Plasmid K401-BCCP-H6 was a gift from Dr. Zvonimir Dogic. This research was supported by Dr. Kun-Ta Wu's start-up fund in Worcester Polytechnic Institute. We thank Dr. Zvonimir Dogic for the protocols to purify and label tubulin and to synthesize active fluids. We are grateful to Dr. Marc Ridilla for his expertise in protein expression and purification. We thank Dr. William Benjamin Roger for assisting us with building the temperature-controlled stage. We acknowledge Brandeis MRSEC (NSF-MRSEC-1420382) for use of the Biological Materials Facility (BMF). We acknowledge the Royal Society of Chemistry for adapting the figures from Bate et al. on Soft Matter33.

The authors have nothing to disclose.

Controlling active matter in situ opens the door to directed self-organization of active matter4,5,24,28,54. In this article, we present a protocol for using temperature to control kinesin-driven, MT-based active fluids in situ, based on the Arrhenius characteristic of the system29,30,31. Because the system is protein-based, maintaining protein functionality throughout the experiment is key to successfully applying the protocol. The main proteins in the system are MTs and kinesin clusters. The former depolymerize below 16 &#176;C and the latter malfunction above 36 &#176;C33. Maintaining the system temperature between 16&#8211;36 &#176;C is therefore vital for active fluids to develop steady dynamics and to enable their response to temperature reversibly (Figure 4 and Figure 5). However, the temperature is controlled based on a PID algorithm, which tends to overshoot the target temperature53. To reduce this overshooting, we recommend setting multiple intermediate target temperatures before setting the final target temperature. For example, to heat up the sample from room temperature (approximately 20 &#176;C) to 35 &#176;C, rather than setting the target temperature directly at 35 &#176;C, we recommend an intermediate target temperature of 30 &#176;C to reduce the chance that the temperature increases above 36 &#176;C, which would irreversibly damage the proteins33. Similarly, when cooling the sample from room temperature to ~16 &#176;C, it is recommended to set an intermediate target temperature of 18 &#176;C, because before reaching a steady state, the PID may cool the sample below 16 &#176;C, depolymerizing the MTs33.
The presented temperature control method relies on cooling and heating using a TEC. The use of the TEC ensures that the sample reaches the target temperature within seconds, whereas a conventional temperature control setup using a temperature-controlled water bath takes minutes to reach the desired temperature (Figure 5)55. The TEC generates nonzero net heat that is dissipated by an aluminum internal water circulation system. However, the water allows mold to grow, which will eventually clog the channel56. A clogged channel inhibits water flow, and the heat will accumulate in the aluminum block, eventually melting the water tubes. The melted tubes cause water to spill over the microscope and camera, damaging the electronic instruments. Therefore, to adopt the presented temperature control setup, we recommend adding 0.1% hydrogen peroxide to the water to inhibit mold growth57,58,59. We expect that this step will ensure that the aluminum internal channel remains clog-free and prevent water damage to nearby electronic devices.
Manipulating the temperature, coating the flow cell surfaces with polyacrylamide, and synthesizing active fluids are three critical steps to realizing this in situ-controlled active fluid. However, this controllability is limited to the temperature range where the involved proteins can function normally. In the active fluid system, the primary proteins are MTs and kinesin clusters, which function normally between 16&#8211;36 &#176;C33. Within this temperature range, active fluids vary their mean flow speeds from 4&#8211;8 &#181;m/s (Figure 4C). Mean speeds outside this range are beyond the limit of the control method presented. In contrast, Ross et al. reported an alternative that allows for the active fluid activities to be switched on and off using light28. The light control also allows for activating active fluids in a 50 &#181;m scale optically-defined boundary. However, such an alternative requires modifying the kinesin structure along with tuning an optical path in a microscope. In comparison, the advantages of adopting the method presented in this article are 1) the active fluids do not need to be redesigned, 2) the microscopes do not need to be modified, 3) the temperature control setup is low-cost and easy to use, and 4) the method is transferrable to other temperature-dependent systems such as the gliding assay29,30,31,32 or more generally enzyme-based systems60. We also expect that the presented method will open the door to designing microfluidic systems where channel flows are controlled locally without valves.

