Name: environmental dynamic mechanical analysis to predict the softening behavior of neural implants
Uploaded: 2019-03-01T14:30:00.000+00:00
Duration: 6 min 59 s
Description: Watch this Scientific Journal Video about Environmental Dynamic Mechanical Analysis to Predict the Softening Behavior of Neural Implants at JoVE.com

The authors want to thank Dr. Taylor Ware for allowing us to use his environmental DMA.
This work was supported by the Office of the Assistant Secretary of Defense for Health Affairs through the Peer Reviewed Medical Research Program [W81XWH-15-1-0607]. Opinions, interpretations, conclusions, and recommendations are those of the authors and not necessarily endorsed by the Department of Defense.

1. Preparation of polymer samples for testing
<ol>
	<li>Synthesize the softening thiol-ene polymer according to previous protocols inside a fume hood.1,2,4,18 Briefly, mix quantitative amounts of thiol to alkene monomers with a total of 0.1 wt% photo initiator.
	<ol>
		<li>Prepare a 20 mL glass vial for polymer mixing. Cover the vial in aluminum foil to prevent incident light from contacting the monomer solution and keep at room temperature (RT). Use all chemicals as received without further purification.</li>
		<li>For fully softening polymer, add 50 mol% 1,3,5-triallyl-1,3,5-triazine-2,4,6(1H,3H,5H)-trione (TATATO), 45 mol% trimethylolpropane tris(3-mercaptopropionate) (TMTMP), and 5 mol% Tris[2-(3-mercaptopropionyloxy)ethyl] isocyanurate (TMICN) to the covered vial using a disposable plastic pipette.</li>
		<li>Add 0.1 wt% of the photoinitiatior 2,2-dimethoxy-2-phenylacetophenone (DMPA) to the polymer solution.</li>
		<li>Mix the contents inside the vial thoroughly by planetary speed mixing without exposing the solution to light. 
		NOTE: The polymer solution is sensitive to light and will start to polymerize after 45 to 60 min, even if covered with foil. Therefore, use the polymer solution as quickly as possible after mixing.</li>
	</ol>
	</li>
	<li>Spin coat the polymer solution prepared in section 1.1 as thin films between 5 to 50 &#181;m in thickness on microscopic glass slides or silicon wafers as a carrier substrate according to the spin curve (Figure 2). For 30 &#181;m film thickness, spin at 600 rpm for 30 s. 
	NOTE: When using a different SMP formulation, the spin speed and time may vary depending on the viscosity of the polymer solution.</li>
	<li>Transfer the polymer films on&#160;the carrier substrate immediately after spinning to the crosslinking chamber. Photo-polymerize the films for 60 min under 365 nm UV bulbs and post-cure for 24 h in a vacuum oven at 120 &#176;C to further complete the conversion.</li>
	<li>Cut the cured polymer films into rectangular samples with widths of 4.5 mm and lengths of 50 mm for the DMA testing. Thicknesses may vary from 5 to 50 &#181;m. The samples can be brought into measuring geometry applying two different methods (choose step 1.4.1 or 1.4.2).
	<ol>
		<li>Cut the cured polymer films into rectangles using a CO2 laser. Set the CO2 laser micromachining parameters to 5.0% power (2.0 W) and 10.0% speed (0.254 m/s) (Figure 3A).</li>
		<li>Define the DMA samples using photolithography in a Class 10000 cleanroom facility (Figure 3B). Use the SMP-on-glass or wafer substrates as the starting substrates in the cleanroom.
		<ol>
			<li>Deposit low temperature silicon nitride to act as a hard mask for the following plasma etching processes. Pattern the device outline/shape using standard lithography techniques. Use a plasma etcher with SF6 and O2 plasma to remove the hard mask and SMP layer, respectively.</li>
			<li>After the SMP layer is plasma etched down to the glass slide/wafer, etch the remaining silicon nitride hard mask away in diluted 10:1 HF dip.</li>
		</ol>
		</li>
	</ol>
	</li>
	<li>Delaminate the test devices from the glass slide/wafer by soaking in deionized water as the last step.</li>
</ol>
2. Machine setup
<ol>
	<li>Use a dynamic mechanical analyzer (DMA) with an immersion system. Equip the machine with the immersion fixture in tension mode (Figure 1). Connect the liquid nitrogen to the machine and enable LN2/air as a gas source for the furnace.</li>
	<li>Write the method for dry measurements with the machine software, including the following three steps: conditioning, oscillation temperature ramp, and conditioning end of test, then set up the parameters as follows:
	<ol>
		<li>Set the following parameters for the conditioning options: mode = active, select &#8220;tension&#8221;, axial force = 0.05 N, set initial value to &#8220;on&#8221;, sensitivity = 0.0 N, proportional force mode = force tracking, compensate for modulus = on, select &#8220;axial force&#8221; then set dynamic force to 25.0%, minimum axial force = 0.05 N, programmed extension below 0.0 Pa, mode enabled, strain adjust = 0.05%, minimum strain = 0.1%, maximum strain = 0.5%, minimum force = 0.05 N, maximum force = 0.2 N.</li>
		<li>Set the following parameters for the oscillation temperature ramp: start temperature = 10 &#176;C, inherit set point = off, soak time = 0.0 s, wait for temperature = on, ramp rate = 2.0 &#176;C/min, end temperature = 100 &#176;C, soak time after ramp = 0.0 s, sampling rate = 1 pts/s, strain % = 0.275%, single point, frequency = 1 Hz.</li>
		<li>Set the following parameters for the conditioning end of test: environmental control = off, axial force adjustment = on, mode disabled, transducer/motor = off.</li>
	</ol>
	</li>
	<li>Write the method for the immersion testing with the machine software including the following four steps: conditioning, oscillation-time, oscillation-temperature ramp, and conditioning-end of test, then set up the parameters as follows:
	<ol>
		<li>Set the following parameters for the conditioning options: mode = active, select &#8220;tension&#8221;, axial force = 0.05 N, set initial value to &#8220;on&#8221;, sensitivity = 0.0 N, proportional force mode = force tracking, compensate for modulus = on, select &#8220;axial force&#8221; and set dynamic force to 25.0%, minimum axial force = 0.05 N, programmed extension below 0.0 Pa, mode enabled, strain adjust = 0.05%, minimum strain = 0.1%, maximum strain = 0.5%, minimum force = 0.05 N, maximum force = 0.2 N.</li>
		<li>Set the following parameters for the oscillation time: temperature = 39.5 &#176;C, inherit set point = off, soak time = 0.0 s, wait for temperature = off, duration = 3600.0 s, sampling rate = 1 pts/s, strain % = 0.275%, single point, frequency = 1 Hz.</li>
		<li>Set the following parameters for the oscillation temperature ramp: start temperature = 10 &#176;C, inherit set point = off, soak time = 300.0 s, wait for temperature = off, ramp rate = 2.0 &#176;C/min, end temperature = 85 &#176;C, soak time after ramp = 300.0 s, sampling rate = 1 pts/s, strain % = 0.275%, single point, frequency = 1 Hz.</li>
		<li>Set the following parameters for the conditioning end of test: environmental control = off, axial force adjustment = on, mode disabled, transducer/motor = off.</li>
	</ol>
	</li>
</ol>
3. Sample loading and unloading for dry measurements
<ol>
	<li>Measure the actual thickness of the polymer specimen for dry (in air) testing with Caliper with 0.001 mm precision.</li>
	<li>Enter the sample name, description, and sample geometry into the software.</li>
	<li>Set the loading gap to 15 mm and load the sample. Make sure to center and align specimen before the clamps are screwed hand tight or use a torque wrench with 0.1 N (Figure 3C).</li>
	<li>Close the furnace and start the measurement using the methods described in section 2.2.</li>
	<li>Wait until the measurement is over. Open the furnace and remove the polymer sample from the machine.</li>
</ol>
4. Sample loading and unloading for immersion testing
<ol>
	<li>Measure the actual thickness of the polymer specimen for immersion testing in PBS with Caliper with 0.001 mm precision.</li>
	<li>Enter the sample name, description, and sample geometry into the software.</li>
	<li>Prepare the setup with the immersion beaker fixed with a clamp at the upper grip (Figure 4A,B).</li>
	<li>Set the loading gap to 15 mm and load the sample (Figure 4C). Make sure to center and align the specimen (Figure 5) before the clamps are screwed hand tight or use a torque wrench with 0.1 N.</li>
	<li>Place the immersion bath on the bottom fixture and screw it tightly (Figure 4D). Fill the bath with RT PBS (Figure 4E), place the lid on top (Figure 4F), close the furnace (Figure 4G), and start the measurement immediately using the methods described in section 2.3. Ensure that the drain is closed (Figure 4H).</li>
	<li>Wait until the measurement is over. Remove the PBS from the immersion baths using the drain. Open the furnace, remove the lid from the beaker, unscrew the immersion beaker, lift it, and remove the polymer sample from the machine.</li>
	<li>Clean the clamps and immersion beaker with de-ironed water to remove any remaining salt from the PBS.</li>
</ol>
5. Measurements
<ol>
	<li>Measure the polymer in air without the immersion beaker. Follow the instructions for sample loading and unloading as described in section 3. Repeat this measurement at least 3x to gather results with statistical relevance.</li>
	<li>Measure the polymer inside the immersion bath following the steps described in section 4. Repeat the measurement at least 3x to gather results with statistical relevance.</li>
</ol>
6. Data interpretation
<ol>
	<li>Open the results tab in the machine software, where the raw data can be viewed in a table format or plotted as a graph.</li>
	<li>Plot the first part of the immersion measurement, the oscillation-time measurement, as storage modulus over time to evaluate the softening kinetics. The curve displays how fast the modulus of the polymer decreases over time while immersed in PBS.</li>
	<li>Note the time at which the modulus levels out. This represents the time for softening under physiological conditions.</li>
	<li>If the polymer is not fully softened after the set immersion time of 1 h, repeat the measurement with increased immersion time.</li>
	<li>Display the oscillation-temperature ramps of the measurements in air and PBS as storage modulus on the left axis and tan delta on the right axis over temperature to display the thermomechanical properties of the polymer before (dry) and after (in PBS) plasticization.</li>
	<li>Plot the data for the dry (air) and PBS measurements together to better display the changes in thermomechanical properties due to plasticization.</li>
	<li>Note the storage modulus of the dry material at 25 &#176;C and of the soaked sample at 37 &#176;C, as these are relevant numbers for evaluating how much the polymer will soften during implantation.</li>
	<li>Note the changes in tan delta peak between the dry and soaked samples.</li>
	<li>Export the data as a .txt or .csv file for further data interpretation and plotting with other software.</li>
</ol>

