Bumjin Jang

Wei Wang

Samuel Wiget

Andrew J Petruska

Xiangzhong Chen

Chenzhi Hu

David Folio

Antoine Ferreira

Salvador Pané

Bradley J Nelson

Catalytic locomotion of Au/Ru core-shell nanowire motors

Keywords: coreshell nanowires, catalytic micronanomotors, selfelectroosmosis, self diffusiophoresis, net propulsive force

Fifteen years ago, Whitesides et al. pioneered a work on self-propelled catalytic millimeter-scale ships, 1 triggering a mighty development of synthetic micro and nanomotors. These devices move autonomously in the presence of a fuel solution by converting chemical to mechanical energy. 2 Most of reported catalytic micro and nanomotors are driven by the decomposition of hydrogen peroxide, although there are other recent examples using glucose and urea as fuels. 3-5 Several applications for these miniaturized motors have been demonstrated such

as electronic circuit healing, [START_REF] Li | Self Propelled Nanomotors Autonomously Seek and Repair Cracks[END_REF] biosensing, [START_REF] Ma | EnzymePowered Hollow Mesoporous Janus Nanomotors[END_REF][START_REF] Dey | Micromotors Powered by Enzyme Catalysis[END_REF] nanolithography, [START_REF] Li | Nanomotor lithography[END_REF] microfluidic pumping, 9 nanocargo transport [START_REF] Sundararajan | Catalytic Motors for Transport of Colloidal Cargo[END_REF] , or environmental remediation (for details, see recent review articles [START_REF] Dey | Catalytic Motors-Quo Vadimus?[END_REF][START_REF] Yadav | Anatomy of Nanoscale Propulsion[END_REF][START_REF] Sanchez | Chemically powered micro and nanomotors[END_REF][START_REF] Wang | From one to many: dynamic assembly and collective behavior of selfpropelled colloidal motors[END_REF][START_REF] Guix | Nano/micromotors in (bio)chemical science applications[END_REF][START_REF] Zhou | Chemistry pumps: a review of chemically powered micropumps[END_REF] ). Most of these applications require a precise control over the speed and directionality of the motors. [START_REF] Colberg | Chemistry in motion: tiny synthetic motors[END_REF] , which are highly dependent on the motor design parameters, such as its geometry, size and composition. Understanding the mechanisms involved in the propulsion of catalytic micro and nanomotors as a function of their design is crucial for controlling their motion. Different propulsion mechanisms have been identified, such as bubble propulsion, selfelectroosmosis, selfdiffusiophoresis or interfacial tension. [START_REF] Zhou | Chemistry pumps: a review of chemically powered micropumps[END_REF] In the first mechanism, bubbles are formed on the catalytic surface and impart momentum to the motor. For example, tubular microjets [START_REF] Solovev | Catalytic microtubular jet engines selfpropelled by accumulated gas bubbles[END_REF][START_REF] Huang | Catalytic Nanoshell Micromotors[END_REF] are driven by oxygen bubble propulsion, caused by the decomposition of H 2 O 2 at the inner catalytic surface. While bubble propulsion seems at first glance the most intuitive way to explain the motion of these motors, this mechanism cannot be used to rationalize, for instance, the motion of bimetallic structures such as Pt/Au or Cu/Pt nanowires (NWs) in H 2 O 2 and I 2 solutions respectively. [START_REF] Liu | Autonomous nanomotor based on copperplatinum segmented nanobattery[END_REF][START_REF] Paxton | Catalytic Nanomotors: Autonomous Movement of Striped Nanorods[END_REF] For bimetallic NWs, self-electroosmosis [START_REF] Wang | Bipolar Electrochemical Mechanism for the Propulsion of Catalytic Nanomotors in Hydrogen Peroxide Solutions †[END_REF][START_REF] Pumera | Electrochemically powered selfpropelled electrophoretic nanosubmarines[END_REF] is generally the most accepted mechanism compared to others [START_REF] Paxton | Catalytic Nanomotors: Autonomous Movement of Striped Nanorods[END_REF] , [START_REF] Fournierbidoz | Synthetic selfpropelled nanorotors[END_REF] . In this case, a selfinduced electric field is generated by redox reactions occurring on the two different metallic segments. The motion of other micro-and nanomotors has been explained in terms of self-diffusiophoresis. Several works suggest that micro-and nanoparticles asymmetrically coated with Pt move due to an O 2 concentration gradient, caused by the asymmetric decomposition of H 2 O 2 along the body of the motor. 25-27 28, 29 Recent investigations evaluating more complex motor architectures are pointing towards a concomitant occurrence of mechanisms. [START_REF] Wilson | Autonomous movement of platinumloaded stomatocytes[END_REF][START_REF] Wang | Selecting the swimming mechanisms of colloidal particles: bubble propulsion versus selfdiffusiophoresis[END_REF] Among all micro-and nanomotors' structures, NWs are particularly appealing due to their unique physical properties. [START_REF] Hu | Chemistry and Physics in One Dimension: Synthesis and Properties of Nanowires and Nanotubes[END_REF] Additionally, NWs can be compartmentalized in several functional modules by means of segmentation [START_REF] Nicewarnerpena | Submicrometer metallic barcodes[END_REF] or coaxial lithography 34 to increase their capabilities and functionalities. For instance, the trajectory of catalytic NW motor consisting of Pt/Ni/Au/Ni/Au segments can be controlled by means of magnetic fields. [START_REF] Kline | Catalytic Nanomotors: Remote Controlled Autonomous Movement of Striped Metallic Nanorods[END_REF] However, interfacing certain building blocks can be detrimental for several applications. For example, the combination of two dissimilar metallic segments such as Au and Ni can lead to an increase of the corrosion rate of the magnetic part due to galvanic coupling. [START_REF] Hangarter | Nanopeapods by galvanic displacement reaction[END_REF] Also, the interaction of certain materials (e.g. Ni) with the surrounding environment (e.g.biological or aquatic media) must be avoided to prevent undesired leakage of toxic ions. Alternatively, coreshell arrangements can provide an inert housing to prevent certain components to be in direct contact to the surrounding environment. Coreshell NWs have been manufactured using a wide variety of methods, and usually they imply two or more steps. [START_REF] Lauhon | Epitaxial coreshell and core multishell nanowire heterostructures[END_REF][START_REF] Hwang | 2O3 Nanotubes Fabricated by Wet Etching of ZnO/Al2O3 Core/Shell Nanofibers[END_REF][START_REF] Yin | Silver Nanowires Can Be Directly Coated with Amorphous Silica To Generate WellControlled Coaxial Nanocables of Silver/Silica[END_REF][START_REF] Kong | Metal-Semiconductor Zn-ZnO Core-Shell Nanobelts and Nanotubes[END_REF][START_REF] Cao | Template Synthesis and Magnetic Behavior of an Array of Cobalt Nanowires Encapsulated in Polyaniline Nanotubules[END_REF][START_REF] Hu | Fabrication of GermaniumFilled Silica Nanotubes and Aligned Silica Nanofibers[END_REF] In this work, we capitalize on a recently developed method, which allows for the batch fabrication of freestanding NWs based on template-assisted electrodeposition and wafer bonding.