Active matter is differentiated from conventional passive matter due to its capability to convert chemical energy into mechanical work. A material that possesses such capability can consist of living or non-living entities such as bacteria, insects, colloids, grains, and cytoskeletal filaments1,2,3,4,5,6,7,8,9,10. These material entities interact with their neighbors. At a larger scale, they self-organize into either turbulent-like vortices (active turbulence) or material flows11,12,13,14,15,16,17,18,19,20. An understanding of self-organization of active matter has led to various applications in molecular shuttles, optical devices, and parallel computation21,22,23. To bring applications to the next level requires control beyond self-organization. For example, Palacci et al. developed a hematite-encapsulated colloid that self-propelled only when exposed to manually controlled blue light, which led to the emergence of living crystals24. Morin et al. established the control of rolling Quincke colloids by using a tunable external electric field, resulting in colloidal flocking in a racetrack-like channel25. These previous works demonstrate the role of local control in applications and advance the knowledge base of active matter.
In this article, we focus on the controllability of kinesin-driven, microtubule (MT)-based 3D active fluids. The fluids consist of three main components: MTs, kinesin molecular motors, and depletants. The depletants induce a depletion force to bundle the MTs, which are later bridged by motor clusters. These motors walk along the MTstoward the plus end. When a pair of bridged MTsis antiparallel, the corresponding motors walk in opposite directions. However, the motors are bound in a cluster and are unable to walk apart, so they cooperatively slide apart pairs of MTs (interfilament sliding, Figure 1A). These sliding dynamics accumulate, causing bundles of MTsto extend until reaching their buckling instability point and break (extensile bundles, Figure 1B)26. The broken bundles are annealed by the depletion force, which subsequently extends again, and the dynamics repeat. During the process of the repeating dynamics, the bundle movements stir the nearby liquid, inducing flows that can be visualized by doping with micron-scale tracers (Figure 1C). Sanchez et al. and Henkin et al. have characterized the mean speeds of tracers, finding that the speeds were tunable by varying the concentrations of adenosine triphosphate (ATP), depletants, motor clusters, and MTs19,27. However, such tunability existed only prior to active fluid synthesis. After synthesis, the tunability was lost, and the fluids self-organized in their own way. To control active fluid activity after synthesis, Ross.et al. reported a method using the light-activated dimerization of motor proteins, allowing fluid activity to be tuned on and off using light28. While light control is convenient in terms of locally activating the fluids, the method requires redesigning the structures of motor proteins, along with modifying the optical paths in a microscope. Here, we provide an easy-to-use method for locally controlling fluid flows without microscope modification while keeping the motor structure intact.
Our method of locally tuning active fluid flow is based on the Arrhenius law because the kinesin-MT reaction has been reported to increase with temperature29,30,31,32. Our previous studies showed that the temperature dependence of the mean speed of an active fluid flow followed the Arrhenius equation: v = A exp(-Ea/RT), where A is a pre-exponential factor, R is the gas constant, Ea is the activation energy, and T is the system temperature33. Therefore, fluid activity is sensitive to the temperature environment, and the system temperature needs to be consistent to stabilize the motor performance, and consequently, the fluid flow speed34. In this article, we demonstrate the use of the motor&#39;s temperature dependence to continuously tune the flow speeds of active fluids by adjusting the system temperature. We also demonstrate the preparation of an active fluid sample, followed by mounting the sample on a microscope stage whose temperature is controlled via computer software. Increasing the temperature from 16 &#176;C to 36 &#176;C speeds up the mean flow speeds from 4 to 8 &#181;m/s. Additionally, the tunability is reversible: repeatedly increasing and decreasing the temperature sequentially accelerates and decelerates the flow. The demonstrated method is applicable to a wide range of systems where the main reactions obey the Arrhenius law, such as the MT gliding assay29,30,31,32.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>(&#177;)-6-Hydroxy-2,5,7,8-tetramethylchromane-2-carboxylic acid</td>
			<td>Sigma-Aldrich</td>
			<td>238813</td>
			<td>Trolox</td>
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		<tr>
			<td>2-Mercaptoethanol</td>
			<td>Sigma-Aldrich</td>
			<td>M6250</td>
			<td></td>
		</tr>
		<tr>
			<td>3-(Trimethoxysilyl)propyl methacrylate, 98%, ACROS Organics</td>
			<td>Fisher Scientific</td>
			<td>AC216550050</td>
			<td></td>
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			<td>3.2mm I.D. Tygon Tubing R-3603</td>
			<td>HACH</td>
			<td>2074038</td>
			<td>Water tubes</td>
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			<td>31.75 mm diameter uncoated, sapphire window</td>
			<td>Edmund Optics</td>
			<td>43-637</td>
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			<td>3M 1181 Copper Tape - 1/2 IN Width X 18 YD Length - 2.6 MIL Total Thickness - 27551</td>
			<td>R.S. HUGHES</td>
			<td>054007-27551</td>
			<td>Copper tape</td>
		</tr>
		<tr>
			<td>Acetic Acid</td>
			<td>Sigma-Aldrich</td>
			<td>A6283</td>
			<td></td>
		</tr>
		<tr>
			<td>Acrylamide Solution (40%/Electrophoresis), Fisher BioReagents</td>
			<td>Fisher Scientific</td>
			<td>BP1402-1</td>
			<td></td>
		</tr>
		<tr>
			<td>Adenosine 5&#39;-triphosphate dipotassium salt hydrate</td>