<ol>
	<li>Garcia-Sandoval, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Chronic+softening+spinal+cord+stimulation+arrays.">Chronic softening spinal cord stimulation arrays.</a> Journal of Neural Engineering. 15 (4), 045002 (2018).</li><li>Ecker, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Sterilization+of+Thiol-ene/Acrylate+Based+Shape+Memory+Polymers+for+Biomedical+Applications.">Sterilization of Thiol-ene/Acrylate Based Shape Memory Polymers for Biomedical Applications.</a> Macromolecular Materials and Engineering. 302 (2), 1600331 (2017).</li><li>Simon, D. M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Design+and+demonstration+of+an+intracortical+probe+technology+with+tunable+modulus.">Design and demonstration of an intracortical probe technology with tunable modulus.</a> Journal of Biomedical Materials Research Part A. 105 (1), 159-168 (2017).</li><li>Do, D. -. H., Ecker, M., Voit, W. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Characterization+of+a+Thiol-Ene/Acrylate-Based+Polymer+for+Neuroprosthetic+Implants.">Characterization of a Thiol-Ene/Acrylate-Based Polymer for Neuroprosthetic Implants.</a> ACS Omega. 2 (8), 4604-4611 (2017).</li><li>Ware, T., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Thiol-ene/acrylate+substrates+for+softening+intracortical+electrodes.">Thiol-ene/acrylate substrates for softening intracortical electrodes.</a> Journal of Biomedical Materials Research Part B-Applied Biomaterials. 102 (1), 1-11 (2014).</li><li>Ware, T., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Thiol-Click+Chemistries+for+Responsive+Neural+Interfaces.">Thiol-Click Chemistries for Responsive Neural Interfaces.</a> Macromolecular Bioscience. 13 (12), 1640-1647 (2013).</li><li>Ware, T., Simon, D., Rennaker, R. L., Voit, W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Smart+Polymers+for+Neural+Interfaces.">Smart Polymers for Neural Interfaces.</a> Polymer Reviews. 53 (1), 108-129 (2013).</li><li>Ware, T., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Fabrication+of+Responsive,+Softening+Neural+Interfaces.">Fabrication of Responsive, Softening Neural Interfaces.</a> Advanced Functional Materials. 22 (16), 3470-3479 (2012).</li><li>Stiller, A. M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Chronic+Intracortical+Recording+and+Electrochemical+Stability+of+Thiol-ene/Acrylate+Shape+Memory+Polymer+Electrode+Arrays.">Chronic Intracortical Recording and Electrochemical Stability of Thiol-ene/Acrylate Shape Memory Polymer Electrode Arrays.</a> Micromachines. 9 (10), 500 (2018).</li><li>Biran, R., Martin, D. C., Tresco, P. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Neuronal+cell+loss+accompanies+the+brain+tissue+response+to+chronically+implanted+silicon+microelectrode+arrays.">Neuronal cell loss accompanies the brain tissue response to chronically implanted silicon microelectrode arrays.</a> Experimental Neurology. 195 (1), 115-126 (2005).</li><li>Polikov, V. S., Tresco, P. A., Reichert, W. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Response+of+brain+tissue+to+chronically+implanted+neural+electrodes.">Response of brain tissue to chronically implanted neural electrodes.</a> Journal of Neuroscience Methods. 148 (1), 1-18 (2005).</li><li>Lacour, S. P., Courtine, G., Guck, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Materials+and+technologies+for+soft+implantable+neuroprostheses.">Materials and technologies for soft implantable neuroprostheses.</a> Nature Reviews Materials. 1 (10), 16063 (2016).</li><li>Stiller, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+Meta-Analysis+of+Intracortical+Device+Stiffness+and+Its+Correlation+with+Histological+Outcomes.">A Meta-Analysis of Intracortical Device Stiffness and Its Correlation with Histological Outcomes.</a> Micromachines. 9 (9), 443 (2018).</li><li>Lecomte, A., Descamps, E., Bergaud, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+review+on+mechanical+considerations+for+chronically-implanted+neural+probes.">A review on mechanical considerations for chronically-implanted neural probes.</a> Journal of Neural Engineering. 15 (3), 031001 (2018).</li><li>Nguyen, J. K., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Mechanically-compliant+intracortical+implants+reduce+the+neuroinflammatory+response.">Mechanically-compliant intracortical implants reduce the neuroinflammatory response.</a> Journal of Neural Engineering. 11 (5), 056014 (2014).</li><li>Ecker, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=From+Softening+Polymers+to+Multi-Material+Based+Bioelectronic+Devices.">From Softening Polymers to Multi-Material Based Bioelectronic Devices.</a> Multifunctional Materials. , (2018).</li><li>Hess, A. E., Potter, K. A., Tyler, D. J., Zorman, C. A., Capadona, J. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Environmentally-controlled+Microtensile+Testing+of+Mechanically-adaptive+Polymer+Nanocomposites+for+ex+vivo+Characterization.">Environmentally-controlled Microtensile Testing of Mechanically-adaptive Polymer Nanocomposites for ex vivo Characterization.</a> Journal of Visualized Experiments. (78), e50078 (2013).</li><li>Black, B. J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=In+vitro+compatibility+testing+of+thiol-ene/acrylate-based+shape+memory+polymers+for+use+in+implantable+neural+interfaces.">In vitro compatibility testing of thiol-ene/acrylate-based shape memory polymers for use in implantable neural interfaces.</a> Journal of Biomedical Materials Research Part A. 106 (11), 2891-2898 (2018).</li><li>Hassler, C., Boretius, T., Stieglitz, T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Polymers+for+neural+implants.">Polymers for neural implants.</a> Journal of Polymer Science Part B: Polymer Physics. 49 (1), 18-33 (2011).</li></ol>

