This work was funded by the National Institutes of Health (R21 AR079095) and the National Science Foundation (2142627).

This study was approved by the Pennsylvania State University Institutional Animal Care and Use Committee.
1. Tissue preparation
<ol>
	<li>For this protocol, harvest the Achilles tendons from 2-4 month old male C57BL/6 mice. 
	NOTE: Different tendons or ligaments from mice or other small animals could also be used.
	<ol>
		<li>Make an incision to the skin superficial to the Achilles tendon to expose the plantaris tendon and the surrounding connective tissue. Then, remove them using a surgical blade.</li>
		<li>Separate the exposed soleus and gastrocnemius muscles from the hind limb, and carefully scrape them off the Achilles tendon with the surgical blade</li>
		<li>Separate the calcaneus from the rest of the foot with a cutting wheel attachment on a rotary tool.</li>
	</ol>
	</li>
	<li>Stain the tissue in 1.5 mL of a 5 &#181;g/mL solution of 5-(4,6-dichlorotriazinyl) aminofluorescein (DTAF) and 0.1 M sodium bicarbonate buffer for 20 min on a rotating mixer at room temperature. This solution stains proteins (e.g., extracellular matrix) in the tissue. 
	NOTE: During this 20 min period, step 1.3 should be completed.</li>
	<li>Prepare a 1:1,000 solution of DRAQ5 in phosphate-buffered saline (PBS) to stain the nuclei. Use a vortex mixer to homogenize the solution.</li>
	<li>After the 20 min incubation period in step 1.2, transfer the tissue from the DTAF solution to the DRAQ5 solution, and incubate in a dark space for 10 min at room temperature.</li>
</ol>
2. Tendon loading and image acquisition
NOTE: This protocol requires a tensile device that can be mounted on top of a confocal microscope. For this study, the microtensile device described by Peterson and Szczesny13 was used.
<ol>
	<li>Place the tendon into the grips of the tensile loading device. Prior to mounting the grips in the loading device, use digital calipers to measure the distance between the calcaneus attachment and the opposite grip. This distance is the tendon gauge length.
	<ol>
		<li>Alternatively, mount the grips into the loading device prior to inserting the tendon, and push into contact to define the zero-displacement motor position. The displacement of the motors after inserting the tendon could provide a potentially more accurate grip-to-grip gauge length.</li>
	</ol>
	</li>
	<li>Mount the grips into the loading device, which contains PBS to maintain tissue hydration. Align the tendon as best as possible with either the x-axis or y-axis of the microscope images so that the x-strain and y-strain outputs of the algorithm correspond with the tendon axes. 
	&#8203;NOTE: In this study, the tendons were aligned with the x-axis. If it is not possible to perfectly align the tendon with the image axes, then the x-strain and y-strain outputs of the algorithm can be transformed to align with the longitudinal/perpendicular axes of the tendon using standard strain transformation equations14.</li>
	<li>Preload the tendon with 1 g of tension, and, if desired, apply cyclic loading to precondition the sample. In this protocol, no preconditioning was used since the study objective was to validate the measured local tissue strains rather than measure the tissue material properties. If there is interest in measuring the macroscale material properties, which are dependent on the loading history, then preconditioning is recommended. Following preconditioning and recovery, reapply a 1 g preload.</li>
	<li>If desired, photobleach a set of four lines spaced 80 &#181;m apart in the center region of the tissue (see Peterson and Szczesny13 for more details). 
	NOTE: The photobleached lines were used to validate the measurements of the ALDIC algorithm and are not necessary for performing the ALDIC itself. The number and spacing of the lines can be adjusted, and the location of the lines should be chosen to avoid any artifacts in the sample that would decrease the clarity of the line.</li>
	<li>Repeat the photobleaching procedure on the left and right extremes of the tissue near the grips.</li>
	<li>Using the confocal microscope, acquire volumetric images (x,y: 1.25 &#181;m/pixel, z: 2.5 &#181;m/pixel) of the DTAF and DRAQ5 fluorescence at 1 g of preload.</li>
	<li>Perform a strain ramp at 0.5%/s to 2% strain. Note that the strain rate and incremental strain magnitude can be adjusted.</li>
	<li>Allow the tissue to stress relax for 10 min. 
	NOTE: The duration of stress relaxation should be chosen such that the sample is under an approximately quasistatic load during image acquisition. To determine if the stress relaxation duration is acceptable, determine the slope of the force-time curve during the final minute of stress relaxation (Supplementary Figure 1), and multiply this slope by the total imaging duration. In this study, the force applied at the largest strain increment never changed by more than 5%.</li>