As the NWs are self-supported on the wafer after template removal, a second electrodeposition step can be realized to produce a core-shell architecture. This newly developed method can be adopted to fabricate core-shell NWs with various material combinations. Here, we have produced different types of bimetallic core-shell NWs such as Au/Ru, Rh/Au, Au/Rh and we have investigated for the first time their motion in H 2 O 2 solutions. We observed abnormal behaviors of these core-shell devices including a speed increase with the length of Au/Ru coreshell NWs and a large speed difference among Au/Ru, Rh/Au and Au/Rh, which have not been observed for the segmented (conventional bimetallic) NWs. The experimental results suggest that self-electroosmosis cannot be the sole mechanism for the locomotion of these architectures. Indeed, we suggest that self-diffusiophoresis is additionally playing a role as well. Additionally, magnetically guided motion of a (Au-Ni)/Ru core-shell NW is demonstrated. In this configuration, the magnetic component is enclosed within the shell, thus preventing the Ni core to be in contact with the surrounding solution.

Results

Numerical model of electroosmotic mechanism on core-shell NWs. It has previously been established that the electroosmotic force is the main driving force for locomotion of nanomotors based on surface redox reactions. [START_REF] Moran | Locomotion of electrocatalytic nanomotors due to reaction induced charge autoelectrophoresis[END_REF][START_REF] Moran | Electrokinetic locomotion due to reactioninduced charge auto electrophoresis[END_REF] The redox reaction consists of an oxidation reaction and reduction reaction as illustrated in Figure 1a. Protons, oxygen molecules, and electrons are generated in the oxidation process at the anode (Au) by decomposing H 2 O 2 . The generated protons travel by convection, diffusion, and migration to the cathode (Ru), where they react with electrons, oxygen and H 2 O 2 molecules to produce H 2 O in the reduction process. As a consequence of this redox reaction, protons are asymmetrically distributed around the nanomotor and, accordingly, an asymmetric electric field is developed. This self-generated electric field causes protons to migrate inside the electrical double layer that naturally formed when the nanomotor is immerged in a water solution. The proton migration leads to an electroosmotic flow, which eventually causes the motion of the nanomotor. The influence of the electroosmotic mechanism on the locomotion of our newly designed nanomotor is simulated using commercial multiphysics simulation package COMSOL (5.1). Figure S1a shows that the proton concentration around the Au/Ru coreshell NW is highly asymmetric, as expected. The protons are highly concentrated at the Au (anode) end the NW, where they are generated, and have a low concentration at the Ru (cathode) end, where they are consumed. As expected, Figure S1b shows that the electric potential is also asymmetric because of the asymmetric distribution of protons. Figure 1b shows that the fluid flows from the anode to the cathode, translating to a directional motion of the Au/Ru coreshell NW with the anode forward. The simulation also reveals that the fluid speed is highest at the interface between the cathode and anode of the core shell NW, where the majority of the electroosmosis flow occurs.

We notice in our simulation (Figure 1b) that near the opposite tip to the anode there is a convergent flow loop on each side of the rod due to the coupling between the electrosmotic flow moving from the anode to the cathode, and a flow from the surrounding solution due to the electric field pointing towards the cathode. We further investigate how the length of Au/Ru coreshell NW affects the convergent flow. When the length is small enough (~ 500 nm, Figure 1b (i)), the electric field generated at the anode dominates the overall electroosmotic flow (Figure S1c), and the disturbance to the fluid flow at the tip of the cathode is negligible. At approximately 1.5 µm, a flow reversal at the tip occurs, which is caused by the migration of protons from the surrounding solution to the cathode (Figure 1b (ii)). The reverse flow gains enough strength to create vortices near the surface of the NW tip. Further increase in length increase the size of vortices (Figure 1b (iii), (iv)). These vortices represent a local increase in the fluidic drag at the tail of the NW, according to the observed negative fluidic speed at the tip of the cathode as seen from the fluid profile along the NW long axis (Figure S1d).