The authors declare that they have no competing financial interests.

The use of environmental DMA allows the study of the behavior of various polymers used as substrates for neural implants19 or other biomedical devices in solution and to mimic in vivo conditions. This includes, but is not limited to, polyimide, parylene-C, PDMS, and SU-8. Hydrogels and extracellular matrix (ECM) materials can also be investigated using this method. The differences of overall softening of the polymer as well as its softening kinetics can be easily compared between different solutions, including water, heavy water, and PBS. It is also possible to test the influence of different immersion temperatures or differences resulting from varying polymer thicknesses and compositions.
This method also allows studying of the influence of various treatments on softening behaviors of polymers and hydrogels. Treatments include application of various sterilization methods, accelerated aging in various media, and surface modification. This in vitro method will help researchers learn about the behavior and durability of these materials, obtain reliable in vitro measurements, and avoid unnecessary animal experiments. However, measuring in PBS is just one approach to mimic the biological environments. In vivo conditions may vary in many aspects, such as ion concentration and the availability of antibodies, proteins, and other species inside biological media/tissues. Depending on the targeted area, experimenters may also consider using different media for environmental measurements, such as tris-buffered saline (TBS), TBS-T (TBS with polysorbate 20), bovine serum albumin (BSA), cerebrospinal fluid (CSF), and other body fluids.
In addition, it is possible to characterize the mechanical properties of probes after explantation from an animal after an in vivo study is completed. This will allow the investigation of probe behavior after softening in a body environment and comparison to in vitro data.
It should be noted that there is an offset between the temperature set for the solution bath and the actual temperature. This is due to the fact that two different temperature controllers are being used: one for temperature control (outside the immersion bath) and another for measuring the temperature (inside the immersion bath). We found that when the outside temperature is set to 39.5 &#176;C, the temperature inside the bath stabilized at 37 &#176;C.
The temperature range for measurements inside solutions are naturally limited by their crystallization and boiling temperatures. It is recommended to remain at least 10 K above and below these temperatures, respectively.
It is debated whether the starting temperature of the immersion solution used for soaking/softening measurements should be room temperature or pre-warmed to body temperature to best mimic the conditions during probe implantation. The use of RT PBS takes into account the fact that the probe is kept at RT before implantation and that it is usually kept in close proximity to the implantation side while it is aligned to the right position. At this stage, the probe may already start to soften due to the moist milieu. Starting with 37 &#176;C PBS will better mimic a shotgun approach for insertion.
The described results were measured on polymer films in tension mode; however, the environmental DMA is also capable of measurements in compression and in shear when using the respective fixture. Therefore, this also allows for the measurement of other sample geometries. It should be noted that the available space inside the immersion beaker is limited and thus the samples used for measurements inside this beaker are restricted by their sizes.
Another limitation of this method is the load cell, which is used to detect the forces generated by the samples during the measurement (in dry and wet conditions). The load cell can only measure forces up to 35 N, which therefore limits the sample size/geometry.