	<li>Take another volumetric image of the tissue after deformation.</li>
	<li>Repeat steps 2.7-2.9 until the desired final strain is reached. In this paper, a final strain value of 12% was chosen.</li>
</ol>
3. Image processing
<ol>
	<li>Use ImageJ or Fiji to create maximum z-projections of each volumetric image of the DRAQ5 (nuclear) channel. This will serve as the 2D speckled images for the ALDIC.</li>
	<li>Save the max-intensity z-projections as .tiff files, and name them according to the following naming convention.
	<ol>
		<li>Use a number as the first character of the image name.</li>
		<li>Have the number correspond to the order in which the images will be considered during the strain analysis. For example, the first image should begin with one, and the second image should begin with two. Different numbers can be chosen, but they must sequentially increase. An example naming convention is as follows: &#34;0_Experiment1_MaxZProjection&#34;.</li>
	</ol>
	</li>
	<li>Save all the renamed max-intensity z-projections to a folder.</li>
</ol>
4. Photobleached line analysis code installation and application
NOTE: These steps are only necessary if it is desired to confirm the accuracy of the ALDIC algorithm using photobleached lines. The code calculates the local tissue strain as the average normalized change in distance between each photobleached line within the photobleached line set. In this study, the average local values were then averaged across all the photobleached line sets (i.e., at the center and the left/right ends) to determine a single average local tissue strain value for each sample. This value was then used to estimate the accuracy of the ALDIC algorithm.
<ol>
	<li>Download the &#34;PBL Code&#34; folder from GitHub (https://github.com/Szczesnytendon/TendonStrainCalc), and move all the contents to the working directory in MATLAB.</li>
	<li>Open the &#34;Micro_Mech_Template.m&#34; MATLAB script.
	<ol>
		<li>Press Run, and select one of the image files containing the volumetric images. The volumetric images can be any of the following file types: .lsm, .tiff, .nd2.</li>
		<li>The software will automatically load all the images in the folder and display a projected image of the reference volumetric image. When prompted, left-click to create multi-point lines that trace the left and right ends of the sample. Right-click to terminate a line. Once the input has been processed, if the edges are correct, press Ok to accept the result.</li>
		<li>Draw a random diagonal line across the sample as a reference line when prompted.</li>
		<li>Input the number of photobleached lines created, and trace the photobleached lines with multi-point lines.</li>
		<li>If the result is acceptable, accept it. If the result is erroneous, adjust it and reprocess.</li>
	</ol>
	</li>
	<li>Repeat step 4.2 for all the images, and move all the images of traced lines into a single folder.</li>
	<li>Open the script &#34;Micro_Mech_Strain.m&#34;.
	<ol>
		<li>Press Run to execute the code, and select one of the saved images where the photobleached lines are traced.</li>
		<li>Confirm that the selected accompanying images are correct once the image is selected by pressing Ok.</li>
	</ol>
	</li>
</ol>
5. Creating digitally transformed images
NOTE: These steps are only necessary if it is desired to confirm the accuracy of the ALDIC algorithm using digitally transformed images. These images simulate homogenous 2D strain fields of a known magnitude by artificially transforming the reference image.
<ol>
	<li>Download the code &#34;Digital_strain.m&#34; from GitHub (https://github.com/Szczesnytendon/TendonStrainCalc).</li>
	<li>Open and run the code.</li>
	<li>When prompted, insert desired values for the maximum applied strain, applied strain increment, and Poisson&#39;s ratio. Press Ok. 
	NOTE: For this experiment, the maximum applied strain was 0.1 (10%), the applied strain increment was 0.02 (2%), and a Poisson&#39;s ratio of 1 was used, which is consistent with experimental data of tendon tensile testing15,16. The code uses the embedded MATLAB function imwarp and the input values (e.g., strain increments, Poisson&#39;s ratio) to create the digitally transformed images.</li>
	<li>When prompted, select the undeformed reference image.</li>
	<li>For each strain increment, an overlay of the reference image and the transformed image is displayed. The transformed image will be saved to the directory under the title &#34;DigitallyTransformedX%Strain&#34;, where X is the strain increment.</li>
</ol>
6. Strain calculation and validation code installation and application
<ol>
	<li>Download the &#34;Strain Calculation and Validation Code&#34; folder from GitHub (https://github.com/Szczesnytendon/TendonStrainCalc), and move all the contents to the MATLAB working directory</li>
