexible oar motion of a synthetic microswimmer can be largely affected by the viscosity of the fluid.  An increase in viscosity decreases the motion of the microswimmer using either method, however, the decrease in movement at higher viscosity is larger for microswimmers using the flexible oar propulsion system.  This is due to the reduction in helical motion of the body of the microswimmer causing an increase in the drag experienced by the body.  Another effect experienced is a reduction in the bending of the tail which reduces the microswimmer’s ability to overcome the time-reversal symmetry.

Another way microswimmers can propel is through catalytic reactions. Taking inspiration from Whitesides, who used the decomposition of hydrogen peroxide (H₂O₂) to propel cm/mm-scale objects on a water surface, ^[135] Sen et al. (2004) fabricated catalytic motors in the micrometer range. ^[98] These microswimmers were rod-shaped particles 370 nm in diameter and consisted of 1 μm long Pt and Au segments. They propelled via the decomposition of hydrogen peroxide in solution which would be catalyzed into water and oxygen. The Pt/Au rods were able to consistently reach speeds of up to 8 μm/s in a solution of 3.3% hydrogen peroxide. The decomposition of hydrogen peroxide in the Pt side produces oxygen, two protons and two electrons. The two protons and electrons will travel towards the Au, where they will be used to react with another hydrogen peroxide molecule, to produce two water molecules. The movements of the two protons and the two electrons through the rod drag the fluid towards the Au side, thus this fluid flow will propel the rod in the opposite direction. This self-electrophoresis mechanism is what powers the motion of these rods. ^[99] Further analysis of the Pt/Au rods showed that they were capable of performing chemotaxis towards higher hydrogen peroxide concentrations, ^[100] transport cargo, ^[101] and exhibited steerable motion in an external magnetic field when inner Ni segments were added. ^[101]

Interest has been shown in using high-frequency sound waves for microswimmer navigation due to being cleared as safe for clinical studies by the U.S. Food and Drug Administration which would allow them to be used in biomedical applications. ^[136]   The microswimmer is designed to have a hydrophobic surface from being manufactured with a resin and small cavities which produce an air bubble when immersed in a liquid.  When the high-frequency sound waves are applied to the microswimmer the bubble produces oscillations and generates enough movement to propel the microswimmer in a controlled direction.  

An additional method that microswimmers can travel is through a response to a difference in temperature.  Huang et al. ^[137] designed a microswimmer to study the control of the shape of a 3D microswimmer.  The microswimmer contains a rigid, non-expandable poly (ethylene glycol) diacrylate (PEGDA) combined with an N-isopropylacrylamide (NIPAAm) hydrogel layer that is thermally responsive.  Within the hydrogel layer, there are magnetic nanoparticles that can control the folding axes.  Through the alignment of the magnetic particles along the folding axes, the change in temperature can cause an active control of the microswimmer shape to propel in a fluid.  Another example of thermal-based control is through the use of a pNIPAM-AAc hydrogel embedded with iron oxide which can be controlled through a magnetic field. ^[138]   Through the combination of magnetic fields and temperature-responsive materials, a dynamic control can be achieved.

Movement without external forces has been demonstrated for microswimmers.  Bioinspired by microvelia beetles which are capable of gliding on the water at high speeds, microswimmers are proposed to take advantage of the Marangoni effect which is the mass transfer across a gradient of the surface tension for a fluid.  Choi et al. demonstrated that photopatterned microswimmers without any mechanical actuation system or external force are capable of traversing a fluid through a polyvinyl alcohol (PVA) fuel source which causes the surface tension of the water once it is dissolved. ^[139]

Responding to stimuli

Symmetric self-thermophoretic active particle 
scale bar has a length of 1 μm

Reconfigurable synthetic or artificial microswimmers need internal feedback ^[141] Self-propelling microparticles are often proposed as synthetic models for biological microswimmers, yet they lack the internally regulated adaptation of their biological counterparts. Conversely, adaptation can be encoded in larger-scale soft-robotic devices but remains elusive to transfer to the colloidal scale. ^[141]

The ubiquity and success of motile bacteria are strongly coupled to their ability to autonomously adapt to different environments as they can reconfigure their shape, metabolism, and motility via internal feedback mechanisms. ^{[142] [143]} Realizing artificial microswimmers with similar adaptation capabilities and autonomous behavior might substantially impact technologies ranging from optimal transport to sensing and microrobotics. ^[144] Focusing on adaptation, existing approaches at the colloidal scale mostly rely on external feedback, either to regulate motility via the spatiotemporal modulation of the propulsion velocity and direction  ^{[145] [140] [146] [147]} or to induce shape changes via the same magnetic or electric fields, ^{[148] [149] [150]} which are also driving the particles. On the contrary, endowing artificial microswimmers with an internal feedback mechanism, which regulates motility in response to stimuli that are decoupled from the source of propulsion, remains an elusive task. ^[141]

A promising route to achieve this goal is to exploit the coupling between particle shape and motility. For example, through simple shape design, platinum micromotors can execute a variety of motion trajectories, from highly linear to orbital, when exposed to hydrogen peroxide. ^[151] Efficient switching between different propulsion states can, for instance, be reached by the spontaneous aggregation of symmetry-breaking active clusters of varying geometry, ^{[152] [153] [154] [155]} albeit this process does not have the desired deterministic control. Conversely, designing colloidal clusters with fixed shapes and compositions offers fine control on motility  ^{[156] [157] [158]} but lacks adaptation. Although progress on reconfigurable robots at the sub-millimeter scale has been made, ^{[159] [160] [161] [162] [163]} downscaling these concepts to the colloidal level demands alternative fabrication and design. Shape-shifting colloidal clusters reconfiguring along a predefined pathway in response to local stimuli  ^[164] would combine both characteristics, with high potential toward the vision of realising adaptive artificial microswimmers. ^[141]

Biohybrid microswimmers

Main article: Biohybrid microswimmer

Types of bacterial biohybrid microswimmers 

Bacterial biohybrid microswimmers development 
capture, delivery, sensing, and release

The so-called biohybrid microswimmer can be defined as a microswimmer that consist of both biological and artificial parts, for instance, one or several living microorganisms attached to one or various synthetic parts. The biohybrid approach directly employs living microorganisms to be a main component or modified base of a functional microswimmer. ^{[167] [168]} Initially microorganisms were used as the motor units for artificial devices, but in recent years this role has been extended and modified toward other functionalities that take advantage of the biological capabilities of these organisms considering their means of interacting with other cells and living matter, specifically for applications inside the human body like drug delivery or fertilisation. ^{[169] [170] [74]}

A distinct advantage of microorganisms is that they naturally integrate motility and various biological functions in a conveniently miniaturised package, coupled with autonomous sensing and decision-making capabilities. They are able to adapt and thrive in complex in vivo environments and are capable of self-repair and self-assembly upon interaction with their surroundings. In that sense, self-sufficient microorganisms naturally function very similar to what we envision for artificially created microrobots: They harvest chemical energy from their surroundings to power molecular motor proteins that serve as actuators, they employ ion channels and microtubular networks to act as intracellular wiring, they rely on RNA or DNA as memory for control algorithms, and they feature an array of various membrane proteins to sense and evaluate their surroundings. All these abilities act together to allow microbes to thrive and pursue their goal and function. In principle, these abilities also qualify them as biological microrobots for novel operations like theranostics, the combination of diagnosis and therapy, if we are able to impose such functions artificially, for example, by functionalisation with therapeutics. Further, artificial extensions may be used as handles for external control and supervision mechanisms or to enhance the microbe's performance to guide and tailor its functions for specific applications. ^[74]

In fact, the biohybrid approach can be conceived in a dualistic way, with respect to the three basic ingredients of an in vivo microrobot, which are motility, control, and functionality. Figure 1 illustrates how these three ingredients can be either realized biologically, i.e., by the microorganism, or artificially, i.e., by the synthetic component. For example, a hybrid biomicromotor based on a sperm cell can be driven by the flagellum of the sperm or by an attached artificial helical flagellum. ^{[171] [172]} It can orient itself autonomously via biological interactions with its surroundings and other cells, or be controlled and supervised externally via artificial sensors and actuators. Finally, it can carry out a biological function, like its inherent ability to fertilize an egg cell, or an artificially imposed function, like the delivery of synthetic drugs or DNA vectors. A biohybrid device may deploy any feasible combination of such biological and artificial components in order to carry out a specific application. ^[74]

Another example of the biohybrid approach is by designing a microswimmer that is designed for studying skeletal muscle stimulation.  The microswimmer is fabricated from an Iron Oxide nanoparticles being dip-coated with chlorella microalgae allowing for the microrobot to be used in different biological environments while still having a high degree of control due to the superparamagnetic nanoparticles. ^[62]   Guided by an external magnetic field the microswimmer is capable of reaching its target.  The microswimmer can be safely irradiated with a near-infrared (NIR) laser causing the nanoparticles to be heated through a photothermal effect and to trigger a targeted contraction in the skeletal muscle.  This technique demonstrates a safe and controllable method for movement within a biological environment.

Navigation

Hydrodynamics can determine the optimal route for microswimmer navigation ^[173] Compared to the well explored problem of how to steer a macroscopic agent, like an airplane or a moon lander, to optimally reach a target, optimal navigation strategies for microswimmers experiencing hydrodynamic interactions with walls and obstacles are far-less understood. ^[173] Furthermore, hydrodynamic interactions in suspensions of microswimmers produce complex behavior. ^{[174] [175]} The quest on how to navigate or steer to optimally reach a target is important, e.g., for airplanes to save fuel while facing complex wind patterns on their way to a remote destination, or for the coordination of the motion of the parts of a space-agent to safely land on the moon. These classical problems are well-explored and are usually solved using optimal control theory. ^[176] Likewise, navigation and search strategies are frequently encountered in a plethora of biological systems, including the foraging of animals for food, ^[177] or of T cells searching for targets to mount an immune response. ^[178]

There is growing interest in optimal navigation problems and search strategies  ^{[179] [180] [181] [182] [183] [184]} of microswimmers  ^{[64] [110] [185] [186]} and "dry" active Brownian particles, ^{[187] [106] [188] [189] [173]} The general problem regarding the optimal trajectory of a microswimmer which can freely steer but cannot control its speed toward a predefined target (point-to-point navigation) can be referred to as "the optimal microswimmer navigation problem". The characteristic differences between the optimal microswimmer navigation problem and conventional optimal control problems for macroagents like airplanes, cruise-ships, or moon-landers root in the presence of a low-Reynolds-number solvent in the former problem only. They comprise (i) overdamped dynamics, (ii) thermal fluctuations, and (iii) long-ranged fluid-mediated hydrodynamic interactions with interfaces, walls, and obstacles, all of which are characteristic for microswimmers. ^[106] In particular, the non-conservative hydrodynamic forces which microswimmers experience call for a distinct navigation strategy than the conservative gravitational forces acting, e.g. on space vehicles. Recent work has explored optimal navigation problems of dry active particles (and particles in external flow fields) accounting for (i) and partly also for (ii). Specifically recent research has pioneered the use of reinforcement learning  ^{[190] [191] [192]} such as determining optimal steering strategies of active particles to optimally navigate toward a target position  ^{[179] [180] [183] [184]} or to exploit external flow fields to avoid getting trapped in certain flow structures by learning smart gravitaxis. ^[193] Deep reinforcement learning has been used to explore microswimmer navigation problems in mazes and obstacle arrays  ^[194] assuming global  ^[180] or only local  ^[181] knowledge of the environment. Analytical approaches to optimal active particle navigation  ^{[182] [183]} complement these works and allow testing machine-learned results. ^{[183] [184] [173]}

An example of a successful machine learning locomotion being used in navigation is by Zou et al. where it was inspired by microorganisms having the ability to naturally switch between locomotory gaits such as a run-and-tumble or a roll-and-flick motion depending on the need to navigate the environment. ^[195]   The artificial intelligence system allowed for the development of distinct gaits for steering, transition, and translational movement.