A new generation of materials used as substrates for neural implants comprises softening shape memory polymers1,2,3,4,5,6,7,8,9. These materials are stiff enough during implantation to overcome critical buckling forces, but they become up to three orders of magnitude softer after implantation in a body environment. It is predicted that these materials show a better device-tissue interaction due to the reduced mismatch in modulus as compared to traditional materials used in neural implants, such as tungsten or silicon. Traditional, stiff devices show inflammatory response after implantation, followed by tissue encapsulation and astroglial scarring which often results in device failure10,11. It is a common assumption that less stiff devices minimize the foreign body response12,13,14. The stiffness of a device is dictated by its cross-sectional area and modulus. Therefore, it is important to reduce both factors to improve the device compliance and, ultimately, the device tissue interaction.
The work on softening polymers was inspired by the work of Nguyen et al.15, who demonstrated that mechanically-compliant intracortical implants reduce the neuroinflammatory response. They have previously used mechanically-adaptive poly(vinyl acetate)/tunicate cellulose nanocrystal (tCNC) nanocomposites (NC), which become compliant after implantation.
The Voit lab, on the other hand, uses the highly tunable system of thiol-ene and thiol-ene/acrylate polymers. These materials are advantageous in that the degree of softening after exposure to in vivo conditions can easily be tuned by the polymer design. By choosing the right polymer composition and crosslink density, the glass transition temperature and Young&#39;s modulus of the polymer can be modified2,4,5,6,8. The underlying effect of the softening is the plasticization of the polymer in an aqueous environment. By having a polymer with a glass transition temperature (Tg) above body temperature when dry (the state during implantation), but below body temperature after being immersed in water or PBS, the resulting stiffness/modulus of the polymer can shift from glassy (stiff) when dry to rubbery (soft) when implanted16.
However, exact and reliable measurements of the softening due to plasticization and the shift of Tg from the dry to wet states have not been able to be measured in the past. Traditional dynamic mechanical analysis is performed in air or inert gases and does not allow for measuring of the thermomechanical properties of polymers inside a solution. In previous studies, the polymers have been immersed in PBS for various amounts of time. Swollen samples were then used to perform dynamic mechanical analysis (DMA)6,7,8. However, since the procedure involves a temperature ramp, samples start to dry during the measurement and do not yield representative data. This is especially true if the sample size becomes smaller. In order to predict the softening of neural probes, it would be necessary to test 5 to 50 &#181;m-thin polymer films, which is not possible with traditional DMA due to the abovementioned drying of the samples during the measurement.
Hess et al.17 have designed a custom-built microtensile testing machine to assess the mechanical properties of mechanically adaptive materials using an environmentally controlled method. They have previously used an airbrush system to spray water on samples during the measurement to prevent them from drying out.
The use of environmental DMA (Figure 1), however, allows for measurement of polymer films in solutions, such as water and PBS, at various temperatures. This allows not only measurement of the polymer&#39;s thermomechanical properties in the soaked/softened state but also measurement of its softening kinetics. Even tensile tests and swelling measurements are possible inside the immersion bath of this machine. This allows for exact studies of the plasticization-induced softening of polymer substrates to predict in vivo behaviors.

<table><tbody><tr><td>1,3,5-Triallyl-1,3,5-triazine-2,4,6(1H,3H,5H)-trione (TATATO)</td><td>Sigma-Aldrich</td><td>114235-100G</td><td></td></tr><tr><td>2,2-Dimethoxy-2-phenylacetophenone (DMPA)</td><td>Sigma-Aldrich</td><td>196118-50G</td><td></td></tr><tr><td>CO2 laser Gravograph LS100</td><td>Gravotech, Inc.</td><td></td><td></td></tr><tr><td>Corning Large Glass Microscope Slides, 75 x 50mm</td><td>Ted Pella</td><td>26005</td><td></td></tr><tr><td>Environmental DMA: RSA-G2 Solids Analyzer</td><td>TA Instruments</td><td></td><td></td></tr><tr><td>ESD Safe Plastic Tweezer, Tips; Flat, Duckbill, 11.5 cm</td><td>Cole Palmer</td><td>EW-07387-17</td><td></td></tr><tr><td>Laurell WS-650-8B spin coater</td><td>Laurell Technologies Corporation</td><td></td><td></td></tr><tr><td>liquid nitrogen</td><td>Air gas</td><td></td><td></td></tr><tr><td>PBS, 1X Solution, Fisher BioReagents</td><td>Fisher Scientific</td><td>BP243820</td><td></td></tr><tr><td>SHEL LAB vacuum oven</td><td>VWR International</td><td>89409-484</td><td></td></tr><tr><td>Silicon wafer</td><td>University Wafer</td><td></td><td>Mechanical grade</td></tr><tr><td>The RSA-G2 Immersion System</td><td>TA Instruments</td><td></td><td></td></tr><tr><td>Trimethylolpropane tris(3-mercaptopropionate) (TMTMP)</td><td>Sigma-Aldrich</td><td>381489-100ML</td><td></td></tr><tr><td>UVP CL-1000 crosslinking chamber with 365 nm bulbs</td><td>VWR International</td><td>21474-598</td><td></td></tr></tbody></table>

environmental dynamic mechanical analysis to predict the softening behavior of neural implants

When using dynamically softening substrates for neural implants, it is important to have a reliable in vitro method to characterize the softening behavior of these materials. In the past, it has not been possible to satisfactorily measure the softening of thin films under conditions mimicking body environment without substantial effort. This publication presents a new and simple method that allows dynamic mechanical analysis (DMA) of polymers in solutions, such as phosphate buffered saline (PBS), at relevant temperatures. The use of environmental DMA allows measurement of the softening effects of polymers due to plasticization in various media and temperatures, which therefore allows a prediction of the materials behavior under in vivo conditions.