	<li>Install a mex C/C++ compiler according to Yang and Bhattacharya12. The steps are summarized below.
	<ol>
		<li>Check MATLAB to see if a mex C/C++ compiler has been installed by typing &#34;mex -setup&#34; in the MATLAB Command Window and pressing Enter.</li>
		<li>If an error appears indicating that a compiler is not supported or present, proceed to step 6.3 and step 6.4.</li>
		<li>If no error is present, proceed to step 6.5</li>
	</ol>
	</li>
	<li>To download a mex C/C++ compiler, go to &#34;https:/tdm-gcc.tdragon.net/&#34;, and choose the TDM-gcc compiler.</li>
	<li>Install the downloaded compiler to a known location.</li>
	<li>Return to the MATLAB command window, and type: &#34;setenv(&#34;MW_MINGW64_LOC&#34;,&#34;[Type your install path here]&#34;)&#34;. For example, this could be &#34;setenv(&#34;MW_MINGW64_LOC&#34;,&#34;C:\TDM-GCC-64&#34;)&#34;. If this command executes successfully, the mex compiler is properly installed.</li>
	<li>Enter the &#34;main_aldic.m&#34; function script, and change line 22 to match the command executed in step 6.5.</li>
	<li>Open the script &#34;Strain_calc_and_validate.m&#34;.</li>
	<li>Press Run to begin the image analysis.</li>
	<li>When prompted, alter the values for the ALDIC parameters as desired. 
	NOTE: The window size should be 0.25 to 1 times the subset size. For more information about the parameter choices, refer to the online user manual: (https://www.researchgate.net/publication/344796296_Augmented_Lagrangian_Digital 
	_Image_Correlation_AL-DIC_Code_Manual).&#160;
	<ol>
		<li>The following values were used in&#160;this study: 
		Subset Size (pixels): 20 
		Window Size (pixels): 10 
		Method to solve ALDIC: Finite Difference (1) 
		Parallel computing was not used (1) 
		Method to compute initial guess: Multigrid search based on image pyramid (0)</li>
	</ol>
	</li>
	<li>When prompted, select the &#34;Yes&#34; checkbox to have the algorithm automatically save the mean value, standard deviation, and 2D maps for the desired collection of variables (e.g., x-strain, y-strain, shear strain, bad regions, etc.). Select which variables should be saved, and press Ok.</li>
	<li>When prompted, alter the parameters as desired.
	<ol>
		<li>The following values were used in this experiment: 
		Surrounding points to calculate strain (numP): 12 
		Correlation coefficient for bad region identification (corr_threshold): 0.5 
		Subregion size (pixels) for bad region analysis (Subsize): 32</li>
	</ol>
	</li>
	<li>When prompted, select the folder that contains the renamed max-intensity z-projections. Note that the software automatically performs incremental ALDIC to determine the strain fields of the deformed images. That is, each deformed image serves as the new &#34;reference&#34; image for the next deformed image. This improves the accuracy of the results (Supplementary Figure 2) compared to performing cumulative ALDIC, where each deformed image is compared back to the original (0% strain) reference image. To perform a cumulative analysis, load the images but only select the original reference image and the deformed image of interest. 
	NOTE: The normal strain is calculated as &#955; - 1, where&#160;&#955; is the tissue stretch. The tissue stretch is calculated according to <img alt="Equation 1" src="/files/ftp_upload/64921/64921eq01.jpg" style="margin: auto;" />, where N = [1 0]T&#160;or [0 1]T for the x-direction and y-direction, respectively, and C = FT F, where F is the deformation gradient calculated using &#34;numP&#34; points surrounding each data point output by the ALDIC algorithm. The shear strain is calculated as <img alt="Equation 2" src="/files/ftp_upload/64921/64921eq02.jpg" style="margin: auto;" />, where <img alt="Equation 3" src="/files/ftp_upload/64921/64921eq03.jpg" style="margin: auto;" />.</li>
	<li>When prompted, left-click to create a four-point polygon to define the region of interest for measuring the strains. Begin with the point in the top-left corner, and assign the subsequent points in a clockwise manner. 
	NOTE: The variable &#34;Storage&#34; saved in the MATLAB workspace contains all the values for the average x-strain, x-strain standard deviation, average y-strain, y-strain standard deviation, average shear strain, shear strain standard deviation, and percentage of bad regions. The bad regions are defined according to the correlation coefficient analysis within the&#160;region of interest selected in step 6.13. The folder &#34;NuclearTrackingResults&#34; (which can be renamed by adjusting lines 555 and 556) stores all the plots specified in step 6.10. This folder also contains a spreadsheet file with the name &#34;Results&#34;, which stores all the means and standard deviations specified in step 6.10.</li>
</ol>

