15 research outputs found
Aerosol-jet-printed, conformable microfluidic force sensors.
Force sensors that are thin, low-cost, flexible, and compatible with commercial microelectronic chips are of great interest for use in biomedical sensing, precision surgery, and robotics. By leveraging a combination of microfluidics and capacitive sensing, we develop a thin, flexible force sensor that is conformable and robust. The sensor consists of a partially filled microfluidic channel made from a deformable material, with the channel overlaying a series of interdigitated electrodes coated with a thin, insulating polymer layer. When a force is applied to the microfluidic channel reservoir, the fluid is displaced along the channel over the electrodes, thus inducing a capacitance change proportional to the applied force. The microfluidic molds themselves are made of low-cost sacrificial materials deposited via aerosol-jet printing, which is also used to print the electrode layer. We envisage a large range of industrial and biomedical applications for this force sensor
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Research data supporting "Complex Architectural Control of Ice-Templated Collagen Scaffolds Using a Predictive Model"
The data set includes data for anisotropic ice templated collagen scaffold production including temperature and strut orientation data. Data was collected via uCT imaging of ice templated collagen scaffolds. Data was also collected via time dependent thermocouple readings and compression testing. The data set also includes simulation results from a 3D diffusion model of water solidification designed to mimic the experimental set up for ice templating. Data was exported from comsol.SMB and REC gratefully acknowledge the financial support of the British Heart Foundation [Grants NH/11/1/28922, RG/15/4/31268 and SP/15/7/31561] and EPSRC [Grant EP/N019938/1]. JC gratefully acknowledges support from by the Gates Cambridge Trust, 33 Bridge Street, Cambridge, CB2 1UW UK. AH and REC acknowledge support from ERC [Advanced Grant 320598 3D-E]
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Research data supporting In-situ ESEM imaging of the vapor-pressure-dependent sublimation-induced morphology of ice
The following data files are provided: Python file containing the raw data for wavelengths and velocities measured at each pressure and Zip file containing all ESEM images taken at each pressure used for publication
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Research Data supporting "The influence of thermal processing parameters on pore size and lamellar alignment in anisotropic ice-templated collagen scaffolds"
This zip folder contains the raw data used in the publication ‘The influence of thermal processing parameters on pore size and lamellar alignment in anisotropic ice-templated collagen scaffolds.’ Temperature data were collected at 4-second intervals (Omega RDXL6SD-USB data logger) via k-type thermocouples placed at a height of 0 mm (bottom) and 26 mm (top) in the mould. Temperature data (.xlsx files) are included in this zip file. The freezing times for collagen slurry (1 w.t.%; 9 ml) or deionized (D.I.) water (9 ml) were recorded. Samples were frozen with a cold finger which was stabilized at -60 °C prior to mould introduction and the temperature was held at -60 °C until frozen. The time from mould introduction to complete solidification, termed, "freezing time", was recorded. Freezing times for both conditions (.xlsx file) are included in this zip file. Time lapse image sequences of each protocol condition were recorded with a GoPro Hero 5 at 10 second intervals. The freezing progression binarized with ImageJ software. Binarized time lapse image stacks (.tif files) are included in this zip file. Alignment and pore size data were measured using X-ray micro-computed tomography (µCT) images (Skyscan 1172). µCT images were taken of each scaffold with a voltage of 25 kV, current of 138 mA, and a pixel size of 5.46 µm. Reconstructions of mCT images were performed with NRecon software by Skyscan and analysed in ImageJ. Results for scaffold alignment (.txt files) and scaffold pore size (.txt files) are included in the zip file.SMB and REC gratefully acknowledge the financial support of the British Heart Foundation [Grants NH/11/1/28922, RG/15/4/31268 and SP/15/7/31561] and EPSRC [Grant EP/N019938/1]. JC gratefully acknowledges support from by the Gates Cambridge Trust, 33 Bridge Street, Cambridge, CB2 1UW UK. AH and REC acknowledge support from ERC [Advanced Grant 320598 3D-E]
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Research data supporting "Investigation of the intrinsic permeability of ice-templated collagen scaffolds as a function of their structural and mechanical properties"
The record contains a sample stack of images of a 0.75wt% collagen scaffold. These images were obtained from 3D micro-CT scanning of the scaffold. They were imported on ImageJ with Fiji and they were image processing was carried out. They were segmented to distinguished between the pores and walls and image filtering was applied to ensure that the images were smooth. Once they were processed, the images were analysed for pore sizes, percolation diameter, interconnectivity and tortuosity
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Research data supporting "Investigation of the intrinsic permeability of ice-templated collagen scaffolds as a function of their structural and mechanical properties"
The record contains a sample stack of images of a 0.75wt% collagen scaffold. These images were obtained from 3D micro-CT scanning of the scaffold. They were imported on ImageJ with Fiji and they were image processing was carried out. They were segmented to distinguished between the pores and walls and image filtering was applied to ensure that the images were smooth. Once they were processed, the images were analysed for pore sizes, percolation diameter, interconnectivity and tortuosity
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Research data supporting "Highly sensitive piezotronic pressure sensors based on undoped GaAs nanowire ensembles"
‘Figure 2a Device B.txt’: data for Figure 2a. Raw data for four measurements including time, applied voltage and measured current for applied weights of 0g, 50g, 100g and 0g rec (‘rec’ for recovered which was taken after 100g was applied).