The use of environmental DMA allows the analysis of softening kinetics and overall softening capabilities of polymers. By using the temperature-time measuring mode of the protocol, the softening profiles of different polymer formulations can be compared to each other (Figure 6). This method can also be used to quantify softening and swelling rates of polymers. It can be seen in Figure 4 that different polymer formulations may undergo different degrees of softening while being immersed in the 37 &#176;C PBS. The non-softening version remains in the GPa range, whereas the semi-softening polymer softens from 1700 MPa to 370 MPa, and the fully softening polymer to 40 MPa. The softening of all three polymer formulations takes place within 10 to 15 min.
The use of the combination of dry DMA measurements and measurements in PBS allows the assessment of water-induced plasticization of different polymer formulations, which is shown by depression of the Tg and overall downshift of the modulus curves (Figure 7). The softening of the polymers is working most effectively when the dry polymer has a Tg above body temperature but below that in the wet state. Thus, the modulus of the polymer drops from the glassy to rubbery modulus upon immersion under physiological conditions (Figure 7A). When the Tg of both the dry and wet states of the polymer are well above body temperature, the polymer will not soften under physiological conditions (Figure 7B).
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/59209/59209fig1.jpg" /> 
Figure 1: Environmental DMA with immersion system. (A) A more detailed view of the fixture for dry (B) and wet (C) measuring conditions. (B) and (C) are previously published by Ecker et al.2. <a href="https://www.jove.com/files/ftp_upload/59209/59209fig1large.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/59209/59209fig2.jpg" /> 
Figure 2: Spin curves for fully softening thiol-ene polymer. Spin curves for fully softening thiol-ene polymer showing the relationship between spin speed and time and the resulting film thickness. <a href="https://www.jove.com/files/ftp_upload/59209/59209fig2large.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/59209/59209fig3.jpg" /> 
Figure 3: Fabrication of DMA test stripes on microscopic glass slides. Fabrication of DMA test stripes on microscopic glass slides (A) or silicon wafers (B) using photolithography. <a href="https://www.jove.com/files/ftp_upload/59209/59209fig3large.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/59209/59209fig4.jpg" /> 
Figure 4: Sample loading for measurement with immersion bath. A () DMA equipped with immersion fixture, (B) immersion beaker temporarily fixed with clamps around upper grip, (C) loading of polymer sample at a clamp distance of 15 mm, (D) lowering of immersion beaker to lower fixture and fixation with screws, (E) filling the immersion beaker with PBS, (F) closing the lid, (G) closing the furnace, and (H) ensuring that drain is closed. <a href="https://www.jove.com/files/ftp_upload/59209/59209fig4large.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/59209/59209fig5.jpg" /> 
Figure 5: Alignment of sample. (A) The sample must be straight and centered between the top and bottom clamps. Samples should not be diagonal (B), too high or too low (C), or too much toward the edges (D). Sample should also not be buckled (E) but should be straight (F) to ensure reliable measurements. <a href="https://www.jove.com/files/ftp_upload/59209/59209fig5large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 6" class="xfigimg" src="/files/ftp_upload/59209/59209fig6.jpg" /> 
Figure 6: Softening kinetics of three different thiol-ene polymers. Softening kinetics of three different thiol-ene polymers as measured with the oscillation-time protocol inside PBS at 37 &#176;C for 1 h. <a href="https://www.jove.com/files/ftp_upload/59209/59209fig6large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 7" class="xfigimg" src="/files/ftp_upload/59209/59209fig7.jpg" /> 
Figure 7: Displays DMA measurements of two different SMP formulations. Displays DMA measurements of two different SMP formulations before (orange) and after (blue) soaking in PBS, respectively. (A) A fully-softening (FS) version and (B) slightly-softening version (SS) of SMP. This figure has been modified from Ecker et al.2. <a href="https://www.jove.com/files/ftp_upload/59209/59209fig7large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

Watch this Scientific Journal Video about Environmental Dynamic Mechanical Analysis to Predict the Softening Behavior of Neural Implants at JoVE.com

Environmental Dynamic Mechanical Analysis to Predict the Softening Behavior of Neural Implants

To allow reliable predictions of the softening of polymeric substrates for neural implants in an in vivo environment, it is important to have a reliable in vitro method. Here, the use of dynamic mechanical analysis in phosphate buffered saline at body temperature is presented.

When using dynamically softening substrates for neural implants, it is important to have a reliable in vitro method to characterize the softening behavior ...

environmental-dynamic-mechanical-analysis-to-predict-softening

Southern Medical University (China)

Research

JoVE Journal

Bioengineering

7.6K Views.  University of Texas at Dallas. To allow reliable predictions of the softening of polymeric substrates for neural implants in an in vivo environment, it is important to have a reliable in vitro method. Here, the use of dynamic mechanical analysis in phosphate buffered saline at body temperature is presented.