The nanomotor speeds are calculated from the fluid velocities around the core-shell NWs using the Solomentsev and Anderson model. [START_REF] Wang | Understanding the efficiency of autonomous nano and microscale motors[END_REF] Figure 1c shows the speed as a function of length of the core-shell NW. The decreased speed with length in the numerical simulation can be attributed to the increase in drag force of the nanomotor as its length increases, since no-slip boundary condition was chosen for the nanomotor surface in the simulation.

Design and fabrication of the Au/Ru core shell NWs. The fabrication process is illustrated in Figure 2a. First, an anodic aluminium oxide (AAO) membrane was transferred onto a Si substrate via the Au/Au compressive bonding technique previously developed in our group. [START_REF] Jang | Siliconsupported aluminum oxide membranes with ultrahigh aspect ratio nanopores[END_REF] Next, Au was electrodeposited into the pores of the AAO. The AAO was then chemically etched to obtain a freestanding Au NW array on the Si substrate. Ru was then electrochemically deposited on the freestanding Au NW array, hence the formation of a coreshell structure. The strong adhesion between the freestanding array and the Si substrate is able to withstand the electrophoretic and fluidic forces exerted on the NWs during Ru electrodeposition. We importantly note that the freestanding NWs must be kept in solution to prevent drying, because the surface tension forces during drying will cause severe NW aggregation [START_REF] Hill | Eliminating capillary coalescence of nanowire arrays with applied electric fields[END_REF] and result in a non uniform deposition of the Ru shell. After the Ru deposition, the NWs are released by sonication.

A scanning electron microscope (SEM) image obtained using a back-scattered electron (BSE) detector in Figure 2b shows a typical Au/Ru core-shell NW with an approximate diameter and a length of 350 nm and 2.5 µm, respectively, where the length varies with the deposition time of the Au core. Since the intensity of the BSE signal is proportional to the atomic number, the bright area and the dark area can be assigned to Au and Ru, respectively. The Au core appears only at the base (left end) as a consequence of being released from the Si substrate. No obvious defects are observed in the Au/Ru core-shell NW structure as is seen in the high-angle annular dark field (HAADF) scanning transmission electron microscopy (STEM) image (Figure 2c). The corresponding energy dispersive X-ray (EDX) mapping verifies that the core-shell NW is composed of Au and Ru (Figure 2d). For a better understanding of the Ru distribution, we further selectively etched the Au core in Au etchant. Representative SEM and STEM images (Figure 2e,f) show that the remaining structure after etching is a Ru shell. The Ru shell has an approximate diameter of 350 nm, which is similar to that of a Au/Ru core-shell NW. A smooth surface of the nanoshell without holes or cracks on the rod is observed and confirms the uniform distribution and the conformal growth of the Ru shell. Subsequent EDX mapping verifies that the nanoshell solely consists of Ru (Figure 2g). To systematically understand the locomotion, we studied the dynamic motion behavior of the core-shell NW with different lengths (1.8, 2.6 and 3.4 µm). We calculated the mean-square displacement (MSD) from the recorded motion trajectory (see also supporting movie1) using:

Catalytic propulsion of the

Δ L 2 (t) ≡⟨ [r(t ) -r (0)] 2 ⟩ (1)
where r (t)=( x (t ) , y (t )) .

Similar to catalytic motors of other shapes, the MSD curves in Figure 3b and S2a,b show sinusoidal oscillations, which indicate that our motors exhibit a circular motion. [START_REF] Marine | Diffusive behaviors of circleswimming motors[END_REF][START_REF] Teeffelen | Dynamics of a Brownian circle swimmer[END_REF][START_REF] Ebbens | Selfassembled autonomous runners and tumblers[END_REF] The translational velocity (v), rotational velocity (ω), translational diffusivity (D) and rotational diffusivity (D r ) for a moving particle can be extracted by fitting the MSD curves with [START_REF] Ebbens | Selfassembled autonomous runners and tumblers[END_REF] :

Δ L 2 (t )=4 D t + 2 v 2 D r t D r 2 +ω 2 + 2 v 2 (ω 2 -D r 2 ) D r 2 + ω 2 + 2 v 2 e D r t ( D r 2 + ω 2 ) 2 [( D r 2 -ω 2 )cosωt -2 ω D r sin ω t ]
For comparison, the translational diffusivity D t,b and the rotational diffusivity D r,b of passive Brownian motion (i.e. in the absence of H 2 O 2 ) of our core-shell NW are calculated by the Einstein relation:

D t , b = k B T f t , ∥ (3)
ΔL 2 ( t ) =4D t + 2v 2 D r t D r 2 +ω 2 + 2v 2 ( ω 2 -D r 2 ) D r 2 +ω 2 + 2v 2 e D r t ( D r 2 +ω 2 ) 2 [ ( D r 2 -ω 2 ) c o s ωt -2ωD r s i nωt ] (4)
where k B is the Boltzmann constant, T is the absolute temperature, f t ∥ is the translational frictional drag force and f r ∥ is the rotational frictional drag force parallel to the long axis of NW with a length (L) and a diameter (d) in a viscous media

D t,b = k B T f t, ∥ . D r , b = k B T f r ∥
are defined as: 54

f t ∥ = 2 πηL ln ( L/ d ) -0.207+ 0.980 ( L/d ) - 0.133 ( L /d ) 2 (5) k B (6)
All of the values, as summarized in Table 1, vary as the length changes. The translational diffusivity D t , rotational diffusivity D r , and rotational velocity decrease with increasing length which are easy to understood by the Einstein law; as the length increases, both translational and rotational drag forces increase, which results in a decrease in both the diffusivity and velocity.