Applications

As is the case for microtechnology and nanotechnology in general, the history of microswimmer applications arguably starts with Richard Feynman’s famous lecture There's Plenty of Room at the Bottom . ^[196] In the visionary speech, among other topics, Feynman addressed the idea of microscopic surgeons, saying: "...it would be interesting in surgery if you could swallow the surgeon. You put the mechanical surgeon inside the blood vessel and it goes into the heart and <<looks>> around (of course the information has to be fed out). It finds out which valve is the faulty one and takes a little knife and slices it out. Other small machines might be permanently incorporated in the body to assist some inadequately-functioning organ." The concept of the surgeon one could swallow was soon after presented in the science-fiction movie Fantastic Voyage and in Isaac Asimov’s writings. ^[1]

Magnetotactic bacteria, such as Magnetococcus marinus , as potential drug-carriers capable of penetrating a tumour 

Only a few decades later, microswimmers aiming to become true microscale surgeons evolved from an intriguing science-fiction concept to a reality explored in many research laboratories around the world, as already highlighted by Metin Sitti in 2009. ^{[198] [1]} These active agents that can self-propel in a low Reynolds number environment might play a key role in the future of nanomedicine, as popularised in 2016 by Yuval Noah Harari in Homo Deus: A Brief History of Tomorrow . ^[199] In particular, they might become useful for the targeted delivery of genes  ^[200] or drugs  ^{[201] [202]} and other cargo  ^{[203] [204]} to a certain target (e.g. a cancer cell) through our blood vessels, requiring them to find a good, or ideally optimal, path toward the target avoiding, e.g., obstacles and unfortunate flow field regions. ^[173]

Already in 2010, Nelson et al. reviewed the existing and envisioned applications of microrobots in minimally invasive medicine. ^[205] Since then, the field has grown, and it has become clear that microswimmers have much potential for biomedical applications. ^[1] Already, many interesting tasks can be performed in vitro using tailored microswimmers. Still, as of 2020, a number of challenges regarding in vivo control, biocompatibility and long-term biosafety need to be overcome before microswimmers can become a viable option for many clinical applications. ^{[206] [1]}

A schematic representation of the classification of biomedical applications is shown in the diagram on the left below. This includes the use of microswimmers for cargo transport in drug delivery and other biomedical applications, as well as assisted fertilisation, sensing, micromanipulation and imaging. Some of the more complex microswimmers fit into multiple categories, as they are applied simultaneously for e.g., sensing and drug delivery. ^[1]

Biomedical applications of microswimmers 

Essentials for a microswimmer to function
with medical interventional capabilities 

The design of an untethered microscopic mobile machine or microrobot to function in vivo with medical interventional capabilities should assume an integrated approach where design 3D body shape, material composition, manufacturing technique, deployment strategy, actuation and control methods, imaging modality, permeation of biological barriers, and the execution of the prescribed medical tasks need to be considered altogether, as illustrated in the diagram on the right above. Each of these essential aspects contains a special design consideration, which must be reflected at the physical design of the microrobot. ^[207]

Delivering therapeutic agents to precise locations in deep tissue remains a significant challenge since magnetic actuation becomes less effective as the magnetic flux density weakens farther away from the electromagnetic control platform. Biohybrid microswimmers have demonstrated promise in drug delivery, capable of precise drug delivery to deep tissue cancer tumors, and one such example is magnetostatic bacteria. Magnetostatic bacteria (MTB) were discovered in the 1970s, and since then, their mechanism and dynamics have been heavily studied. Microaerophilic alphaproteobacterium Magnetospirillum gryphiswaldense is one of the most well-characterized magnetostatic bacteria, and it contains a prominent amount of magnetite Fe3O4 which acts internal compass that guides the bacteria with the surrounding magnetic field. ^[208] A recent study by Mirkhani N, et al. demonstrated drug delivery using rotating magnetic fields (RMF) controlled magnetostatic bacteria (MTB) on a mouse tumor model. ^[209] RMF concentrates magnetic torque density within specific regions by inducing a magnetostatic selection field with a field-free point or field-free line. Experimental validation using the mouse tumor model has confirmed the efficacy of the RMF control in enhancing the translational velocity and the penetration of MTB into deep tissues. This strategy holds the potential for systemic drug delivery with heightened spatial selectivity.

Another proposed application is for microswimmers to help improve the protection of the environment by reducing the amount of waste and pollutants in different parts of the environment.  Some examples of pollutants that can be reduced or removed from the field include microplastics, oil-based chemicals, and other waste.  A wide variety of microswimmers are being developed in research labs for this purpose using different variants of actuation systems using different multiphysics including light, magnetic, and chemical gradients. ^[210]   One example is the removal of Bisphenol A (BPA) which is a common waste product from factories producing plastics-based products.  One example is a microswimmer developed by Dekanovsky et al. using a Mxene-based microswimmer which is controlled by light. The propulsion system has two components one being the Mxene component grafted with nanoparticles and an iron oxide layer. The components within the propulsion system produce a chemical reaction with BPA where the products produce oxygen bubbles which can propel the microswimmer forward. ^[211]   Research is being conducted on designing synthetic microswimmers to react with other waste chemicals to reduce pollution in the environment.  Many of the current synthetic microswimmers are being designed to use a multiphysics propulsion system with a magnetic force along with a chemical or optical-based system.

See also

• Bioinspiration
• Bio-inspired robotics
• Bio-inspired engineering
• Gray goo
• Robotic sperm
• Soft robotics
• Squirmer