Video: Environmental Dynamic Mechanical Analysis to Predict the Softening Behavior of Neural Implants

Environmentally-controlled Microtensile Testing of Mechanically-adaptive Polymer Nanocomposites for ex vivo Characterization

Implantable microdevices are gaining significant attention for several biomedical applications1-4. Such devices have been made from a range of materials, each offering its own advantages and shortcomings5,6. Most prominently, due to the microscale device dimensions, a high modulus is required to facilitate implantation into living tissue. Conversely, the stiffness of the device should match the surrounding tissue to minimize induced local strain7-9. Therefore, we recently developed a new class of bio-inspired materials to meet these requirements by responding to environmental stimuli with a change in mechanical properties10-14. Specifically, our poly(vinyl acetate)-based nanocomposite (PVAc-NC) displays a reduction in stiffness when exposed to water and elevated temperatures (e.g. body temperature). Unfortunately, few methods exist to quantify the stiffness of materials in vivo15, and mechanical testing outside of the physiological environment often requires large samples inappropriate for implantation. Further, stimuli-responsive materials may quickly recover their initial stiffness after explantation. Therefore, we have developed a method by which the mechanical properties of implanted microsamples can be measured ex vivo, with simulated physiological conditions maintained using moisture and temperature control13,16,17. 
To this end, a custom microtensile tester was designed to accommodate microscale samples13,17 with widely-varying Young's moduli (range of 10 MPa to 5 GPa). As our interests are in the application of PVAc-NC as a biologically-adaptable neural probe substrate, a tool capable of mechanical characterization of samples at the microscale was necessary. This tool was adapted to provide humidity and temperature control, which minimized sample drying and cooling17. As a result, the mechanical characteristics of the explanted sample closely reflect those of the sample just prior to explantation.
The overall goal of this method is to quantitatively assess the in vivo mechanical properties, specifically the Young's modulus, of stimuli-responsive, mechanically-adaptive polymer-based materials. This is accomplished by first establishing the environmental conditions that will minimize a change in sample mechanical properties after explantation without contributing to a reduction in stiffness independent of that resulting from implantation. Samples are then prepared for implantation, handling, and testing (Figure 1A). Each sample is implanted into the cerebral cortex of rats, which is represented here as an explanted rat brain, for a specified duration (Figure 1B). At this point, the sample is explanted and immediately loaded into the microtensile tester, and then subjected to tensile testing (Figure 1C). Subsequent data analysis provides insight into the mechanical behavior of these innovative materials in the environment of the cerebral cortex.

Environmentally-controlled Microtensile Testing of Mechanically-adaptive Polymer Nanocomposites for ex vivo Characterization

A method is discussed by which the in vivo mechanical behavior of stimuli-responsive materials is monitored as a function of time. Samples are tested ex vivo using a microtensile tester with environmental controls to simulate the physiological environment. This work further promotes understanding the in vivo behavior of our material.

Implantable microdevices are gaining significant attention for several biomedical applications1-4. Such devices have been made from a range of materials, ...

Insertion of Flexible Neural Probes Using Rigid Stiffeners Attached with Biodissolvable Adhesive

Microelectrode arrays for neural interface devices that are made of biocompatible thin-film polymer are expected to have extended functional lifetime because the flexible material may minimize adverse tissue response caused by micromotion. However, their flexibility prevents them from being accurately inserted into neural tissue. This article demonstrates a method to temporarily attach a flexible microelectrode probe to a rigid stiffener using biodissolvable polyethylene glycol (PEG) to facilitate precise, surgical insertion of the probe. A unique stiffener design allows for uniform distribution of the PEG adhesive along the length of the probe. Flip-chip bonding, a common tool used in microelectronics packaging, enables accurate and repeatable alignment and attachment of the probe to the stiffener. The probe and stiffener are surgically implanted together, then the PEG is allowed to dissolve so that the stiffener can be extracted leaving the probe in place. Finally, an in vitro test method is used to evaluate stiffener extraction in an agarose gel model of brain tissue. This approach to implantation has proven particularly advantageous for longer flexible probes (&gt;3 mm). It also provides a feasible method to implant dual-sided flexible probes. To date, the technique has been used to obtain various in vivo recording data from the rat cortex.

Insertion of flexible neural microelectrode probes is enabled by attaching probes to rigid stiffeners with polyethylene glycol (PEG). A unique assembly process ensures uniform and repeatable attachment. After insertion into tissue, the PEG dissolves and the stiffener is extracted. An in vitro test method evaluates the technique in agarose gel.

Microelectrode  arrays for neural interface devices that are made of biocompatible thin-film  polymer are expected to have extended functional lifetime ...

A Coupled Experiment-finite Element Modeling Methodology for Assessing High Strain Rate Mechanical Response of Soft Biomaterials

This study offers a combined experimental and finite element (FE) simulation approach for examining the mechanical behavior of soft biomaterials (e.g. brain, liver, tendon, fat, etc.) when exposed to high strain rates. This study utilized a Split-Hopkinson Pressure Bar (SHPB) to generate strain rates of 100-1,500 sec-1. The SHPB employed a striker bar consisting of a viscoelastic material (polycarbonate). A sample of the biomaterial was obtained shortly postmortem and prepared for SHPB testing. The specimen was interposed between the incident and transmitted bars, and the pneumatic components of the SHPB were activated to drive the striker bar toward the incident bar. The resulting impact generated a compressive stress wave (i.e. incident wave) that traveled through the incident bar. When the compressive stress wave reached the end of the incident bar, a portion continued forward through the sample and transmitted bar (i.e. transmitted wave) while another portion reversed through the incident bar as a tensile wave (i.e. reflected wave). These waves were measured using strain gages mounted on the incident and transmitted bars. The true stress-strain behavior of the sample was determined from equations based on wave propagation and dynamic force equilibrium. The experimental stress-strain response was three dimensional in nature because the specimen bulged. As such, the hydrostatic stress (first invariant) was used to generate the stress-strain response. In order to extract the uniaxial (one-dimensional) mechanical response of the tissue, an iterative coupled optimization was performed using experimental results and Finite Element Analysis (FEA), which contained an Internal State Variable (ISV) material model used for the tissue. The ISV material model used in the FE simulations of the experimental setup was iteratively calibrated (i.e. optimized) to the experimental data such that the experiment and FEA strain gage values and first invariant of stresses were in good agreement.

The current study prescribes a coupled experiment-finite element simulation methodology to obtain the uniaxial dynamic mechanical response of soft biomaterials (brain, liver, tendon, fat, etc.). The multiaxial experimental results that arose because of specimen bulging obtained from Split-Hopkinson Pressure Bar testing were rendered to a uniaxial true stress-strain behavior when simulated through iterative optimization of the finite element analysis of the biomaterial.

This study offers a combined experimental and finite element (FE) simulation approach for examining the mechanical behavior of soft biomaterials (e.g. ...