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All authors have no conflicts of interest to disclose.

The objective of this paper was to provide an open-source, validated method to measure the 2D strain fields in tendons under tensile load. The foundation of the software was based on a publicly available ALDIC algorithm12. This algorithm was embedded into a larger MATLAB code with the added functionality of incremental (versus cumulative) strain analysis. This adapted algorithm was then applied to the tensile testing of tendons, and its accuracy was assessed by two different techniques (i.e., digi...

Tendons are mechanosensitive tissues that adapt and degenerate in response to mechanical loading1,2,3,4. Due to the role that mechanical stimuli play in tendon cell biology, there is a large interest in understanding the strains that tendon cells experience in the native tissue environment during loading. Several experimental and analytical techniques have been developed to measure local tissue strains in tendons. These include 2D/3D digital image correlation (DIC) analyses of surface strains using either speckle patterns or photobleached lines (PBLs)5,6,7,8, measurement of the changes in the centroid-to-centroid distance of individual nuclei within the tissue9,10, and a recent full-field 3D DIC method that considers out-of-plane motion and 3D deformations11. However, the accuracy and sensitivity of these techniques have been reported in only a few cases, and none of these techniques have been made publicly available, which makes the widespread adoption and utilization of these techniques difficult.
The objective of this work was to create a validated analysis tool for measuring local tissue strains in tendon explants that is readily available and easy to use. The chosen method is based on a publicly available augmented-Lagrangian digital image correlation (ALDIC) algorithm written in MATLAB that was developed by Yang and Bhattacharya12. This algorithm was adapted for analyzing tendon samples and validated by applying it to digitally transformed images and by comparing the strains measured in actual tendon samples to the results obtained from photobleached lines. Furthermore, additional functionality was implemented in the algorithm to confirm the accuracy of the calculated displacement field even in the absence of known strain values or a secondary measurement technique. Therefore, this algorithm is a highly useful and adaptable tool for accurately measuring local 2D tissue strains in tendons.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>5-DTAF (5-(4,6-Dichlorotriazinyl) Aminofluorescein), single isomer</td>
			<td>ThermoFisher</td>
			<td>D16</td>
			<td></td>
		</tr>
		<tr>
			<td>Calipers</td>
			<td>Mitutoyo</td>
			<td>500-196-30</td>
			<td></td>
		</tr>
		<tr>
			<td>Confocal Microscope</td>
			<td>Nikon</td>
			<td>A1R HD</td>
			<td></td>
		</tr>
		<tr>
			<td>Corning LSE Vortex Mixer</td>
			<td>Coning</td>
			<td>6775</td>
			<td></td>
		</tr>
		<tr>
			<td>DRAQ5 Fluorescent Probe Solution (5 mM)</td>
			<td>ThermoFisher</td>
			<td>62554</td>
			<td></td>
		</tr>
		<tr>
			<td>MATLAB</td>
			<td>MathWorks</td>
			<td>R2022b</td>
			<td></td>
		</tr>
		<tr>
			<td>Tensile Loading Device</td>
			<td>N/A</td>
			<td>N/A</td>
			<td>Tensile loading device described in Peterson et al, 2020. (ref 13)&#160;</td>
		</tr>
		<tr>
			<td>Tube Revolver Rotator</td>
			<td>ThermoFisher</td>
			<td>88881001</td>
			<td></td>
		</tr>
	</tbody>
</table>

measuring local tissue strains in tendons via open-source digital image correlation

There is considerable scientific interest in understanding the strains that tendon cells experience in situ and how these strains influence tissue remodeling. Based on this interest, several analytical techniques have been developed to measure local tissue strains within tendon explants during loading. However, in several cases, the accuracy and sensitivity of these techniques have not been reported, and none of the algorithms are publicly available. This has made it difficult for the more widespread measurement of local tissue strains in tendon explants. Therefore, the objective of this paper was to create a validated analysis tool for measuring local tissue strains in tendon explants that is readily available and easy to use. Specifically, a publicly available augmented-Lagrangian digital image correlation (ALDIC) algorithm was adapted for measuring 2D strains by tracking the displacements of cell nuclei within mouse Achilles tendons under uniaxial tension. Additionally, the accuracy of the calculated strains was validated by analyzing digitally transformed images, as well as by comparing the strains with values determined from an independent technique (i.e., photobleached lines). Finally, a technique was incorporated into the algorithm to reconstruct the reference image using the calculated displacement field, which can be used to assess the accuracy of the algorithm in the absence of known strain values or a secondary measurement technique. The algorithm is capable of measuring strains up to 0.1 with an accuracy of 0.00015. The technique for comparing a reconstructed reference image with the actual reference image successfully identified samples that had erroneous data and indicated that, in samples with good data, approximately 85% of the displacement field was accurate. Finally, the strains measured in mouse Achilles tendons were consistent with the prior literature. Therefore, this algorithm is a highly useful and adaptable tool for accurately measuring local tissue strains in tendons.