However, while D t is quite close to D t,b , a discrepancy of several orders of magnitude is observed between D r and D r,b for longer core-shell NWs. The vortices at the tail of the cathode, found in the numerical modelling (see again Figure 1b) may explain the observed discrepancy between D r and D r,b for a longer NW. This additional fluidic drag caused by the vortices, electrochemically formed contributes to the large reduction in the rotational diffusivity, hence reducing its tendency to rotate and, therefore, increasing its motion stability (or directionality.) For a detailed elucidation, we example macro-scaled flying objects, such as rockets or airplanes. For those flying objects, it is commonly known that the center of drag force should locate behind the center of thrust for a directional stability, and the stability further increases with the distance of the two centers. In our case, the center of thrust locates at the tip of the anode, while the center of drag force, supposed to be at the center for this rectangular shaped-nanomotor is actually shifted to the side of the cathode tip due to the additional drag force. The distance of the two centers further increases with the size of vortex as the length increases.

In addition, the increase in translational velocity (ν) with increasing length is unexpected and somewhat counter-intuitive. Previous study conducted on Cu/Pt nanomotors show a decrease in velocity with increasing nanomotor length, most likely due to the increased drag force for a longer length. [START_REF] Liu | Autonomous nanomotor based on copperplatinum segmented nanobattery[END_REF] However, in our experiments the speed of Au-Ru core-shell nanomotor increased with the nanomotor length, as shown by the averaged measured velocities in Figure 3c for lengths of 1.5, 2.5 and 3.5 µm (N>8 for each length). Our experimental values show a dissimilar trend with the numerical simulations (Figure 1c). The increase in speed with length in the experimental data implies that there might be an extra driving force that increases with length.

This extra driving force superimposes on the main driving force (electroosmotic force), resulting in increase in the net propulsive force of the nanomotor (Figure 3d). We can assume that the extra driving force can be related to a self-diffusiophoresis mechanism because O 2 concentration gradient around the motor surface is reinforced as the catalytically reactive surface area (Ru) increases with length. Although it is intuitively compelling that self-diffusiophoresis due to the generated O 2 concentration on the Ru surface leads to a speed enhancement, the exact magnitude of such enhancement remains unknown, and it is therefore difficult to directly compare the relative contribution from the enhanced force due to O 2 generation, and the larger viscous drag that a longer rod would experience slowing down the motor.

Catalytic propulsion of the core-shell NWs with various material combinations and the

prediction of their propulsive force. In order to further investigate the role of diffusiophoresis in the catalytic propulsion of core-shell naonomtors, we fabricated Au/Rh and Rh/Au core-shell NWs with a length of 2.5 um. Their motion in the presence of 20 wt% H 2 O 2 were compared with that of Au/Ru core-shell NW. We note that Ru/Au core-shell NW was not fabricated due to the growth limit of Ru cores beyond 200 nm in a length. The trajectories of the core-shell NWs are represented in Figure 4. The speed comparison of these three types of nanomotors was Au/Ru >> Rh/Au > Au/Rh (See the Supplementary movie2).

Such a trend cannot be explained by existing theory on self-electroosmosis, and we reason as follows. The speed of a self-electroosmotic nanomotor is in general correlated to a combination of the surface charge of the nanomotor (zeta potential), and the self-generated electric field as a result of surface electrochemical reactions. To understand the effects of these two factors, we first measured the zeta potential of Au/Ru, Rh/Au and Au/Rh core-shell NWs, all yielding results around ~ -26 mV. This indicates that the observed speed difference among these three samples cannot be explained solely by the differences in their zeta potential. Secondly, as far as self-generated electric field is concerned, we believe that the difference among these three samples are to be insignificant, supported by a previous study by Wang et al. [START_REF] Wang | Bipolar Electrochemical Mechanism for the Propulsion of Catalytic Nanomotors in Hydrogen Peroxide Solutions †[END_REF] showing similar speed of Au-Ru, Rh-Au and Au-Rh segmented NW motors. To briefly summarize, selfelectroosmosis mechanism would predict similar speeds for these three samples, which is in clear

contrast to what we have experimentally observed.

Given the inadequacy of self-electroosmosis in explaining the observed speed trend, we attempt to reconcile our results with self-diffusiophoresis. The magnitude of the selfdiffusiophoretic force (extra driving force) depends on the O 2 concentration gradient, developed by the decomposition of H 2 O 2 on the catalytically active layer. According to the previous studies, the decomposition rate of H 2 O 2 on Ru is twice as high as Rh, while it is 10 times slower on Au than on Rh. [START_REF] Mckee | Catalytic decomposition of hydrogen peroxide by metals and alloys of the platinum group[END_REF] This implies that the predicted magnitude of the selfdiffusiophoretic forces on the coreshell NWs are in the order of Au/Rh >> Rh/Au > Au/Ru core-shell NWs, provided that the total number of H 2 O 2 decomposition is defined as the product of the decomposition rate and the reactive surface area. With the regards, diffusiophoretic force directions on Au/Rh, Rh/Au, Au/Ru core-shell nanomotors can be predicted as Au→Rh, Rh→Au and Au→Ru respectively.

The predicted force magnitudes and directions are illustrated in the left column of Figure 4. We note that the arrow sizes are relative among three core-shell samples.

To this end,, the relative speeds among three nanomotors are predicted by comparing the magnitudes of the propulsive forces that is the sum of the self-electroosmotic force and selfdiffusiophoretic force. Since the Au/Ru and Au/Rh core-shell NW exhibits the highest and lowest propulsive force respectively, the expected speeds for the core-shell NWs decrease in the following order: Au/Ru > Rh/Au > Au/Rh. This prediction is in accordance with our experimental observation, confirming our propulsion mechanism.