References

1. Bunea, Ada-Ioana; Taboryski, Rafael (2020). "Recent Advances in Microswimmers for Biomedical Applications". Micromachines. 11 (12): 1048. doi: 10.3390/mi11121048 . PMC   7760273 . PMID   33261101. Material was copied from this source, which is available under a Creative Commons Attribution 4.0 International License.
2. Medina-Sánchez, Mariana; Schwarz, Lukas; Meyer, Anne K.; Hebenstreit, Franziska; Schmidt, Oliver G. (2016). "Cellular Cargo Delivery: Toward Assisted Fertilization by Sperm-Carrying Micromotors". Nano Letters. 16 (1): 555–561. Bibcode:2016NanoL..16..555M. doi:10.1021/acs.nanolett.5b04221. PMID   26699202.
3. Schauer, Oliver; Mostaghaci, Babak; Colin, Remy; Hürtgen, Daniel; Kraus, David; Sitti, Metin; Sourjik, Victor (2018). "Motility and chemotaxis of bacteria-driven microswimmers fabricated using antigen 43-mediated biotin display". Scientific Reports. 8 (1): 9801. Bibcode:2018NatSR...8.9801S. doi:10.1038/s41598-018-28102-9. PMC   6023875 . PMID   29955099.
4. Magdanz, Veronika; Sanchez, Samuel; Schmidt, Oliver G. (2013). "Development of a Sperm-Flagella Driven Micro-Bio-Robot". Advanced Materials. 25 (45): 6581–6588. Bibcode:2013AdM....25.6581M. doi: 10.1002/adma.201302544 . PMID   23996782. S2CID   5125033.
5. Srivastava, Sarvesh Kumar; Medina-Sánchez, Mariana; Koch, Britta; Schmidt, Oliver G. (2016). "Medibots: Dual-Action Biogenic Microdaggers for Single-Cell Surgery and Drug Release". Advanced Materials. 28 (5): 832–837. Bibcode:2016AdM....28..832S. doi:10.1002/adma.201504327. PMID   26619085. S2CID   40955542.
6. Singh, Ajay Vikram; Dad Ansari, Mohammad Hasan; Dayan, Cem Balda; Giltinan, Joshua; Wang, Shuo; Yu, Yan; Kishore, Vimal; Laux, Peter; Luch, Andreas; Sitti, Metin (2019). "Multifunctional magnetic hairbot for untethered osteogenesis, ultrasound contrast imaging and drug delivery". Biomaterials. 219: 119394. doi:10.1016/j.biomaterials.2019.119394. PMID   31382208. S2CID   199451792.
7. Bhuyan, Tamanna; Singh, Amit Kumar; Dutta, Deepanjalee; Unal, Aynur; Ghosh, Siddhartha Sankar; Bandyopadhyay, Dipankar (2017). "Magnetic Field Guided Chemotaxis of i Mushbots for Targeted Anticancer Therapeutics". ACS Biomaterials Science & Engineering. 3 (8): 1627–1640. doi:10.1021/acsbiomaterials.7b00086. PMID   33429648.
8. Magdanz, Veronika; Khalil, Islam S. M.; Simmchen, Juliane; Furtado, Guilherme P.; Mohanty, Sumit; Gebauer, Johannes; Xu, Haifeng; Klingner, Anke; Aziz, Azaam; Medina-Sánchez, Mariana; Schmidt, Oliver G.; Misra, Sarthak (2020). "IRONSperm: Sperm-templated soft magnetic microrobots". Science Advances. 6 (28): eaba5855. Bibcode:2020SciA....6.5855M. doi:10.1126/sciadv.aba5855. PMC   7450605 . PMID   32923590.
9. Bhuyan, Tamanna; Dutta, Deepanjalee; Bhattacharjee, Mitradip; Singh, Amit Kumar; Ghosh, Siddhartha Sankar; Bandyopadhyay, Dipankar (2019). "Acoustic Propulsion of Vitamin C Loaded Teabots for Targeted Oxidative Stress and Amyloid Therapeutics". ACS Applied Bio Materials. 2 (10): 4571–4582. doi:10.1021/acsabm.9b00677. PMID   35021416. S2CID   203945671.
10. Bunea, Ada-Ioana; Glückstad, Jesper (2019). "Strategies for Optical Trapping in Biological Samples: Aiming at Microrobotic Surgeons" (PDF). Laser & Photonics Reviews. 13 (4). Bibcode:2019LPRv...1300227B. doi:10.1002/lpor.201800227. S2CID   128326068.
11. Bhuyan, Tamanna; Simon, Anitha T.; Maity, Surjendu; Singh, Amit Kumar; Ghosh, Siddhartha Sankar; Bandyopadhyay, Dipankar (2020). "Magnetotactic T-Budbots to Kill-n-Clean Biofilms". ACS Applied Materials & Interfaces. 12 (39): 43352–43364. doi:10.1021/acsami.0c08444. PMID   32864951. S2CID   221383266.
12. Wang, Xiaopu; Chen, Xiang-Zhong; Alcântara, Carlos C. J.; Sevim, Semih; Hoop, Marcus; Terzopoulou, Anastasia; De Marco, Carmela; Hu, Chengzhi; De Mello, Andrew J.; Falcaro, Paolo; Furukawa, Shuhei; Nelson, Bradley J.; Puigmartí-Luis, Josep; Pané, Salvador (2019). "MOF-Based Microrobots: MOFBOTS: Metal–Organic-Framework-Based Biomedical Microrobots (Adv. Mater. 27/2019)". Advanced Materials. 31 (27). Bibcode:2019AdM....3170192W. doi: 10.1002/adma.201970192 . S2CID   198797318.
13. Brown, James F. (1852). "XXIV. On some salts and products of decomposition of pyromeconic acid". The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science. 4 (24): 161–168. doi:10.1080/14786445208647098.
14. Einstein, A. (1905). "Über die von der molekularkinetischen Theorie der Wärme geforderte Bewegung von in ruhenden Flüssigkeiten suspendierten Teilchen". Annalen der Physik. 322 (8): 549–560. Bibcode:1905AnP...322..549E. doi: 10.1002/andp.19053220806 .
15. Bastos-Arrieta, Julio; Revilla-Guarinos, Ainhoa; Uspal, William E.; Simmchen, Juliane (2018). "Bacterial Biohybrid Microswimmers". Frontiers in Robotics and AI. 5: 97. doi: 10.3389/frobt.2018.00097 . PMC   7805739 . PMID   33500976. Material was copied from this source, which is available under a Creative Commons Attribution 4.0 International License.
16. Purcell, E. M. (1977). "Life at low Reynolds number". American Journal of Physics. 45 (1): 3–11. Bibcode:1977AmJPh..45....3P. doi:10.1119/1.10903.
17. Qiu, Tian; Lee, Tung-Chun; Mark, Andrew G.; Morozov, Konstantin I.; Münster, Raphael; Mierka, Otto; Turek, Stefan; Leshansky, Alexander M.; Fischer, Peer (2014). "Swimming by reciprocal motion at low Reynolds number". Nature Communications. 5: 5119. Bibcode:2014NatCo...5.5119Q. doi:10.1038/ncomms6119. PMC   4241991 . PMID   25369018.
18. Abbott, Jake J.; Peyer, Kathrin E.; Lagomarsino, Marco Cosentino; Zhang, Li; Dong, Lixin; Kaliakatsos, Ioannis K.; Nelson, Bradley J. (November 2009). "How Should Microrobots Swim?". The International Journal of Robotics Research. 28 (11–12): 1434–1447. doi:10.1177/0278364909341658. ISSN   0278-3649.
19. Low Reynolds number hydrodynamics. Mechanics of fluids and transport processes. Vol. 1. 1983. doi:10.1007/978-94-009-8352-6. ISBN   978-90-247-2877-0.
20. Peyer, Kathrin E.; Zhang, Li; Nelson, Bradley J. (2013-02-04). "Bio-inspired magnetic swimming microrobots for biomedical applications". Nanoscale. 5 (4): 1259–1272. Bibcode:2013Nanos...5.1259P. doi:10.1039/C2NR32554C. ISSN   2040-3372. PMID   23165991.
21. Purcell, E. M. (December 2013), "Life at Low Reynolds Number", Physics and Our World, WORLD SCIENTIFIC, pp. 47–67, doi:10.1142/9789814434973_0004, ISBN   978-981-4434-96-6 , retrieved 2024-06-06
22. G. I., Taylor (1967). ""Film Notes for Low Reynolds-Number Flows,"" (PDF). Cambridge University.
23. Zhang, Li; Abbott, Jake J.; Dong, Lixin; Kratochvil, Bradley E.; Bell, Dominik; Nelson, Bradley J. (2009). "Artificial bacterial flagella: Fabrication and magnetic control". Applied Physics Letters. 94 (6): 064107. Bibcode:2009ApPhL..94f4107Z. doi:10.1063/1.3079655.
24. Abbott, Jake J.; Peyer, Kathrin E.; Lagomarsino, Marco Cosentino; Zhang, Li; Dong, Lixin; Kaliakatsos, Ioannis K.; Nelson, Bradley J. (2009). "How Should Microrobots Swim?". The International Journal of Robotics Research. 28 (11–12): 1434–1447. doi:10.1177/0278364909341658. S2CID   62330062.
25. Schamel, Debora; Mark, Andrew G.; Gibbs, John G.; Miksch, Cornelia; Morozov, Konstantin I.; Leshansky, Alexander M.; Fischer, Peer (2014). "Nanopropellers and Their Actuation in Complex Viscoelastic Media". ACS Nano. 8 (9): 8794–8801. doi:10.1021/nn502360t. PMID   24911046.
26. Rogowski, Louis William; Oxner, Micah; Tang, Jiannan; Kim, Min Jun (2020). "Heterogeneously flagellated microswimmer behavior in viscous fluids". Biomicrofluidics. 14 (2): 024112. doi:10.1063/1.5137743. PMC   7173976 . PMID   32341723.
27. Ceylan, Hakan; Yasa, Immihan Ceren; Yasa, Oncay; Tabak, Ahmet Fatih; Giltinan, Joshua; Sitti, Metin (2019). "3D-Printed Biodegradable Microswimmer for Theranostic Cargo Delivery and Release". ACS Nano. 13 (3): 3353–3362. doi:10.1021/acsnano.8b09233. PMC   6728090 . PMID   30742410.
28. Peyer, Kathrin E.; Zhang, Li; Nelson, Bradley J. (2013). "Bio-inspired magnetic swimming microrobots for biomedical applications". Nanoscale. 5 (4): 1259–1272. Bibcode:2013Nanos...5.1259P. doi:10.1039/C2NR32554C. PMID   23165991.
29. Chowdhury, Sagar; Jing, Wuming; Cappelleri, David J. (2015). "Controlling multiple microrobots: Recent progress and future challenges". Journal of Micro-Bio Robotics. 10 (1–4): 1–11. doi:10.1007/s12213-015-0083-6. S2CID   53644820.
30. Servant, Ania; Qiu, Famin; Mazza, Mariarosa; Kostarelos, Kostas; Nelson, Bradley J. (2015). "Controlled in Vivo Swimming of a Swarm of Bacteria-Like Microrobotic Flagella". Advanced Materials. 27 (19): 2981–2988. Bibcode:2015AdM....27.2981S. doi:10.1002/adma.201404444. PMID   25850420. S2CID   22780031.
31. Dong, Xiaoguang; Sitti, Metin (2020). "Controlling two-dimensional collective formation and cooperative behavior of magnetic microrobot swarms". The International Journal of Robotics Research. 39 (5): 617–638. doi: 10.1177/0278364920903107 . S2CID   213942288.
32. Liang, Xiong; Mou, Fangzhi; Huang, Zhen; Zhang, Jianhua; You, Ming; Xu, Leilei; Luo, Ming; Guan, Jianguo (2020). "Hierarchical Microswarms with Leader–Follower-Like Structures: Electrohydrodynamic Self-Organization and Multimode Collective Photoresponses". Advanced Functional Materials. 30 (16). doi:10.1002/adfm.201908602. S2CID   214408287.
33. Zheng, Jing; Dai, Baohu; Wang, Jizhuang; Xiong, Ze; Yang, Ya; Liu, Jun; Zhan, Xiaojun; Wan, Zhihan; Tang, Jinyao (2017). "Orthogonal navigation of multiple visible-light-driven artificial microswimmers". Nature Communications. 8 (1): 1438. Bibcode:2017NatCo...8.1438Z. doi:10.1038/s41467-017-01778-9. PMC   5681650 . PMID   29127414.
