Analyte Sensing with Catalytic Micromotors
Abstract
Catalytic micromotors can be used to detect molecules of interest in several ways. The straightforward approach is to use such motors as sensors of their “fuel” (i.e., of the species consumed for self-propulsion). Another way is in the detection of species which are not fuel but still modulate the catalytic processes facilitating self-propulsion. Both of these require analysis of the motion of the micromotors because the speed (or the diffusion coefficient) of the micromotors is the analytical signal. Alternatively, catalytic micromotors can be used as the means to enhance mass transport, and thus increase the probability of specific recognition events in the sample. This latter approach is based on “classic” (e.g., electrochemical) analytical signals and does not require an analysis of the motion of the micromotors. Together with a discussion of the current limitations faced by sensing concepts based on the speed (or diffusion coefficient) of catalytic micromotors, we review the findings of the studies devoted to the analytical performances of catalytic micromotor sensors. We conclude that the qualitative (rather than quantitative) analysis of small samples, in resource poor environments, is the most promising niche for the catalytic micromotors in analytical chemistry.
Full text
Citation: Popescu, M.N.; Gáspár, S. Analyte Sensing with Catalytic Micromotors. Biosensors 2023,13, 45. https://doi.org/10.3390/ bios13010045 Received: 15 November 2022 Revised: 21 December 2022 Accepted: 23 December 2022 Published: 28 December 2022 Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). biosensors Review Analyte Sensing with Catalytic Micromotors Mihail N. Popescu 1and Szilveszter Gáspár2,* 1Física Teórica, Universidad de Sevilla, Apdo. 1065, E-41080 Sevilla, Spain 2International Centre of Biodynamics, 1B Intrarea Portocalelor, 060101 Bucharest, Romania *Correspondence: [email protected]o Abstract: Catalytic micromotors can be used to detect molecules of interest in several ways. The straightforward approach is to use such motors as sensors of their “fuel” (i.e., of the species consumed for self-propulsion). Another way is in the detection of species which are not fuel but still modulate the catalytic processes facilitating self-propulsion. Both of these require analysis of the motion of the micromotors because the speed (or the diffusion coefficient) of the micromotors is the analytical signal. Alternatively, catalytic micromotors can be used as the means to enhance mass transport, and thus increase the probability of specific recognition events in the sample. This latter approach is based on “classic” (e.g., electrochemical) analytical signals and does not require an analysis of the motion of the micromotors. Together with a discussion of the current limitations faced by sensing concepts based on the speed (or diffusion coefficient) of catalytic micromotors, we review the findings of the studies devoted to the analytical performances of catalytic micromotor sensors. We conclude that the qualitative (rather than quantitative) analysis of small samples, in resource poor environments, is the most promising niche for the catalytic micromotors in analytical chemistry. Keywords: catalytic micromotor; self-propulsion; motion-based sensing; enhanced diffusion coefficient 1. Introduction Catalytic micromotors are micrometer-sized objects which self-propel when the solution in which they are suspended contains species which they can chemically convert. It is important to note that in order to achieve self-propulsion, the object should be, in general, sufficiently asymmetric either in shape or/and in the surface distribution of the catalytic processes (e.g., only on half of a spherical microbead, see Figure 1) such that a preferred direction can be defined. (The breaking of the isotropic symmetry, which is necessary for motion, can also be induced by boundaries; e.g., a spherical catalytic particle near a wall would exhibit motion in the direction normal to the wall.) While at short times the motion of a typical micromotor is ballistic, at long times it crosses over to Brownian motion, albeit with an effectively enhanced diffusion coefficient that depends on the activity of the particle [1]. The first catalytic micromotors were reported in 2004: bimetallic nanorods (1–2 µ m long, ~200 nm in diameter, half Au and half Pt), which self-propelled by catalyzing the decomposition of H 2 O 2 [ 2 ]. This report has been followed by intense research efforts aimed at, among others: determining the mechanism of propulsion [ 3 , 4 ], the development of micromotors propelling with other “fuel” than H 2 O 2 [ 5 – 7 ], controlling the trajectory of the micromotors [ 8 – 10 ], and, obviously, related to the question of applications for catalytic micromotors [ 11 , 12 ]. The efforts aimed at determining the mechanism of propulsion, for example, have shown that self-propulsion can occur via self-diffusiophoresis [ 1 ], selfelectrophoresis [3,4], or bubble ejection [13]. Biosensors 2023,13, 45. https://doi.org/10.3390/bios13010045 https://www.mdpi.com/journal/biosensors
Biosensors 2023,13, 45 2 of 21 Biosensors2023,13,xFORPEERREVIEW2of22 Figure1.(A)SchematicrepresentationofaJanus‐typecatalyticmicromotorself‐propellingbythe photochemicaldecompositionofH2O2inthevicinityofawall;(B)CalculatedconcentrationgradientsproducedbyaJanus‐typecatalyticmicromotorinthevicinityofawall(colorcoded,inarbitrary units)superimposedovercalculatedhydrodynamicflowsproducedbysuchacatalyticmicromotor (streamlines,white);FiguresreproducedwithpermissionfromRef.[14]. Oneverypromisingapplicationofcatalyticmicromotorsisinanalyticalchemistry, inthedetectionandquantificationofdifferentmoleculesofinterestinfood,environmental,orclinicalsamples.Catalyticmicromotorscanbeusedtodetectmoleculesofinterest inseveralways(seealsoFigure2).Themoststraightforwardwayistousethemicromotorstodetecttheir“fuel”(i.e.,thespeciestheyareconsumingforself‐propulsion)(see Figure2A).Thisapproachrequiresthemotionofthemicromotorstobecarefullyanalyzed (usingamicroscope,adigitalcamera,andtheappropriatesoftwaretools)becausethe speed(orthediffusioncoefficient)ofthemicromotors,whichdependsontheconcentrationoftheanalyteofinterest,istheanalyticalsignal.Anotherpossibilityistousethe micromotorstodetectspecieswhichdonotactasfuelforthemicromotorbutstillcan modulate(e.g.,inhibit,activate,etc.)thecatalyticprocessesfacilitatingself‐propulsion. Thisapproachalsorequiresananalysisofthemotionofthemicromotorsbecausethe speed(ordiffusioncoefficient)ofthemicromotorsremainstheusefulanalyticalsignal. Finally,catalyticmicromotorscanalsobeusedastoolsforenhancingthemasstransport (andtheprobabilityofspecificrecognitionevents)inthesample.Thisapproachisbased on“classic”(e.g.,electrochemical,fluorescent,etc.)analyticalsignalsanddoesnotrequire anin‐depthanalysisofthemotionofthemicromotors(seeFigure2B). Figure2.Twowaysofusingcatalyticmicromotorsforsensing.(A)Fuelmolecules(orspecieswhich inhibit/activatethecatalyticprocess)aretheanalytesofinterestandeitherthespeedorthediffusion coefficientofthecatalyticmicromotorsistheanalyticalsignal;(B)Theself‐propulsionofthecatalyticmicromotorshastheroleofincreasingmasstransportwithintheinvestigatedsample(byinducingflowofthesolutionandbymovingthroughthesolution).Thecatalyticmicromotorscarry classicbiomoleculedetectionschemes(suchasthe“sandwich‐type”detectionschemebasedon Figure 1. ( A ) Schematic representation of a Janus-type catalytic micromotor self-propelling by the photochemical decomposition of H 2 O 2 in the vicinity of a wall; ( B ) Calculated concentration gradients produced by a Janus-type catalytic micromotor in the vicinity of a wall (color coded, in arbitrary units) superimposed over calculated hydrodynamic flows produced by such a catalytic micromotor (streamlines, white); Figures reproduced with permission from Ref. [14]. One very promising application of catalytic micromotors is in analytical chemistry, in the detection and quantification of different molecules of interest in food, environmental, or clinical samples. Catalytic micromotors can be used to detect molecules of interest in several ways (see also Figure 2). The most straightforward way is to use the micromotors to detect their “fuel” (i.e., the species they are consuming for self-propulsion) (see Figure 2A). This approach requires the motion of the micromotors to be carefully analyzed (using a microscope, a digital camera, and the appropriate software tools) because the speed (or the diffusion coefficient) of the micromotors, which depends on the concentration of the analyte of interest, is the analytical signal. Another possibility is to use the micromotors to detect species which do not act as fuel for the micromotor but still can modulate (e.g., inhibit, activate, etc.) the catalytic processes facilitating self-propulsion. This approach also requires an analysis of the motion of the micromotors because the speed (or diffusion coefficient) of the micromotors remains the useful analytical signal. Finally, catalytic micromotors can also be used as tools for enhancing the mass transport (and the probability of specific recognition events) in the sample. This approach is based on “classic” (e.g., electrochemical, fluorescent, etc.) analytical signals and does not require an in-depth analysis of the motion of the micromotors (see Figure 2B). Biosensors2023,13,xFORPEERREVIEW2of22 Figure1.(A)SchematicrepresentationofaJanus‐typecatalyticmicromotorself‐propellingbythe photochemicaldecompositionofH2O2inthevicinityofawall;(B)CalculatedconcentrationgradientsproducedbyaJanus‐typecatalyticmicromotorinthevicinityofawall(colorcoded,inarbitrary units)superimposedovercalculatedhydrodynamicflowsproducedbysuchacatalyticmicromotor (streamlines,white);FiguresreproducedwithpermissionfromRef.[14]. Oneverypromisingapplicationofcatalyticmicromotorsisinanalyticalchemistry, inthedetectionandquantificationofdifferentmoleculesofinterestinfood,environmental,orclinicalsamples.Catalyticmicromotorscanbeusedtodetectmoleculesofinterest inseveralways(seealsoFigure2).Themoststraightforwardwayistousethemicromotorstodetecttheir“fuel”(i.e.,thespeciestheyareconsumingforself‐propulsion)(see Figure2A).Thisapproachrequiresthemotionofthemicromotorstobecarefullyanalyzed (usingamicroscope,adigitalcamera,andtheappropriatesoftwaretools)becausethe speed(orthediffusioncoefficient)ofthemicromotors,whichdependsontheconcentrationoftheanalyteofinterest,istheanalyticalsignal.Anotherpossibilityistousethe micromotorstodetectspecieswhichdonotactasfuelforthemicromotorbutstillcan modulate(e.g.,inhibit,activate,etc.)thecatalyticprocessesfacilitatingself‐propulsion. Thisapproachalsorequiresananalysisofthemotionofthemicromotorsbecausethe speed(ordiffusioncoefficient)ofthemicromotorsremainstheusefulanalyticalsignal. Finally,catalyticmicromotorscanalsobeusedastoolsforenhancingthemasstransport (andtheprobabilityofspecificrecognitionevents)inthesample.Thisapproachisbased on“classic”(e.g.,electrochemical,fluorescent,etc.)analyticalsignalsanddoesnotrequire anin‐depthanalysisofthemotionofthemicromotors(seeFigure2B). Figure2.Twowaysofusingcatalyticmicromotorsforsensing.(A)Fuelmolecules(orspecieswhich inhibit/activatethecatalyticprocess)aretheanalytesofinterestandeitherthespeedorthediffusion coefficientofthecatalyticmicromotorsistheanalyticalsignal;(B)Theself‐propulsionofthecatalyticmicromotorshastheroleofincreasingmasstransportwithintheinvestigatedsample(byinducingflowofthesolutionandbymovingthroughthesolution).Thecatalyticmicromotorscarry classicbiomoleculedetectionschemes(suchasthe“sandwich‐type”detectionschemebasedon Figure 2. Two ways of using catalytic micromotors for sensing. ( A ) Fuel molecules (or species which inhibit/activate the catalytic process) are the analytes of interest and either the speed or the diffusion coefficient of the catalytic micromotors is the analytical signal; ( B ) The self-propulsion of the catalytic micromotors has the role of increasing mass transport within the investigated sample (by inducing flow of the solution and by moving through the solution). The catalytic micromotors carry classic biomolecule detection schemes (such as the “sandwich-type” detection scheme based on antibodies schematically shown here). Depending on the detection scheme, in this approach, the analytical signal is either optical or electrochemical.