Magnetic guided motion of (Au-Ni)/Ru core-shell NWs.

In this section, we demonstrate that the trajectory of our core-shell NWs can be controlled under an applied magnetic field in H 2 O 2 solution. For a magnetically guided motion, a

Ni segment was additionally deposited on a Au segment, and subsequently coated with a Ru shell (see Figure S3 for SEM images and EDX mapping of the (Au-Ni)/Ru core-shell NWs). A magnetic guided motion was performed under the magnetic field ( ⃗ B ) of 1mT with a negligible magnetic field gradient. Supplementary movie 3 shows rectangular trajectories of two (Au-Ni)/Ru core-shell NWs when the field orientation is switched counterclockwise with a step of 90°. Interestingly, the magnetic field only serves to align the motors parallel to the magnetic field lines, but it does not determine the heading direction along them.

In architecture as a magnetic responsive material. The coreshell fabrication method presented here also allows for the integration of materials within the shell such as magnetic parts, and prevent them to be in direct contact with the surrounding environment.

  Au/Ru core-shell NWs. The catalytic locomotion of the Au/Ru core-shell NWs (approximate length of 2.5 µm) in H 2 O 2 solution was investigated at various concentrations (0, 0.1, 0.3, 2.5, 5, 10 and 20 wt %). As expected, the speed of the core-shell NW increases with the concentration of H 2 O 2 (Figure3a), showing the maximum speed of 41 ± 7.6 µm/s at 20 wt % H 2 O 2 . The positive correlation between the speed of the motor and the concentration of H 2 O 2 is in accordance with previous findings.[START_REF] Marine | Diffusive behaviors of circleswimming motors[END_REF][START_REF] Lee | Self propelling nanomotors in the presence of strong Brownian forces[END_REF][START_REF] Demirok | Ultrafast catalytic alloy nanomotors[END_REF][START_REF] Laocharoensuk | CarbonNanotubeInduced Acceleration of Catalytic Nanomotors[END_REF] A representative core-shell-NW trajectory is shown in Figure3a(i). A large displacement is observed over 4.55 sec in the presence of H 2 O 2 (5 wt %) in contrast to the control experiment, conducted in the absence of H 2 O 2 , which shows Brownian motion and a very small displacement (Figure3a(ii)).

FiguresFigure 1 .

 1 Figures

Figure 2 .

 2 Figure 2. (a) A schematic drawing of the fabrication of core-shell NWs: the fabrication process includes AAO/Si substrate bonding and electrodeposition. BSE images of (b) a Au/Ru core-shell NW and (e) a Ru shell. *Insets in (b) and (e) are schematic drawings of Au/Ru core-shell NW and Ru shell, respectively. HAADF STEM images of (c) a Au/Ru core-shell NW and (f) a Ru shell. TEM-edx maps of (d) a Au/Ru core-shell NW and (g) a Ru shell, respectively.

Figure3.

  Figure3. Experimental data of Au/Ru core-shell NW in the presence of H2O2: (a) Averaged speed as a function of the concentration of H2O2. Speed was averaged from at least 11 samples with an approximate length of 2.5 μm. Inset (i): A tracked path of a core-shell NW at 5 wt % H2O2. And inset (ii): A tracked path of a core-shell NW at 0 wt % H2O2. (b) a MSD curve of Au/Ru core-shell NWs at the approximate length of 2.5 µm. (c) Speed of Au/Ru core-shell NWs as a function of a length of NWs in the presence of 5 wt % H2O2. *The length of core-shell NWs ranges from 1 to 2 µm, 2 to 3 µm and 3 to 4 µm. Averaged speed from at least eight samples for each range. (d) A schematic drawing of our proposed mechanism for the propulsion of the Au/Ru core-shell NWs. We note the force arrows are represented from the point of view of the nanomotor, rather than fluid.

Figure 4 .

 4 Figure 4. The predictions of the magnitude and direction of self-electroosmotic and self-diffusiophoretic forces, and the experimentally observed tracked paths of (a) Au/Rh, (b) Rh/Au and (c) Au/Ru core-shell NWs over 4.8 sec. * The arrow size are relatively predicted in three samples and force arrows are represented from the point of view of the nanomotor, rather than fluid.

TOC

  conclusion, coreshell NWs composed Au/Ru have been fabricated and studied as a potential catalytic nanomotor when submerged in H 2 O 2 . Their maximum speed was observed to be 41±7.6 µm/s at a 20% H 2 O 2 concentration. Moreover, decreased rotational diffusivity or, equivalently, enhanced directionality is observed with increased length, which results from the formation of vortices at the end of the cathode as revealed by the electroosmotic mechanism based numerical simulation. Experimentally, this nanomotor design exhibits a positive length speed correlation, which is contrary to what occurs in other designs or in our simulation. In the case of our coreshell nanostructures, we assume that the propulsion comes from two simultaneous mechanisms: selfdiffusiophoresis and selfelectroosmosis. When the catalytic active surface area (Ru) increases with length, the selfdiffusiophoretic force increases due to a higher O 2 concentration gradient. For a coreshell Au/Ru NW, the generated selfdiffusiophoretic force is superimposed to the selfelectroosmotic force with the same force direction. Therefore, the net propulsion force is reinforced. The suggested propulsion mechanism also justifies the large difference in speed among Au/Rh, Rh/Au and Au/Ru coreshell NWs, which has not been observed for conventional bimetallic NWs. Finally, we have successfully demonstrated magnetically guided motion of coreshell NWs by inserting a Ni segment in the coreshell

Table 1 .