Bioelectric Analyses of an Osseointegrated Intelligent Implant Design System for Amputees

The projected number of American amputees is expected to rise to 3.6 million by 2050.  Many of these individuals depend on artificial limbs to perform routine activities, but prosthetic suspensions using traditional socket technology can prove to be cumbersome and uncomfortable for a person with limb loss.  Moreover, for those with high proximal amputations, limited residual limb length may prevent exoprosthesis attachment all together. Osseointegrated implant technology is a novel operative procedure which allows firm skeletal attachment between the host bone and an implant. Preliminary results in European amputees with osseointegrated implants have shown improved clinical outcomes by allowing direct transfer of loads to the bone-implant interface.  Despite the apparent advantages of osseointegration over socket technology, the current rehabilitation procedures require long periods of restrictive load bearing prior which may be reduced with expedited skeletal attachment via electrical stimulation.  The goal of the osseointegrated intelligent implant design (OIID) system is to make the implant part of an electrical system to accelerate skeletal attachment and help prevent periprosthetic infection. To determine optimal electrode size and placement, we initiated proof of concept with computational modeling of the electric fields and current densities that arise during electrical stimulation of amputee residual limbs.  In order to provide insure patient safety, subjects with retrospective computed tomography scans were selected and three dimensional reconstructions were created using customized software programs to ensure anatomical accuracy (Seg3D and SCIRun) in an IRB and HIPAA approved study.  These software packages supported the development of patient specific models and allowed for interactive manipulation of electrode position and size. Preliminary results indicate that electric fields and current densities can be generated at the implant interface to achieve the homogenous electric field distributions   required to induce osteoblast migration, enhance skeletal fixation and may help prevent periprosthetic infections.  Based on the electrode configurations experimented with in the model, an external two band configuration will be advocated in the future.

There is a need to develop alternative prosthesis attachment due to limb loss attributed to vascular occlusive diseases and trauma. The goal of the work is to introduce an osseointegrated intelligent implant design system to increase skeletal fixation and reduce periprosthetic infection rates for patients needing osseointegrated technology.

The projected number of American amputees is expected to rise to 3.6 million by 2050.  Many of these individuals depend on artificial limbs to perform ...

Biology

Intact Histological Characterization of Brain-implanted Microdevices and Surrounding Tissue

Research into the design and utilization of brain-implanted microdevices, such as microelectrode arrays, aims to produce clinically relevant devices that interface chronically with surrounding brain tissue. Tissue surrounding these implants is thought to react to the presence of the devices over time, which includes the formation of an insulating "glial scar" around the devices. However, histological analysis of these tissue changes is typically performed after explanting the device, in a process that can disrupt the morphology of the tissue of interest.
Here we demonstrate a protocol in which cortical-implanted devices are collected intact in surrounding rodent brain tissue. We describe how, once perfused with fixative, brains are removed and sliced in such a way as to avoid explanting devices. We outline fluorescent antibody labeling and optical clearing methods useful for producing an informative, yet thick tissue section. Finally, we demonstrate the mounting and imaging of these tissue sections in order to investigate the biological interface around brain-implanted devices.

Here we present a histological method for capturing, labeling, optically clearing, and imaging the intact brain tissue interface around chronically implanted microdevices in rodent brain tissue. Results from the techniques comprising this method are useful for understanding the impact of various penetrating brain-implants on their surrounding tissue.

Research  into the design and utilization of brain-implanted microdevices, such as  microelectrode arrays, aims to produce clinically relevant devices ...

Neuroscience

A Method for Systematic Electrochemical and Electrophysiological Evaluation of Neural Recording Electrodes

New materials and designs for neural implants are typically tested separately, with a demonstration of performance but without reference to other implant characteristics.&#160;This precludes a rational selection of a particular implant as optimal for a particular application and the development of new materials based on the most critical performance parameters.&#160;This article develops a protocol for in vitro and in vivo testing of neural recording electrodes.&#160;Recommended parameters for electrochemical and electrophysiological testing are documented with the key steps and potential issues discussed.&#160;This method eliminates or reduces the impact of many systematic errors present in simpler in vivo testing paradigms, especially variations in electrode/neuron distance and between animal models.&#160;The result is a strong correlation between the critical in vitro and in vivo responses, such as impedance and signal-to-noise ratio.&#160;This protocol can easily be adapted to test other electrode materials and designs.&#160;The in vitro techniques can be expanded to any other nondestructive method to determine further important performance indicators.&#160;The principles used for the surgical approach in the auditory pathway can also be modified to other neural regions or tissue.

Different electrode coatings affect neural recording performance through changes to electrochemical, chemical and mechanical properties. Comparison of electrodes in vitro is relatively simple, however comparison of in vivo response is typically complicated by variations in electrode/neuron distance and between animals. This article provides a robust method to compare neural recording electrodes.

New materials and designs for neural implants are typically tested separately, with a demonstration of performance but without reference to other implant ...

Improved 3D Hydrogel Cultures of Primary Glial Cells for In Vitro Modelling of Neuroinflammation

In the central nervous system, numerous acute injuries and neurodegenerative disorders, as well as implanted devices or biomaterials engineered to enhance function result in the same outcome: excess inflammation leads to gliosis, cytotoxicity, and/or formation of a glial scar that collectively exacerbate injury or prevent healthy recovery. With the intent of creating a system to model glial scar formation and study inflammatory processes, we have generated a 3D cell scaffold capable of housing primary cultured glial cells: microglia that regulate the foreign body response and initiate the inflammatory event, astrocytes that respond to form a fibrous scar, and oligodendrocytes that are typically vulnerable to inflammatory injury. The present work provides a detailed step-by-step method for the fabrication, culture, and microscopic characterization of a hyaluronic acid-based 3D hydrogel scaffold with encapsulated rat brain-derived glial cells. Further, protocols for characterization of cell encapsulation and the hydrogel scaffold by confocal immunofluorescence and scanning electron microscopy are demonstrated, as well as the capacity to modify the scaffold with bioactive substrates, with incorporation of a commercial basal lamina mixture to improved cell integration.

Improved 3D Hydrogel Cultures of Primary Glial Cells for In Vitro Modelling of Neuroinflammation

Herein, we present a protocol for the 3D culture of rat brain-derived glia cells, including astrocytes, microglia, and oligodendrocytes. We demonstrate primary cell culture, methacrylated hyaluronic acid (HAMA) hydrogel synthesis, HAMAphoto-polymerization and cell encapsulation, and sample processing for confocal and scanning electron microscopic imaging.