			<td>Sigma-Aldrich</td>
			<td>A8937</td>
			<td>ATP</td>
		</tr>
		<tr>
			<td>Alexa Fluor 647 NHS Ester (Succinimidyl Ester)</td>
			<td>Thermo Fisher Scientific</td>
			<td>A20006</td>
			<td>Far-red fluorescent dye. Alexa 647 can be pre suspended in dimethylsulfoxide (DMSO) before mixing with microtubules (1.3.3.2.)</td>
		</tr>
		<tr>
			<td>Amicon Ultra-4 Centrifugal Filter Unit</td>
			<td>Sigma-Aldrich</td>
			<td>UFC801024</td>
			<td>Centrifugal filter tube. Cutoff molecular weight: 10 kDa</td>
		</tr>
		<tr>
			<td>Ammonium Persulfate, 100g, MP Biomedicals</td>
			<td>Fisher Scientific</td>
			<td>ICN802829</td>
			<td>APS</td>
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		<tr>
			<td>Ampicillin Sodium Salt (Crystalline Powder), Fisher BioReagents</td>
			<td>Fisher Scientific</td>
			<td>BP1760</td>
			<td>Ampicillin</td>
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		<tr>
			<td>Antivibration Table</td>
			<td>Nikon</td>
			<td>63-7590S</td>
			<td></td>
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		<tr>
			<td>Avanti J-E Centrifuge</td>
			<td>Beckman Coulter</td>
			<td>369001</td>
			<td></td>
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		<tr>
			<td>Bacto Agar Soldifying Agent, BD Diagnostics</td>
			<td>VWR</td>
			<td>90000-760</td>
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			<td>Biotin</td>
			<td>Alfa Aesar</td>
			<td>A14207</td>
			<td></td>
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		<tr>
			<td>Bucket-plastic white - 2 gallon</td>
			<td>Bon</td>
			<td>84-715</td>
			<td>Water bucket</td>
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		<tr>
			<td>Calcium Chloride</td>
			<td>Sigma-Aldrich</td>
			<td>746495</td>
			<td>CaCl2</td>
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		<tr>
			<td>Catalase from bovine liver</td>
			<td>Sigma-Aldrich</td>
			<td>C40</td>
			<td></td>
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		<tr>
			<td>CFI Plan Apo Lambda 4x Obj</td>
			<td>Nikon</td>
			<td>MRD00045</td>
			<td>4x air objective</td>
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		<tr>
			<td>C-FLLL-FOV GFP HC HC HISN ero Shift</td>
			<td>Nikon</td>
			<td>96372</td>
			<td>GFP filter cube</td>
		</tr>
		<tr>
			<td>CH-109-1.4-1.5</td>
			<td>TE Technology</td>
			<td>CH-109-1.4-1.5</td>
			<td>Thermoelectric Cooler (TEC)</td>
		</tr>
		<tr>
			<td>Chloramphenicol, 98%, ACROS Organics</td>
			<td>Fisher Scientific</td>
			<td>C0378</td>
			<td></td>
		</tr>
		<tr>
			<td>Cooling block</td>
			<td>N/A</td>
			<td>N/A</td>
			<td>Custom milled aluminum</td>
		</tr>
		<tr>
			<td>Coomassie Brilliant Blue R-250 #1610400</td>
			<td>Bio-Rad</td>
			<td>1610400</td>
			<td>Triphenylmethane dye</td>
		</tr>
		<tr>
			<td>D-(+)-Glucose</td>
			<td>Sigma-Aldrich</td>
			<td>G7528</td>
			<td></td>
		</tr>
		<tr>
			<td>Dimethyl Sulfoxide (Certified ACS), Fisher Chemical</td>
			<td>Fisher Scientific</td>
			<td>D128</td>
			<td>DMSO</td>
		</tr>
		<tr>
			<td>DL-1,4-Dithiothreitol, 99%, for biochemistry, ACROS Organics</td>
			<td>Fisher Scientific</td>
			<td>AC165680050</td>
			<td>DTT</td>
		</tr>
		<tr>
			<td>DOWSIL 340 Heat Sink Compound</td>
			<td>Dow</td>
			<td>1446622</td>
			<td>Thermal paste</td>
		</tr>
		<tr>
			<td>ETHYL ALCOHOL, 200 PROOF ACS/USP/NF GRADE 5 GALLON POLY CUBE</td>
			<td>Pharmco by Greenfield Global</td>
			<td>111000200CB05</td>
			<td>Ethanol</td>
		</tr>
		<tr>
			<td>Ethylene glycol-bis(2-aminoethylether)-N,N,N&#39;,N&#39;-tetraacetic acid</td>
			<td>Sigma-Aldrich</td>
			<td>E3889</td>
			<td>EGTA</td>
		</tr>
		<tr>
			<td>Ethylenediaminetetraacetic acid</td>
			<td>Sigma-Aldrich</td>
			<td>798681</td>
			<td>EDTA</td>
		</tr>
		<tr>
			<td>Fisher BioReagents Microbiology Media Additives: Tryptone</td>
			<td>Fisher Scientific</td>
			<td>BP1421</td>
			<td>Tryptone</td>
		</tr>
		<tr>
			<td>Fisher BioReagents Microbiology Media Additives: Yeast Extract</td>
			<td>Fisher Scientific</td>
			<td>BP1422</td>
			<td>Yeast extract</td>
		</tr>
		<tr>
			<td>Fluoresbrite YG Microspheres, Calibration Grade 3.00 &#181;m</td>
			<td>Polysciences</td>
			<td>18861</td>
			<td>Tracer particles</td>
		</tr>
		<tr>
			<td>Glucose Oxidase from Aspergillus niger</td>
			<td>Sigma-Aldrich</td>
			<td>G2133</td>
			<td></td>
		</tr>
		<tr>
			<td>Glycerol</td>
			<td>Sigma-Aldrich</td>
			<td>G5516</td>
			<td></td>
		</tr>
		<tr>
			<td>GpCpp</td>
			<td>Jena Bioscience</td>
			<td>NU-405L</td>
			<td>Guanosine-5'[(&#945;,&#946;)-methyleno]triphosphate (GMPCPP)</td>
		</tr>
		<tr>
			<td>GS Power&#39;s 18 Gauge (True American Wire Ga), 100 feet, 99.9% Stranded Oxygen Free Copper OFC, Red/Black 2 Conductor Bonded Zip Cord Power/Speaker Electrical Cable for Car, Audio, Home Theater</td>
			<td>Amazon</td>
			<td>B07428NBCW</td>
			<td>Copper wire</td>
		</tr>
		<tr>
			<td>Guanosine 5&#39;-triphosphate sodium salt hydrate</td>
			<td>Sigma-Aldrich</td>
			<td>G8877</td>
			<td>GTP</td>
		</tr>
		<tr>
			<td>Hellmanex III</td>
			<td>Sigma-Aldrich</td>
			<td>Z805939</td>
			<td>Detergent</td>
		</tr>
		<tr>
			<td>HEPES Sodium Salt (White Powder), Fisher BioReagents</td>
			<td>Fisher Scientific</td>
			<td>BP410</td>
			<td>NaHEPES</td>
		</tr>
		<tr>
			<td>High performance blender machine</td>
			<td>AIMORES</td>
			<td>AS-UP1250</td>
			<td>Blender</td>
		</tr>
		<tr>
			<td>His GraviTrap</td>
			<td>GE Healthcare</td>
			<td>11003399</td>
			<td>Gravity Column</td>
		</tr>
		<tr>
			<td>Imidazole</td>
			<td>Sigma-Aldrich</td>
			<td>I5513</td>
			<td></td>
		</tr>
		<tr>
			<td>IPTG</td>
			<td>Sigma-Aldrich</td>
			<td>I6758</td>
			<td>Isopropyl &#946;-D-1-thiogalactopyranoside</td>
		</tr>
		<tr>
			<td>Isopropyl Alcohol 99%</td>
			<td>Pharmco by Greenfield Global</td>
			<td>231000099</td>
			<td>Isopropanol</td>
		</tr>
		<tr>
			<td>JA-10 rotor</td>
			<td>Beckman Coulter</td>
			<td>369687</td>
			<td></td>
		</tr>