34. Koleoso, M.; Feng, X.; Xue, Y.; Li, Q.; Munshi, T.; Chen, X. (2020). "Micro/Nanoscale magnetic robots for biomedical applications". Materials Today Bio. 8: 100085. doi:10.1016/j.mtbio.2020.100085. PMC   7702192 . PMID   33299981.
35. Rao, K. Jagajjanani; Li, Fei; Meng, Long; Zheng, Hairong; Cai, Feiyan; Wang, Wei (2015). "A Force to be Reckoned with: A Review of Synthetic Microswimmers Powered by Ultrasound". Small. 11 (24): 2836–2846. doi:10.1002/smll.201403621. PMID   25851515.
36. Dreyfus, Rémi; Baudry, Jean; Roper, Marcus L.; Fermigier, Marc; Stone, Howard A.; Bibette, Jérôme (2005). "Microscopic artificial swimmers". Nature. 437 (7060): 862–865. Bibcode:2005Natur.437..862D. doi:10.1038/nature04090. PMID   16208366. S2CID   3025635.
37. Palagi, Stefano; Mark, Andrew G.; Reigh, Shang Yik; Melde, Kai; Qiu, Tian; Zeng, Hao; Parmeggiani, Camilla; Martella, Daniele; Sanchez-Castillo, Alberto; Kapernaum, Nadia; Giesselmann, Frank; Wiersma, Diederik S.; Lauga, Eric; Fischer, Peer (2016). "Structured light enables biomimetic swimming and versatile locomotion of photoresponsive soft microrobots". Nature Materials. 15 (6): 647–653. Bibcode:2016NatMa..15..647P. doi:10.1038/nmat4569. hdl: 2158/1105540 . PMID   26878315.
38. Kim, Min-Soo; Lee, Hyun-Taek; Ahn, Sung-Hoon (2019). "Laser Controlled 65 Micrometer Long Microrobot Made of Ni-Ti Shape Memory Alloy". Advanced Materials Technologies. 4 (12). doi:10.1002/admt.201900583. S2CID   210801365.
39. Peng, Xiaolei; Chen, Zhihan; Kollipara, Pavana Siddhartha; Liu, Yaoran; Fang, Jie; Lin, Linhan; Zheng, Yuebing (2020). "Opto-thermoelectric microswimmers". Light: Science & Applications. 9 (1): 141. Bibcode:2020LSA.....9..141P. doi:10.1038/s41377-020-00378-5. PMC   7429954 . PMID   32864116.
40. Bastos-Arrieta, Julio; Revilla-Guarinos, Ainhoa; Uspal, William E.; Simmchen, Juliane (2018). "Bacterial Biohybrid Microswimmers". Frontiers in Robotics and AI. 5: 97. doi: 10.3389/frobt.2018.00097 . PMC   7805739 . PMID   33500976.
41. Bente, Klaas; Codutti, Agnese; Bachmann, Felix; Faivre, Damien (2018). "Biohybrid and Bioinspired Magnetic Microswimmers". Small. 14 (29): e1704374. doi:10.1002/smll.201704374. PMID   29855143. S2CID   46918320.
42. Kaynak, Murat; Ozcelik, Adem; Nourhani, Amir; Lammert, Paul E.; Crespi, Vincent H.; Huang, Tony Jun (2017). "Acoustic actuation of bioinspired microswimmers". Lab on a Chip. 17 (3): 395–400. doi:10.1039/C6LC01272H. PMC   5465869 . PMID   27991641.
43. Xin, Hongbao; Zhao, Nan; Wang, Yunuo; Zhao, Xiaoting; Pan, Ting; Shi, Yang; Li, Baojun (2020). "Optically Controlled Living Micromotors for the Manipulation and Disruption of Biological Targets". Nano Letters. 20 (10): 7177–7185. Bibcode:2020NanoL..20.7177X. doi:10.1021/acs.nanolett.0c02501. PMID   32935992. S2CID   221747106.
44. Nagai, Moeto; Hirano, Takahiro; Shibata, Takayuki (2019). "Phototactic Algae-Driven Unidirectional Transport of Submillimeter-Sized Cargo in a Microchannel". Micromachines. 10 (2): 130. doi: 10.3390/mi10020130 . PMC   6412834 . PMID   30781488.
45. Lozano, Celia; Ten Hagen, Borge; Löwen, Hartmut; Bechinger, Clemens (2016). "Phototaxis of synthetic microswimmers in optical landscapes". Nature Communications. 7: 12828. arXiv: 1609.09814 . Bibcode:2016NatCo...712828L. doi:10.1038/ncomms12828. PMC   5056439 . PMID   27687580. S2CID   7924312.
46. Singh, Dhruv P.; Uspal, William E.; Popescu, Mihail N.; Wilson, Laurence G.; Fischer, Peer (2018). "Photogravitactic Microswimmers" (PDF). Advanced Functional Materials. 28 (25). doi:10.1002/adfm.201706660. S2CID   247697846.
47. Dai, Baohu; Wang, Jizhuang; Xiong, Ze; Zhan, Xiaojun; Dai, Wei; Li, Chien-Cheng; Feng, Shien-Ping; Tang, Jinyao (2016). "Programmable artificial phototactic microswimmer". Nature Nanotechnology. 11 (12): 1087–1092. Bibcode:2016NatNa..11.1087D. doi:10.1038/nnano.2016.187. PMID   27749832.
48. Akolpoglu, Mukrime Birgul; Dogan, Nihal Olcay; Bozuyuk, Ugur; Ceylan, Hakan; Kizilel, Seda; Sitti, Metin (2020). "High-Yield Production of Biohybrid Microalgae for On-Demand Cargo Delivery". Advanced Science. 7 (16). doi:10.1002/advs.202001256. PMC   7435244 . PMID   32832367.
49. Tu, Yingfeng; Peng, Fei; Wilson, Daniela A. (2017). "Motion Manipulation of Micro- and Nanomotors". Advanced Materials. 29 (39). Bibcode:2017AdM....2901970T. doi: 10.1002/adma.201701970 . hdl: 2066/181774 . PMID   28841755. S2CID   205280841.
50. Luo, Ming; Feng, Youzeng; Wang, Tingwei; Guan, Jianguo (2018). "Micro-/Nanorobots at Work in Active Drug Delivery". Advanced Functional Materials. 28 (25). doi:10.1002/adfm.201706100. S2CID   104145610.
51. Srivastava, Sarvesh Kumar; Clergeaud, Gael; Andresen, Thomas L.; Boisen, Anja (2019). "Micromotors for drug delivery in vivo: The road ahead" (PDF). Advanced Drug Delivery Reviews. 138: 41–55. doi:10.1016/j.addr.2018.09.005. PMID   30236447. S2CID   52310451.
52. Plutnar, Jan; Pumera, Martin (2019). "Chemotactic Micro- and Nanodevices". Angewandte Chemie International Edition. 58 (8): 2190–2196. doi:10.1002/anie.201809101. PMID   30216620. S2CID   52278805.
53. Yang, Qingliang; Xu, Lei; Zhong, Weizhen; Yan, Qinying; Gao, Ying; Hong, Weiyong; She, Yuanbin; Yang, Gensheng (2020). "Recent Advances in Motion Control of Micro/Nanomotors". Advanced Intelligent Systems. 2 (8). doi: 10.1002/aisy.202000049 . S2CID   221418150.
54. Kanu, Nand Jee; Gupta, Eva; Vates, Umesh Kumar; Singh, Gyanendra Kumar (2019). "An insight into biomimetic 4D printing". RSC Advances. 9 (65): 38209–38226. Bibcode:2019RSCAd...938209K. doi: 10.1039/C9RA07342F . PMC   9075844 . PMID   35541793. S2CID   214386444.
55. Lui, Yuan Siang; Sow, Wan Ting; Tan, Lay Poh; Wu, Yunlong; Lai, Yuekun; Li, Huaqiong (2019). "4D printing and stimuli-responsive materials in biomedical aspects". Acta Biomaterialia. 92: 19–36. doi:10.1016/j.actbio.2019.05.005. hdl: 10356/143207 . PMID   31071476. S2CID   149445838.
56. Spiegel, Christoph A.; Hippler, Marc; Münchinger, Alexander; Bastmeyer, Martin; Barner-Kowollik, Christopher; Wegener, Martin; Blasco, Eva (2020). "4D Printing at the Microscale". Advanced Functional Materials. 30 (26). doi: 10.1002/adfm.201907615 . S2CID   210959593.
57. Yang, Qingzhen; Gao, Bin; Xu, Feng (2020). "Recent Advances in 4D Bioprinting". Biotechnology Journal. 15 (1): e1900086. doi:10.1002/biot.201900086. PMID   31486199. S2CID   201837838.
58. Zhang, Yabin; Yuan, Ke; Zhang, Li (16 January 2019). "Micro/Nanomachines: from Functionalization to Sensing and Removal". Advanced Materials Technologies. 4 (4). Wiley: 1800636. doi:10.1002/admt.201800636. ISSN   2365-709X. S2CID   139612870.
59. Bunea, Ada-Ioana; Jakobsen, Mogens Havsteen; Engay, Einstom; Bañas, Andrew R.; Glückstad, Jesper (2019). "Optimization of 3D-printed microstructures for investigating the properties of the mucus biobarrier". Micro and Nano Engineering. 2. Elsevier BV: 41–47. doi: 10.1016/j.mne.2018.12.004 . ISSN   2590-0072. S2CID   215751974.
60. Zhang, Yabin; Yuan, Ke; Zhang, Li (2019). "Micro/Nanomachines: From Functionalization to Sensing and Removal". Advanced Materials Technologies. 4 (4). doi:10.1002/admt.201800636. S2CID   139612870.
61. Bunea, Ada-Ioana; Jakobsen, Mogens Havsteen; Engay, Einstom; Bañas, Andrew R.; Glückstad, Jesper (2019). "Optimization of 3D-printed microstructures for investigating the properties of the mucus biobarrier". Micro and Nano Engineering. 2: 41–47. doi: 10.1016/j.mne.2018.12.004 . S2CID   215751974.
62. Wang, Yuhang; Chen, Jun; Su, Guangfei; Mei, Jiaxi; Li, Junyang (September 2023). "A Review of Single-Cell Microrobots: Classification, Driving Methods and Applications". Micromachines. 14 (9): 1710. doi: 10.3390/mi14091710 . ISSN   2072-666X. PMC   10537272 . PMID   37763873.
63. Singh, Ajay Vikram; Kishore, Vimal; Santomauro, Giulia; Yasa, Oncay; Bill, Joachim; Sitti, Metin (28 April 2020). "Mechanical Coupling of Puller and Pusher Active Microswimmers Influences Motility". Langmuir. 36 (19). American Chemical Society (ACS): 5435–5443. doi:10.1021/acs.langmuir.9b03665. ISSN   0743-7463. PMC   7304893 . PMID   32343587. Material was copied from this source, which is available under a Creative Commons Attribution 4.0 International License.
64. Lauga, Eric; Powers, Thomas R. (2009). "The hydrodynamics of swimming microorganisms". Reports on Progress in Physics. 72 (9): 096601. arXiv: 0812.2887 . Bibcode:2009RPPh...72i6601L. doi:10.1088/0034-4885/72/9/096601. S2CID   3932471.
65. Vogel, Pia D. (2005). "Nature's design of nanomotors". European Journal of Pharmaceutics and Biopharmaceutics. 60 (2): 267–277. doi:10.1016/j.ejpb.2004.10.007. PMID   15939237.
66. Patra, Debabrata; Sengupta, Samudra; Duan, Wentao; Zhang, Hua; Pavlick, Ryan; Sen, Ayusman (2013). "Intelligent, self-powered, drug delivery systems". Nanoscale. 5 (4): 1273–1283. Bibcode:2013Nanos...5.1273P. doi:10.1039/C2NR32600K. PMID   23166050.
67. Feringa, Ben L. (2001). "In Control of Motion: From Molecular Switches to Molecular Motors". Accounts of Chemical Research. 34 (6): 504–513. doi:10.1021/ar0001721. hdl: 11370/a0b20090-34b9-4e2d-8450-bc2afbea2fcf . PMID   11412087.
68. Sokolov, A.; Apodaca, M. M.; Grzybowski, B. A.; Aranson, I. S. (2010). "Swimming bacteria power microscopic gears". Proceedings of the National Academy of Sciences. 107 (3): 969–974. Bibcode:2010PNAS..107..969S. doi: 10.1073/pnas.0913015107 . PMC   2824308 . PMID   20080560.
69. Zhao, Xi; Gentile, Kayla; Mohajerani, Farzad; Sen, Ayusman (2018-10-16). "Powering Motion with Enzymes". Accounts of Chemical Research. 51 (10): 2373–2381. doi:10.1021/acs.accounts.8b00286. ISSN   0001-4842. PMID   30256612. S2CID   52845451.