Prior to analyzing the strain fields in actual tissue samples, the ALDIC protocol was first validated using digitally strained/transformed images of nuclei within mouse Achilles tendons. Specifically, the images were transformed to digitally produce uniform strains in the x-direction of 2%, 4%, 6%, 8%, and 10% strain with a simulated Poisson&#39;s ratio of 115,16. The accuracy of the ALDIC algorithm was then assessed by comparing the mean calculated strain values...

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Measuring Local Tissue Strains in Tendons via Open-Source Digital Image Correlation

This paper describes an open-source digital image correlation algorithm for measuring local 2D tissue strains within tendon explants. The accuracy of the technique has been validated using multiple techniques, and it is available for public use.

Measuring Local Tissue Strains in Tendons via Open-Source Digital Image Correlation

There is considerable scientific interest in understanding the strains that tendon cells experience in situ and how these strains influence tissue ...

measuring-local-tissue-strains-tendons-via-open-source-digital-image

Universidad de Cartagena UDEC

Research

JoVE Journal

Engineering

2.0K Views.  Pennsylvania State University. This paper describes an open-source digital image correlation algorithm for measuring local 2D tissue strains within tendon explants. The accuracy of the technique has been validated using multiple techniques, and it is available for public use. 

Video: Measuring Local Tissue Strains in Tendons via Open-Source Digital Image Correlation

Echo Particle Image Velocimetry

The transport of mass, momentum, and energy in fluid flows is ultimately determined by spatiotemporal distributions of the fluid velocity field.1 Consequently, a prerequisite for understanding, predicting, and controlling fluid flows is the capability to measure the velocity field with adequate spatial and temporal resolution.2 For velocity measurements in optically opaque fluids or through optically opaque geometries, echo particle image velocimetry (EPIV) is an attractive diagnostic technique to generate "instantaneous" two-dimensional fields of velocity.3,4,5,6 In this paper, the operating protocol for an EPIV system built by integrating a commercial medical ultrasound machine7 with a PC running commercial particle image velocimetry (PIV) software8 is described, and validation measurements in Hagen-Poiseuille (i.e., laminar pipe) flow are reported.
For the EPIV measurements, a phased array probe connected to the medical ultrasound machine is used to generate a two-dimensional ultrasound image by pulsing the piezoelectric probe elements at different times. Each probe element transmits an ultrasound pulse into the fluid, and tracer particles in the fluid (either naturally occurring or seeded) reflect ultrasound echoes back to the probe where they are recorded. The amplitude of the reflected ultrasound waves and their time delay relative to transmission are used to create what is known as B-mode (brightness mode) two-dimensional ultrasound images. Specifically, the time delay is used to determine the position of the scatterer in the fluid and the amplitude is used to assign intensity to the scatterer. The time required to obtain a single B-mode image, t, is determined by the time it take to pulse all the elements of the phased array probe. For acquiring multiple B-mode images, the frame rate of the system in frames per second (fps) = 1/&delta;t. (See 9 for a review of ultrasound imaging.)
For a typical EPIV experiment, the frame rate is between 20-60 fps, depending on flow conditions, and 100-1000 B-mode images of the spatial distribution of the tracer particles in the flow are acquired. Once acquired, the B-mode ultrasound images are transmitted via an ethernet connection to the PC running the PIV commercial software. Using the PIV software, tracer particle displacement fields, D(x,y)[pixels], (where x and y denote horizontal and vertical spatial position in the ultrasound image, respectively) are acquired by applying cross correlation algorithms to successive ultrasound B-mode images.10 The velocity fields, u(x,y)[m/s], are determined from the displacements fields, knowing the time step between image pairs, &Delta;T[s], and the image magnification, M[meter/pixel], i.e., u(x,y) = MD(x,y)/&Delta;T. The time step between images &Delta;T = 1/fps + D(x,y)/B, where B[pixels/s] is the time it takes for the ultrasound probe to sweep across the image width. In the present study, M = 77[&mu;m/pixel], fps = 49.5[1/s], and B = 25,047[pixels/s]. Once acquired, the velocity fields can be analyzed to compute flow quantities of interest.