		<tr>
			<td>L-Glutamic acid potassium salt monohydrate</td>
			<td>Sigma-Aldrich</td>
			<td>G1501</td>
			<td>K-Glutamate</td>
		</tr>
		<tr>
			<td>Lysozyme from chicken egg white</td>
			<td>Sigma-Aldrich</td>
			<td>L6876</td>
			<td></td>
		</tr>
		<tr>
			<td>Magnesium chloride hexahydrate</td>
			<td>Sigma-Aldrich</td>
			<td>M2670</td>
			<td>MgCl2&#8226;6H2O</td>
		</tr>
		<tr>
			<td>MES sodium salt</td>
			<td>Sigma-Aldrich</td>
			<td>M5057</td>
			<td>2-(N-Morpholino)ethanesulfonic acid sodium salt</td>
		</tr>
		<tr>
			<td>MOPS</td>
			<td>Sigma-Aldrich</td>
			<td>M1254</td>
			<td>3-(N-Morpholino)propanesulfonic acid</td>
		</tr>
		<tr>
			<td>MP-3022</td>
			<td>TE Technology</td>
			<td>MP-3022</td>
			<td>Thermocouple</td>
		</tr>
		<tr>
			<td>N,N,N&#39;,N&#39;-Tetramethylethylenediamine 99%, ACROS Organics</td>
			<td>Fisher Scientific</td>
			<td>AC138450500</td>
			<td>TEMED</td>
		</tr>
		<tr>
			<td>Nanodrop 2000c UV-VIS Spectrophotometer</td>
			<td>Thermo Fisher Scientific</td>
			<td>E112352</td>
			<td>Spectrometer</td>
		</tr>
		<tr>
			<td>Nikon Ti2-E Nikon Inverted Microscope</td>
			<td>Nikon</td>
			<td>MEA54000</td>
			<td></td>
		</tr>
		<tr>
			<td>Norland Optical Adhesive 81</td>
			<td>Norland Products</td>
			<td>NOA81</td>
			<td>UV glue</td>
		</tr>
		<tr>
			<td>Novex Sharp Pre-stained Protein Standard</td>
			<td>Thermo Fisher Scientific</td>
			<td>LC5800</td>
			<td>Protein standard ladder</td>
		</tr>
		<tr>
			<td>NuPAGE 4-12% Bis-Tris Protein Gels, 1.5 mm, 10-well</td>
			<td>Thermo Fisher Scientific</td>
			<td>NP0335BOX</td>
			<td>SDS gel</td>
		</tr>
		<tr>
			<td>Optima L-90K Ultracentrifuge</td>
			<td>Beckman Coulter</td>
			<td>365672</td>
			<td></td>
		</tr>
		<tr>
			<td>Parafilm PM996 Wrap , 4&#34; Wide; 125 Ft/Roll</td>
			<td>Cole-Parmer</td>
			<td>EW-06720-40</td>
			<td>Wax film</td>
		</tr>
		<tr>
			<td>Pe 300 ultra Illumination System Single 
			Band , 3mm Light Guide control Pod 
			power supply</td>
			<td>Nikon</td>
			<td>PE-300-UT-L-SB-40</td>
			<td>Cool LED Illuminator</td>
		</tr>
		<tr>
			<td>Phenylmethanesulfonyl fluoride</td>
			<td>Sigma-Aldrich</td>
			<td>78830</td>
			<td>PMSF</td>
		</tr>
		<tr>
			<td>Phosphoenolpyruvic acid monopotassium salt, 99%</td>
			<td>BeanTown Chemical</td>
			<td>129745</td>
			<td>PEP</td>
		</tr>
		<tr>
			<td>Pierce Coomassie (Bradford) Protein Assay Kit</td>
			<td>Thermo Fisher Scientific</td>
			<td>23200</td>
			<td></td>
		</tr>
		<tr>
			<td>Pierce Protease Inhibitor Mini Tablets</td>
			<td>Thermo Fisher Scientific</td>
			<td>A32953</td>
			<td></td>
		</tr>
		<tr>
			<td>PIPES</td>
			<td>Sigma-Aldrich</td>
			<td>P6757</td>
			<td>1,4-Piperazinediethanesulfonic acid</td>
		</tr>
		<tr>
			<td>Pluronic F-127</td>
			<td>Sigma-Aldrich</td>
			<td>P2443</td>
			<td></td>
		</tr>
		<tr>
			<td>Poly(ethylene glycol)</td>
			<td>Sigma-Aldrich</td>
			<td>81300</td>
			<td>PEG. Average molecular weight 20,000 Da</td>
		</tr>
		<tr>
			<td>Potassium Hydroxide (Pellets/Certified ACS), Fisher Chemical</td>
			<td>Fisher Scientific</td>
			<td>P250-500</td>
			<td>KOH</td>
		</tr>
		<tr>
			<td>PowerEase 300W Power Supply (115 VAC)</td>
			<td>ThermoFisher Scientific</td>
			<td>PS0300</td>
			<td>DC power supply of the gel box</td>
		</tr>
		<tr>
			<td>PS-12-8.4A</td>
			<td>TE Technology</td>
			<td>PS-12-8.4A</td>
			<td>DC power supply of the temperature controller</td>
		</tr>
		<tr>
			<td>Pyruvate Kinase/Lactic Dehydrogenase enzymes from rabbit muscle</td>
			<td>Sigma-Aldrich</td>
			<td>P-0294</td>
			<td>PK/LDH</td>
		</tr>
		<tr>
			<td>Quiet One Lifegard Fountain Pump, 296-Gallon Per Hour</td>
			<td>Amazon</td>
			<td>B005JWA612</td>
			<td>Fish tank pump</td>
		</tr>
		<tr>
			<td>Rosetta 2(DE3)pLysS Competent Cells - Novagen</td>
			<td>Millipore Sigma</td>
			<td>71403</td>
			<td>Competent cells</td>
		</tr>
		<tr>
			<td>Sharp Microwave ZSMC0912BS Sharp 900W Countertop Microwave Oven, 0.9 Cubic Foot, Stainless Steel</td>
			<td>Amazon</td>
			<td>B01MT6JZMR</td>
			<td>Microwave for boiling the water</td>
		</tr>
		<tr>
			<td>Sodium Chloride (Crystalline/Certified ACS), Fisher Chemical</td>
			<td>Fisher Scientific</td>
			<td>S271-500</td>
			<td>NaCl</td>
		</tr>
		<tr>
			<td>Sodium dodecyl sulfate</td>
			<td>Sigma-Aldrich</td>
			<td>L3771</td>
			<td>SDS</td>
		</tr>
		<tr>
			<td>Sodium phosphate monobasic</td>
			<td>Sigma-Aldrich</td>
			<td>S8282</td>
			<td>NaH2PO4</td>
		</tr>
		<tr>
			<td>Streptavidin Protein</td>
			<td>Thermo Fisher Scientific</td>
			<td>21122</td>
			<td></td>
		</tr>
		<tr>
			<td>Sucrose</td>
			<td>Sigma-Aldrich</td>
			<td>S7903</td>
			<td></td>
		</tr>
		<tr>
			<td>TC-720</td>
			<td>TE Technology</td>
			<td>TC-720</td>
			<td>Temperature controller</td>
		</tr>
		<tr>
			<td>Tris Base, Molecular Biology Grade - CAS 77-86-1 - Calbiochem</td>
			<td>Sigma-Aldrich</td>
			<td>648310</td>
			<td>Tris-HCL</td>
		</tr>
		<tr>
			<td>Type 45 Ti rotor</td>
			<td>Beckman Coulter</td>
			<td>339160</td>
			<td></td>
		</tr>
		<tr>
			<td>Type 70 Ti rotor</td>
			<td>Beckman Coulter</td>
			<td>337922</td>
			<td></td>
		</tr>
		<tr>
			<td>Type 70.1 Ti rotor</td>
			<td>Beckman Coulter</td>
			<td>342184</td>
			<td></td>
		</tr>
		<tr>
			<td>VWR General-Purpose Laboratory Labeling Tape</td>
			<td>VWR</td>
			<td>89097-916</td>
			<td>Paper tapes</td>
		</tr>
		<tr>
			<td>VWR Micro Cover Glasses, Square, No. 1 1/2</td>
			<td>VWR</td>
			<td>48366-227</td>
			<td>Glass coverslips</td>
		</tr>
		<tr>
			<td>VWR Plain and Frosted Micro Slides, Premium</td>
			<td>VWR</td>
			<td>75799-268</td>
			<td>Glass slides</td>
		</tr>
		<tr>
			<td>XCell SureLock Mini-Cell</td>
			<td>ThermoFisher Scientific</td>
			<td>EI0001</td>
			<td>Gel box</td>
		</tr>
		<tr>
			<td>ZYLA 5.5 USB3.0 Camera</td>
			<td>Nikon</td>
			<td>ZYLA5.5-USB3</td>
			<td>Monochrome CCD camera</td>
		</tr>
	</tbody>
</table>