Biosensors 2023,13, 45 3 of 21 Catalytic micromotors are often obtained by complicated, multistep procedures. Many of them also rely on rather expensive materials (e.g., noble metals, purified enzymes, antibodies, etc.). Cost-effective mass production of highly reproducible catalytic micromotors is still a problem in spite of improvements brought about by, for example, Pickering emulsionbased methods [ 15 , 16 ]. Why are catalytic micromotors still interesting for sensing in such conditions? There are several features which recommend catalytic micromotors for sensing: (i). First, catalytic micromotors can analyze tiny samples. They can be suspended and their motion investigated in few microliters of sample. This is useful when large volumes of samples are not available (e.g., blood samples collected from very low birth weight infants [ 17 – 19 ]). One should also note that the ability to work with small samples advantageously translates into smaller amounts of chemical/biological waste. (ii). Second, catalytic micromotors (via their motion) can facilitate enhanced mass transport within the investigated sample without using laboratory equipment (such as stirrers, vortexes, or pumps). This can be important for investigations carried out outside specialized laboratories, in remote areas with limited resources. It can also be important when analyzing a few microliters of sample (e.g., a drop of blood, sweat, tear, or saliva placed on a microscope glass slide). There are very few tools for stirring/mixing within tiny liquid droplets. (iii). Third, the signal of the catalytic micromotors can be documented using a mobile phone instead of a bulky, expensive laboratory equipment. In turn, this can facilitate sensing outside specialized laboratories and, eventually, by untrained users. While this possibility represents an advantage over classic analytical approaches (which most often require bulky and expensive instrumentation that is used by trained staff in specialized laboratories), only a few times it has been demonstrated. Both the collective behavior of catalytic micromotors [ 20 ] and the individual behavior of catalytic micromotors [ 21 – 23 ] were already documented using mobile phone cameras and linked to the concentration of the analyte of interest. Reading the fluorescence of catalytic micromotors using a mobile phone was also recently reported [ 24 ]. Important to note, some of these mobile phone-based approaches to study micromotors are still relying on image analysis carried out on a computer. (iv). Forth, sensing with catalytic micromotors is characterized by high spatial resolution because each tiny motor reports on the concentration of the analyte in the solution adjacent to the micromotor. However, achieving sensing with high spatial resolution (i.e., building high resolution chemical 2D/3D maps with catalytic micromotors) is currently still hindered by the heterogeneity of the catalytic micromotors. One cannot be 100% sure that a micromotor self-propels faster/slower than the other because of the local availability/unavailability of the targeted analyte or because of intrinsic, but yet not well understood, heterogeneity from batch to batch, or even within the same batch, in the properties of the individual micromotors (see also Section 2), or for various other reasons related to the experimental setup. For example, it has been observed that hydrazine-fueled micromotors propel faster at the edges of a water droplet than in the middle of it, immediately after the droplet is exposed to hydrazine vapors [ 25 ]; this is so because in that setup the hydrazine vapors reach the micromotors faster through the shallow edges of the water droplet [25]. (v). Last but not least, catalytic micromotors can combine sensing with other functions (e.g., with neutralization of dangerous chemicals). However, this possibility was also seldom explored. Metal ions were both detected and collected/removed using some H2O2-propeled catalytic micromotors [26,27]. The above advantages (or possible advantages) of catalytic micromotors as sensors are currently explored by research groups worldwide and were highlighted in a good number of papers. The present (focused and thorough) overview of the catalytic micromotors used to detect and quantify analytes of interest complements several recent reviews about sensing with micromotors [28–37].
Biosensors 2023,13, 45 4 of 21 2. The Speed (or Diffusion Coefficient) of Catalytic Micromotors as Analytical Signal When considering potential ways to employ catalytic micromotors as sensors, it is intuitively appealing to attempt exploiting the dependence of the speed (or, alternatively, the effective diffusion coefficient) of the micromotor on the presence of the analyte of interest. This dependency can occur in several scenarios. The analyte of interest can be the reactant in the catalytic process responsible for the self-propulsion, that is, the analyte of interest acts as fuel for the catalytic micromotors. For example, micrometer sized objects modified with enzyme were used as sensors for the substrate of the respective enzyme [ 6 , 38 ]. The analyte of interest can be an inhibitor or activator of the catalytic process responsible for self-propulsion. For example, Au-Pt bimetallic nanorods were used to detect Ag + ions as these ions activated the decomposition of H 2 O 2 self -propelling such nanorods [ 39 ]. Finally, in certain situations, the analyte of interest can slow down the micromotor despite limited (or no) impact on the catalytic process responsible for self-propulsion. For example, the already mentioned Au-Pt nanorods were observed to be slowed down by common ions in rather low concentrations because ions impact the reaction-induced self-generated electric field [39,40]. In the following, we focus, for simplicity, on the case of self-phoretic spheroidal catalytic micromotors (see Figure 3A), with the analyte of interest being the reactant in the first-order chemical reaction powering the micromotor. Biosensors2023,13,xFORPEERREVIEW4of22 numberofpapers.Thepresent(focusedandthorough)overviewofthecatalyticmicromotorsusedtodetectandquantifyanalytesofinterestcomplementsseveralrecentreviewsaboutsensingwithmicromotors[28–37]. 2.TheSpeed(orDiffusionCoefficient)ofCatalyticMicromotorsasAnalyticalSignal Whenconsideringpotentialwaystoemploycatalyticmicromotorsassensors,itis intuitivelyappealingtoattemptexploitingthedependenceofthespeed(or,alternatively, theeffectivediffusioncoefficient)ofthemicromotoronthepresenceoftheanalyteofinterest.Thisdependencycanoccurinseveralscenarios.Theanalyteofinterestcanbethe reactantinthecatalyticprocessresponsiblefortheself‐propulsion,thatis,theanalyteof interestactsasfuelforthecatalyticmicromotors.Forexample,micrometersizedobjects modifiedwithenzymewereusedassensorsforthesubstrateoftherespectiveenzyme [6,38].Theanalyteofinterestcanbeaninhibitororactivatorofthecatalyticprocessresponsibleforself‐propulsion.Forexample,Au‐Ptbimetallicnanorodswereusedtodetect Ag+ionsastheseionsactivatedthedecompositionofH2O2self‐propellingsuchnanorods [39].Finally,incertainsituations,theanalyteofinterestcanslowdownthemicromotor despitelimited(orno)impactonthecatalyticprocessresponsibleforself‐propulsion.For example,thealreadymentionedAu‐Ptnanorodswereobservedtobesloweddownby commonionsinratherlowconcentrationsbecauseionsimpactthereaction‐inducedselfgeneratedelectricfield[39,40]. Inthefollowing,wefocus,forsimplicity,onthecaseofself‐phoreticspheroidalcatalyticmicromotors(seeFigure3A),withtheanalyteofinterestbeingthereactantinthe first‐orderchemicalreactionpoweringthemicromotor. Figure3.(A)Schematicrepresentationofspheroidalcatalyticmicromotorscharacterizedbythe samepolarsemi‐axis(R1),differentequatorialsemi‐axes(R2),and,thus,differentaspectratiossr= R2/R1.(Obs.:Thepartofthecatalyticmicromotorcoveredbycatalyst,thatis,theactivecapofthe catalyticmicromotor,isdepictedasablackarea.)(B)Thedependenceofthescaledphoreticvelocity (Vpr/V0)onthefractionofthesurfaceofthecatalyticmicromotorcoveredbythecatalyst(η0=−1,0, 1,respectively,correspondtoamicromotorsurfacewithnocatalyst,withthelowerhalfcoveredby thecatalyst,andcompletelycoveredbythecatalyst,respectively),foraprolate‐shapedspheroidal Figure 3. ( A ) Schematic representation of spheroidal catalytic micromotors characterized by the same polar semi-axis (R 1 ), different equatorial semi-axes (R 2 ), and, thus, different aspect ratios s r =R 2 /R 1 . (Obs.: The part of the catalytic micromotor covered by catalyst, that is, the active cap of the catalytic micromotor, is depicted as a black area). ( B ) The dependence of the scaled phoretic velocity (V pr /V 0 ) on the fraction of the surface of the catalytic micromotor covered by the catalyst ( η0 = − 1, 0, 1, respectively, correspond to a micromotor surface with no catalyst, with the lower half covered by the catalyst, and completely covered by the catalyst, respectively), for a prolate-shaped spheroidal catalytic micromotor with aspect ratio parameter ξ0 = (1 − s r2 ) −1/2 = 80 (s r = 0.9999), 2 (s r = 0.866), 1.1 (s r = 0.42), 1.01 (s r = 0.14), and 1.005 (s r = 0.099). The large ξ0 values correspond to a quasispherical shape, while the limit towards 1 corresponds to needle-like shapes (approximating a long rod). Figures reproduced with permission from Ref. [41].