An echo particle image velocimetry (EPIV) system capable of acquiring two-dimensional fields of velocity in optically opaque fluids or through optically opaque geometries is described, and validation measurements in pipe flow are reported.

The transport of mass, momentum, and energy in fluid flows is ultimately determined by spatiotemporal distributions of the fluid velocity field.1 ...

High-speed Particle Image Velocimetry Near Surfaces

Multi-dimensional and transient flows play a key role in many areas of science, engineering, and health sciences but are often not well understood. The complex nature of these flows may be studied using particle image velocimetry (PIV), a laser-based imaging technique for optically accessible flows. Though many forms of PIV exist that extend the technique beyond the original planar two-component velocity measurement capabilities, the basic PIV system consists of a light source (laser), a camera, tracer particles, and analysis algorithms. The imaging and recording parameters, the light source, and the algorithms are adjusted to optimize the recording for the flow of interest and obtain valid velocity data. 
Common PIV investigations measure two-component velocities in a plane at a few frames per second. However, recent developments in instrumentation have facilitated high-frame rate (&gt; 1 kHz) measurements capable of resolving transient flows with high temporal resolution. Therefore, high-frame rate measurements have enabled investigations on the evolution of the structure and dynamics of highly transient flows. These investigations play a critical role in understanding the fundamental physics of complex flows.
A detailed description for performing high-resolution, high-speed planar PIV to study a transient flow near the surface of a flat plate is presented here. Details for adjusting the parameter constraints such as image and recording properties, the laser sheet properties, and processing algorithms to adapt PIV for any flow of interest are included.

A procedure for studying transient flows near boundaries using high-resolution, high-speed particle image velocimetry (PIV) is described here. PIV is a non-intrusive measurement technique applicable to any optically accessible flow by optimizing several parameter constraints such as the image and recording properties, the laser sheet properties, and analysis algorithms.

Multi-dimensional and transient flows play a key role in many areas of science, engineering, and health sciences but are often not well understood. The ...

Real-Time DC-dynamic Biasing Method for Switching Time Improvement in Severely Underdamped Fringing-field Electrostatic MEMS Actuators

Mechanically underdamped electrostatic fringing-field MEMS actuators are well known for their fast switching operation in response to a unit step input bias voltage. However, the tradeoff for the improved switching performance is a relatively long settling time to reach each gap height in response to various applied voltages. Transient applied bias waveforms are employed to facilitate reduced switching times for electrostatic fringing-field MEMS actuators with high mechanical quality factors. Removing the underlying substrate of the fringing-field actuator creates the low mechanical damping environment necessary to effectively test the concept. The removal of the underlying substrate also a has substantial improvement on the reliability performance of the device in regards to failure due to stiction. Although DC-dynamic biasing is useful in improving settling time, the required slew rates for typical MEMS devices may place aggressive requirements on the charge pumps for fully-integrated on-chip designs. Additionally, there may be challenges integrating the substrate removal step into the back-end-of-line commercial CMOS processing steps. Experimental validation of fabricated actuators demonstrates an improvement of 50x in switching time when compared to conventional step biasing results. Compared to theoretical calculations, the experimental results are in good agreement.

The robust device design of fringing-field electrostatic MEMS actuators results in inherently low squeeze-film damping conditions and long settling times when performing switching operations using conventional step biasing. Real-time switching time improvement with DC-dynamic waveforms reduces the settling time of fringing-field MEMS actuators when transitioning between up-to-down and down-to-up states.

Mechanically underdamped electrostatic fringing-field MEMS actuators are well known for their fast switching operation in response to a unit step input ...

Scanning SQUID Study of Vortex Manipulation by Local Contact

Local, deterministic manipulation of individual vortices in type 2 superconductors is challenging. The ability to control the position of individual vortices is necessary in order to study how vortices interact with each other, with the lattice, and with other magnetic objects. Here, we present a protocol for vortex manipulation in thin superconducting films by local contact, without applying current or magnetic field. Vortices are imaged using a scanning superconducting quantum interference device (SQUID), and vertical stress is applied to the sample by pushing the tip of a silicon chip into the sample, using a piezoelectric element. Vortices are moved by tapping the sample or sweeping it with the silicon tip. Our method allows for effective manipulation of individual vortices, without damaging the film or affecting its topography. We demonstrate how vortices were relocated to distances of up to 0.8 mm. The vortices remained stable at their new location up to five days. With this method, we can control vortices and move them to form complex configurations. This technique for vortex manipulation could also be implemented in applications such as vortex based logic devices.