70. Muddana, Hari S.; Sengupta, Samudra; Mallouk, Thomas E.; Sen, Ayusman; Butler, Peter J. (2010-02-24). "Substrate Catalysis Enhances Single-Enzyme Diffusion". Journal of the American Chemical Society. 132 (7): 2110–2111. doi:10.1021/ja908773a. ISSN   0002-7863. PMC   2832858 . PMID   20108965.
71. Ghosh, Subhadip; Mohajerani, Farzad; Son, Seoyoung; Velegol, Darrell; Butler, Peter J.; Sen, Ayusman (2019-09-11). "Motility of Enzyme-Powered Vesicles". Nano Letters. 19 (9): 6019–6026. Bibcode:2019NanoL..19.6019G. doi:10.1021/acs.nanolett.9b01830. ISSN   1530-6984. PMID   31429577.
72. Madigan, Michael T.; Bender, Kelly S.; Buckley, Daniel H.; Brock, Thomas D.; Matthew Sattley, W.; Stahl, David Allan (29 January 2018). Brock Biology of Microorganisms. Pearson. ISBN   9781292235103.
73. Dufrêne, Yves F. (2015). "Sticky microbes: Forces in microbial cell adhesion". Trends in Microbiology. 23 (6): 376–382. doi:10.1016/j.tim.2015.01.011. PMID   25684261.
74. Schwarz, Lukas; Medina-Sánchez, Mariana; Schmidt, Oliver G. (2017). "Hybrid Bio Micromotors". Applied Physics Reviews. 4 (3): 031301. Bibcode:2017ApPRv...4c1301S. doi: 10.1063/1.4993441 . Material was copied from this source, which is available under a Creative Commons Attribution 4.0 International License.
75. Darnton, Nicholas C.; Turner, Linda; Rojevsky, Svetlana; Berg, Howard C. (2007). "On Torque and Tumbling in Swimming Escherichia coli". Journal of Bacteriology. 189 (5): 1756–1764. doi:10.1128/JB.01501-06. PMC   1855780 . PMID   17189361.
76. Edwards, Matthew R.; Carlsen, Rika Wright; Zhuang, Jiang; Sitti, Metin (2014). "Swimming characterization of Serratia marcescens for bio-hybrid micro-robotics". Journal of Micro-Bio Robotics. 9 (3–4): 47–60. doi:10.1007/s12213-014-0072-1. S2CID   84413776.
77. Magariyama, Yukio; Sugiyama, Shigeru; Kudo, Seishi (2001). "Bacterial swimming speed and rotation rate of bundled flagella". FEMS Microbiology Letters. 199 (1): 125–129. doi: 10.1111/j.1574-6968.2001.tb10662.x . PMID   11356579.
78. Ito, Masahiro; Terahara, Naoya; Fujinami, Shun; Krulwich, Terry Ann (2005). "Properties of Motility in Bacillus subtilis Powered by the H+-coupled MotAB Flagellar Stator, Na+-coupled MotPS or Hybrid Stators MotAS or MotPB". Journal of Molecular Biology. 352 (2): 396–408. doi:10.1016/j.jmb.2005.07.030. PMC   2578835 . PMID   16095621.
79. Higashi, Kazuhiko; Miki, Norihisa (2014). "A self-swimming microbial robot using microfabricated nanofibrous hydrogel". Sensors and Actuators B: Chemical. 202: 301–306. Bibcode:2014SeAcB.202..301H. doi:10.1016/j.snb.2014.05.068.
80. Kawagishi, I.; Maekawa, Y.; Atsumi, T.; Homma, M.; Imae, Y. (1995). "Isolation of the polar and lateral flagellum-defective mutants in Vibrio alginolyticus and identification of their flagellar driving energy sources". Journal of Bacteriology. 177 (17): 5158–5160. doi:10.1128/jb.177.17.5158-5160.1995. PMC   177299 . PMID   7665498.
81. Xie, L.; Altindal, T.; Chattopadhyay, S.; Wu, X.-L. (2011). "Bacterial flagellum as a propeller and as a rudder for efficient chemotaxis". Proceedings of the National Academy of Sciences. 108 (6): 2246–2251. doi: 10.1073/pnas.1011953108 . PMC   3038696 . PMID   21205908.
82. Lacayo, Catherine I.; Theriot, Julie A. (2004). "Listeria monocytogenes Actin-based Motility Varies Depending on Subcellular Location: A Kinematic Probe for Cytoarchitecture". Molecular Biology of the Cell. 15 (5): 2164–2175. doi:10.1091/mbc.E03-10-0747. PMC   404013 . PMID   15004231.
83. McGrath, James L.; Eungdamrong, Narat J.; Fisher, Charles I.; Peng, Fay; Mahadevan, Lakshminarayanan; Mitchison, Timothy J.; Kuo, Scot C. (2003). "The Force-Velocity Relationship for the Actin-Based Motility of Listeria monocytogenes". Current Biology. 13 (4): 329–332. Bibcode:2003CBio...13..329M. doi: 10.1016/S0960-9822(03)00051-4 . PMID   12593799. S2CID   6459972.
84. Chen, Yifan; Kosmas, Panagiotis; Martel, Sylvain (2013). "A Feasibility Study for Microwave Breast Cancer Detection Using Contrast-Agent-Loaded Bacterial Microbots". International Journal of Antennas and Propagation. 2013: 1–11. doi: 10.1155/2013/309703 .
85. Ruan, J.; Kato, T.; Santini, C.-L.; Miyata, T.; Kawamoto, A.; Zhang, W.-J.; Bernadac, A.; Wu, L.-F.; Namba, K. (2012). "Architecture of a flagellar apparatus in the fast-swimming magnetotactic bacterium MO-1". Proceedings of the National Academy of Sciences. 109 (50): 20643–20648. Bibcode:2012PNAS..10920643R. doi: 10.1073/pnas.1215274109 . PMC   3528567 . PMID   23184985.
86. Martel, Sylvain; Tremblay, Charles C.; Ngakeng, Serge; Langlois, Guillaume (2006). "Controlled manipulation and actuation of micro-objects with magnetotactic bacteria". Applied Physics Letters. 89 (23): 233904. Bibcode:2006ApPhL..89w3904M. doi:10.1063/1.2402221.
87. Miyata, Makoto; Ryu, William S.; Berg, Howard C. (2002). "Force and Velocity of Mycoplasma mobile Gliding". Journal of Bacteriology. 184 (7): 1827–1831. doi:10.1128/JB.184.7.1827-1831.2002. PMC   134919 . PMID   11889087.
88. Weibel, D. B.; Garstecki, P.; Ryan, D.; Diluzio, W. R.; Mayer, M.; Seto, J. E.; Whitesides, G. M. (2005). "Microoxen: Microorganisms to move microscale loads". Proceedings of the National Academy of Sciences. 102 (34): 11963–11967. Bibcode:2005PNAS..10211963W. doi: 10.1073/pnas.0505481102 . PMC   1189341 . PMID   16103369.
89. Kim, Dal Hyung; Cheang, U. Kei; Kőhidai, László; Byun, Doyoung; Kim, Min Jun (2010). "Artificial magnetotactic motion control of Tetrahymena pyriformis using ferromagnetic nanoparticles: A tool for fabrication of microbiorobots". Applied Physics Letters. 97 (17): 173702. Bibcode:2010ApPhL..97q3702K. doi:10.1063/1.3497275.
90. Hill, Kent L. (2003). "Biology and Mechanism of Trypanosome Cell Motility". Eukaryotic Cell. 2 (2): 200–208. doi:10.1128/EC.2.2.200-208.2003. PMC   154846 . PMID   12684369.
91. Krüger, Timothy; Engstler, Markus (2016). "Trypanosomes – versatile microswimmers". The European Physical Journal Special Topics. 225 (11–12): 2157–2172. Bibcode:2016EPJST.225.2157K. doi:10.1140/epjst/e2016-60063-5. S2CID   125623927.
92. Maree, L.; Van Der Horst, G. (2013). "Quantification and identification of sperm subpopulations using computer-aided sperm analysis and species-specific cut-off values for swimming speed". Biotechnic & Histochemistry. 88 (3–4): 181–193. doi:10.3109/10520295.2012.757366. hdl: 10566/3120 . PMID   23331185. S2CID   19603301.
93. Eamer, Lise; Nosrati, Reza; Vollmer, Marion; Zini, Armand; Sinton, David (2015). "Microfluidic assessment of swimming media for motility-based sperm selection". Biomicrofluidics. 9 (4): 044113. doi:10.1063/1.4928129. PMC   4529441 . PMID   26339314.
94. Gomendio, Montserrat; Roldan, Eduardo R.S. (2008). "Implications of diversity in sperm size and function for sperm competition and fertility". The International Journal of Developmental Biology. 52 (5–6): 439–447. doi: 10.1387/ijdb.082595mg . PMID   18649256.
95. Tung, Chih-Kuan; Ardon, Florencia; Fiore, Alyssa G.; Suarez, Susan S.; Wu, Mingming (2014). "Cooperative roles of biological flow and surface topography in guiding sperm migration revealed by a microfluidic model". Lab Chip. 14 (7): 1348–1356. doi:10.1039/C3LC51297E. PMC   4497544 . PMID   24535032.
96. Ishikawa, Takuji (2019) Special Issue "Microswimmer" Micromachines, ISSN   2072-666X.
97. Dumé, Isabelle (2020) Microswimmers benefit from thermoelectric guidance Physics World.
98. Paxton, Walter F.; Kistler, Kevin C.; Olmeda, Christine C.; Sen, Ayusman; St. Angelo, Sarah K.; Cao, Yanyan; Mallouk, Thomas E.; Lammert, Paul E.; Crespi, Vincent H. (2004-10-01). "Catalytic Nanomotors: Autonomous Movement of Striped Nanorods". Journal of the American Chemical Society. 126 (41): 13424–13431. doi:10.1021/ja047697z. ISSN   0002-7863. PMID   15479099.
99. Paxton, Walter F.; Baker, Paul T.; Kline, Timothy R.; Wang, Yang; Mallouk, Thomas E.; Sen, Ayusman (2006-11-01). "Catalytically Induced Electrokinetics for Motors and Micropumps". Journal of the American Chemical Society. 128 (46): 14881–14888. doi:10.1021/ja0643164. ISSN   0002-7863. OSTI   1118642. PMID   17105298.
100. Hong, Yiying; Blackman, Nicole M. K.; Kopp, Nathaniel D.; Sen, Ayusman; Velegol, Darrell (2007-10-26). "Chemotaxis of Nonbiological Colloidal Rods". Physical Review Letters. 99 (17): 178103. Bibcode:2007PhRvL..99q8103H. doi:10.1103/PhysRevLett.99.178103. PMID   17995374.
101. Sundararajan, Shakuntala; Lammert, Paul E.; Zudans, Andrew W.; Crespi, Vincent H.; Sen, Ayusman (2008-05-01). "Catalytic Motors for Transport of Colloidal Cargo". Nano Letters. 8 (5): 1271–1276. Bibcode:2008NanoL...8.1271S. doi:10.1021/nl072275j. ISSN   1530-6984. PMID   18416540.
102. Zhou, Dekai; Ren, Liqiang; Li, Yuguang C.; Xu, Pengtao; Gao, Yuan; Zhang, Guangyu; Wang, Wei; Mallouk, Thomas E.; Li, Longqiu (2017). "Visible light-driven, magnetically steerable gold/iron oxide nanomotors". Chem. Commun. 53 (83): 11465–11468. doi:10.1039/C7CC06327J. ISSN   1359-7345. PMID   28983536.
103. Unruh, Angus; Savage, Ethan J.; Sen, Ayusman (2023-12-12). "Remote Magnetically Controlled Chemically Fueled Micromotor Disks". Chemistry of Materials. 35 (23): 10099–10105. doi:10.1021/acs.chemmater.3c02156. ISSN   0897-4756.
104. Wang, Wei; Castro, Luz Angelica; Hoyos, Mauricio; Mallouk, Thomas (2012). "Autonomous motion of metallic microrods propelled by ultrasound". ACS Nano. 6 (7): 6122–6132. doi:10.1021/nn301312z. PMID   22631222.
105. Guix, Maria; Mayorga-Martinez, Carmen C.; Merkoçi, Arben (2014). "Nano/Micromotors in (Bio)chemical Science Applications". Chemical Reviews. 114 (12): 6285–6322. doi:10.1021/cr400273r. PMID   24827167.