Biosensors 2023,13, 45 5 of 21 One notes that from a theoretical perspective, this set-up seems optimal, in that the speed is maximal at half-coverage of the micromotor with catalyst (see Figure 3B). The speed is also quite robust against fabrication defects, if the axial-symmetry is preserved: the speed maximum is relatively flat with respect to the coverage, and thus variations in coverage have small influence on the value of the speed [ 41 ]. Furthermore, in the range of few micrometers of sizes, and for first-order kinetics of the catalytic chemical reaction, the self-phoretic speeds are independent of the size of the particle [1]. Using a spherical shape as an example, the arguments above can be made more quantitative in that the speed V, as a function of the coverage θ (the angle between the axis of the particle and the rim of the active cap; it runs from 0, for zero coverage, to π , for a fully covered sphere) by the catalyst, is given by V= (1 – cos 2θ )V 0 (where V 0 ~C is a characteristic velocity directly proportional with the concentration Cof fuel and, in a first-order approximation, independent on the size of the particle). Thus, in order to see, for example, a change in velocity by 10% from the nominal value at θ = π /2, which is the case of a Janus particle, the coverage θ ’should change to the one obeying cos 2θ ’ = 0.1, i.e., θ’≈0.4 π . This is a rather large variation, and most of the modern methods of manufacturing Janus colloids can perform better than that without particular technical demands. Although the theoretical point of view suggests a robust operation of Janus particles as motors, there is an increasing body of experimental evidence that there must exist additional parameters, beyond the geometrical aspects discussed above, that play an important role in the emerging motion. For example, for seemingly identical TiO 2 on SiO 2 Janus-type motors (with radius of 275 ± 8 nm), the active velocity is measured by Sachs et al. [ 42 ] to be a stochastic variable with a quasi-Gaussian distribution of a width comparable to the average value (7.9 µm/s; see Figure 4A). Biosensors2023,13,xFORPEERREVIEW5of22 catalyticmicromotorwithaspectratioparameterξ0=(1−sr2)−1/2=80(sr=0.9999),2(sr=0.866),1.1(sr =0.42),1.01(sr=0.14),and1.005(sr=0.099).Thelargeξ0valuescorrespondtoaquasi‐spherical shape,whilethelimittowards1correspondstoneedle‐likeshapes(approximatingalongrod).FiguresreproducedwithpermissionfromRef.[41]. Onenotesthatfromatheoreticalperspective,thisset‐upseemsoptimal,inthatthe speedismaximalathalf‐coverageofthemicromotorwithcatalyst(seeFigure3B).The speedisalsoquiterobustagainstfabricationdefects,iftheaxial‐symmetryispreserved: thespeedmaximumisrelativelyflatwithrespecttothecoverage,andthusvariationsin coveragehavesmallinfluenceonthevalueofthespeed[41].Furthermore,intherangeof fewmicrometersofsizes,andforfirst‐orderkineticsofthecatalyticchemicalreaction,the self‐phoreticspeedsareindependentofthesizeoftheparticle[1]. Usingasphericalshapeasanexample,theargumentsabovecanbemademorequantitativeinthatthespeedV,asafunctionofthecoverageθ(theanglebetweentheaxisof theparticleandtherimoftheactivecap;itrunsfrom0,forzerocoverage,toπ,forafully coveredsphere)bythecatalyst,isgivenbyV=(1–cos2θ)V0(whereV0~Cisacharacteristic velocitydirectlyproportionalwiththeconcentrationCoffueland,inafirst‐orderapproximation,independentonthesizeoftheparticle).Thus,inordertosee,forexample,a changeinvelocityby10%fromthenominalvalueatθ=π/2,whichisthecaseofaJanus particle,thecoverageθ’shouldchangetotheoneobeyingcos2θ’=0.1,i.e.,θ’≈0.4π.This isaratherlargevariation,andmostofthemodernmethodsofmanufacturingJanuscolloidscanperformbetterthanthatwithoutparticulartechnicaldemands. AlthoughthetheoreticalpointofviewsuggestsarobustoperationofJanusparticles asmotors,thereisanincreasingbodyofexperimentalevidencethattheremustexistadditionalparameters,beyondthegeometricalaspectsdiscussedabove,thatplayanimportantroleintheemergingmotion.Forexample,forseeminglyidenticalTiO2onSiO2 Janus‐typemotors(withradiusof275±8nm),theactivevelocityismeasuredbySachset al.[42]tobeastochasticvariablewithaquasi‐Gaussiandistributionofawidthcomparabletotheaveragevalue(7.9μm/s;seeFigure4A). Figure4.(A)VelocitydistributionsforTiO2onSiO2Janus‐typemotors(d~550nm)in2.5%H2O2; (B)VelocitydistributionforPtonpolystyreneJanus‐typemotors(d~2.8μm)in0.1%(top),1.0% (center)and3.0%(bottom)H2O2;(Obs.:(A)isbuiltusingtheaveragespeedofindividualmotors while(B)showsexperimentalvelocitydistributionsfor~30individualmicromotors).FiguresreproducedwithpermissionfromRef.[42](panel(A))andRef.[43](panel(B)). Figure 4. ( A ) Velocity distributions for TiO 2 on SiO 2 Janus-type motors (d ~550 nm) in 2.5% H 2 O 2 ; ( B ) Velocity distribution for Pt on polystyrene Janus-type motors (d ~2.8 µ m) in 0.1% (top), 1.0% (center) and 3.0% (bottom) H2O2; (Obs.: (A) is built using the average speed of individual motors while (B) shows experimental velocity distributions for ~30 individual micromotors). Figures reproduced with permission from Ref. [42] (panel (A)) and Ref. [43] (panel (B)). Similar findings are reported for nanorods (either classic Au-Pt or the more rapidly moving ones made with carbon nanotubes) [ 44 ], as well as for a variety of other types of Janus spheres in aqueous solutions of H 2 O 2 [ 43 , 45 ] (see Figure 4B). In all cases, this has been attributed to an intrinsic variability of the catalytic activity of the materials, but why this is happening, and how can it be controlled, remains thus far unclear. The impact of such large dispersion around the average value as in the data in Figure 4A is dramatic in what concerns the accuracy of an analytic method based on the value of the velocity
Biosensors 2023,13, 45 6 of 21 (which, in the first approximation, is proportional to the fuel concentration): a change in the velocity from the average value of ~8 µ m/s by a standard deviation of ~2 µ m/s represents a 25% variation which, unknowingly, would render an over (or under) estimated fuel concentration by the same percentage. The experimentally observed variability of the speed of catalytic micromotors impacts also the way catalytic micromotors are used as sensors. It is clear that the speed of a large sample of micromotors must be averaged into an average speed both during the calibration of the micromotors with solutions containing known concentrations of analyte and during the analysis of samples of unknown chemical composition. Only such average speeds will reflect the correct analyte concentration in the standard solutions and investigated samples. The wider the distribution of self-propulsion speeds, the larger the number of micromotors which must be analyzed for correct results. Analyzing the speed of large numbers of micromotors requires time, careful experiments (e.g., the experiments must have an optimal number of micromotors in the field of view, the micromotors must stay long enough in the field of view, etc.), and also important computing power. The experimentally observed variability of the speed also compromises the idea of using catalytic micromotors for sensing with spatial resolution (an idea that is based on the assumption that all catalytic micromotors taken into work are characterized by similar parameters of the motion in similar experimental conditions). In spite of the above-described complication, the first studies exploring catalytic micromotors as sensors did use the speed (or the diffusion coefficient) of the micromotors as the analytical signal (see, for example, [ 39 ]). Therefore, the following two sections will shortly review catalytic micromotor sensors which report on the concentration of the analyte of interest via the parameters of their motion. As we will see, the analyte of interest is either the fuel of the catalytic micromotors (Section 3) or a molecule which modulates the catalytic process without being the fuel of the catalytic micromotors (Section 4). 3. Sensing Analytes Which Are Also Fuel for the Catalytic Micromotors Most catalytic micromotors developed up to now use H 2 O 2 as fuel. These H 2 O 2 -fueled micromotors have been reported to exhibit dependence of the velocity on the concentration of H 2 O 2 . For example, already the first catalytic micromotors, developed by Paxton et al., self-propelled at speeds of 3.9 µ m/s in 0.03% H 2 O 2 and at speeds of 7.9 µ m/s in 3.3% H 2 O 2 [ 2 ]. Ni on Pt microtubes (L ~10–20 µ m, φ ~2–3 µ m) were shown to self-propel at speeds of 75 µ m/s in 1% H 2 O 2 and at speeds of 165 µ m/s in 5% H 2 O 2 [ 46 ]. Polycaprolactone microspheres (d ~30–40 µ m) carrying MnO 2 particles were also shown to self-propel at speeds of 15 µ m/s in 5% H 2 O 2 and at speeds of 43 µ m/s in 25% H 2 O 2 [ 47 ]. As a consequence of this repeatedly documented dependence, the idea of using catalytic micromotors as sensors for the quantification of their fuel molecules has emerged. However, a convincing practical implementation of the idea was slowed down by several findings. Particles presumably moving by self-electrophoresis or self-diffusiophoresis (two mechanisms of self-propulsion) were found to be very sensitive to the presence of ions in the solution, which is actually a ubiquitous feature in most real-world samples [ 39 , 48 ]. The speed of catalytic micromotors was found to disadvantageously depend on the sample matrix as well. For example, Mg-based catalytic micromotors (which self-propel by ejecting H 2 bubbles in H 2 O, a mechanism considered more robust than self-phoresis) were found to self-propel with speeds of 296 ± 40 µ m/s in water, 223 ± 38 µ m/s in whiskey, 108 ±18 µm/s in milk, and only 40 ± 8 µ m/s in serum at the same H 2 O 2 concentration [ 49 ]. The speed of catalytic micromotors was found to depend significantly also on the working temperature. For example, the speed of Pt-based rolled-up microtubes (self-propelling due to the O 2 bubbles produced in 1% H 2 O 2 ) was found to be ~400 µ m/s at 20 ◦ C and ~550 µ m/s at 25 ◦ C [ 50 ]. These additional dependencies (of the self-propulsion speed on sample features which are not always easy to control) very much weaken the robustness and reliability of catalytic micromotors as analytical tools. They add to the already mentioned problem related to