controlling flow speeds of microtubule-based 3d active fluids using temperature

We present a method for using temperature to tune the flow speeds of kinesin-driven, microtubule-based three-dimensional (3D) active fluids. This method allows for tuning the speeds in situ without the need to manufacture new samples to reach different desired speeds. Moreover, this method enables the dynamic control of speed. Cycling the temperature leads the fluids to flow fast and slow, periodically. This controllability is based on the Arrhenius characteristic of the kinesin-microtubule reaction, demonstrating a controlled mean flow speed range of 4&#8211;8 &#181;m/s. The presented method will open the door to the design of microfluidic devices where the flow rates in the channel are locally tunable without the need for a valve.

Preparing the kinesin-driven, MT-based active fluids requires both kinesin and MTs. The MTs were polymerized from labeled tubulins (steps 1.3 and 1.4) that were purified from bovine brains (step 1.1, Figure 2A), followed by recycling to enhance purity (step 1.2, Figure 2B). The kinesin motor proteins were expressed in and purified from E. coli (steps 2.1 and 2.2, Figure 2B)41,52. The concentration of the prepared kinesin stock was measured with an SDS gel by comparing the main band brightness with that of recycled tubulins with known concentrations (step 2.3, Figure 2B inset). The brightness values of the tubulin bands (B) were linearly fit to their corresponding concentration (C) using the equation C = aB + &#1013;, yielding a = 3.1 &#215; 10-2 mg/mL and &#1013; = 8.7 &#215; 10-4 mg/mL (Figure 2C). The fitted equation was used to determine the concentration of a kinesin band by applying the measured band brightness, Bk = 1,060, yielding a kinesin concentration of Ck = 0.95 mg/mL. MT and kinesin samples with known concentrations were used to prepare active fluid samples. The active fluids were synthesized, loaded into a polyacrylamide-coated flow cell, and the cell was sealed with UV glue (sections 3 and 4)46.
To demonstrate the control of the active fluid activity with the temperature, the active fluid sample was mounted on the homemade temperature stage (step 5.2, Figure 3A-C). The sample temperature was monitored and controlled by the controller according to a proportional-integral-derivative (PID) algorithm53. Samples controlled at 10 &#176;C, 20 &#176;C, 30 &#176;C, and 40 &#176;C appeared to fluctuate in temperature within 0.1&#8211;0.3 &#176;C for 4 h, demonstrating the stability and reliability of this temperature control setup (Figure 3D).
To observe the sample, the setup was mounted onto an epifluorescence microscope. The sample was doped with Alexa 488-labeled tracers, which were imaged with fluorescence microscopy via a GFP channel (step 6.1). The tracers were imaged every &#916;t = 2 s. The sequential images allowed for tracking tracer trajectories ri, where i represents the tracer index (step 6.2, Figure 4A). The trajectories revealed a mean speed v(t) &#8801; &#60;|ri(t) - ri(t- &#916;t)|/&#916;t&#62;I (step 6.3). The mean speeds measured at 20&#8211;36 &#176;C appeared to be nearly time-independent at t = 0&#8211;2 h, whereas at 10 &#176;C and 40 &#176;C the mean speeds decayed quickly (Figure 4B). The decay at 10 &#176;C was caused by MT depolymerization below 16 &#176;C. The decay at 40 &#176;C was due to the kinesin clusters malfunctioning above 36 &#176;C. According to our previous studies these kinesin motor clusters lose the ability to drive pairs of MTs after preincubation at &#62;36 &#176;C33. To characterize the mean speeds for each temperature while reflecting the decay induced by these factors, the mean speeds were averaged between t = 1&#8211;2 h (steps 6.3 and 6.4)33. The time-averaged mean speeds were measured between 10 &#176;C&#8211;40 &#176;C (step 6.5, Figure 4C). Below 16 &#176;C and above 36 &#176;C the mean speeds decayed quickly due to MT depolymerization and malfunctioning kinesin clusters33, whereas increasing temperatures from 16 &#176;C to 36 &#176;C accelerated the mean speeds from 4 to 8 &#181;m/s, demonstrating the feasibility of tuning the mean speed of an active fluid flow using temperature. To further demonstrate the capability of the temperature control, the system temperatures were alternated between 20 &#176;C and 30 &#176;C every 30 min. The mean speeds of the active fluids did not only accelerate and decelerate accordingly, but they also responded to the temperature change within 10 s (Figure 5). Such a reversible and quick response of the active fluid demonstrates the workability of using temperature to dynamically control fluid activities.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/60484/60484fig01.jpg" /> 
Figure 1: Introduction of kinesin-driven, MT-based 3D active fluids. (A) Schematics of interfilament sliding. Pairs of antiparallel MTs were bundled by depletants and driven apart by motor clusters. (B) Motor clusters collectively drove pairs of MTs, leading MT bundles to extend. (C) The extensile bundles constituted a MT-based active gel (green) that stirred the surrounding liquid to induce a flow. To track the flow, the liquid was doped with tracers (red). This figure was adapted from Bate et al.33. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/60484/60484fig01large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/60484/60484fig02.jpg" /> 
Figure 2: Images of SDS gels from the tubulin and kinesin purifications. (A) Image of an SDS gel of purified tubulin. (B) Image of an SDS gel of recycled tubulins and purified kinesin. The lanes from left to right were protein standards, blank, 1.25&#8211;0.25 mg/mL recycled tubulins, blank, and kinesin stock. (C) The concentration of purified kinesin was determined by comparing the band brightness with the sequential bands of tubulins with measured concentrations (red and blue dashed rectangles in inset). The brightness of each band was measured by summing the pixel values within a cropped band image. To reduce the background noise, before cropping the gel image was transferred to grayscale, black-and-white inverted, and then contrast-enhanced to reveal a black background (inset). <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/60484/60484fig02large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/60484/60484fig03.jpg" /> 
Figure 3: Temperature control setup. (A) Schematics of the aluminum block for the temperature control setup. The block contains an internal channel for water to flow through it and carry away the TEC-generated heat. The central hole allows the sample to be illuminated by bright field microscopy on one side and imaged with an objective on the other side. (B) Schematics of the temperature control setup. Silicon thermal paste is applied between the aluminum cooling block and the TEC and between the TEC and the sapphire disc. (C) Image of a sample mounted on the temperature-controlled stage. Water tubes are connected to a pump immersed in a water reservoir. (D) Recorded sample temperatures vs. time for target temperatures 10 &#176;C, 20 &#176;C, 30 &#176;C, and 40 &#176;C, respectively. Sample temperatures were maintained at temperatures with a fluctuation of 0.1&#8211;0.3 &#176;C. B and D were adapted from Bate et al.33. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/60484/60484fig03large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/60484/60484fig04.jpg" /> 
Figure 4: Tuning active fluid flows via temperature. (A) Imaging tracers (white dots) allowed for tracking the flow trajectories sequentially (miscellaneously colored curves). The tracers were monitored every 5 s (&#916;t = 5 s) at room temperature (~20 &#176;C). (B) Tracer mean speed vs. time at 10 &#176;C, 20 &#176;C, 30 &#176;C, 36 &#176;C, and 40 &#176;C, respectively. (C) Tracer mean speed vs. temperature. Each point represents the average tracer mean speed during the first and second hours. Below 16 &#176;C the MTs depolymerized, and above 36 &#176;C, kinesin clusters malfunctioned. Therefore, the working temperature is between 16&#8211;36 &#176;C, where mean speeds varied from 4&#8211;8 &#181;m/s. The error bars represent the standard deviation of the time-averaged mean speeds. B and C were adapted from Bate et al.33. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/60484/60484fig04large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/60484/60484fig05.jpg" /> 
Figure 5: Alternating the flow speed of active fluids by periodically alternating the system temperature. Switching the temperature between 20 &#176;C and 30 &#176;C every 30 min accelerated and decelerated flow speeds repeatedly, demonstrating local control of active fluid activities with the temperature. This figure was adapted from Bate et al.33. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/60484/60484fig05large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Supplemental File 1: MATLAB script 1. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/60484/particle_tracking.m" target="_blank">Please click here to view this file (Right click to download).</a>
Supplemental File 2: MATLAB script 2. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/60484/analysis.m" target="_blank">Please click here to view this file (Right click to download).</a>