 1 Measured parameters (Dt, ν, Dr and ω) and theoretical values (Dt,b and Dr,b) of Au/Ru core-shell NWs

	Parameters	Dt (µm 2 /s)	Dt,b	ν (µm/s)	Dr (µm 2 /rad)	Dr,b	Ω
	Length		(µm 2 /s)			(µm 2 /rad)	(rad/s)
	1.8 µm	0.97	0.71	9.83	1.84	2.13	6.97
	2.6 µm	0.58	0.58	16.35	0.44	1.52	5.73
	3.4 µm	0.31	0.50	27.88	0.0015	1.18	1.43

Funding support from the European Community's Seventh Framework Programme (FP7/2007 2013) under grant agreement 296679 (MANAQA) is acknowledged. S.P. acknowledges financial support by the European Research Council Starting Grant "Magnetoelectric Chemonanomotorics for Chemical and Biomedical Applications (ELECTROCHEMBOTS)", by the ERC grant agreement n. 336456. W.W. is grateful for the financial support from National Natural Science Foundation of China (Grant No. 11402069) and the city government of Shenzhen (Grant No. KQCX20140521144102503). We especially thank Prof. Mahmut Selman Sakar from the Mechanical Engineering (School of Engineering, EPFL), and Carlos C.J. Alcantara and Ayoung Hong from the MultiScale Robotics Lab (ETH Zürich) for constructive discussions.

ASSOCIATED CONTENT Supporting Information.

Experimental procedures, numerical modelling procedures, supplementary figures and supplementary movies are included. This material is available free of charge via the Internet at http://pubs.acs.org.

AUTHOR INFORMATION

Corresponding Author

*Email: bjang@ethz.ch, vidalp@ethz.ch

Notes

The authors declare no competing financial interests.


Said Hamou

Abdelmadjid Cheikh
email: acheikh@mai.univ-tlemcen.dz.

Jean Marc Linares

Machining dispersions based procedures for computer aided process plan simulation SAID HAMOU and ABDELMADJID CHEIKHJEAN MARC LINARESALI BENAMAR Abstract. Among the whole manufacturing cycle of a product, a sequence of manufacturing stages needs to be optimized using the increasingly available computing resources. Computer aided process planning is seen as the missing link between CAD and CAM, which relates to the translation of design tolerances into manufacturing tolerances to be executed in the shop ¯oor. A computerized module for process plan simulation, taking into account the manufacturing dispersions, has been developed. The process plan simulation program, which consists of three procedures, uses a combination of the minimal transfer method and a modi®ed form of the dispersions method. The ®rst procedure performs a veri®cation of the feasibility of the project's process plans through tolerance transfer. The second procedure performs an optimization of the tolerance distribution using the process capability data. The third procedure computes the manufacturing dimensions, which ensure the quality of the components and products. The simulation module has been validated on complex problems and shows that it gives good results in a short time. The manual work requires several days to solving this manufacturing problem.

activities. Several stages of the manufacturing process of a product are therefore increasingly bene®ting, to power. However, despite the computerization of design (CAD) and manufacturing (CAM) it is extremely dif®cult to ensure a communication between the two activities when several types of technological data are used, especially tolerances [START_REF] Cheikh | Integrating functional tolerancing in computer aided engineering environments[END_REF]. Computer aided process planning (CAPP) is sometimes seen as the missing link between CAD and CAM (Weill 1988, Halevi and[START_REF] Halevi | Principles of Process Planning a Logical Approach[END_REF]. In fact CAPP can provide direct links to the design activity in terms of geometric de®nition and component attributes, such as functional dimensions and tolerances (Graves and Biscard 1999). At the same time it can create links towards the control and inspection activities of manufacture. Therefore, special attention is directed towards the analysis of manufacturing dimensions and tolerances, which ensure the quality of the product as dictated by the functional requirements. The process plan simulation, sometimes called manufacturing dimensions planning (see ®gure 1), is a very dif®cult step to computerize in process planning. In fact the technical literature [START_REF] Hamou | A module for process plan simulation and tolerance transfer in computer aided manufacturing[END_REF] shows that this procedure has 1. Introduction been used manually and, in some cases, partially automated. [START_REF] Bourdet | Les chaà õnes de cotes de fabrication[END_REF] ®rst laid down the basis The last two decades have seen an enormous for process plan simulation based on the manufacturincrease in the use of computers in industrial ing process capabilities in terms of minimal machining engineering activities. In fact, nowadays, computer dispersions. The simulation was performed manually as resources can be exploited to speed-up and improve a tolerance transfer and optimization from design to the accuracy of design, manufacture and assembly manufacture. Taking into account the minimal machining dispersions, [START_REF] Duret | Simulation de gamme d'usinage[END_REF] proposed the minimal transfer method, which automates the veri®cation different degrees, from this substantial computing of the feasibility of a process plan. [START_REF] Fainguelernt | Computer aided tolerancing and dimensioning in process planning[END_REF] presented a computer program that automates the tolerance optimization procedure, starting from a data matrix of minimal reference dispersions. However, the program lacks any automated procedure for the feasibility check of the process plan and the solution to impossible tolerance transfer conditions prior to the optimization. This paper presents a programmed module that completely automates the process plan simulation of a manufacturing pre-project. The mini-mal transfer method and the machining dispersions method are combined in order to automate the process plan feasibility veri®cation. In addition, the paper proposes a modi®ed and programmable methodology, which integrates the veri®cation and optimization in the same procedure, starting with a data matrix of unknown dispersions instead of a data matrix between the two methodologies is given in this paper.