Biosensors 2023,13, 45 7 of 21 the variability of the speed of self-propulsion for seemingly similar catalytic micromotors (Section 2). The few studies in which catalytic micromotors are used as sensors for the detection of their fuel are shortly presented in Table 1. As highlighted in the table, the catalytic micromotors used to sense their fuel self-propel by either self-diffusiophoresis or selfelectrophoresis. The fascinating details of these two mechanisms are nicely presented in a review by Moran and Posner [40]. Table 1. Examples of catalytic micromotors which were used to detect/quantify their fuel. (Used abbreviations: GOx = glucose oxidase, GluOx = glutamate oxidase, XOD = xanthine oxidase, DL = detection limit, and LR = linear range). Targeted Analyte Motor Structure; Self Propulsion Mechanism Analytical Performances Ref. Glucose, glutamate and hypoxanthine ~2 µm long, half Pt and half poly(pyrrole) nanorods modified with GOx, GluOX or XOD; Self-diffusiophoresis; DL: 0.05 mM for glucose, 0.03 mM for hypoxanthine, and 0.19 mM for glutamate; LR: up to 1 mM for glucose and glutamate and up to 0.1 mM for hypoxanthine; Selectivity proved in diluted horse serum and cell culture medium; [6] Hydrazine (vapors) ~1 µm diameter Au microsphere with one hemisphere covered with a 20 nm thick layer of Ir; Self-electrophoresis; Vapors from a 2.5 mm diameter droplet containing 5–30% hydrazine induced self-propulsion of motors found in a second 2.5 mm diameter droplet (at 0.5–3 cm from the first); [25] Urea ~2 µm diameter hollow silica microcapsules modified with urease; Self-diffusiophoresis; Self-propulsion was investigated at urea concentrations from 10 mM to 200 mM; Impact of the purity of urease on the speed of self-propulsion was discovered; Some stability problems were also noted; [38] Figure 5completes Table 1and shows typical calibration curves which link the speed (or the diffusion coefficient) of the micromotors to the concentration of the fuel (i.e., analyte of interest). On one hand, the rather similar calibration curves obtained in water and serum (see Figure 5A) argue that enzyme-based micromotors can be used for sensing in relatively complex samples, such as 10 × diluted serum. On the other hand, these calibration curves also highlight yet another difficulty in using enzyme-based micromotors to detect their fuel molecules: the dependence of the speed (or the diffusion coefficient) of the micromotors on the concentration of the fuel is linear for a very narrow range of concentrations. (Moreover, in the case of GOX-based micromotors, there is the additional complication of the re-entrant behavior, i.e., for a range of increases of the diffusion coefficient, there are two possible values of the substrate concentration for each specific value of that increase.) The samples to be investigated must be diluted or preconcentrated in order to fit that range. However, while not convenient, this is not something unusual with other analytical tools as well.
Biosensors 2023,13, 45 8 of 21 Biosensors2023,13,xFORPEERREVIEW8of22 Figure5.(A)ThedependenceoftherelativediffusioncoefficientofXOD‐modifiednanorodsand ofGOx‐modifiednanorodsontheconcentrationofhypoxanthineandglucose,respectively,inwater andin10×dilutedserum;(B)Thedependenceofthereactionrate(inblue)andoftheself‐propulsion speed(inred)ofsilicamicrocapsulesmodifiedwithureaseontheconcentrationofureainwater; FiguresreproducedwithpermissionfromRef.[6](panel(A))andfromRef.[38](panel(B)). 4.SensingAnalytesWhichModulatetheSpeedofSelf‐PropulsionwithoutBeing FuelfortheCatalyticMicromotors Asnotedintheprevioussection,catalyticmicromotorswereusedonlyafewtimes todetectanalyteswhichalsoactasfuelforthem.Wehavediscussedanumberofcomplicationswhichseemtoexplainthelimitedpopularityofthatapproach.However,catalytic processescanbesloweddownoracceleratedbycertainspeciesotherthantheirreactants, andthecatalyticprocesseswhichself‐propelthecatalyticmicromotorsmakenoexception fromthisrule.Thisopensthepossibilitytousecatalyticmicromotorsalsoforthedetection oftheseinhibitorsandactivators.Asanotherpossibility,catalyticactivity(insteadofbeingsloweddownbyinhibitorsoracceleratedbyactivators)canbeconferredtomicrostructures(e.g.,amicrotube)fromscratch,viabiorecognitioneventsselectivefortheanalyteofinterest.ThisapproachwasusedtodetectDNA,forexample(seedetailsin[51]). ThedetectionrequiredmicrotubestobemodifiedwithcaptureDNA.Whenthetarget DNAwasavailableintheinvestigatedsample,themodifiedmicrotubeswereabletobind notonlytargetDNAbutalsodetectorDNAthatwaspreviouslylabeledwithPtnanoparticles.Thelatter,togetherwithH2O2asfuel,putthemicrotubesintomotionwithspeeds whichwereproportionalwiththeconcentrationofDNAtargetinthesample(seeFigure 6).Catalyticmicromotorsfacilitatednotonlythis“signalon”(orOFF‐ON)typebiosensingconceptbutalso“signaloff”(orON–OFF)typedetectionconcepts(see,forexample, inRef.[52]).Asafinalpossibility,onecanexploitthefactthataccumulationofantibody– antigen–antibodycomplexesonthesurfaceofcatalyticmicromotorscanslowdownthe self‐propulsionofcatalyticmicromotorsbyattachingsignificantweighttothecatalytic micromotors[53]. Figure 5. ( A ) The dependence of the relative diffusion coefficient of XOD-modified nanorods and of GOx-modified nanorods on the concentration of hypoxanthine and glucose, respectively, in water and in 10 × diluted serum; ( B ) The dependence of the reaction rate (in blue) and of the self-propulsion speed (in red) of silica microcapsules modified with urease on the concentration of urea in water; Figures reproduced with permission from Ref. [6] (panel (A)) and from Ref. [38] (panel (B)). 4. Sensing Analytes Which Modulate the Speed of Self-Propulsion without Being Fuel for the Catalytic Micromotors As noted in the previous section, catalytic micromotors were used only a few times to detect analytes which also act as fuel for them. We have discussed a number of complications which seem to explain the limited popularity of that approach. However, catalytic processes can be slowed down or accelerated by certain species other than their reactants, and the catalytic processes which self-propel the catalytic micromotors make no exception from this rule. This opens the possibility to use catalytic micromotors also for the detection of these inhibitors and activators. As another possibility, catalytic activity (instead of being slowed down by inhibitors or accelerated by activators) can be conferred to microstructures (e.g., a microtube) from scratch, via biorecognition events selective for the analyte of interest. This approach was used to detect DNA, for example (see details in [ 51 ]). The detection required microtubes to be modified with capture DNA. When the target DNA was available in the investigated sample, the modified microtubes were able to bind not only target DNA but also detector DNA that was previously labeled with Pt nanoparticles. The latter, together with H 2 O 2 as fuel, put the microtubes into motion with speeds which were proportional with the concentration of DNA target in the sample (see Figure 6). Catalytic micromotors facilitated not only this “signal on” (or OFF-ON) type biosensing concept but also “signal off” (or ON–OFF) type detection concepts (see, for example, in Ref. [ 52 ]). As a final possibility, one can exploit the fact that accumulation of antibody–antigen–antibody complexes on the surface of catalytic micromotors can slow down the self-propulsion of catalytic micromotors by attaching significant weight to the catalytic micromotors [53]. A summary of the studies employing catalytic micromotors to detect species which modulate the speed of the self-propulsion without being fuel for the catalytic micromotors is presented in Table 2.
