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2,679 results for “softness”
Dataset of "Structural Development on Ru and RuO2 Electrodes during Oxygen Evolution – an operando soft X-ray Absorption Spectroscopy Approach"
<p>Time resolved in-situ X-ray absorption spectroscopy (XAS) in soft X-ray region was used to characterize polarized interphase on Ru and Ru oxide based electrodes under oxygen evolution reaction (OER) conditions. XAS spectra were used to align the type and population of oxygen-containing species formed at electrodes at anodic potentials with local electronic structure of the OER catalyst. The operando soft XAS data do not identify a single rate limiting process at potentials negative to 1.4 V vs Ag/AgCl. Individual intermediates of the oxygen evolution process coexist at the surface at potentials preceding the actual OER onset. The OER is accompanied with redistribution of the electron density resulting for a start of the catalytic cycle reflecting increased population of oxygen vacancies at the surface. The observed spectral behavior indicates a confinement of the OER to the coordination unsaturated sites (cus) at the surface. </p>
Dataset for paper entitled "A Wireless Inductive Sensing Technology for Soft Pneumatic Actuators Using Magnetorheological Elastomers"
<p>This dataset includes all the experimental and FE results presented in the RoboSoft2019 paper "A Wireless Inductive Sensing Technology for Soft Pneumatic Actuators Using Magnetorheological Elastomers" (DOI: <a href="https://doi.org/10.1109/ROBOSOFT.2019.8722800">10.1109/ROBOSOFT.2019.8722800</a>).</p> <p>https://ieeexplore.ieee.org/abstract/document/8722800</p> <p>List of data:</p> <p>Fig.3-EXP_Coil size.xlsx<br> Fig.4-MRE Characterization.xlsx<br> Fig.6-FE modeling results.xlsx<br> Fig.8-Flat SPA Characterization.xlsx<br> Fig.9-EXP-external load.xlsx<br> Fig.10-Exp-Bending SPA.xlsx</p>
Dataset for paper entitled "Folding and Bending Planar Coils for Highly Precise Soft Angle Sensing"
<p>This dataset includes all results presented in the paper entitled "Folding and Bending Planar Coils for Highly Precise Soft Angle Sensing", published in Advanced Materials Technologies, vol.5, 2000659, 2020<br> DOI: 10.1002/admt.202000659.<br> URL:<br> https://onlinelibrary.wiley.com/doi/full/10.1002/admt.202000659</p> <p>List of data in this dataset:<br> Fig.1-Theoretical Analysis and Basic characteristics.xlsx<br> Fig.2-Experimental results-Coil design.xlsx<br> Fig.3-Cyclic Bending and Folding.xlsx<br> Fig.4-Folding Angle Sensing Performance Evaluation.xlsx<br> Fig.5-Case studies.xlsx</p> <p>All the data included in this dataset were collected and processed by Dr. Hongbo Wang.</p> <p>Contact person:<br> Dr. Hongbo Wang, ustcwhb@gmail.com</p>
Data and code related to the paper: "Integrated stretchable pneumatic strain gauges for electronics-free soft robots"
<p>This folder contains the raw data and Matlab scripts to reproduce the plots and supplementary movies for the paper:</p> <p>Anastasia Koivikko, Vilma Lampinen, Mika Pihlajamäki, Kyriacos Yiannacou, Vipul Sharma & Veikko Sariola, "Integrated Stretchable Pneumatic Strain Gauges for Electronics-Free Soft Robots", Communications Engineering, 1, 14 (2022).</p> <p><a href="https://doi.org/10.1038/s44172-022-00015-6">Link to the paper</a>.</p> <p>The scripts were tested on Matlab R2021a on Windows.</p> <p>Generally speaking, there is a folder containing the plotting scripts for each figure. In most cases, the folder contains scripts named <strong>plot<...>.m</strong> that recreate the actual plots. Some folders also have a scripts <strong>analyze<...>.m</strong> to analyze the data; these need to be run before the actual plotting.</p> <p>For more details, please see the paper.</p>
Measuring the counterion cloud of soft microgels using SANS with contrast variation
<p>The behavior of microgels and other soft and compressible colloidal particles depends on particle concentration in ways that are absent in their hard-particulate counterparts. For instance, poly-N-isopropylacrylamide (pNIPAM) microgels can spontanously deswell and reduce suspension polydispersity at high concentrations. Despite the pNIPAM network in these microgels is uncharged, the key to understand this distinct behavior relies on the existence of peripheric charged groups, which provide stability when deswollen, and the associated counterion cloud. When in close proximity, clouds of different particles overlap, effectively freeing the associated counterions, which are then able to exert an osmotic pressure that can potentially cause the microgels to change size. Up to to now, however, no direct measurement of such an ionic cloud exists, perhaps even for hard colloids, where it is referred to as electric double layer. Here, we use small-angle neutron scattering with contrast variation with different ions to isolate the change in the form factor directly related to the counterion cloud and obtain its radius and width. Our results highlight that modeling of microgel suspensions must unavoidably and explicitly consider the presence of this cloud, which is present for nearly all microgel particles synthesized today.</p>
Relative Abundance of Soft Algae From the Comprehensive Everglades Restoration Plan (CERP) Study (FCE), Florida, USA, September 2005 to November 2011
Relative soft algae data collected between September 2005 and November 2011 "The Comprehensive Everglades Restoration Plan (CERP) focuses on “getting the water right” in the south Florida ecosystem—getting the right amount of water of the right quality to the right places at the right time" (USACE & DoI, 2015. Central and Southern Florida Project Comprehensive Everglades Restoration Plan) in the Everglades ecosystems. To inform CERP, since February 2005 we have been investigating the spatio-temporal variations of distribution, biomass and diversity of algae (key aquatic primary producers) in periphyton mats in relation to hydrology, nutrients and pH, and other environmental conditions.