We present a protocol for manipulation of individual vortices in thin superconducting films, using local mechanical contact. The method does not include applying current, magnetic field or additional fabrication steps.

Local, deterministic manipulation of individual vortices in type 2 superconductors is challenging. The ability to control the position of individual ...

Recombination Dynamics in Thin-film Photovoltaic Materials via Time-resolved Microwave Conductivity

A method for investigating recombination dynamics of photo-induced charge carriers in thin film semiconductors, specifically in photovoltaic materials such as organo-lead halide perovskites is presented. The perovskite film thickness and absorption coefficient are initially characterized by profilometry and UV-VIS absorption spectroscopy. Calibration of both laser power and cavity sensitivity is described in detail. A protocol for performing Flash-photolysis Time Resolved Microwave Conductivity (TRMC) experiments, a non-contact method of determining the conductivity of a material, is presented. A process for identifying the real and imaginary components of the complex conductivity by performing TRMC as a function of microwave frequency is given. Charge carrier dynamics are determined under different excitation regimes (including both power and wavelength). Techniques for distinguishing between direct and trap-mediated decay processes are presented and discussed. Results are modelled and interpreted with reference to a general kinetic model of photoinduced charge carriers in a semiconductor. The techniques described are applicable to a wide range of optoelectronic materials, including organic and inorganic photovoltaic materials, nanoparticles, and conducting/semiconducting thin films.

A Time Resolved Microwave Conductivity technique for investigating direct and trap-mediated recombination dynamics and determining carrier mobilities of thin film semiconductors is presented here.

A method for investigating recombination dynamics of photo-induced charge carriers in thin film semiconductors, specifically in photovoltaic materials ...

Measurement of Dynamic Force Acted on Water Strider Leg Jumping Upward by the PVDF Film Sensor

This study aimed to make an explanation for the phenomenon in nature that water strider usually jumps or glides on the water surface easily but quickly, with its peak locomotion speed reaching 150 cm/s. First of all, we observed the microstructure and hierarchy of water strider legs using the scanning electron microscope. On the basis of the observed morphology of the legs, a theoretical model of the detachment from water surface was established, which explained water striders' capability to slide on water surface effortlessly in terms of energy reduction. Secondly, a dynamic force measurement system was devised using the PVDF film sensor with excellent sensitivity, which could detect the whole interaction process. Subsequently, a single leg in contact with water was pulled upward at different speeds, and the adhesion force was measured at the same time. The results of the departing experiment suggested a deep understanding of the fast jumping of water striders.

The protocol here is dedicated to investigating the free and quick maneuvering of water strider on water surface. The protocol includes observing the microstructure of legs and measuring the adhesion force when departing from water surface at different speeds.

This study aimed to make an explanation for the phenomenon in nature that water strider usually jumps or glides on the water surface easily but quickly, ...

Full-field Strain Measurements for Microstructurally Small Fatigue Crack Propagation Using Digital Image Correlation Method

A novel measurement approach is used to reveal the cumulative deformation field at a sub-grain level and to study the influence of microstructure on the growth of microstructurally small fatigue cracks. The proposed strain field analysis methodology is based on the use of a unique pattering technique with a characteristic speckle size of approximately 10 &#181;m. The developed methodology is applied to study the small fatigue crack behavior in body centered cubic (bcc) Fe-Cr ferritic stainless steel with a relatively large grain size allowing a high spatial measurement accuracy at the sub-grain level. This methodology allows the measurement of small fatigue crack growth retardation events and associated intermittent shear strain localization zones ahead of the crack tip. In addition, this can be correlated with the grain orientation and size. Thus, the developed methodology can provide a deeper fundamental understanding of the small fatigue crack growth behavior, required for the development of robust theoretical models for the small fatigue crack&#160;propagation in polycrystalline materials.

Microstructurally small fatigue crack growth behavior is investigated using a novel methodological approach combining crack growth rate measurement and strain-field analysis to reveal the cumulative deformation field at sub-grain level.

A novel measurement approach is used to reveal the cumulative deformation field at a sub-grain level and to study the influence of microstructure on the ...