Biosensors 2023,13, 45 9 of 21 Biosensors2023,13,xFORPEERREVIEW9of22 Figure6.(A)Modificationoftheinnersurfaceofpoly(3,4‐ethylenedioxythiophene)(PEDOT)onAu microtubeswithcaptureDNA,andofPtnanoparticleswithdetectorDNA;(B)Theprinciplefor DNAdetectionbyintroducingPtnanoparticle‐DNAconjugatesintothemicrotubesviaspecific DNAhybridizationmediatedbytargetDNA;FigurereproducedwithpermissionfromRef.[51]. Asummaryofthestudiesemployingcatalyticmicromotorstodetectspecieswhich modulatethespeedoftheself‐propulsionwithoutbeingfuelforthecatalyticmicromotors ispresentedinTable2. Table2.Examplesofcatalyticmicromotorswhichwereusedtodetectanalyteswhichmodulatethe speedofself‐propulsionwithoutbeingfuelforthecatalyticmicromotors;(Usedabbreviation:BSA =bovineserumalbumin). TargetedAnalyte MicromotorStructure ; Self‐Propulsion MechanismAnalyticalPerformancesRef. A.Catalyticmicromotorsusedtodetectinorganiccompounds: Ag+Bimetallicnanorod(halfAuandhalfPt); Self‐electrophoresis; LR:from0.5μMto100μM;Interferencefrom otherionswasobserved;[39] Hg2+ Halloysiteclaynanotube(L~700nm,ϕ~80– 100nm)partiallycoveredwithPt;O2bubble ejection; DL:3.24ppb;Detectedanalyteconcentrations from0.25ppbto1000ppb;Thespeedofthemicromotorsisalsoaffectedbyotherheavymetals; [54] Catalaseinhibitors(Hg2+,Cu2+ , NaN3,andaminotriazole) PEDOTonAumicrotube(L~8μm,ϕ~2μm) withitsinnersurfacemodifiedwithcatalase;O2bubbleejection; Impactonspeedobservedfor50–200μMHg2+, 0.2–1mMCu2+,2.5–25μMNaN3,and375–750 mMaminotriazole;Theconceptdoesnotdistinguishinbetweencatalaseinhibitors; [55] Pb2+CuonPtmicrotube(ϕ=2μm);O2bubble ejection; Detectedconcentrations:from0.48mMto1.92 mM;Someselectivitywasobservedwhentested withCd2+;Interferencefromcompoundsableto adsorbontoPtcanbeexpected; [56] pH Cylindricalgelatincartridge(L~15μm,ϕ~8 μm)withitsinnersurfacedecoratedwith3 nmPtnanoparticles;O2bubbleejection; Boththespeedofself‐propulsionandthedistancetraveledduring5swerefoundproportionalwithpH;SensitivitytopHfrom0to14 wasobserved; [57] B.Catalyticmicromotorsusedtodetectnucleicacids: Figure 6. ( A ) Modification of the inner surface of poly(3,4-ethylenedioxythiophene) (PEDOT) on Au microtubes with capture DNA, and of Pt nanoparticles with detector DNA; ( B ) The principle for DNA detection by introducing Pt nanoparticle-DNA conjugates into the microtubes via specific DNA hybridization mediated by target DNA; Figure reproduced with permission from Ref. [51]. Table 2. Examples of catalytic micromotors which were used to detect analytes which modulate the speed of self-propulsion without being fuel for the catalytic micromotors; (Used abbreviation: BSA = bovine serum albumin). Targeted Analyte Micromotor Structure; Self-Propulsion Mechanism Analytical Performances Ref. A. Catalytic micromotors used to detect inorganic compounds: Ag+Bimetallic nanorod (half Au and half Pt); Self-electrophoresis; LR: from 0.5 µ M to 100 µ M; Interference from other ions was observed; [39] Hg2+ Halloysite clay nanotube (L ~700 nm, φ~80–100 nm) partially covered with Pt; O2bubble ejection; DL: 3.24 ppb; Detected analyte concentrations from 0.25 ppb to 1000 ppb; The speed of the micromotors is also affected by other heavy metals; [54] Catalase inhibitors (Hg2+, Cu2+, NaN3, and aminotriazole) PEDOT on Au microtube (L ~8 µm, φ~2µm) with its inner surface modified with catalase; O2bubble ejection; Impact on speed observed for 50–200 µM Hg2+, 0.2–1 mM Cu2+, 2.5–25 µM NaN3, and 375–750 mM aminotriazole; The concept does not distinguish in between catalase inhibitors; [55] Pb2+ Cu on Pt microtube (φ=2 µm); O2 bubble ejection; Detected concentrations: from 0.48 mM to 1.92 mM; Some selectivity was observed when tested with Cd2+; Interference from compounds able to adsorb onto Pt can be expected; [56] pH Cylindrical gelatin cartridge (L ~15 µm, φ~8 µm) with its inner surface decorated with 3 nm Pt nanoparticles; O2 bubble ejection; Both the speed of self-propulsion and the distance traveled during 5 s were found proportional with pH; Sensitivity to pH from 0 to 14 was observed; [57]
Biosensors 2023,13, 45 16 of 21 Table 3. Cont. Targeted Analyte Micromotor Structure; Self-Propulsion Mechanism Analytical Performances Ref. Salmonella enterica lipopolysaccharide ~20 µm diameter polycaprolactone microbead containing 100 nm Pt nanoparticles and graphene quantum dots carrying receptor molecules; O2 bubble ejection; Fluorescence detection; DL: 0.07 ng/mL; LR: up to 1 ng/mL; Analysis time of 15 min; Measurements in milk, mayo, egg yolk, and egg white were made; [82] E. Catalytic micromotors used to detect small organic molecules: L-tryptophan Ni on Pt microtube (L = 10–20 µm, φ= 2–3 µm) modified with CdS quantum dots and with β-cyclodextrin; O2bubble ejection; Fluorescence and electrochemical detection; Some preference for L-tryptophan is demonstrated; No other analytical performances were detailed; [46] 2,4,6-Trinitrophenol ~30–40 µm diameter polycaprolactone microspheres loaded with fluorescent covalent-organic-frameworks, MnO2 microurchins, and Fe3O4nanoparticles; O2bubble ejection; Fluorescence detection; 2,4,6-trinitrophenol turns off the fluorescence of the micromotors; Detection of 1 ppm of 2,4,6-trinitrophenol was demonstrated; [47] Diphenyl phthalate ~20 µm diameter Mg microparticles partially covered with Au; OH-ions, which degrade diphenyl phthalate to phenol, are also produced during self-propulsion; H2bubble ejection; Electrochemical detection; DL: 0.039 mM; LR: from 0.12 mM to 1 mM; Recovery > 97 ±8%; Analysis time ~5 min; Analysis of tap water, whiskey, milk, and human serum samples was successfully carried out; [49] Crystal violet (as model analyte) SiO2-coated Ag nanowire (~150 nm in diameter, L ~15 µm) with a spherical AgCl head; Self-electrophoresis; SERS detection; Measured concentration: 0.1 mM; Positive phototaxis of the micromotors led to an increase in the analytical signal (e.g., 6.2×); [65] Methyl paraoxon ~40 µm diameter Mg microbead partially covered with a 80 nm thick layer of Ni and a 10 nm thick layer of Au; H2bubble ejection; Electrochemical detection; LR: from ~5 mM to 20 mM; Relative standard deviation < 5%; [66] Ortho-phenylenediamine Carbon nanotubes and 50 nm Fe2O3 nanoparticles on MnO2microtube (L ~12 µm, φ~2 µm); O2bubble ejection; Colorimetric detection; DL: 5 µM; LR: from 16.7 µM to 500 µM; Analysis time ~15 min; Interference from 20 µM Cu2+ and 20 µM Fe3+ was observed; Similar results were obtained for para-phenylenediamine; [67] Diethyl chlorophosphate (nerve agent simulant) Silica particle covered with fluoresceinamine and then partially covered with Pt; O2bubble ejection; Fluorescence detection; 10 mM diethyl chlorophosphate is detected in 1 min while 10 µM diethyl chlorophosphate is detected in about 3 min; Selectivity was proved with ethanol, toluene, acetone, and isopropanol; [83] Cortisol Microtube (L ~10 µm, φ= 5 µm) made of an outer layer of PEDOT, a middle layer of Ni and an inner layer of Pt; The outer layer was further modified with Au and anti-cortisol antibodies; O2 bubble ejection; Colorimetric detection; Lowest detected concentration 0.1 µg/mL; Analysis time 2 min; [84] Glucose ~30 µm diameter Mg microbead partially covered with Pt; H2 bubble ejection; Electrochemical detection; DL: 33.2 µM; LR: from 1 mM to 15 mM; Measurements in diluted human serum were made; [85] If one compares Table 3with Tables 1and 2, it becomes clear that sensing with catalytic micromotors is dominated by concepts which are not using the parameters of the motion of catalytic micromotors as the analytical signal. There are several other points one can make based on the information summarized in Table 3. Instead of using the motion of catalytic micromotors as the analytical signal, the sensing concepts listed in Table 3deliver either optical signals (76% of the micromotors) or electrochemical signals (24% of the micromotors). As such, these sensing concepts carry the problems characterizing the optical and electrochemical detection principles.