In the central nervous system, numerous acute injuries and neurodegenerative disorders, as well as implanted devices or biomaterials engineered to enhance ...

Rodent Behavioral Testing to Assess Functional Deficits Caused by Microelectrode Implantation in the Rat Motor Cortex

Medical devices implanted in the brain hold tremendous potential. As part of a Brain Machine Interface (BMI) system, intracortical microelectrodes demonstrate the ability to record action potentials from individual or small groups of neurons. Such recorded signals have successfully been used to allow patients to interface with or control&#160;computers, robotic limbs, and their own limbs. However, previous animal studies have shown that a microelectrode implantation in the brain not only damages the surrounding tissue but can also result in functional deficits. Here, we discuss a series of behavioral tests to quantify potential motor impairments following the implantation of intracortical microelectrodes into the motor cortex of a rat. The methods for open field grid, ladder crossing, and grip strength testing provide valuable information regarding the potential complications resulting from a microelectrode implantation. The results of the behavioral testing are correlated with endpoint histology, providing additional information on the pathological outcomes and impacts of this procedure on the adjacent tissue.

We have shown that a microelectrode implantation in the motor cortex of rats causes immediate and lasting motor deficits. The methods proposed herein outline a microelectrode implantation surgery and three rodent behavioral tasks to elucidate potential changes in the fine or gross motor function due to implantation-caused damage to the motor cortex.

Medical devices implanted in the brain hold tremendous potential. As part of a Brain Machine Interface (BMI) system, intracortical microelectrodes ...

Experimental and Data Analysis Workflow for Soft Matter Nanoindentation

Nanoindentation refers to a class of experimental techniques where a micrometric force probe is used to quantify the local mechanical properties of soft biomaterials and cells. This approach has gained a central role in the fields of mechanobiology, biomaterials design&#160;and tissue engineering, to obtain a proper mechanical characterization of soft materials with a resolution comparable to the size of single cells (&#956;m). The most popular strategy to acquire such experimental data is to employ an atomic force microscope (AFM); while this instrument offers an unprecedented resolution in force (down to pN) and space (sub-nm), its usability is often limited by its complexity that prevents routine measurements of integral indicators of mechanical properties, such as Young&#39;s Modulus (E). A new generation of nanoindenters, such as those based on optical fiber sensing technology, has recently gained popularity for its ease of integration while allowing to apply sub-nN forces with &#181;m spatial resolution, therefore being suitable to probe local mechanical properties of hydrogels and cells.
In this protocol, a step-by-step guide detailing the experimental procedure to acquire nanoindentation data on hydrogels and cells using a commercially available ferrule-top optical fiber sensing nanoindenter is presented. Whereas some steps are specific to the instrument used herein, the proposed protocol can be taken as a guide for other nanoindentation devices, granted some steps are adapted according to the manufacturer&#39;s guidelines. Further, a new open-source Python software equipped with a user-friendly graphical user interface for the analysis of nanoindentation data is presented, which allows for screening of incorrectly acquired curves, data filtering, computation of the contact point through different numerical procedures, the conventional computation of E, as well as a more advanced analysis particularly suited for single-cell nanoindentation data.

The protocol presents a complete workflow for soft material nanoindentation experiments, including hydrogels and cells. First, the experimental steps to acquire force spectroscopy data are detailed; then, the analysis of such data is detailed through a newly developed open-source Python software, which is free to download from GitHub.

Nanoindentation refers to a class of experimental techniques where a micrometric force probe is used to quantify the local mechanical properties of soft ...

Tools for Surface Treatment of Silicon Planar Intracortical Microelectrodes

Intracortical microelectrodes hold great therapeutic potential. But they are challenged with significant performance reduction after modest implantation durations. A substantial contributor to the observed decline is the damage to the neural tissue proximal to the implant and subsequent neuroinflammatory response. Efforts to improve device longevity include chemical modifications or coating applications to the device surface to improve the tissue response. Development of such surface treatments is typically completed using non-functional "dummy" probes that lack the electrical components required for the intended application. Translation to functional devices requires additional consideration given the fragility of intracortical microelectrode arrays. Handling tools greatly facilitate surface treatments to assembled devices, particularly for modifications that require long procedural times. The handling tools described here are used for surface treatments applied via gas-phase deposition and aqueous solution exposure. Characterization of the coating is performed using ellipsometry and x-ray photoelectron spectroscopy. A comparison of electrical impedance spectroscopy recordings before and after the coating procedure on functional devices confirmed device integrity following modification. The described tools can be readily adapted for alternative electrode devices and treatment methods that maintain chemical compatibility.

The present protocol describes tools for handling silicon planar intracortical microelectrodes during treatments for surface modification via gas deposition and aqueous solution reactions. The assembly of the components used to handle the devices throughout the procedure is explained in detail.

Intracortical microelectrodes hold great therapeutic potential. But they are challenged with significant performance reduction after modest implantation ...

Environmental Dynamic Mechanical Analysis to Predict the Softening Behavior of Neural Implants

Summary

Explore More Videos

Chapters in this video

Environmentally-controlled Microtensile Testing of Mechanically-adaptive Polymer Nanocomposites for ex vivo Characterization

Insertion of Flexible Neural Probes Using Rigid Stiffeners Attached with Biodissolvable Adhesive

A Coupled Experiment-finite Element Modeling Methodology for Assessing High Strain Rate Mechanical Response of Soft Biomaterials

Bioelectric Analyses of an Osseointegrated Intelligent Implant Design System for Amputees

Intact Histological Characterization of Brain-implanted Microdevices and Surrounding Tissue

A Method for Systematic Electrochemical and Electrophysiological Evaluation of Neural Recording Electrodes

Improved 3D Hydrogel Cultures of Primary Glial Cells for In Vitro Modelling of Neuroinflammation

Rodent Behavioral Testing to Assess Functional Deficits Caused by Microelectrode Implantation in the Rat Motor Cortex

Experimental and Data Analysis Workflow for Soft Matter Nanoindentation

Tools for Surface Treatment of Silicon Planar Intracortical Microelectrodes