Dataset for "Reconfigurable Magnonic Crystals Based on Imprinted Magnetization Textures in Hard and Soft Dipolar-Coupled Bilayers"
<p>The dataset consist of the data of the numerical simulations used to prepare the figures for the manuscript: </p><p>Krzysztof Szulc, Silvia Tacchi, Aurelio Hierro-Rodríguez, Javier Díaz, Paweł Gruszecki, Piotr Graczyk, Carlos Quirós, Daniel Markó, José Ignacio Martín, María Vélez, David S. Schmool, Giovanni Carlotti, Maciej Krawczyk, and Luis Manuel Álvarez-Prado. <i>Reconfigurable Magnonic Crystals Based on Imprinted Magnetization Textures in Hard and Soft Dipolar-Coupled Bilayers</i>. ACS Nano <strong>2022</strong> <i>16</i> (9), 14168-14177.</p><p>Please read README.txt file to see the description of the data in the files.</p>
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Supramolecular Self-Healing Sensor Fiber Composites for Damage Detection in Piezoresistive Electronic Skin for Soft Robots
<p>Self-healing materials can prolong the lifetime of structures and products by enabling the repairing of damage. However, detecting the damage and the progress of the healing process remains an important issue. In this study, self-healing, piezoresistive strain sensor fibers (ShSFs) are used for detecting strain deformation and damage in a self-healing elastomeric matrix. The ShSFs were embedded in the self-healing matrix for the development of self-healing sensor fiber composites (ShSFC) with elongation at break values of up to 100%. A quadruple hydrogen-bonded supramolecular elastomer was used as a matrix material. The ShSFCs exhibited a reproducible and monotonic response. The ShSFCs were investigated for use as sensorized electronic skin on 3D-printed soft robotic modules, such as bending actuators. Depending on the bending actuator module, the electronic skin was loaded under either compression (pneumatic-based module) or tension (tendon-based module). In both configurations, the ShSFs could be successfully used as deformation sensors, and in addition, detect the presence of damage based on the sensor signal drift. The sensor under tension showed better recovery of the signal after healing, and smaller signal relaxation. Even with the complete severing of the fiber, the piezoresistive properties returned after the healing, but in that case, thermal heat treatment was required. With their resilient response and self-healing properties, the supramolecular fiber composites can be used for the next generation of soft robotic modules</p>
Fabrication of a Soft Robotic Gripper With Integrated Strain Sensing Elements Using Multi-Material Additive Manufacturing
<p>With the purpose of making soft robotic structures with embedded sensors, additive manufacturing techniques like fused deposition modeling (FDM) are popular. Thermoplastic polyurethane (TPU) filaments, with and without conductive fillers, are now commercially available. However, conventional FDM still has some limitations because of the marginal compatibility with soft materials. Material selection criteria for the available material options for FDM have not been established. In this study, an open-source soft robotic gripper design has been used to evaluate the FDM printing of TPU structures with integrated strain sensing elements in order to provide some guidelines for the material selection when an elastomer and a soft piezoresistive sensor are combined. Such soft grippers, with integrated strain sensing elements, were successfully printed using a multi-material FDM 3D printer. Characterization of the integrated piezoresistive sensor function, using dynamic tensile testing, revealed that the sensors exhibited good linearity up to 30% strain, which was sufficient for the deformation range of the selected gripper structure. Grippers produced using four different TPU materials were used to investigate the effect of the Shore hardness of the TPU on the piezoresistive sensor properties. The results indicated that the <em>in situ</em> printed strain sensing elements on the soft gripper were able to detect the deformation of the structure when the tentacles of the gripper were open or closed. The sensor signal could differentiate between the picking of small or big objects and when an obstacle prevented the tentacles from opening. Interestingly, the sensors embedded in the tentacles exhibited good reproducibility and linearity, and the sensitivity of the sensor response changed with the Shore hardness of the gripper. Correlation between TPU Shore hardness, used for the gripper body and sensitivity of the integrated <em>in situ</em> strain sensing elements, showed that material selection affects the sensor signal significantly.</p>
A soft pneumatic actuator with integrated deformation sensing elements produced exclusively with extrusion based additive manufacturing