Biosensors 2023,13, 45 17 of 21 For example, photobleaching remains a major problem of concepts based on fluorescence. Interferences and drifts (e.g., due to electrode fouling) can complicate biosensing concepts based on electrochemical detection. The catalytic micromotors get a new role in the concepts listed in Table 3(as compared to the concepts listed in Tables 1and 2): they are used to enhance the mass transport in the investigated sample. Interestingly, the analytical signals obtained with mass transport enhanced by the self-propulsion of catalytic micromotors were found sometimes larger (e.g., in Refs. [ 78 , 81 ]), sometimes equal, and sometimes smaller (e.g., in Ref. [ 82 ]) than the analytical signals obtained with classic ways for stirring and mixing liquid samples (e.g., magnetic stirring). A thorough comparison of catalytic micromotors with classic tools for stirring and mixing liquid samples was not yet carried out. However, one must keep in mind that stirring and mixing liquid samples with catalytic micromotors requires no additional laboratory equipment and that there are very few tools to carry out stirring and mixing in samples of only few microliters volume. Enhancing the mass transport in the investigated sample was most often done with catalytic micromotors ejecting O 2 bubbles by the decomposition of H 2 O 2 (and only few times with micromotors which generate H 2 bubbles via the reaction of Mg and H 2 O). Self-propulsion based on the ejection of O 2 bubbles generated in H 2 O 2 , while more robust than phoretic self-propulsion, is still characterized by weaknesses (some of which were already mentioned in previous sections and will not be repeated here). O 2 bubbles are only generated at high H 2 O 2 concentrations (0.8–7.5%) and high H 2 O 2 concentrations can oxidize sample components and cause problems both in electrochemical detection (as H 2 O 2 is oxidized and reduced at relatively low applied potentials) and in optical detection (e.g., H 2 O 2 concentrations higher than 7% were found to quench the CdTe quantum dots-based fluorescence of some catalytic micromotors [ 70 ]). In addition, the chemical composition of the investigated sample was also found to impact the speed of O 2 bubble ejection-based catalytic micromotors. Some organic species (e.g., dimethyl sulfoxide) can quench radicals involved in the decomposition of H 2 O 2 while others (e.g., thiols, furfural, and ethanol) can irreversibly adsorb onto the catalyst with detrimental effects on its ability to decompose H 2 O 2 [ 86 , 87 ]. It is currently not clear (as it was not studied) how much the speed of the self-propulsion can decrease/increase (due to the combined effects of viscosity, organic species, temperature, etc.) without affecting the analytical signal in the sensing concepts listed in Table 3. One can also note that selectivity tests are sketchy, in the best case, as the number of samples analyzed (after calibration with standard solutions) is very low (e.g., 2–3) in most of the available studies. The fact that catalytic micromotors were not tested with a larger number of complex (i.e., “real world”) samples is most likely due to the complexity of making the catalytic micromotors and using them for sensing purposes, as well to the (prohibitively large for most of the laboratories) time and costs demanded by such extensive validation studies. 6. Conclusions Proof-of-principle studies of detection of analytes by using catalytic micromotors have been reported for a large number of very different type of analytes: e.g., metal ions, low molecular weight biomolecules, biomacromolecules, viruses, gases, etc. The analytes detected with catalytic micromotors are of interest in various fields, such as biomedicine, environment protection and remediation, and food safety. Few of these analytes were detected as they could also act as fuel for the catalytic micromotors (see Section 3). Other analytes were detected because they could modulate (e.g., inhibit or activate) the catalytic processes behind the self-propulsion of catalytic micromotors (without being the actual fuel of the catalytic micromotors; see Section 4). Most of the analytes were detected based on optical or electrochemical methods enhanced by catalytic micromotors. The ability of self-propelling catalytic micromotors to stir and mix the investigated sample was very important in this latter case (see Section 5). This ability is critical when the investigated sample is a tiny liquid droplet that cannot be stirred and mixed with classic laboratory equipment.
Biosensors 2023,13, 45 18 of 21 Real-Time PCR [ 21 , 72 ], electrochemical biosensors [19,58,64,68,69,81,82] , optical biosensors [ 19 , 58 , 64 , 69 , 75 , 81 , 82 ], enzyme-linked immunosorbent assays [ 22 , 64 ], and the limulus amoebocyte lysate test [ 69 , 81 , 82 ] are among the analytical tools compared to catalytic micromotor sensors. The analytical performances (most often the detection limit, sometimes the accuracy) of catalytic micromotor sensors were found sometimes better [ 64 , 68 , 75 , 81 , 82 ], sometimes similar [ 21 , 22 , 72 , 75 , 82 ], and sometimes worse [ 19 , 58 , 69 , 75 ] than those of the classic counterparts. The outcome of such a comparison obviously depends very much on the analytical tools selected as reference points. The shorter analysis times they facilitate and the extremely low volumes of sample they need are still undeniable advantages of the catalytic micromotor sensors. However, as discussed in this review, there remain, in our opinion, a lot of aspects to be improved in what concerns the fabrication and use of catalytic micromotors as sensors. Most of the catalytic micromotors are produced by putting together several different materials via complicated, multistep procedures. We are clearly far from being able to mass produce highly reproducible, catalytic micromotors. Important properties (e.g., the speed of self-propulsion) of the currently produced catalytic micromotors show inconveniently large dispersions. Moreover, numerous details of the sample (e.g., viscosity, temperature, ion content, concentration of thiols, etc.), rather than just the concentration of the analyte of interest, seem to impact the behavior of catalytic micromotors, and thus the result of the “measurement”, in seemingly uncontrolled, and not well understood, ways. Most studies considered only a small number of “real-world” samples (e.g., 2–3) and thus the reliability of the sensing concepts based on catalytic micromotors remains poorly demonstrated; moreover, the proof-of-concepts studies are only seldom followed-up by thorough comparisons with reference analytical methods. Accordingly, the conclusion that emerges from the available studies is that the qualitative (rather than quantitative) analysis of small samples in resource poor environments is the most promising niche area for the catalytic micromotors in analytical chemistry. Author Contributions: Writing—original draft preparation, M.N.P. and S.G.; Writing—review and editing, M.N.P. and S.G. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by the Romanian Executive Unit for Higher Education, Research, Development and Innovation Funding through grants PN-III-P4-PCE-2021-1231 (contract no. PCE 21 from 26 May 2022) and ERA-Net EuroNanoMed (contract no. 135 from 9 March 2020). Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: Not applicable. Conflicts of Interest: The authors declare no conflict of interest. References 1. Golestanian, R.; Liverpool, T.B.; Ajdari, A. Designing Phoretic Micro- and Nano-Swimmers. New J. Phys. 2007 ,9, 126. [CrossRef] 2. Paxton, W.F.; Kistler, K.C.; Olmeda, C.C.; Sen, A.; Angelo, S.K.S.; Cao, Y.; Mallouk, T.E.; Lammert, P.E.; Crespi, V.H. Catalytic Nanomotors: Autonomous Movement of Striped Nanorods. J. Am. Chem. Soc. 2004,126, 13424–13431. [CrossRef] [PubMed] 3. Paxton, W.F.; Baker, P.T.; Kline, T.R.; Wang, Y.; Mallouk, T.E.; Sen, A. Catalytically Induced Electrokinetics for Motors and Micropumps. J. Am. Chem. Soc. 2006,128, 14881–14888. [CrossRef] 4. Wang, Y.; Hernandez, R.M.; Bartlett, D.J.; Bingham, J.M.; Kline, T.R.; Sen, A.; Mallouk, T.E. Bipolar Electrochemical Mechanism for the Propulsion of Catalytic Nanomotors in Hydrogen Peroxide Solutions. Langmuir 2006 ,22, 10451–10456. [CrossRef] [PubMed] 5. Gao, W.; Uygun, A.; Wang, J. Hydrogen-Bubble-Propelled Zinc-Based Microrockets in Strongly Acidic Media. J. Am. Chem. Soc. 2012,134, 897–900. [CrossRef] 6. Bunea, A.-I.; Pavel, I.-A.; David, S.; Gáspár, S. Sensing Based on the Motion of Enzyme-Modified Nanorods. Biosens. Bioelectron. 2015,67, 42–48. [CrossRef] [PubMed] 7. Ma, X.; Jannasch, A.; Albrecht, U.-R.; Hahn, K.; Miguel-López, A.; Schäffer, E.; Sánchez, S. Enzyme-Powered Hollow Mesoporous Janus Nanomotors. Nano Lett. 2015,15, 7043–7050. [CrossRef] [PubMed] 8. Kline, T.R.; Paxton, W.F.; Mallouk, T.E.; Sen, A. Catalytic Nanomotors: Remote-Controlled Autonomous Movement of Striped Metallic Nanorods. Angew. Chem. Int. Ed. 2005,44, 744–746. [CrossRef]