Modelling of the process plan simulation

In order to automate completely the process plan simulation, a programmable methodology is proposed introduced by [START_REF] Bourdet | Les chaà õnes de cotes de fabrication[END_REF] and the minimal transfer method introduced by [START_REF] Duret | Simulation de gamme d'usinage[END_REF]. On one hand, these two methods are rebuilt so that they can solve any tolerance transfer problem. On the other hand, the methodology is built so that it can calculate an optimal distribution of the machining dispersions (capabilities) and ®nally to compute optimal manufacturing dimensions and tolerances. The ®nal simulation module is designed in the form of three chronological procedures. The ®rst procedure performs a veri®cation of the manufacturing pre-project (A222 in ®gure 1). The second procedure consists of the optimization of dispersions (A223 in ®gure 1). The third procedure computes the ®nal optimized manufacturing dimensions (A224 in ®gure 1).

Fundamental dispersions model

If a machine is set up to execute a given operation in quantity production there are inevitably various uncontrollable stochastic factors that affect the ®nal size for the dimension l of a machined component. The measured sizes usually give a variation (scattering) Dl called machining dispersion. It is the difference between the largest size l n and the lowest size l 1 for a given batch as follows:

Dl ‹ l n ÿ l 1 :

-1ƒ

dispersions Dl i for a machined surface or Dl' i for a positioning surface represents the positions occupied by surface i in relation to the machine referential system.

Procedure for the veri®cation of the pre-project based on of minimal reference dispersions. A comparison minimal dispersions

The veri®cation of the process plan pre-project is carried out by checking the feasibility of the process plan with regards to the capabilities of the available manufacturing processes in the workshop. This condition is ful®lled when the manufacturing means can produce the design set dimensions imposed by the as a combination of the machining dispersions method design of®ce. In technical terms, this condition is satis®ed when the tolerance interval (IT) of the design dimension BE is greater than or equals the manufacturing tolerance due to the summation of all dispersions Dl i and is given as:

These different dispersions will affect the process capability and consequently the manufacturing dimensions. They are usually due to localization and ®xture errors, machine rigidity and tool rigidity. The objective of the simulation is to verify the feasibility of the design speci®cations (dimensions BE) and the manufacturing conditions (dimensions BM) using a dispersions based fundamental model introduced by [START_REF] Bourdet | Les chaà õnes de cotes de fabrication[END_REF]. In this model, for each phase in the process plan, the total X IT -dimension BE ƒ5

Dl i : -2ƒ

only one dispersion Dl are eliminated because a phase needs at least two dispersions Dl, one for the machined surface and another for the positioning surface. This elimination process is carried out until the minimal transfer condition is satis®ed, which is the presence of zero Dl or two Dl per column, except for surfaces that are limits to the design dimension (BE). At the end, we verify equation ( 2) by summing all the remaining Dl i dispersions in the matrix.

In the case when equation ( 2) is not satis®ed, the pre-project is not veri®ed. The tolerance transfer is impossible. This problem can be solved in three ways. The ®rst solution consists of changing the tolerance interval IT for the design functional dimension (BE). This decision is the responsibility of the design of®ce. The second one involves changing datum elements and reference planes, which means changing the rooting and operations sheets of the whole process plan. This solution means rewriting a new process plan pre-project. The third solution is more adequate and consists of diminishing the IT of one or more manufacturing dimensions (CF) in the tolerance chain that makes Figure 2. Sample example of a mechanical part pre-project. up the IT of the design dimension as given by In order to automate the task of the veri®cation using the minimal transfer method in conjunction with equation ( 2), the manufacturing pre-project of a sample, an example of which is shown in ®gure 2 for a mechanical part, is written in a matrix format of I p lines and I s columns. Figure 3 highlights a sample ve r i ®c at i o n s e q u e nc e f o r d e s i g n d i m e ns i o n BE = 16 + 0.6. In this ®gure, I s represents a surface varying between 1 to 8 and I p represents a phase varying between 1 to 5. The element A (I p , I s ) of the initial matrix contains a dispersion value only when surface I s intervenes in phase I p as a machined surface (Dl) or as a positioning surface (Dl', Dl '', Dl ''', . . .) in an subsequent phase. Otherwise the value is set to zero. The manufacturing tolerance interval given by SDl i is computed using the method of minimal transfer and is then compared to IT, the tolerance interval of the design dimension (BE). This method is explained in the sample example of ®gure 3 and the ¯owchart steps of ®gure 4. The veri®cation procedure is carried out for each design dimension (BE) by successive elimination of the dispersions from single element columns and lines, except for columns whose surfaces are limits to a design dimension (BE). In fact, on one hand, columns (surfaces) with one dispersion Dl are eliminated because they are not taking part in the localization of Once the pre-project has been veri®ed and retained, the manufacturing tolerance intervals can be computed. To do this, we build an optimization matrix B(I c , I d ) of p lines and q columns where p represents the number of design dimensions and manufacturing dimensions and q the number of dispersions. The ®rst ®gure 2. Each column represents a dispersion Dl which is affected by a surface beginning with the ®rst one, the second one and so on. Then we compute the residuals e given by the following equation:

X e ‹ IT -dimension BE ƒ ÿ

Dl i : -3ƒ table of ®gure 5 contains this matrix (column 3 to After the initial distribution matrix has been built column 12) corresponding to the mechanical part of and the residuals e computed, we determine the processing order of the lines in this matrix depending on the values of these residuals. This order is given by the increasing values of the residuals. As explained in ®gure 5, this optimization process consists of an equal z z Figure 5. Classical optimization procedure for the sample example.

elements Dl of each line in the matrix so that the following equation is satis®ed: X IT -dimension BE ƒ ‹ Dl i :

distribution of each residual e on the dispersion before going to the next line. The tolerance intervals are updated during this process using the optimized dispersions from the previously processed ones. Figure 5 gives the optimization results for the sample example -4ƒ of ®gure 2.