Watch this Scientific Journal Video about Controlling Flow Speeds of Microtubule-Based 3D Active Fluids Using Temperature at JoVE.com

Controlling Flow Speeds of Microtubule-Based 3D Active Fluids Using Temperature

The goal of this protocol is to use temperature to control the flow speeds of three-dimensional active fluids. The advantage of this method not only allows for regulating flow speeds in situ but also enables dynamic control, such as periodically tuning flow speeds up and down.

We present a method for using temperature to tune the flow speeds of kinesin-driven, microtubule-based three-dimensional (3D) active fluids. This method ...

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7.1K Views.  Worcester Polytechnic Institute. The goal of this protocol is to use temperature to control the flow speeds of three-dimensional active fluids. The advantage of this method not only allows for regulating flow speeds in situ but also enables dynamic control, such as periodically tuning flow speeds up and down.

Video: Controlling Flow Speeds of Microtubule-Based 3D Active Fluids Using Temperature

Controlling the Size, Shape and Stability of Supramolecular Polymers in Water

For aqueous based supramolecular polymers, the simultaneous control over shape, size and stability is very difficult1. At the same time, the ability to do so is highly important in view of a number of applications in functional soft matter including electronics, biomedical engineering, and sensors. In the past, successful strategies to control the size and shape of supramolecular polymers typically focused on the use of templates2,3, end cappers4 or selective solvent techniques5. 
Here we disclose a strategy based on self-assembling discotic amphiphiles that leads to the control over stack length and shape of ordered, chiral columnar aggregates. By balancing electrostatic repulsive interactions on the hydrophilic rim and attractive non-covalent forces within the hydrophobic core of the polymerizing building block, we manage to create small and discrete spherical objects6,7. Increasing the salt concentration to screen the charges induces a sphere-to-rod transition. Intriguingly, this transition is expressed in an increase of cooperativity in the temperature-dependent self-assembly mechanism, and more stable aggregates are obtained. 
For our study we select a benzene-1,3,5-tricarboxamide (BTA) core connected to a hydrophilic metal chelate via a hydrophobic, fluorinated L-phenylalanine based spacer (Scheme 1). The metal chelate selected is a Gd(III)-DTPA complex that contains two overall remaining charges per complex and necessarily two counter ions. The one-dimensional growth of the aggregate is directed by &pi;-&pi; stacking and intermolecular hydrogen bonding. However, the electrostatic, repulsive forces that arise from the charges on the Gd(III)-DTPA complex start limiting the one-dimensional growth of the BTA-based discotic once a certain size is reached. At millimolar concentrations the formed aggregate has a spherical shape and a diameter of around 5 nm as inferred from 1H-NMR spectroscopy, small angle X-ray scattering, and cryogenic transmission electron microscopy (cryo-TEM). The strength of the electrostatic repulsive interactions between molecules can be reduced by increasing the salt concentration of the buffered solutions. This screening of the charges induces a transition from spherical aggregates into elongated rods with a length &gt; 25 nm. Cryo-TEM allows to visualise the changes in shape and size. In addition, CD spectroscopy permits to derive the mechanistic details of the self-assembly processes before and after the addition of salt. Importantly, the cooperativity -a key feature that dictates the physical properties of the produced supramolecular polymers- increases dramatically upon screening the electrostatic interactions. This increase in cooperativity results in a significant increase in the molecular weight of the formed supramolecular polymers in water.

The goal of this experiment is to determine and control the size, shape and stability of self-assembled discotic amphiphiles in water. For aqueous based supramolecular polymers such level of control is very difficult. We apply a strategy using both repulsive and attractive interactions. The experimental techniques applied to characterize this system are broadly applicable.

For aqueous based supramolecular polymers, the simultaneous control over shape, size and stability is very difficult1. At the same time, the ability to do ...

Determining 3D Flow Fields via Multi-camera Light Field Imaging

In the field of fluid mechanics, the resolution of computational schemes has outpaced experimental methods and widened the gap between predicted and observed phenomena in fluid flows. Thus, a need exists for an accessible method capable of resolving three-dimensional (3D) data sets for a range of problems. We present a novel technique for performing quantitative 3D imaging of many types of flow fields. The 3D technique enables investigation of complicated velocity fields and bubbly flows. Measurements of these types present a variety of challenges to the instrument. For instance, optically dense bubbly multiphase flows cannot be readily imaged by traditional, non-invasive flow measurement techniques due to the bubbles occluding optical access to the interior regions of the volume of interest. By using Light Field Imaging we are able to reparameterize images captured by an array of cameras to reconstruct a 3D volumetric map for every time instance, despite partial occlusions in the volume. The technique makes use of an algorithm known as synthetic aperture (SA) refocusing, whereby a 3D focal stack is generated by combining images from several cameras post-capture 1. Light Field Imaging allows for the capture of angular as well as spatial information about the light rays, and hence enables 3D scene reconstruction. Quantitative information can then be extracted from the 3D reconstructions using a variety of processing algorithms. In particular, we have developed measurement methods based on Light Field Imaging for performing 3D particle image velocimetry (PIV), extracting bubbles in a 3D field and tracking the boundary of a flickering flame. We present the fundamentals of the Light Field Imaging methodology in the context of our setup for performing 3DPIV of the airflow passing over a set of synthetic vocal folds, and show representative results from application of the technique to a bubble-entraining plunging jet.

A technique for performing quantitative three-dimensional (3D) imaging for a range of fluid flows is presented. Using concepts from the area of Light Field Imaging, we reconstruct 3D volumes from arrays of images. Our 3D results span a broad range including velocity fields and multi-phase bubble size distributions.