Biosensors 2023,13, 45 19 of 21 9. Ma, X.; Wang, X.; Hahn, K.; Sánchez, S. Motion Control of Urea-Powered Biocompatible Hollow Microcapsules. ACS Nano 2016 , 10, 3597–3605. [CrossRef] 10. Simmchen, J.; Katuri, J.; Uspal, W.E.; Popescu, M.N.; Tasinkevych, M.; Sánchez, S. Topographical Pathways Guide Chemical Microswimmers. Nat. Commun. 2016,7, 10598. [CrossRef] [PubMed] 11. Soler, L.; Sánchez, S. Catalytic Nanomotors for Environmental Monitoring and Water Remediation. Nanoscale 2014 ,6, 7175–7182. [CrossRef] [PubMed] 12. Mujtaba, J.; Liu, J.; Dey, K.K.; Li, T.; Chakraborty, R.; Xu, K.; Makarov, D.; Barmin, R.A.; Gorin, D.A.; Tolstoy, V.P.; et al. Micro- Bio-Chemo-Mechanical-Systems: Micromotors, Microfluidics, and Nanozymes for Biomedical Applications. Adv. Mater. 2021 , 33, 2007465. [CrossRef] 13. Wrede, P.; Medina-Sánchez, M.; Fomin, V.M.; Schmidt, O.G. Switching Propulsion Mechanisms of Tubular Catalytic Micromotors. Small 2021,17, 2006449. [CrossRef] [PubMed] 14. Wittmann, M.; Popescu, M.N.; Domínguez, A.; Simmchen, J. Active Spheres Induce Marangoni Flows That Drive Collective Dynamics. Eur. Phys. J. E 2021,44, 15. [CrossRef] [PubMed] 15. Archer, R.J.; Parnell, A.J.; Campbell, A.I.; Howse, J.R.; Ebbens, S.J. A Pickering Emulsion Route to Swimming Active Janus Colloids. Adv. Sci. 2018,5, 1700528. [CrossRef] 16. Kaang, B.K.; Mestre, R.; Kang, D.-C.; Sánchez, S.; Kim, D.-P. Scalable and Integrated Flow Synthesis of Triple-Responsive Nano-Motors via Microfluidic Pickering Emulsification. Appl. Mater. Today 2020,21, 100854. [CrossRef] 17. Molinero-Fernández, Á.; Arruza, L.; López, M.Á.; Escarpa, A. On-the-Fly Rapid Immunoassay for Neonatal Sepsis Diagnosis: C-Reactive Protein Accurate Determination Using Magnetic Graphene-Based Micromotors. Biosens. Bioelectron. 2020 ,158, 112156. [CrossRef] 18. Molinero-Fernández, Á.; López, M.Á.; Escarpa, A. Electrochemical Microfluidic Micromotors-Based Immunoassay for C-Reactive Protein Determination in Preterm Neonatal Samples with Sepsis Suspicion. Anal. Chem. 2020,92, 5048–5054. [CrossRef] 19. Molinero-Fernández, Á.; Moreno-Guzmán, M.; Arruza, L.; López, M.Á.; Escarpa, A. Polymer-Based Micromotor Fluorescence Immunoassay for On-the-Move Sensitive Procalcitonin Determination in Very Low Birth Weight Infants’ Plasma. ACS Sens. 2020 , 5, 1336–1344. [CrossRef] 20. Russell, S.M.; Alba-Patiño, A.; Borges, M.; de la Rica, R. Multifunctional Motion-to-Color Janus Transducers for the Rapid Detection of Sepsis Biomarkers in Whole Blood. Biosens. Bioelectron. 2019,140, 111346. [CrossRef] 21. Draz, M.S.; Kochehbyoki, K.M.; Vasan, A.; Battalapalli, D.; Sreeram, A.; Kanakasabapathy, M.K.; Kallakuri, S.; Tsibris, A.; Kuritzkes, D.R.; Shafiee, H. DNA Engineered Micromotors Powered by Metal Nanoparticles for Motion Based Cellphone Diagnostics. Nat. Commun. 2018,9, 4282. [CrossRef] 22. Draz, M.S.; Lakshminaraasimulu, N.K.; Krishnakumar, S.; Battalapalli, D.; Vasan, A.; Kanakasabapathy, M.K.; Sreeram, A.; Kallakuri, S.; Thirumalaraju, P.; Li, Y.; et al. Motion-Based Immunological Detection of Zika Virus Using Pt-Nanomotors and a Cellphone. ACS Nano 2018,12, 5709–5718. [CrossRef] 23. Yuan, K.; Cuntín-Abal, C.; Jurado-Sánchez, B.; Escarpa, A. Smartphone-Based Janus Micromotors Strategy for Motion-Based Detection of Glutathione. Anal. Chem. 2021,93, 16385–16392. [CrossRef] [PubMed] 24. Yuan, K.; de la Asunción-Nadal, V.; Cuntín-Abal, C.; Jurado-Sánchez, B.; Escarpa, A. On-Board Smartphone Micromotor-Based Fluorescence Assays. Lab Chip 2022,22, 928–935. [CrossRef] [PubMed] 25. Dong, R.; Li, J.; Rozen, I.; Ezhilan, B.; Xu, T.; Christianson, C.; Gao, W.; Saintillan, D.; Ren, B.; Wang, J. Vapor-Driven Propulsion of Catalytic Micromotors. Sci. Rep. 2015,5, 13226. [CrossRef] 26. Villa, K.; Manzanares Palenzuela, C.L.; Sofer, Z.; Matˇejková, S.; Pumera, M. Metal-Free Visible-Light Photoactivated C 3 N 4 Bubble-Propelled Tubular Micromotors with Inherent Fluorescence and On/Off Capabilities. ACS Nano 2018 ,12, 12482–12491. [CrossRef] [PubMed] 27. Yang, W.; Li, J.; Xu, Z.; Yang, J.; Liu, Y.; Liu, L. A Eu-MOF/EDTA-NiAl-CLDH Fluorescent Micromotor for Sensing and Removal of Fe3+ from Water. J. Mater. Chem. C 2019,7, 10297–10308. [CrossRef] 28. Chałupniak, A.; Morales-Narváez, E.; Merkoçi, A. Micro and Nanomotors in Diagnostics. Adv. Drug Deliv. Rev. 2015 ,95, 104–116. [CrossRef] 29. Yánez-Sedeño, P.; Campuzano, S.; Pingarrón, J.M. Janus Particles for (Bio)Sensing. Appl. Mater. Today 2017 ,9, 276–288. [CrossRef] 30. Jurado-Sánchez, B. Nanoscale Biosensors Based on Self-Propelled Objects. Biosensors 2018,8, 59. [CrossRef] 31. Kong, L.; Guan, J.; Pumera, M. Micro- and Nanorobots Based Sensing and Biosensing. Curr. Opin. Electrochem. 2018 ,10, 174–182. [CrossRef] 32. Pacheco, M.; López, M.Á.; Jurado-Sánchez, B.; Escarpa, A. Self-Propelled Micromachines for Analytical Sensing: A Critical Review. Anal. Bioanal. Chem. 2019,411, 6561–6573. [CrossRef] 33. Jurado-Sánchez, B.; Campuzano, S.; Pingarrón, J.M.; Escarpa, A. Janus Particles and Motors: Unrivaled Devices for Mastering (Bio)Sensing. Microchim. Acta 2021,188, 416. [CrossRef] [PubMed] 34. Ye, H.; Wang, Y.; Xu, D.; Liu, X.; Liu, S.; Ma, X. Design and Fabrication of Micro/Nano-Motors for Environmental and Sensing Applications. Appl. Mater. Today 2021,23, 101007. [CrossRef] 35. Duan, W.; Wang, W.; Das, S.; Yadav, V.; Mallouk, T.E.; Sen, A. Synthetic Nano- and Micromachines in Analytical Chemistry: Sensing, Migration, Capture, Delivery, and Separation. Annu. Rev. Anal. Chem. 2015,8, 311–333. [CrossRef]
Biosensors 2023,13, 45 20 of 21 36. Campuzano, S.; Kagan, D.; Orozco, J.; Wang, J. Motion-Driven Sensing and Biosensing Using Electrochemically Propelled Nanomotors. Analyst 2011,136, 4621–4630. [CrossRef] 37. Kim, K.; Guo, J.; Liang, Z.; Fan, D. Artificial Micro/Nanomachines for Bioapplications: Biochemical Delivery and Diagnostic Sensing. Adv. Funct. Mater. 2018,28, 1705867. [CrossRef] 38. Valles, M.; Pujals, S.; Albertazzi, L.; Sánchez, S. Enzyme Purification Improves the Enzyme Loading, Self-Propulsion, and Endurance Performance of Micromotors. ACS Nano 2022,16, 5615–5626. [CrossRef] 39. Kagan, D.; Calvo-Marzal, P.; Balasubramanian, S.; Sattayasamitsathit, S.; Manesh, K.M.; Flechsig, G.-U.; Wang, J. Chemical Sensing Based on Catalytic Nanomotors: Motion-Based Detection of Trace Silver. J. Am. Chem. Soc. 2009 ,131, 12082–12083. [CrossRef] 40. Moran, J.L.; Posner, J.D. Phoretic Self-Propulsion. Annu. Rev. Fluid Mech. 2017,49, 511–540. [CrossRef] 41. Popescu, M.N.; Dietrich, S.; Tasinkevych, M.; Ralston, J. Phoretic Motion of Spheroidal Particles Due to Self-Generated Solute Gradients. Eur. Phys. J. E 2010,31, 351–367. [CrossRef] [PubMed] 42. Sachs, J.; Kottapalli, S.N.; Fischer, P.; Botin, D.; Palberg, T. Characterization of Active Matter in Dense Suspensions with Heterodyne Laser Doppler Velocimetry. Colloid Polym. Sci. 2021,299, 269–280. [CrossRef] 43. Dietrich, K.; Renggli, D.; Zanini, M.; Volpe, G.; Buttinoni, I.; Isa, L. Two-Dimensional Nature of the Active Brownian Motion of Catalytic Microswimmers at Solid and Liquid Interfaces. New J. Phys. 2017,19, 065008. [CrossRef] 44. Laocharoensuk, R.; Burdick, J.; Wang, J. Carbon-Nanotube-Induced Acceleration of Catalytic Nanomotors. ACS Nano 2008 ,2, 1069–1075. [CrossRef] [PubMed] 45. Singh, K.; Yadav, A.; Dwivedi, P.; Mangal, R. Interaction of Active Janus Colloids with Tracers. Langmuir 2022 ,38, 2686–2698. [CrossRef] 46. Muñoz, J.; Urso, M.; Pumera, M. Self-Propelled Multifunctional Microrobots Harboring Chiral Supramolecular Selectors for “Enantiorecognition-on-the-Fly”. Angew. Chem. Int. Ed. 2022,61, e202116090. [CrossRef] [PubMed] 47. Wang, K.; Wang, W.; Pan, S.; Fu, Y.; Dong, B.; Wang, H. Fluorescent Self-Propelled Covalent Organic Framework as a Microsensor for Nitro Explosive Detection. Appl. Mater. Today 2020,19, 100550. [CrossRef] 48. Arqué, X.; Andrés, X.; Mestre, R.; Ciraulo, B.; Ortega Arroyo, J.; Quidant, R.; Patiño, T.; Sánchez, S. Ionic Species Affect the Self-Propulsion of Urease-Powered Micromotors. Research 2020,2020, 1–14. [CrossRef] 49. Rojas, D.; Jurado-Sánchez, B.; Escarpa, A. “Shoot and Sense” Janus Micromotors-Based Strategy for the Simultaneous Degradation and Detection of Persistent Organic Pollutants in Food and Biological Samples. Anal. Chem. 2016,88, 4153–4160. [CrossRef] 50. Sanchez, S.; Ananth, A.N.; Fomin, V.M.; Viehrig, M.; Schmidt, O.G. Superfast Motion of Catalytic Microjet Engines at Physiological Temperature. J. Am. Chem. Soc. 2011,133, 14860–14863. [CrossRef] 51. Van Nguyen, K.; Minteer, S.D. DNA-Functionalized Pt Nanoparticles as Catalysts for Chemically Powered Micromotors: Toward Signal-on Motion-Based DNA Biosensor. Chem. Commun. 