‘Figure 2b reverse bias.txt’: data for Figure 2b for device A and device B for applied weights of 0g, 50g, 100g under reversed applied bias
‘Figure 2b V_FB insert.txt’: extracted data for Figure 2b insert, for calculation see paper.
‘Figure 4 Device A phi gauge.txt’, ‘Figure 4 Device B phi gauge.txt’, ‘Figure 4 b insert Delta phi.txt’: data for Figure 4
50g = 1.93kPa, 100g = 2.58kPa (see paper)
Delta phi = - 0.0247 * ln(I_load/I_no_load)
Gauge = (I_load - I_no_load) / (I_no_load * epsilon) (for explanation of epsilon see paper)
‘Figure S2 Device A.txt’: data for Figure S2. Raw data for four measurements including time, applied voltage and measured current for applied weights of 0g, 50g, 100g and 0g rec (‘rec’ for recovered which was taken after 100g was applied).
‘Figure S3 device A.txt’, ‘Figure S3 device B.txt’: data for Figure S3 for both devices A and B under applied weight of 0g
‘Figure S4 forward bias.txt’: data for Figure S4 for device A and device B for applied weights of 0g, 50g, 100g under forward applied bia
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Complex architectural control of ice-templated collagen scaffolds using a predictive model.
The architectural and physiomechanical properties of regenerative scaffolds have been shown to improve engineered tissue function at both a cellular and tissue level. The fabrication of regenerative three-dimensional scaffolds that precisely replicate the complex hierarchical structure of native tissue, however, remains a challenge. The aim of this work is therefore two-fold: i) demonstrate an innovative multidirectional freeze-casting system to afford precise architectural control of ice-templated collagen scaffolds; and ii) present a predictive simulation as an experimental design tool for bespoke scaffold architecture. We used embedded heat sources within the freeze-casting mold to manipulate the local thermal environment during solidification of ice-templated collagen scaffolds. The resultant scaffolds comprised complex and spatially varied lamellar orientations that correlated with the imposed thermal environment and could be readily controlled by varying the geometry and power of the heat sources. The complex macro-architecture did not interrupt the hierarchical features characteristic of ice-templated scaffolds, but pore orientation had a significant impact on the stiffness of resultant structures under compression. Furthermore, our finite element model (FEM) accurately predicted the thermal environment and illustrated the freezing front topography within the mold during solidification. The lamellar orientation of freeze-cast scaffolds was also predicted using thermal gradient vector direction immediately prior to phase change. In combination our FEM and bespoke freeze-casting system present an exciting opportunity for tailored architectural design of ice-templated regenerative scaffolds that mimic the complex hierarchical environment of the native extracellular matrix. STATEMENT OF SIGNIFICANCE: Biomimetic scaffold structure improves engineered tissue function, but the fabrication of three-dimensional scaffolds that precisely replicate the complex hierarchical structure of native tissue remains a challenge. Here, we leverage the robust relationship between thermal gradients and lamellar orientation of ice-templated collagen scaffolds to develop a multidirectional freeze-casting system with precise control of the thermal environment and consequently the complex lamellar structure of resultant scaffolds. Demonstrating the diversity of our approach, we identify heat source geometry and power as control parameters for complex lamellar orientations. We simultaneously present a finite element model (FEM) that describes the three-dimensional thermal environment during solidification and accurately predicts lamellar structure of resultant scaffolds. The model serves as a design tool for bespoke regenerative scaffolds
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Research data supporting "Aerosol-Jet Printed Conformable Microfluidic Force Sensors"
The repository includes original SEM image and data collections for the figures in the paper titled "Aerosol-Jet Printed Conformable Microfluidic Force Sensors" (Figure 3, 4, 5 and figures in Supplemental Information). The data was collected from experimental results and simulation results. The collection of experimental data includes means of impedance analyzer, Arduino board, force gauge, etc. The simulation method is based on Comsol Multiphysics