<p>In recent years, soft pneumatic actuators have come into the spotlight because of their simple control and the wide range of complex motions. To monitor the deformation of soft robotic systems, elastomer-based sensors are being used. However, the embedding of sensors into soft actuator modules by polymer casting is time consuming and difficult to upscale. In this study, it is shown how a pneumatic bending actuator with an integrated sensing element can be produced using an extrusion-based additive manufacturing method, e.g., fused deposition modeling (FDM). The advantage of FDM against direct printing or robocasting is the significantly higher resolution and the ability to print large objectives in a short amount of time. New, commercial launched, pellet-based FDM printers are able to 3D print thermoplastic elastomers of low shore hardness that are required for soft robotic applications, to avoid high pressure for activation. A soft pneumatic actuator with the in situ integrated piezoresistive sensor element was successfully printed using a commercial styrene-based thermoplastic elastomer (TPS) and a developed TPS/carbon black (CB) sensor composite. It has been demonstrated that the integrated sensing elements could monitor the deformation of the pneumatic soft robotic actuator. The findings of this study contribute to extending the applicability of additive manufacturing for integrated soft sensors in large soft robotic systems.</p>
Multi-material 3D Printing of Thermoplastic Elastomers for Development of Soft Robotic Structures with Integrated Sensor Elements
<p>Embedded sensing can benefit soft robots with the ability to interact with their environment but producing embedded soft sensors can be challenging. Multi-material Fused Deposition Modeling (FDM) additive manufacturing allows producing complex structures, by combining more than one kind of polymeric material. For multi-material FDM, conductive thermoplastic elastomer filaments have been developed. This allows the printing of flexible functional structures, based on thermoplastic elastomer structures with conductive paths that are of great interest for stretchable electronics and soft robotic applications. In this study, stretchable piezoresistive elastomer strain sensor composites were successfully produced by using multi-material FDM. A piezoresistive thermoplastic elastomer was printed on the top of a nonconductive, flexible thermoplastic elastomer strip using FDM multi-material 3D printer. FDM elastomer filaments with different shore hardness as substrate materials for the gripper structure were used. The hardness of the elastomer affected the printability and the adhesion to the conductive elastomer material, which was used as a strain sensor material. The hardness affected the strain sensor properties too. The piezoresistive response, dynamic behavior, drift, relaxation and sensitivity of the printed multi-material strips were investigated by tensile tests. Soft robotic grippers with integrated sensing elements to detect deformation while touching the objective were selected as a case study. The soft grippers with the integrated sensors exhibited intelligent response by recognizing when they were griping a small or big object and when an obstacle was inhibiting their function.</p>
Supplementary materials (set 2 of 2) in support of "Signalling Emotions with a Breathing Soft Robot" (Data set and materials used for human-robot interaction experiment)
<p>Supplementary materials (set 2 of 2) in support of "Signalling Emotions with a Breathing Soft Robot" authored by Troels Aske Klausen, Ulrich Farhadi, Evgenios Vlachos, and Jonas Jørgensen.</p> <p>Contents of set 2:<br> - Data set and materials used for the human-robot interaction experiment and for data analysis</p> <p>Files:<br> - "Questionnaire.pdf": Questionnaire used for data collection.<br> - "Video links.txt": Weblinks to stimuli videos used.<br> - "Data set.xls": Collected raw data.<br> - "Matlab_DataAnalysis.mlx": Matlab script used to analyze raw data.<br> - "Linear_Arousal.png": Linear fit between the scoring of arousal and BPM.<br> - "Linear_Dominance.png": Linear fit between the scoring of dominance and BPM.<br> - "Linear_Pleasure.png": Linear fit between the scoring of pleasure and BPM.</p> <p>The experiment procedure is described in the paper.<br> The soft robot used for the experiment is open source and can be manufactured using design files available on Zenodo: 10.5281/zenodo.5565201</p>
Scale Soft Sensor for steel semi-products [CSS4]
<p> </p> <p>During production of steel bars in hot rolling mills there is a formation of scale on the surface of the products. It is differentiated between two scale types: primary scale and secondary scale. During the reheating of the product in the furnace the scale is called primary scale. After reheating and before rolling the scale is removed by a descaler, for instance with high water pressure. After rolling, while the product is located on the cooling bed, the secondary scale grows up.</p>
Oscillatory compression with different frequencies of 2D, dense, soft particle suspensions