Intermediate Strain Rate Material Characterization with Digital Image Correlation

The mechanical response of a material under dynamic load is typically different than its behavior under static conditions; therefore, the common quasistatic equipment and procedures used for material characterization are not applicable for materials under dynamic loads. The dynamic response of a material depends on its deformation rate and is broadly categorized into high (i.e., greater than 200/s), intermediate (i.e., 10&#8722;200/s) and low strain rate regimes (i.e., below 10/s). Each of these regimes calls for specific facilities and testing protocols to ensure the reliability of the acquired data. Due to the limited access to high-speed servo-hydraulic facilities and validated testing protocols, there is a noticeable gap in the results at the intermediate strain rate. The current manuscript presents a validated protocol for the characterization of different materials at these intermediate strain rates. Strain gauge instrumentation and digital image correlation protocols are also included as complimentary modules to extract the utmost level of detailed data from every single test. Examples of raw data, obtained from a variety of materials and test setups (e.g., tensile and shear) is presented and the analysis procedure used to process the output data is described. Finally, the challenges of dynamic characterization using the current protocol, along with the limitations of the facility and methods of overcoming potential problems are discussed.

Here we present a methodology for the dynamic characterization of tensile specimens at intermediate strain rates using a high-speed servo-hydraulic load frame. Procedures for strain gauge instrumentation and analysis, as well as for digital image correlation strain measurements on the specimens, are also defined.

The mechanical response of a material under dynamic load is typically different than its behavior under static conditions; therefore, the common ...

Measuring the Interaction Force Between a Droplet and a Super-hydrophobic Substrate by the Optical Lever Method

The goal of this paper is to investigate the interaction force between droplets and super-hydrophobic substrates in the air. A measurement system based on an optical lever method is designed. A millimetric cantilever is used as a force sensitive component in the measurement system. Firstly, the force sensitivity of the optical lever is calibrated using electrostatic force, which is the critical step in measuring interaction force. Secondly, three super-hydrophobic substrates with different grid fractions are prepared with nanoparticles and copper grids. Finally, the interaction forces between droplets and super-hydrophobic substrates with different grid fractions are measured by the system. This method can be used to measure the force on the scale of sub-micronewton with a resolution on the scale of nanonewton. The in-depth study of the contact process of droplets and super-hydrophobic structures can help to improve the production efficiency in coating, film and printing. The force measurement system designed in this paper can also be used in other fields of microforce measurement.

The protocol aims to investigate the interaction between droplets and super-hydrophobic substrates in the air. This includes calibrating the measurement system and measuring the interaction force at super-hydrophobic substrates with different grid fractions.

The goal of this paper is to investigate the interaction force between droplets and super-hydrophobic substrates in the air. A measurement system based on ...

Crack Monitoring in Resonance Fatigue Testing of Welded Specimens Using Digital Image Correlation

A procedure using digital image correlation (DIC) to detect cracks on welded specimens during fatigue tests on resonance testing machines is presented. It is intended as a practical and reproducible procedure to identify macroscopic cracks at an early stage and monitor crack propagation during fatigue tests. It consists of strain field measurements at the weld using DIC. Images are taken at fixed load cycle intervals. Cracks become visible in the computed strain field as elevated strains. This way, the whole width of a small-scale specimen can be monitored to detect where and when a crack initiates. Subsequently, it is possible to monitor the development of the crack length. Because the resulting images are saved, the results are verifiable and comparable. The procedure is limited to cracks initiating at the surface and is intended for fatigue tests under laboratory conditions. By visualizing the crack, the presented procedure allows direct observation of macrocracks from their formation until rupture of the specimen.

Digital image correlation is used in fatigue tests on a resonance testing machine to detect macroscopic cracks and monitor crack propagation in welded specimens. Cracks on the specimen surface become visible as increased strains.

A procedure using digital image correlation (DIC) to detect cracks on welded specimens during fatigue tests on resonance testing machines is presented. It ...

Measuring Local Tissue Strains in Tendons via Open-Source Digital Image Correlation

Summary

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Chapters in this video

Echo Particle Image Velocimetry

High-speed Particle Image Velocimetry Near Surfaces

Real-Time DC-dynamic Biasing Method for Switching Time Improvement in Severely Underdamped Fringing-field Electrostatic MEMS Actuators

Scanning SQUID Study of Vortex Manipulation by Local Contact

Recombination Dynamics in Thin-film Photovoltaic Materials via Time-resolved Microwave Conductivity

Measurement of Dynamic Force Acted on Water Strider Leg Jumping Upward by the PVDF Film Sensor

Full-field Strain Measurements for Microstructurally Small Fatigue Crack Propagation Using Digital Image Correlation Method

Intermediate Strain Rate Material Characterization with Digital Image Correlation

Measuring the Interaction Force Between a Droplet and a Super-hydrophobic Substrate by the Optical Lever Method

Crack Monitoring in Resonance Fatigue Testing of Welded Specimens Using Digital Image Correlation