106. Bechinger, Clemens; Di Leonardo, Roberto; Löwen, Hartmut; Reichhardt, Charles; Volpe, Giorgio; Volpe, Giovanni (2016). "Active Particles in Complex and Crowded Environments". Reviews of Modern Physics. 88 (4): 045006. arXiv: 1602.00081 . Bibcode:2016RvMP...88d5006B. doi:10.1103/RevModPhys.88.045006. S2CID   14940249.
107. Magdanz, Veronika; Guix, Maria; Schmidt, Oliver G. (2014). "Tubular micromotors: From microjets to spermbots". Robotics and Biomimetics. 1. doi: 10.1186/s40638-014-0011-6 . S2CID   55870000.
108. McNeill, Jeffrey M.; Mallouk, Thomas E. (2023-10-14). "Acoustically Powered Nano- and Microswimmers: From Individual to Collective Behavior". ACS Nanoscience Au. 3 (6): 424–440. doi: 10.1021/acsnanoscienceau.3c00038 . ISSN   2694-2496. PMC   10740144 . PMID   38144701.
109. Ricotti, Leonardo; Cafarelli, Andrea; Iacovacci, Veronica; Vannozzi, Lorenzo; Menciassi, Arianna (2015). "Advanced Micro-Nano-Bio Systems for Future Targeted Therapies". Current Nanoscience. 11 (2): 144–160. Bibcode:2015CNan...11..144R. doi:10.2174/1573413710666141114221246.
110. Elgeti, J.; Winkler, R. G.; Gompper, G. (2015). "Physics of microswimmers—single particle motion and collective behavior: A review". Reports on Progress in Physics. 78 (5): 056601. arXiv: 1412.2692 . Bibcode:2015RPPh...78e6601E. doi:10.1088/0034-4885/78/5/056601. PMID   25919479. S2CID   3909877.
111. Purcell, E. M. (1997). "The efficiency of propulsion by a rotating flagellum". Proceedings of the National Academy of Sciences. 94 (21): 11307–11311. Bibcode:1997PNAS...9411307P. doi: 10.1073/pnas.94.21.11307 . PMC   23452 . PMID   9326605.
112. Morozov, Konstantin I.; Leshansky, Alexander M. (2014). "The chiral magnetic nanomotors". Nanoscale. 6 (3): 1580–1588. arXiv: 1308.6115 . Bibcode:2014Nanos...6.1580M. doi:10.1039/C3NR04853E. PMID   24336860. S2CID   15834620.
113. Katsu-Kimura, Yumiko; Nakaya, Fumio; Baba, Shoji A.; Mogami, Yoshihiro (2009-06-15). "Substantial energy expenditure for locomotion in ciliates verified by means of simultaneous measurement of oxygen consumption rate and swimming speed". Journal of Experimental Biology. 212 (12): 1819–1824. doi:10.1242/jeb.028894. ISSN   1477-9145. PMID   19482999.
114. Tavaddod, S.; Charsooghi, M. A.; Abdi, F.; Khalesifard, H. R.; Golestanian, R. (2011-02-21). "Probing passive diffusion of flagellated and deflagellated Escherichia coli". The European Physical Journal E. 34 (2): 16. doi:10.1140/epje/i2011-11016-9. ISSN   1292-895X. PMID   21337013.
115. Gao, Wei; Feng, Xiaomiao; Pei, Allen; Kane, Christopher R.; Tam, Ryan; Hennessy, Camille; Wang, Joseph (2014-01-08). "Bioinspired helical microswimmers based on vascular plants". Nano Letters. 14 (1): 305–310. Bibcode:2014NanoL..14..305G. doi:10.1021/nl404044d. ISSN   1530-6992. PMID   24283342.
116. Ye, Chengwei; Liu, Jia; Wu, Xinyu; Wang, Ben; Zhang, Li; Zheng, Yuanyi; Xu, Tiantian (March 2019). "Hydrophobicity Influence on Swimming Performance of Magnetically Driven Miniature Helical Swimmers". Micromachines. 10 (3): 175. doi: 10.3390/mi10030175 . ISSN   2072-666X. PMC   6471021 . PMID   30845732.
117. Lauga, Eric; Powers, Thomas R (25 August 2009). "The hydrodynamics of swimming microorganisms". Reports on Progress in Physics. 72 (9). IOP Publishing: 096601. arXiv: 0812.2887 . Bibcode:2009RPPh...72i6601L. doi:10.1088/0034-4885/72/9/096601. ISSN   0034-4885. S2CID   3932471.
118. Berg, Howard C.; Anderson, Robert A. (1973). "Bacteria Swim by Rotating their Flagellar Filaments". Nature. 245 (5425): 380–382. Bibcode:1973Natur.245..380B. doi:10.1038/245380a0. PMID   4593496. S2CID   4173914.
119. Berg, Howard (2004). E. coli in motion (in Italian). New York: Springer. ISBN   978-0-387-21638-6. OCLC   56124142.
120. Mitchell, David R. (2001). "Chlamydomonas flagella". Journal of Phycology. 36 (2): 261–273. doi:10.1046/j.1529-8817.2000.99218.x. S2CID   221921243.
121. Oberholzer, Michael; Lopez, Miguel A.; McLelland, Bryce T.; Hill, Kent L. (2010). "Social Motility in African Trypanosomes". PLOS Pathogens. 6 (1): e1000739. doi: 10.1371/journal.ppat.1000739 . PMC   2813273 . PMID   20126443.
122. Babu, Sujin B.; Stark, Holger (2012). "Modeling the locomotion of the African trypanosome using multi-particle collision dynamics". New Journal of Physics. 14 (8): 085012. Bibcode:2012NJPh...14h5012B. doi: 10.1088/1367-2630/14/8/085012 .
123. Choudhary, Priyanka; Mandal, Subhayan; Babu, Sujin B. (2018). "Locomotion of a flexible one-hinge swimmer in Stokes regime". Journal of Physics Communications. 2 (2): 025009. arXiv: 1707.07451 . Bibcode:2018JPhCo...2b5009C. doi:10.1088/2399-6528/aaa856. S2CID   119229534. Material was copied from this source, which is available under a Creative Commons Attribution 3.0 International License.
124. Taylor, Geoffrey (1951). "Analysis of the swimming of microscopic organisms". Proceedings of the Royal Society of London. Series A. Mathematical and Physical Sciences. 209 (1099): 447–461. Bibcode:1951RSPSA.209..447T. doi:10.1098/rspa.1951.0218. S2CID   120382159.
125. Avron, J. E.; Raz, O. (2008). "A geometric theory of swimming: Purcell's swimmer and its symmetrized cousin". New Journal of Physics. 10 (6): 063016. arXiv: 0712.2047 . Bibcode:2008NJPh...10f3016A. doi:10.1088/1367-2630/10/6/063016. S2CID   14646885.
126. Najafi, Ali; Golestanian, Ramin (2004). "Simple swimmer at low Reynolds number: Three linked spheres". Physical Review E. 69 (6): 062901. arXiv: cond-mat/0402070 . Bibcode:2004PhRvE..69f2901N. doi:10.1103/PhysRevE.69.062901. PMID   15244646. S2CID   27500334.
127. Daddi-Moussa-Ider, Abdallah; Lisicki, Maciej; Mathijssen, Arnold J. T. M. (2020). "Tuning the upstream swimming of microrobots by shape and cargo size". Physical Review Applied. 14 (2): 024071. arXiv: 2004.05694 . Bibcode:2020PhRvP..14b4071D. doi:10.1103/PhysRevApplied.14.024071. S2CID   229547570.
128. Daddi-Moussa-Ider, Abdallah; Lisicki, Maciej; Hoell, Christian; Löwen, Hartmut (2018). "Swimming trajectories of a three-sphere microswimmer near a wall". Journal of Chemical Physics. 148 (13): 134904. arXiv: 1801.01162 . Bibcode:2018JChPh.148m4904D. doi:10.1063/1.5021027. PMID   29626882. S2CID   4718416.
129. Nasouri, Babak; Khot, Aditi; Elfring, Gwynn J. (2017). "Elastic two-sphere swimmer in Stokes flow". Physical Review Fluids. 2 (4): 043101. arXiv: 1611.05847 . Bibcode:2017PhRvF...2d3101N. doi:10.1103/PhysRevFluids.2.043101. S2CID   119474335.
130. Montino, Alessandro; Desimone, Antonio (2015). "Three-sphere low-Reynolds-number swimmer with a passive elastic arm". The European Physical Journal E. 38 (5): 127. doi: 10.1140/epje/i2015-15042-3 . hdl: 11384/84141 . PMID   25990633. S2CID   45431975.
131. Wiggins, Chris H.; Goldstein, Raymond E. (1998). "Flexive and Propulsive Dynamics of Elastica at Low Reynolds Number". Physical Review Letters. 80 (17): 3879–3882. arXiv: cond-mat/9707346 . Bibcode:1998PhRvL..80.3879W. doi:10.1103/PhysRevLett.80.3879. S2CID   10335181.
132. Lagomarsino, M.C.; Capuani, F.; Lowe, C.P. (2003). "A simulation study of the dynamics of a driven filament in an Aristotelian fluid". Journal of Theoretical Biology. 224 (2): 215–224. arXiv: physics/0208022 . Bibcode:2003JThBi.224..215L. doi:10.1016/S0022-5193(03)00159-0. hdl: 2434/802791 . PMID   12927528. S2CID   3200289.
133. Lauga, Eric (2007). "Floppy swimming: Viscous locomotion of actuated elastica". Physical Review E. 75 (4): 041916. arXiv: cond-mat/0610154 . Bibcode:2007PhRvE..75d1916L. doi:10.1103/PhysRevE.75.041916. PMID   17500930. S2CID   13651250.
134. Huang, H.-W.; Uslu, F. E.; Katsamba, P.; Lauga, E.; Sakar, M. S.; Nelson, B. J. (2019-01-18). "Adaptive locomotion of artificial microswimmers". Science Advances. 5 (1): eaau1532. arXiv: 1902.09000 . Bibcode:2019SciA....5.1532H. doi:10.1126/sciadv.aau1532. ISSN   2375-2548. PMC   6357760 . PMID   30746446.
135. Ismagilov, Rustem F.; Schwartz, Alexander; Bowden, Ned; Whitesides, George M. (2002-02-15). "Autonomous Movement and Self-Assembly". Angewandte Chemie International Edition. 41 (4): 652–654. doi: 10.1002/1521-3773(20020215)41:4<652::AID-ANIE652>3.0.CO;2-U . ISSN   1433-7851.
136. Luo, Tao; Wu, Mingming (2021-10-26). "Biologically inspired micro-robotic swimmers remotely controlled by ultrasound waves". Lab on a Chip. 21 (21): 4095–4103. doi:10.1039/D1LC00575H. ISSN   1473-0189. PMID   34549766.
137. Huang, Hen-Wei; Sakar, Mahmut Selman; Petruska, Andrew J.; Pané, Salvador; Nelson, Bradley J. (2016-07-22). "Soft micromachines with programmable motility and morphology". Nature Communications. 7 (1): 12263. Bibcode:2016NatCo...712263H. doi:10.1038/ncomms12263. ISSN   2041-1723. PMC   5512624 . PMID   27447088.
138. Breger, Joyce C.; Yoon, ChangKyu; Xiao, Rui; Kwag, Hye Rin; Wang, Martha O.; Fisher, John P.; Nguyen, Thao D.; Gracias, David H. (2015-02-11). "Self-Folding Thermo-Magnetically Responsive Soft Microgrippers". ACS Applied Materials & Interfaces. 7 (5): 3398–3405. doi:10.1021/am508621s. ISSN   1944-8244. PMC   4326779 . PMID   25594664.
139. Choi, Yeongjae; Park, Cheolheon; Lee, Amos C.; Bae, Junghyun; Kim, Hyeli; Choi, Hansol; Song, Seo woo; Jeong, Yunjin; Choi, Jaewon; Lee, Howon; Kwon, Sunghoon; Park, Wook (2021-08-05). "Photopatterned microswimmers with programmable motion without external stimuli". Nature Communications. 12 (1): 4724. Bibcode:2021NatCo..12.4724C. doi:10.1038/s41467-021-24996-8. ISSN   2041-1723. PMC   8342497 . PMID   34354060.
140. Khadka, Utsab; Holubec, Viktor; Yang, Haw; Cichos, Frank (2018). "Active particles bound by information flows". Nature Communications. 9 (1): 3864. arXiv: 1803.03053 . Bibcode:2018NatCo...9.3864K. doi:10.1038/s41467-018-06445-1. PMC   6154969 . PMID   30242284. Material was copied from this source, which is available under a Creative Commons Attribution 4.0 International License.