<p>This repository contains 5 datasets of cyclically compressed hydrogel packings inside microfluidic channels, with different oscillation frequencies, observed using a microscope. This repository contains the raw data (images) as well as analyzed data of the particles tracked over time. The data format closely resembles information you might obtain from 2D DEM simulations, and could, therefore, be used to calibrate DEM simulations of the compaction of soft particles.</p> <p>The "Readme.md" file contains more in-depth information about the experimental setup, experiments and data structure.</p>
Data for "Multimodal Soft Valve Enables Physical Responsiveness for Pre-emptive Resilience of Soft Robots"
<p>This dataset contains all the data and CAD models needed to replicate the study presented in "Multimodal Soft Valve Enables Physical Responsiveness for Pre-emptive Resilience of Soft Robots".</p>
Data package for "Fast event-driven simulations for soft spheres: from dynamics to Laves phase nucleation"
<p>This dataset contains supporting data for the publication:</p> <p><em>Fast event-driven simulations for soft spheres: from dynamics to Laves phase nucleation</em></p> <p>A. Castagnède, L. Filion, and F. Smallenburg, J. Chem. Phys. 160 (2024), doi:10.1063/5.0209178, arXiv:2403:12755</p> <p> </p> <p><strong>Contents:</strong></p> <p>The main folder <em>data_package</em> contains three subfolders: <em>figures</em>, <em>SLNN</em>, and <em>snapshots</em>. The <em>figures</em> subfolder contains supporting data for each of the figures found in the publication, accompanied by details on statepoints and methods in individual README files. The <em>SLNN</em> subfolder contains the trained neural network classifier used in this work for crystalline phase identification, alongside usage instructions and an exemple system to analyze. Finally, the <em>snapshots</em> subfolder contains supplementary snapshots of the crystalline clusters obtained in simulations. </p> <p> </p>
Data for "Adaptive and Resilient Soft Tensegrity Robots" (Rieffel & Mouret, 2018)
<p>Data (experimental results) for the paper "Adaptive and Resilient Soft Tensegrity Robots", to appear in Soft Robotics (2018).</p> <ul> <li>Source code: <a href="https://github.com/resibots/rieffel_mouret_2018_soft_tensegrity">https://github.com/resibots/rieffel_mouret_2018_soft_tensegrity </a></li> <li>Pre-print: <a href="https://arxiv.org/abs/1702.03258">https://arxiv.org/abs/1702.03258</a></li> </ul>
Pellet-based fused deposition modeling for the development of soft compliant robotic grippers with integrated
<p>Fused deposition modeling (FDM) has some advantages compared to other additive manufacturing techniques, such as the in situ integration of functional components, like sensors, and recyclability of parts. However, conventional filament-based FDM techniques are limited to thermoplastic elastomers with a Shore hardness above 70 A, thus it has marginal compatibility with soft robotic structures. Due to recently emerging pellet-based FDM printer technology, the fabrication of soft grippers with low Shore hardness has become possible. In this study, styrene based thermoplastic elastomers (TPS) were used to print elastic strips and soft gripper structures down to a Shore hardness of 25 A with an integrated strain sensing element (piezoresistive sensor). Printing on a soft rather than rigid substrate affects the integration of the printed thread on the substrate, because of the softness and relaxation, during the printing softness. It was seen that integrating the sensing element on a substrate with higher Shore hardness decreased the elongation at the point of fracture and the sensitivity of the sensing element. A soft compliant gripper structure with an integrated sensing layer was printed with the TPS-based elastomers successfully, and even due to the complex deformation of the compliant gripper structure, several positions could be detected successfully. Opened and closed position of the gripper, as well as, size recognition of spools of different sizes could be monitored by the piezoresistive printed sensor layer. The most sensitive sensing performance was obtained with the TPS of the lower Shore hardness (25 A), as the value of relative change in resistance was 1, followed by the gripper of Shore hardness 65 A and a relative change in resistance of 0.51. With this study, we demonstrated that pellet-based FDM printers can be used, to print potential soft robotic structures with in-situ integrated sensor structures.</p>
Dataset for DOI: 10.1109/LRA.2019.2927936. "Multi-DoF Force Characterization of Soft Actuators"
<p>This dataset contains the raw measurement data and MATLAB scripts for the following publication: S. Joshi and J. Paik, "Multi-DoF Force Characterization of Soft Actuators," in <em>IEEE Robotics and Automation Letters</em>, vol. 4, no. 4, pp. 3679-3686, Oct. 2019. doi: 10.1109/LRA.2019.2927936</p> <p><br> The .csv files contain measured values of soft actuator pressure, displacement and force output. The MATLAB scripts help to extract and plot this raw data.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.