2015,51, 4782–4784. [CrossRef] [PubMed] 52. Fu, S.; Zhang, X.; Xie, Y.; Wu, J.; Ju, H. An Efficient Enzyme-Powered Micromotor Device Fabricated by Cyclic Alternate Hybridization Assembly for DNA Detection. Nanoscale 2017,9, 9026–9033. [CrossRef] [PubMed] 53. Yu, X.; Li, Y.; Wu, J.; Ju, H. Motor-Based Autonomous Microsensor for Motion and Counting Immunoassay of Cancer Biomarker. Anal. Chem. 2014,86, 4501–4507. [CrossRef] [PubMed] 54. Maric, T.; Mayorga-Martinez, C.C.; Nasir, M.Z.M.; Pumera, M. Platinum-Halloysite Nanoclay Nanojets as Sensitive and Selective Mobile Nanosensors for Mercury Detection. Adv. Mater. Technol. 2019,4, 1800502. [CrossRef] 55. Orozco, J.; García-Gradilla, V.; D’Agostino, M.; Gao, W.; Cortés, A.; Wang, J. Artificial Enzyme-Powered Microfish for Water- Quality Testing. ACS Nano 2013,7, 818–824. [CrossRef] 56. Moo, J.G.S.; Wang, H.; Zhao, G.; Pumera, M. Biomimetic Artificial Inorganic Enzyme-Free Self-Propelled Microfish Robot for Selective Detection of Pb2+ in Water. Chem. Eur. J. 2014,20, 4292–4296. [CrossRef] [PubMed] 57. Su, Y.; Ge, Y.; Liu, L.; Zhang, L.; Liu, M.; Sun, Y.; Zhang, H.; Dong, B. Motion-Based pH Sensing Based on the Cartridge-Case-like Micromotor. ACS Appl. Mater. Interfaces 2016,8, 4250–4257. [CrossRef] 58. Zhang, X.; Chen, C.; Wu, J.; Ju, H. Bubble-Propelled Jellyfish-like Micromotors for DNA Sensing. ACS Appl. Mater. Interfaces 2019 , 11, 13581–13588. [CrossRef] 59. Wu, J.; Balasubramanian, S.; Kagan, D.; Manesh, K.M.; Campuzano, S.; Wang, J. Motion-Based DNA Detection Using Catalytic Nanomotors. Nat. Commun. 2010,1, 36. [CrossRef] 60. Xie, Y.; Fu, S.; Wu, J.; Lei, J.; Ju, H. Motor-Based Microprobe Powered by Bio-Assembled Catalase for Motion Detection of DNA. Biosens. Bioelectron. 2017,87, 31–37. [CrossRef] 61. Singh, V.V.; Kaufmann, K.; de Ávila, B.E.-F.; Uygun, M.; Wang, J. Nanomotors Responsive to Nerve-Agent Vapor Plumes. Chem. Commun. 2016,52, 3360–3363. [CrossRef] [PubMed] 62. Pereiro, I.; Fomitcheva-Khartchenko, A.; Kaigala, G.V. Shake It or Shrink It: Mass Transport and Kinetics in Surface Bioassays Using Agitation and Microflidics. Anal. Chem. 2020,92, 10187–10195. [CrossRef] [PubMed] 63. Molinero-Fernández, Á.; Jodra, A.; Moreno-Guzmán, M.; López, M.Á.; Escarpa, A. Magnetic Reduced Graphene Oxide/Nickel/Platinum Nanoparticles Micromotors for Mycotoxin Analysis. Chem. Eur. J. 2018 ,24, 7172–7176. [CrossRef] [PubMed] 64. Ma, E.; Wang, K.; Wang, H. An Immunoassay Based on Nanomotor-Assisted Electrochemical Response for the Detection of Immunoglobulin. Microchim. Acta 2022,189, 47. [CrossRef]
Biosensors 2023,13, 45 21 of 21 65. Wang, Y.; Zhou, C.; Wang, W.; Xu, D.; Zeng, F.; Zhan, C.; Gu, J.; Li, M.; Zhao, W.; Zhang, J.; et al. Photocatalytically Powered Matchlike Nanomotor for Light-Guided Active SERS Sensing. Angew. Chem. 2018,130, 13294–13297. [CrossRef] 66. Cinti, S.; Valdés-Ramírez, G.; Gao, W.; Li, J.; Palleschi, G.; Wang, J. Microengine-Assisted Electrochemical Measurements at Printable Sensor Strips. Chem. Commun. 2015,51, 8668–8671. [CrossRef] 67. María-Hormigos, R.; Jurado-Sánchez, B.; Escarpa, A. Self-Propelled Micromotors for Naked-Eye Detection of Phenylenediamines Isomers. Anal. Chem. 2018,90, 9830–9837. [CrossRef] 68. Mayorga-Martinez, C.C.; Pumera, M. Self-Propelled Tags for Protein Detection. Adv. Funct. Mater. 2020 ,30, 1906449. [CrossRef] 69. Pacheco, M.; Jurado-Sánchez, B.; Escarpa, A. Transition Metal Dichalcogenide-Based Janus Micromotors for on-the-Fly Salmonella Detection. Microchim. Acta 2022,189, 194. [CrossRef] 70. Jurado-Sánchez, B.; Escarpa, A.; Wang, J. Lighting up Micromotors with Quantum Dots for Smart Chemical Sensing. Chem. Commun. 2015,51, 14088–14091. [CrossRef] 71. Liu, M.; Sun, Y.; Wang, T.; Ye, Z.; Zhang, H.; Dong, B.; Li, C.Y. A Biodegradable, All-Polymer Micromotor for Gas Sensing Applications. J. Mater. Chem. C 2016,4, 5945–5952. [CrossRef] 72. Zhang, Y.; Yang, F.; Wei, W.; Wang, Y.; Yang, S.; Li, J.; Xing, Y.; Zhou, L.; Dai, W.; Dong, H. Self-Propelled Janus Mesoporous Micromotor for Enhanced MicroRNA Capture and Amplified Detection in Complex Biological Samples. ACS Nano 2022 ,16, 5587–5596. [CrossRef] [PubMed] 73. Singh, V.V.; Kaufmann, K.; de Ávila, B.E.-F.; Karshalev, E.; Wang, J. Molybdenum Disulfide-Based Tubular Microengines: Toward Biomedical Applications. Adv. Funct. Mater. 2016,26, 6270–6278. [CrossRef] 74. Cogal, G.C.; Karaca, G.Y.; Uygun, E.; Kuralay, F.; Oksuz, L.; Remskar, M.; Oksuz, A.U. RF Plasma-Enhanced Conducting Polymer/W 5 O 14 Based Self-Propelled Micromotors for MiRNA Detection. Anal. Chim. Acta 2020 ,1138, 69–78. [CrossRef] [PubMed] 75. Báez, D.F.; Ramos, G.; Corvalán, A.; Cordero, M.L.; Bollo, S.; Kogan, M.J. Effects of Preparation on Catalytic, Magnetic and Hybrid Micromotors on Their Functional Features and Application in Gastric Cancer Biomarker Detection. Sens. Actuators B Chem. 2020 , 310, 127843. [CrossRef] 76. Zhang, Z. Magnetic Molecularly Imprinted Microsensor for Selective Recognition and Transport of Fluorescent Phycocyanin in Seawater. J. Mater. Chem. A 2015,8, 7437–7444. [CrossRef] 77. Esteban-Fernández de Ávila, B.; Lopez-Ramirez, M.A.; Báez, D.F.; Jodra, A.; Singh, V.V.; Kaufmann, K.; Wang, J. Aptamer-Modified Graphene-Based Catalytic Micromotors: Off–On Fluorescent Detection of Ricin. ACS Sens. 2016,1, 217–221. [CrossRef] 78. Yuan, K.; López, M.Á.; Jurado-Sánchez, B.; Escarpa, A. Janus Micromotors Coated with 2D Nanomaterials as Dynamic Interfaces for (Bio)-Sensing. ACS Appl. Mater. Interfaces 2020,12, 46588–46597. [CrossRef] 79. Molinero-Fernández, Á.; Moreno-Guzmán, M.; López, M.Á.; Escarpa, A. Biosensing Strategy for Simultaneous and Accurate Quantitative Analysis of Mycotoxins in Food Samples Using Unmodified Graphene Micromotors. Anal. Chem. 2017 ,89, 10850–10857. [CrossRef] 80. Jurado-Sánchez, B.; Pacheco, M.; Rojo, J.; Escarpa, A. Magnetocatalytic Graphene Quantum Dots Janus Micromotors for Bacterial Endotoxin Detection. Angew. Chem. Int. Ed. 2017,56, 6957–6961. [CrossRef] 81. Pacheco, M.; de la Asunción-Nadal, V.; Jurado-Sánchez, B.; Escarpa, A. Engineering Janus Micromotors with WS 2 and Affinity Peptides for Turn-on Fluorescent Sensing of Bacterial Lipopolysaccharides. Biosens. Bioelectron. 2020 ,165, 112286. [CrossRef] [PubMed] 82. Pacheco, M.; Jurado-Sánchez, B.; Escarpa, A. Sensitive Monitoring of Enterobacterial Contamination of Food Using Self-Propelled Janus Microsensors. Anal. Chem. 2018,90, 2912–2917. [CrossRef] 83. Singh, V.V.; Kaufmann, K.; Orozco, J.; Li, J.; Galarnyk, M.; Arya, G.; Wang, J. Micromotor-Based on–off Fluorescence Detection of Sarin and Soman Simulants. Chem. Commun. 2015,51, 11190–11193. [CrossRef] [PubMed] 84. de Ávila, B.E.-F.; Zhao, M.; Campuzano, S.; Ricci, F.; Pingarrón, J.M.; Mascini, M.; Wang, J. Rapid Micromotor-Based Naked-Eye Immunoassay. Talanta 2017,167, 651–657. [CrossRef] [PubMed] 85. Kong, L.; Rohaizad, N.; Nasir, M.Z.M.; Guan, J.; Pumera, M. Micromotor-Assisted Human Serum Glucose Biosensing. Anal. Chem. 2019,91, 5660–5666. [CrossRef] [PubMed] 86. Zhao, G.; Sanchez, S.; Schmidt, O.G.; Pumera, M. Poisoning of Bubble Propelled Catalytic Micromotors: The Chemical Environment Matters. Nanoscale 2013,5, 2909–2914. [CrossRef] 87. Greydanus, B.; Saleheen, M.; Wu, H.; Heyden, A.; Medlin, J.W.; Schwartz, D.K. Probing Surface-Adsorbate Interactions through Active Particle Dynamics. J. Colloid Interface Sci. 2022,614, 425–435. [CrossRef] Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.