In the field of fluid mechanics, the resolution of computational schemes has outpaced experimental methods and widened the gap between predicted and ...

High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques

Digital fringe projection (DFP) techniques provide dense 3D measurements of dynamically changing surfaces.&#160;Like the human eyes and brain, DFP uses triangulation between matching points in two views of the same scene at different angles to compute depth.&#160;However, unlike a stereo-based method, DFP uses a digital video projector to replace one of the cameras1. The projector rapidly projects a known sinusoidal pattern onto the subject, and the surface of the subject distorts these patterns in the camera&#8217;s field of view. Three distorted patterns (fringe images) from the camera can be used to compute the depth using triangulation.
Unlike other 3D measurement methods, DFP techniques lead to systems that tend to be faster, lower in equipment cost, more flexible, and easier to develop.&#160;DFP systems can also achieve the same measurement resolution as the camera.&#160;For this reason, DFP and other digital structured light techniques have recently been the focus of intense research (as summarized in1-5). Taking advantage of DFP, the graphics processing unit, and optimized algorithms, we have developed a system capable of 30&#160;Hz 3D video data acquisition, reconstruction, and display for over 300,000 measurement points per frame6,7.&#160;Binary defocusing DFP methods can achieve even greater speeds8.
Diverse applications can benefit from DFP techniques. Our collaborators have used our systems for facial function analysis9, facial animation10, cardiac mechanics studies11, and fluid surface measurements, but many other potential applications exist.&#160;This video will teach the fundamentals of DFP techniques and illustrate the design and operation of a binary defocusing DFP system.

This video describes the fundamentals of digital fringe projection techniques, which provide dense 3D measurements of dynamically changing surfaces.&#160;It also demonstrates the design and operation of a high-speed binary defocusing system based on these techniques.

Digital fringe projection (DFP) techniques provide dense 3D measurements of dynamically changing surfaces.&#160;Like the human eyes and brain, DFP uses ...

Experimental Measurement of Settling Velocity of Spherical Particles in Unconfined and Confined Surfactant-based Shear Thinning Viscoelastic Fluids

An experimental study is performed to measure the terminal settling velocities of spherical particles in surfactant based shear thinning viscoelastic (VES) fluids. The measurements are made for particles settling in unbounded fluids&#160;and fluids between parallel walls. VES fluids over a wide range of rheological properties are prepared and rheologically characterized. The rheological characterization involves steady shear-viscosity and dynamic oscillatory-shear measurements to quantify the viscous and elastic properties respectively. The settling velocities under unbounded conditions are measured in beakers having diameters at least 25x the diameter of particles. For measuring settling velocities between parallel walls, two experimental cells with different wall spacing are constructed. Spherical particles of varying sizes are gently dropped in the fluids and allowed to settle. The process is recorded with a high resolution video camera and the trajectory of the particle is recorded using image analysis software. Terminal settling velocities are calculated from the data.
The impact of elasticity on settling velocity in unbounded fluids is quantified by comparing the experimental settling velocity to the settling velocity calculated by the inelastic drag predictions of Renaud et al.1&#160;Results show that elasticity of fluids can increase or decrease the settling velocity. The magnitude of reduction/increase is a function of the rheological properties of the fluids and properties of particles. Confining walls are observed to cause a retardation effect on settling and the retardation is measured in terms of wall factors.

This paper demonstrates the experimental procedure to measure terminal settling velocities of spherical particles in surfactant-based shear thinning viscoelastic fluids. Fluids over a wide range of rheological properties are prepared and settling velocities are measured for a range of particle sizes in unbounded fluids and fluids between parallel walls.

An experimental study is performed to measure the terminal settling velocities of spherical particles in surfactant based shear thinning viscoelastic ...

Measuring Material Microstructure Under Flow Using 1-2 Plane Flow-Small Angle Neutron Scattering

A new small-angle neutron scattering (SANS) sample environment optimized for studying the microstructure of complex fluids under simple shear flow is presented. The SANS shear cell consists of a concentric cylinder Couette geometry that is sealed and rotating about a horizontal axis so that the vorticity direction of the flow field is aligned with the neutron beam enabling scattering from the 1-2 plane of shear (velocity-velocity gradient, respectively). This approach is an advance over previous shear cell sample environments as there is a strong coupling between the bulk rheology and microstructural features in the 1-2 plane of shear. Flow-instabilities, such as shear banding, can also be studied by spatially resolved measurements. This is accomplished in this sample environment by using a narrow aperture for the neutron beam and scanning along the velocity gradient direction. Time resolved experiments, such as flow start-ups and large amplitude oscillatory shear flow are also possible by synchronization of the shear motion and time-resolved detection of scattered neutrons. Representative results using the methods outlined here demonstrate the useful nature of spatial resolution for measuring the microstructure of a wormlike micelle solution that exhibits shear banding, a phenomenon that can only be investigated by resolving the structure along the velocity gradient direction. Finally, potential improvements to the current design are discussed along with suggestions for supplementary experiments as motivation for future experiments on a broad range of complex fluids in a variety of shear motions.

A shear cell is developed for small-angle neutron scattering measurements in the velocity-velocity gradient plane of shear and is used to characterize complex fluids. Spatially resolved measurements in the velocity gradient direction are possible for studying shear-banding materials. Applications include investigations of colloidal dispersions, polymer solutions, and self-assembled structures.

A new small-angle neutron scattering (SANS) sample environment optimized for studying the microstructure of complex fluids under simple shear flow is ...

Fabrication of Uniform Nanoscale Cavities via Silicon Direct Wafer Bonding

Measurements of the heat capacity and superfluid fraction of confined 4He have been performed near the lambda transition using lithographically patterned and bonded silicon wafers. Unlike confinements in porous materials often used for these types of experiments3, bonded wafers provide predesigned uniform spaces for confinement. The geometry of each cell is well known, which removes a large source of ambiguity in the interpretation of data.
Exceptionally flat, 5 cm diameter, 375 &#181;m thick Si wafers with about 1 &#181;m variation over the entire wafer can be obtained commercially (from Semiconductor Processing Company, for example). Thermal oxide is grown on the wafers to define the confinement dimension in the z-direction. A pattern is then etched in the oxide using lithographic techniques so as to create a desired enclosure upon bonding. A hole is drilled in one of the wafers (the top) to allow for the introduction of the liquid to be measured. The wafers are cleaned2 in RCA solutions and then put in a microclean chamber where they are rinsed with deionized water4. The wafers are bonded at RT and then annealed at ~1,100 &#176;C. This forms a strong and permanent bond. This process can be used to make uniform enclosures for measuring thermal and hydrodynamic properties of confined liquids from the nanometer to the micrometer scale.