141. Alvarez, L.; Fernandez-Rodriguez, M. A.; Alegria, A.; Arrese-Igor, S.; Zhao, K.; Kröger, M.; Isa, Lucio (2021). "Reconfigurable artificial microswimmers with internal feedback". Nature Communications. 12 (1): 4762. arXiv: 2009.08382 . Bibcode:2021NatCo..12.4762A. doi:10.1038/s41467-021-25108-2. PMC   8346629 . PMID   34362934. Material was copied from this source, which is available under a Creative Commons Attribution 4.0 International License.
142. Hamadeh, Abdullah; Roberts, Mark A. J.; August, Elias; McSharry, Patrick E.; Maini, Philip K.; Armitage, Judith P.; Papachristodoulou, Antonis (2011). "Feedback Control Architecture and the Bacterial Chemotaxis Network". PLOS Computational Biology. 7 (5): e1001130. Bibcode:2011PLSCB...7E1130H. doi: 10.1371/journal.pcbi.1001130 . PMC   3088647 . PMID   21573199.
143. Baker, Melinda D.; Wolanin, Peter M.; Stock, Jeffry B. (2006). "Signal transduction in bacterial chemotaxis". BioEssays. 28 (1): 9–22. doi:10.1002/bies.20343. PMID   16369945. S2CID   189870.
144. Ebbens, S.J. (2016). "Active colloids: Progress and challenges towards realising autonomous applications". Current Opinion in Colloid & Interface Science. 21: 14–23. doi: 10.1016/j.cocis.2015.10.003 .
145. Lozano, Celia; Ten Hagen, Borge; Löwen, Hartmut; Bechinger, Clemens (2016). "Phototaxis of synthetic microswimmers in optical landscapes". Nature Communications. 7: 12828. arXiv: 1609.09814 . Bibcode:2016NatCo...712828L. doi:10.1038/ncomms12828. PMC   5056439 . PMID   27687580.
146. Sprenger, Alexander R.; Fernandez-Rodriguez, Miguel Angel; Alvarez, Laura; Isa, Lucio; Wittkowski, Raphael; Löwen, Hartmut (2020). "Active Brownian Motion with Orientation-Dependent Motility: Theory and Experiments". Langmuir. 36 (25): 7066–7073. arXiv: 1911.09524 . doi:10.1021/acs.langmuir.9b03617. PMID   31975603. S2CID   208201932.
147. Fernandez-Rodriguez, Miguel Angel; Grillo, Fabio; Alvarez, Laura; Rathlef, Marco; Buttinoni, Ivo; Volpe, Giovanni; Isa, Lucio (2020). "Feedback-controlled active brownian colloids with space-dependent rotational dynamics". Nature Communications. 11 (1): 4223. arXiv: 1911.02291 . Bibcode:2020NatCo..11.4223F. doi:10.1038/s41467-020-17864-4. PMC   7445303 . PMID   32839447.
148. Han, Koohee; Shields, C. Wyatt; Diwakar, Nidhi M.; Bharti, Bhuvnesh; López, Gabriel P.; Velev, Orlin D. (2017). "Sequence-encoded colloidal origami and microbot assemblies from patchy magnetic cubes". Science Advances. 3 (8): e1701108. Bibcode:2017SciA....3E1108H. doi:10.1126/sciadv.1701108. PMC   5544397 . PMID   28798960.
149. Shields, C. Wyatt; Velev, Orlin D. (2017). "The Evolution of Active Particles: Toward Externally Powered Self-Propelling and Self-Reconfiguring Particle Systems". Chem. 3 (4): 539–559. Bibcode:2017Chem....3..539S. doi: 10.1016/j.chempr.2017.09.006 .
150. Yang, Tao; Sprinkle, Brennan; Guo, Yang; Qian, Jun; Hua, Daoben; Donev, Aleksandar; Marr, David W. M.; Wu, Ning (2020). "Reconfigurable microbots folded from simple colloidal chains". Proceedings of the National Academy of Sciences. 117 (31): 18186–18193. Bibcode:2020PNAS..11718186Y. doi: 10.1073/pnas.2007255117 . PMC   7414297 . PMID   32680965.
151. Unruh, Angus; Brooks, Allan M.; Aranson, Igor S.; Sen, Ayusman (2023-04-28). "Programming Motion of Platinum Microparticles: From Linear to Orbital". ACS Applied Engineering Materials. 1 (4): 1126–1133. doi:10.1021/acsaenm.2c00249. ISSN   2771-9545.
152. Soto, Rodrigo; Golestanian, Ramin (2014). "Self-Assembly of Catalytically Active Colloidal Molecules: Tailoring Activity Through Surface Chemistry". Physical Review Letters. 112 (6): 068301. arXiv: 1306.6596 . Bibcode:2014PhRvL.112f8301S. doi:10.1103/PhysRevLett.112.068301. PMID   24580712. S2CID   37057964.
153. Niu, Ran; Fischer, Andreas; Palberg, Thomas; Speck, Thomas (2018). "Dynamics of Binary Active Clusters Driven by Ion-Exchange Particles". ACS Nano. 12 (11): 10932–10938. doi:10.1021/acsnano.8b04221. PMID   30346687. S2CID   206722021.
154. Ma, Fuduo; Wang, Sijia; Wu, David T.; Wu, Ning (2015). "Electric-field–induced assembly and propulsion of chiral colloidal clusters". Proceedings of the National Academy of Sciences. 112 (20): 6307–6312. Bibcode:2015PNAS..112.6307M. doi: 10.1073/pnas.1502141112 . PMC   4443365 . PMID   25941383.
155. Wang, Zuochen; Wang, Zhisheng; Li, Jiahui; Tian, Changhao; Wang, Yufeng (2020). "Active colloidal molecules assembled via selective and directional bonds". Nature Communications. 11 (1): 2670. Bibcode:2020NatCo..11.2670W. doi:10.1038/s41467-020-16506-z. PMC   7260206 . PMID   32471993.
156. Ebbens, Stephen; Jones, Richard A. L.; Ryan, Anthony J.; Golestanian, Ramin; Howse, Jonathan R. (2010). "Self-assembled autonomous runners and tumblers". Physical Review E. 82 (1 Pt 2): 015304. Bibcode:2010PhRvE..82a5304E. doi:10.1103/PhysRevE.82.015304. PMID   20866681.
157. Ni, Songbo; Marini, Emanuele; Buttinoni, Ivo; Wolf, Heiko; Isa, Lucio (2017). "Hybrid colloidal microswimmers through sequential capillary assembly". Soft Matter. 13 (23): 4252–4259. Bibcode:2017SMat...13.4252N. doi:10.1039/c7sm00443e. PMID   28573270.
158. Wang, Zuochen; Wang, Zhisheng; Li, Jiahui; Cheung, Simon Tsz Hang; Tian, Changhao; Kim, Shin-Hyun; Yi, Gi-Ra; Ducrot, Etienne; Wang, Yufeng (2019). "Active Patchy Colloids with Shape-Tunable Dynamics". Journal of the American Chemical Society. 141 (37): 14853–14863. doi:10.1021/jacs.9b07785. PMID   31448592. S2CID   201748635.
159. Hu, Chengzhi; Pané, Salvador; Nelson, Bradley J. (2018). "Soft Micro- and Nanorobotics". Annual Review of Control, Robotics, and Autonomous Systems. 1: 53–75. doi:10.1146/annurev-control-060117-104947. hdl: 20.500.11850/316345 . S2CID   139844553.
160. Palagi, Stefano; Fischer, Peer (2018). "Bioinspired microrobots". Nature Reviews Materials. 3 (6): 113–124. Bibcode:2018NatRM...3..113P. doi:10.1038/s41578-018-0016-9. S2CID   189929035.
161. Medina-Sánchez, Mariana; Magdanz, Veronika; Guix, Maria; Fomin, Vladimir M.; Schmidt, Oliver G. (2018). "Swimming Microrobots: Soft, Reconfigurable, and Smart". Advanced Functional Materials. 28 (25). doi:10.1002/adfm.201707228. S2CID   103866599.
162. Hu, Wenqi; Lum, Guo Zhan; Mastrangeli, Massimo; Sitti, Metin (2018). "Small-scale soft-bodied robot with multimodal locomotion". Nature. 554 (7690): 81–85. Bibcode:2018Natur.554...81H. doi:10.1038/nature25443. PMID   29364873. S2CID   4461200.
163. Huang, H.-W.; Uslu, F. E.; Katsamba, P.; Lauga, E.; Sakar, M. S.; Nelson, B. J.; Nelson, Bradley J. (2019). "Adaptive locomotion of artificial microswimmers". Science Advances. 5 (1): eaau1532. arXiv: 1902.09000 . Bibcode:2019SciA....5.1532H. doi:10.1126/sciadv.aau1532. PMC   6357760 . PMID   30746446.
164. Dou, Yong; Bishop, Kyle J. M. (2019). "Autonomous navigation of shape-shifting microswimmers". Physical Review Research. 1 (3): 032030. arXiv: 1908.05808 . Bibcode:2019PhRvR...1c2030D. doi:10.1103/PhysRevResearch.1.032030. S2CID   201058417.
165. Zhuang, Jiang; Park, Byung-Wook; Sitti, Metin (2017). "Propulsion and Chemotaxis in Bacteria-Driven Microswimmers". Advanced Science. 4 (9). doi:10.1002/advs.201700109. PMC   5604384 . PMID   28932674. Material was copied from this source, which is available under a Creative Commons Attribution 4.0 International License.
166. Sun, Zhiyong; Popp, Philipp; Loderer, Christoph; Revilla-Guarinos, Ainhoa (28 December 2019). "Genetically Engineered Bacterial Biohybrid Microswimmers for Sensing Applications". Sensors. 20 (1). MDPI AG: 180. Bibcode:2019Senso..20..180S. doi: 10.3390/s20010180 . ISSN   1424-8220. PMC   6982730 . PMID   31905650. Material was copied from this source, which is available under a Creative Commons Attribution 4.0 International License
167. Carlsen, Rika Wright; Sitti, Metin (2014). "Bio-Hybrid Cell-Based Actuators for Microsystems". Small. 10 (19): 3831–3851. doi:10.1002/smll.201400384. PMID   24895215.
168. Hosseinidoust, Zeinab; Mostaghaci, Babak; Yasa, Oncay; Park, Byung-Wook; Singh, Ajay Vikram; Sitti, Metin (2016). "Bioengineered and biohybrid bacteria-based systems for drug delivery". Advanced Drug Delivery Reviews. 106 (Pt A): 27–44. doi:10.1016/j.addr.2016.09.007. PMID   27641944.
169. Magdanz, Veronika; Medina-Sánchez, Mariana; Schwarz, Lukas; Xu, Haifeng; Elgeti, Jens; Schmidt, Oliver G. (2017). "Spermatozoa as Functional Components of Robotic Microswimmers". Advanced Materials. 29 (24). Bibcode:2017AdM....2906301M. doi:10.1002/adma.201606301. PMID   28323360. S2CID   26622101.
170. Medina-Sánchez, Mariana; Schmidt, Oliver G. (2017). "Medical microbots need better imaging and control". Nature. 545 (7655): 406–408. Bibcode:2017Natur.545..406M. doi: 10.1038/545406a . PMID   28541344. S2CID   4388403.
171. Magdanz, Veronika; Medina-Sánchez, Mariana; Schwarz, Lukas; Xu, Haifeng; Elgeti, Jens; Schmidt, Oliver G. (2017). "Spermatozoa as Functional Components of Robotic Microswimmers". Advanced Materials. 29 (24). Bibcode:2017AdM....2906301M. doi:10.1002/adma.201606301. PMID   28323360. S2CID   26622101.
172. Medina-Sánchez, Mariana; Schmidt, Oliver G. (2017). "Medical microbots need better imaging and control". Nature. 545 (7655): 406–408. Bibcode:2017Natur.545..406M. doi: 10.1038/545406a . PMID   28541344. S2CID   4388403.
173. Daddi-Moussa-Ider, Abdallah; Löwen, Hartmut; Liebchen, Benno (2021). "Hydrodynamics can determine the optimal route for microswimmer navigation". Communications Physics. 4 (1): 15. arXiv: 2008.11064 . Bibcode:2021CmPhy...4...15D. doi: 10.1038/s42005-021-00522-6 . S2CID   234012727. Material was copied from this source, which is available under a Creative Commons Attribution 4.0 International License.