After processing a line, the optimized elements of 2. . and Linares 1994) is used in combination with the minimal transfer method to build the dispersions data matrix for the simultaneous veri®cation and optimization procedure. However, since no reference values are used, each dispersion participating in the tolerance chain for the design and manufacturing dimensions is given the letter x as a value. Others are given the value zero in the starting data matrix as highlighted by the ®rst table of ®gure 6. Then, we increasing values of this coef®cient give the processing order for the tolerance distribution. It is noticed that for the ®rst iteration k equals the BE tolerance value. Following this order, each line is processed by distributing the design tolerance value among the participating dispersions and their values are saved in the distribution matrix, as explained in ®gure 6 for the example of ®gure 2. In opposition to the minimal dispersions method, where a complete distribution of the residuals is not guaranteed, the unknown dispersions method always distributes the full tolerance values among the unknown dispersions.

m p k j ‹ IT CBE ÿ P i Dli ; -5ƒ
p is the number of unknown dispersions, j is the iteration order.

2.4. Procedure for computing manufacturing dimensions (1975) and on the matrix of the pre-project, we can calculate an average length l i limited by two Dl i . For each phase, the origin of basic average lengths is taken on the leftmost surface (surface 1, l 1 = 0). Using the design dimensions (BE) and the manufacturing dimensions (BM) we build a system of equations to determine the basic average lengths l i using relations ( 6) and ( 7) as follows:

Cf iÿj ave: ‹ l j ÿ l i ; -6ƒ

Cpm iÿj ave: ‹ l j ÿ l i ; -7ƒ

where Cf i-j ave. is the average design dimension (BE), Cpm i-j ave. is the average minimal stock removal (BM).

Figure 7 explains this procedure for the example of ®gure 2. This ®gure highlights the system of 12 linear compute the distribution coef®cient k for each algebraic equations obtained using the data of the predesign dimension BE using equation ( 5). The project. After the solution of the system of equations, the computed values of the average lengths l i for the eight surfaces are used to calculate the average manufacturing dimensions CF i-j ave. . The tolerance interval IT is then computed for each dimension CF i-j using the previously optimized dispersions Dl i on each surface i delimiting the dimension. Figure 7 shows that In the veri®cation procedure of the design dimensions, and when the condition of minimum transfer is satis®ed, the manufacturing dimensions participating in the design dimensions are those bounded by surfaces having the two dispersions stationed on the same stage. Thus, we obtain all the z manufacturing dimensions in the pre-project. Based on the fundamental model developed by Bourdet dispersions optimization method for the simple example are similar to the results given by the classical dispersions optimization method. However, in reality, the former always gives better results than the latter, as that are not always available in the industrial literature or are dif®cult to assess from experience.

Programming and validation tests for the simulation

The procedures forming the simulation have been integrated in one module and programmed in order to be tested. The ®nal computer program consists of ®ve functions. The ®rst function permits the input and preparation of the pre-project data in matrix format. The second function performs the pre-project veri®catransfer. The third function executes the optimization procedure of dispersions. The fourth function computes the manufacturing dimensions. The ®fth function computes the setting dimensions. The program is validated by practical and complex process plan simulations and is compared to classical manual simulations [START_REF] Hamou | A module for process plan simulation and tolerance transfer in computer aided manufacturing[END_REF]. Figure 8 gives an overview of the output results of the developed module for the pre-project sample of ®gure 2. This software, which is designed as a standalone module, can be easily integrated in a CAPP system or a more general CAD/ CAM system. The program can extract its initial preproject data from the upstream CAPP modules. On the other side, it can communicate its simulation results to the downstream departments of manufacturing, such as numerical control processing activities.

Conclusions and perspectives

The objectives set forth by this research work were to automate the process plan simulation activity as a separate module or within a CAPP system. The manufacturing dispersions or capabilities are at the root of the development of a combined programmable methodology, based on the minimal transfer method, the minimal reference dispersions method and a proposed modi®ed dispersions method. It is shown that the modi®ed dispersions method performs a the ®nal tolerance values given by the modi®ed simultaneous veri®cation and optimization without the need of the minimal reference dispersions that are not always available for all processes. On the basis of the developed programmed methodology, a computerised module has been designed in the form of three was veri®ed with more complex examples. Further-procedures. The ®rst procedure permits the veri®camore, the modi®ed dispersions method overcomes the tion of the process plan pre-project. The second problem of unknown minimal reference dispersions procedure permits an optimization of dispersions.

The third procedure permits the computation of the optimized manufacturing dimensions and tolerances.

The ®nal module of simulation has been tested on complex examples. These results have shown that the methodologies used can help process designers in the activity of manufacturing tolerance analysis and distribution within a CAPP system. Further research is undergone on one side, in order to perform a nonequal redistribution of excess tolerance depending on the complexity of the manufacturing dimensions. On the other side, investigations are being conducted for automating the extraction of a manufacturing tolerance tion and eventual solution of impossible tolerance chain in order to perform a cost-based statistical optimization of tolerance transfers and distribution on the manufacturing dimensions.

Figure 1 .

 1 Figure 1. Process plan simulation module.

Figure 3 .

 3 Figure 3. Veri®cation example of dimension BE = 16 + 0,6.

Figure 4 .

 4 Figure 4. Flowchart of the veri®cation procedure.

Figure 6 .

 6 Figure 6. Modi®ed optimization procedure for the sample example.

Figure 7 .Figure 8 .

 78 Figure 7. Computation of manufacturing dimensions for the sample example.