A method for permanently bonding two silicon wafers so as to realize a uniform enclosure is described. This includes wafer preparation, cleaning, RT bonding, and annealing processes. The resulting bonded wafers (cells) have uniformity of enclosure ~1%1,2. The resulting geometry allows for measurements of confined liquids and gasses.

Measurements of the heat capacity and superfluid fraction of confined 4He have been performed near the lambda transition using lithographically patterned ...

Fast Imaging Technique to Study Drop Impact Dynamics of Non-Newtonian Fluids

In the field of fluid mechanics, many dynamical processes not only occur over a very short time interval but also require high spatial resolution for detailed observation, scenarios that make it challenging to observe with conventional imaging systems. One of these is the drop impact of liquids, which usually happens within one tenth of millisecond. To tackle this challenge, a fast imaging technique is introduced that combines a high-speed camera (capable of up to one million frames per second) with a macro lens with long working distance to bring the spatial resolution of the image down to 10 &#181;m/pixel. The imaging technique enables precise measurement of relevant fluid dynamic quantities, such as the flow field, the spreading distance and the splashing speed, from analysis of the recorded video. To demonstrate the capabilities of this visualization system, the impact dynamics when droplets of non-Newtonian fluids impinge on a flat hard surface are characterized. Two situations are considered: for oxidized liquid metal droplets we focus on the spreading behavior, and for densely packed suspensions we determine the onset of splashing. More generally, the combination of high temporal and spatial imaging resolution introduced here offers advantages for studying fast dynamics across a wide range of microscale phenomena.

Drop impact of non-Newtonian fluids is a complex process since different physical parameters influence the dynamics over a very short time (less than one tenth of a millisecond). A fast imaging technique is introduced in order to characterize the impact behaviors of different non-Newtonian fluids.

In the field of fluid mechanics, many dynamical processes not only occur over a very short time interval but also require high spatial resolution for ...

Writing and Low-Temperature Characterization of Oxide Nanostructures

Oxide nanoelectronics is a rapidly growing field which seeks to develop novel materials with multifunctional behavior at nanoscale dimensions. Oxide interfaces exhibit a wide range of properties that can be controlled include conduction, piezoelectric behavior, ferromagnetism, superconductivity and nonlinear optical properties. Recently, methods for controlling these properties at extreme nanoscale dimensions have been discovered and developed. Here are described explicit step-by-step procedures for creating LaAlO3/SrTiO3 nanostructures using a reversible conductive atomic force microscopy technique. The processing steps for creating electrical contacts to the LaAlO3/SrTiO3 interface are first described. Conductive nanostructures are created by applying voltages to a conductive atomic force microscope tip and locally switching the LaAlO3/SrTiO3 interface to a conductive state. A versatile nanolithography toolkit has been developed expressly for the purpose of controlling the atomic force microscope (AFM) tip path and voltage. Then, these nanostructures are placed in a cryostat and transport measurements are performed. The procedures described here should be useful to others wishing to conduct research in oxide nanoelectronics.

Oxide nanostructures provide new opportunities for science and technology. The interfacial conductivity between LaAlO3 and SrTiO3 can be controlled with near-atomic precision using a conductive atomic force microscopy technique. The protocol for creating and measuring conductive nanostructures at LaAlO3/SrTiO3 interfaces is demonstrated.

Oxide nanoelectronics is a rapidly growing field which seeks to develop novel materials with multifunctional behavior at nanoscale dimensions.  Oxide ...

Visualizing Hyporheic Flow Through Bedforms Using Dye Experiments and Simulation

Advective exchange between the pore space of sediments and the overlying water column, called hyporheic exchange in fluvial environments, drives solute transport in rivers and many important biogeochemical processes. To improve understanding of these processes through visual demonstration, we created a hyporheic flow simulation in the multi-agent computer modeling platform NetLogo. The simulation shows virtual tracer flowing through a streambed covered with two-dimensional bedforms. Sediment, flow, and bedform characteristics are used as input variables for the model. We illustrate how these simulations match experimental observations from laboratory flume experiments based on measured input parameters. Dye is injected into the flume sediments to visualize the porewater flow. For comparison virtual tracer particles are placed at the same locations in the simulation. This coupled simulation and lab experiment has been used successfully in undergraduate and graduate laboratories to directly visualize river-porewater interactions and show how physically-based flow simulations can reproduce environmental phenomena. Students took photographs of the bed through the transparent flume walls and compared them to shapes of the dye at the same times in the simulation. This resulted in very similar trends, which allowed the students to better understand both the flow patterns and the mathematical model. The simulations also allow the user to quickly visualize the impact of each input parameter by running multiple simulations. This process can also be used in research applications to illustrate basic processes, relate interfacial fluxes and porewater transport, and support quantitative process-based modeling.

This manuscript describes how to create regular bedforms in a flume, visualize flow through the bedforms, and use computer simulations to simulate the hyporheic flow. The computer simulations compare well with the experimental observations. This coupled simulation and experiment is well-suited for both research and educational purposes.

Advective exchange between the pore space of sediments and the overlying water column, called hyporheic exchange in fluvial environments, drives solute ...

Characterization of Full Set Material Constants and Their Temperature Dependence for Piezoelectric Materials Using Resonant Ultrasound Spectroscopy

During the operation of high power electromechanical devices, a temperature rise is unavoidable due to mechanical and electrical losses, causing the degradation of device performance. In order to evaluate such degradations using computer simulations, full matrix material properties at elevated temperatures are needed as inputs. It is extremely difficult to measure such data for ferroelectric materials due to their strong anisotropic nature and property variation among samples of different geometries. Because the degree of depolarization is boundary condition dependent, data obtained by the IEEE (Institute of Electrical and Electronics Engineers) impedance resonance technique, which requires several samples with drastically different geometries, usually lack self-consistency. The resonant ultrasound spectroscopy (RUS) technique allows the full set material constants to be measured using only one sample, which can eliminate errors caused by sample to sample variation. A detailed RUS procedure is demonstrated here using a lead zirconate titanate (PZT-4) piezoceramic sample. In the example, the complete set of material constants was measured from room temperature to 120 &#176;C. Measured free dielectric constants <img alt="Equation 1" src="/files/ftp_upload/53461/image001.jpg" /> and&#160;<img alt="Equation 2" src="/files/ftp_upload/53461/image002.jpg" /> were compared with calculated ones based on the measured full set data, and piezoelectric constants d15 and d33 were also calculated using different formulas. Excellent agreement was found in the entire range of temperatures, which confirmed the self-consistency of the data set obtained by the RUS.

This protocol describes the procedure of measuring the temperature dependence of the full set material constants of piezoelectric materials using resonant ultrasound spectroscopy (RUS).

During the operation of high power electromechanical devices, a temperature rise is unavoidable due to mechanical and electrical losses, causing the ...