174. Schwarzendahl, Fabian Jan; Mazza, Marco G. (2018). "Maximum in density heterogeneities of active swimmers". Soft Matter. 14 (23): 4666–4678. arXiv: 1711.08689 . Bibcode:2018SMat...14.4666S. doi: 10.1039/C7SM02301D . PMID   29717736.
175. Theers, Mario; Westphal, Elmar; Qi, Kai; Winkler, Roland G.; Gompper, Gerhard (31 October 2018). "Clustering of microswimmers: interplay of shape and hydrodynamics". Soft Matter. 14 (42): 8590–8603. arXiv: 1807.01211 . Bibcode:2018SMat...14.8590T. doi: 10.1039/C8SM01390J . PMID   30339172.
176. Kirk, Donald (2004). Optimal control theory : an introduction. Mineola, N.Y. ISBN   978-0-486-13507-6.
177. Viswanathan, Gandhimohan. M.; Da Luz, Marcos G. E.; Raposo, Ernesto P.; Stanley, H. Eugene (2011). The Physics of Foraging. doi:10.1017/CBO9780511902680. ISBN   9780511902680.
178. Fricke, G. Matthew; Letendre, Kenneth A.; Moses, Melanie E.; Cannon, Judy L. (2016). "Persistence and Adaptation in Immunity: T Cells Balance the Extent and Thoroughness of Search". PLOS Computational Biology. 12 (3): e1004818. Bibcode:2016PLSCB..12E4818F. doi: 10.1371/journal.pcbi.1004818 . PMC   4798282 . PMID   26990103.
179. Muiños-Landin, S.; Fischer, A.; Holubec, V.; Cichos, F. (2021). "Reinforcement learning with artificial microswimmers". Science Robotics. 6 (52). arXiv: 1803.06425 . doi:10.1126/scirobotics.abd9285. PMID   34043550. S2CID   4938282.
180. Yang, Yuguang; Bevan, Michael A. (2018). "Optimal Navigation of Self-Propelled Colloids". ACS Nano. 12 (11): 10712–10724. doi:10.1021/acsnano.8b05371. PMID   30252442. S2CID   52824752.
181. Yang, Yuguang; Bevan, Michael A.; Li, Bo (2020). "Efficient Navigation of Colloidal Robots in an Unknown Environment via Deep Reinforcement Learning". Advanced Intelligent Systems. 2. arXiv: 1906.10844 . doi: 10.1002/aisy.201900106 . S2CID   199000857.
182. Liebchen, Benno; Löwen, Hartmut (2019). "Optimal navigation strategies for active particles". EPL (Europhysics Letters). 127 (3): 34003. Bibcode:2019EL....12734003L. doi:10.1209/0295-5075/127/34003. S2CID   203038971.
183. Schneider, E.; Stark, H. (2019). "Optimal steering of a smart active particle". EPL (Europhysics Letters). 127 (6): 64003. arXiv: 1909.03243 . Bibcode:2019EL....12764003S. doi:10.1209/0295-5075/127/64003. S2CID   202540395.
184. Biferale, L.; Bonaccorso, F.; Buzzicotti, M.; Clark Di Leoni, P.; Gustavsson, K. (2019). "Zermelo's problem: Optimal point-to-point navigation in 2D turbulent flows using reinforcement learning". Chaos: An Interdisciplinary Journal of Nonlinear Science. 29 (10): 103138. arXiv: 1907.08591 . Bibcode:2019Chaos..29j3138B. doi:10.1063/1.5120370. PMID   31675828. S2CID   197935446.
185. Lauga, Eric (2016). "Bacterial Hydrodynamics". Annual Review of Fluid Mechanics. 48 (1): 105–130. arXiv: 1509.02184 . Bibcode:2016AnRFM..48..105L. doi:10.1146/annurev-fluid-122414-034606. S2CID   13849152.
186. Lauga, Eric (2020). The fluid dynamics of cell motility. Cambridge, United Kingdom New York, NY. ISBN   978-1-107-17465-8.
187. Romanczuk, P.; Bär, M.; Ebeling, W.; Lindner, B.; Schimansky-Geier, L. (2012). "Active Brownian particles". The European Physical Journal Special Topics. 202: 1–162. arXiv: 1202.2442 . doi:10.1140/epjst/e2012-01529-y. S2CID   119100040.
188. Cates, Michael E.; Tailleur, Julien (2015). "Motility-Induced Phase Separation". Annual Review of Condensed Matter Physics. 6: 219–244. arXiv: 1406.3533 . Bibcode:2015ARCMP...6..219C. doi:10.1146/annurev-conmatphys-031214-014710. S2CID   15672131.
189. Zöttl, Andreas; Stark, Holger (11 May 2016). "Emergent behavior in active colloids". Journal of Physics: Condensed Matter. 28 (25). IOP Publishing: 253001. arXiv: 1601.06643 . Bibcode:2016JPCM...28y3001Z. doi:10.1088/0953-8984/28/25/253001. ISSN   0953-8984. S2CID   3948148.
190. Sutton, Richard (2018). Reinforcement learning : an introduction. Cambridge, Massachusetts: The MIT Press. ISBN   978-0-262-35270-3.
191. Cichos, Frank; Gustavsson, Kristian; Mehlig, Bernhard; Volpe, Giovanni (2020). "Machine learning for active matter". Nature Machine Intelligence. 2 (2): 94–103. doi:10.1038/s42256-020-0146-9. S2CID   214355969.
192. Garnier, Paul; Viquerat, Jonathan; Rabault, Jean; Larcher, Aurélien; Kuhnle, Alexander; Hachem, Elie (2021). "A review on deep reinforcement learning for fluid mechanics". Computers & Fluids. 225: 104973. arXiv: 1908.04127 . doi:10.1016/j.compfluid.2021.104973. S2CID   199543817.
193. Colabrese, Simona; Gustavsson, Kristian; Celani, Antonio; Biferale, Luca (2017). "Flow Navigation by Smart Microswimmers via Reinforcement Learning". Physical Review Letters. 118 (15): 158004. arXiv: 1701.08848 . Bibcode:2017PhRvL.118o8004C. doi:10.1103/PhysRevLett.118.158004. PMID   28452499. S2CID   13695532.
194. Yang, Yuguang; Bevan, Michael A.; Li, Bo (2020). "Micro/Nano Motor Navigation and Localization via Deep Reinforcement Learning". Advanced Theory and Simulations. 3 (6). arXiv: 2002.06775 . doi:10.1002/adts.202000034. S2CID   211133324.
195. Zou, Zonghao; Liu, Yuexin; Young, Y.-N.; Pak, On Shun; Tsang, Alan C. H. (2022-06-21). "Gait switching and targeted navigation of microswimmers via deep reinforcement learning". Communications Physics. 5 (1): 158. Bibcode:2022CmPhy...5..158Z. doi: 10.1038/s42005-022-00935-x . ISSN   2399-3650.
196. Feynman, R. (2018). "There’s plenty of room at the bottom". In: Hey, Anthony (2018). Feynman and computation : exploring the limits of computers. Boca Raton: CRC Press. pp. 63–76. ISBN   978-0-429-50045-9.
197. Kuzajewska, Danuta; Wszołek, Agata; Żwierełło, Wojciech; Kirczuk, Lucyna; Maruszewska, Agnieszka (19 May 2020). "Magnetotactic Bacteria and Magnetosomes as Smart Drug Delivery Systems: A New Weapon on the Battlefield with Cancer?". Biology. 9 (5). MDPI AG: 102. doi: 10.3390/biology9050102 . ISSN   2079-7737. PMC   7284773 . PMID   32438567.
198. Sitti, Metin (2009). "Voyage of the microrobots". Nature. 458 (7242): 1121–1122. doi: 10.1038/4581121a . PMID   19407789. S2CID   205044764.
199. Harari, Yuval (2016). Homo deus : a brief history of tomorrow. London: Harvill Secker. ISBN   978-1-4735-4537-3.
200. Qiu, Famin; Fujita, Satoshi; Mhanna, Rami; Zhang, Li; Simona, Benjamin R.; Nelson, Bradley J. (2015). "Magnetic Helical Microswimmers Functionalized with Lipoplexes for Targeted Gene Delivery". Advanced Functional Materials. 25 (11): 1666–1671. doi:10.1002/adfm.201403891. S2CID   95812709.
201. Park, Byung-Wook; Zhuang, Jiang; Yasa, Oncay; Sitti, Metin (2017). "Multifunctional Bacteria-Driven Microswimmers for Targeted Active Drug Delivery". ACS Nano. 11 (9): 8910–8923. doi:10.1021/acsnano.7b03207. PMID   28873304.
202. Wang, Joseph; Gao, Wei (2012). "Nano/Microscale Motors: Biomedical Opportunities and Challenges". ACS Nano. 6 (7): 5745–5751. doi:10.1021/nn3028997. PMID   22770233.
203. Ma, Xing; Hahn, Kersten; Sanchez, Samuel (2015). "Catalytic Mesoporous Janus Nanomotors for Active Cargo Delivery". Journal of the American Chemical Society. 137 (15): 4976–4979. doi:10.1021/jacs.5b02700. PMC   4440854 . PMID   25844893.
204. Demirörs, Ahmet F.; Akan, Mehmet Tolga; Poloni, Erik; Studart, André R. (2018). "Active cargo transport with Janus colloidal shuttles using electric and magnetic fields". Soft Matter. 14 (23): 4741–4749. Bibcode:2018SMat...14.4741D. doi:10.1039/C8SM00513C. PMID   29799053.
205. Nelson, Bradley J.; Kaliakatsos, Ioannis K.; Abbott, Jake J. (2010). "Microrobots for Minimally Invasive Medicine". Annual Review of Biomedical Engineering. 12: 55–85. doi:10.1146/annurev-bioeng-010510-103409. PMID   20415589.
206. Soto, Fernando; Wang, Jie; Ahmed, Rajib; Demirci, Utkan (2020). "Medical Micro/Nanorobots in Precision Medicine". Advanced Science. 7 (21). doi:10.1002/advs.202002203. PMC   7610261 . PMID   33173743.
207. Ceylan, Hakan; Yasa, Immihan C; Kilic, Ugur; Hu, Wenqi; Sitti, Metin (16 July 2019). "Translational prospects of untethered medical microrobots". Progress in Biomedical Engineering. 1 (1). IOP Publishing: 012002. doi: 10.1088/2516-1091/ab22d5 . ISSN   2516-1091. S2CID   199341199. Material was copied from this source, which is available under a Creative Commons Attribution 3.0 International License.
208. Erglis, Kaspars; Wen, Qi; Ose, Velta; Zeltins, Andris; Sharipo, Anatolijs; Janmey, Paul A.; Cēbers, Andrejs (2007-08-15). "Dynamics of magnetotactic bacteria in a rotating magnetic field". Biophysical Journal. 93 (4): 1402–1412. Bibcode:2007BpJ....93.1402E. doi:10.1529/biophysj.107.107474. ISSN   0006-3495. PMC   1929029 . PMID   17526564.
209. Mirkhani, Nima; Christiansen, Michael G.; Gwisai, Tinotenda; Menghini, Stefano; Schuerle, Simone (2024-03-09). "Spatially selective delivery of living magnetic microrobots through torque-focusing". Nature Communications. 15 (1): 2160. Bibcode:2024NatCo..15.2160M. doi:10.1038/s41467-024-46407-4. ISSN   2041-1723. PMC   10924878 . PMID   38461256.
210. Teng, Xiangyu; Qiao, Zezheng; Yu, Shuxuan; Liu, Yujie; Lou, Xinyu; Zhang, Huanbin; Ge, Zhixing; Yang, Wenguang (April 2024). "Recent Advances in Microrobots Powered by Multi-Physics Field for Biomedical and Environmental Applications". Micromachines. 15 (4): 492. doi: 10.3390/mi15040492 . ISSN   2072-666X. PMC   11051856 . PMID   38675303.
211. Dekanovsky, Lukas; Huang, Hai; Akir, Sana; Ying, Yulong; Sofer, Zdenek; Khezri, Bahareh (August 2023). "Light-Driven MXene-Based Microrobots: Mineralization of Bisphenol A to CO 2 and H 2 O". Small Methods. 7 (8): e2201547. doi: 10.1002/smtd.202201547 . ISSN   2366-9608. PMID   37075736.