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19 results for “Phase change material”
Database of New Phase Change Materials
<p>Database of structural and optical properties of Gallium sulfide (GaS), Antimony sulfide (Sb2S3), Gallium Selenide (GaSe), Selenium (Se), and Molybdenum oxide (MoOx) thin film phase change materials. Contains Raman spectra and room temperature optical constants from spectroscopic ellipsometry for amorphous and polycrystalline phases. Also includes temperature dependent optical constants measured in-situ during thermal annealing to induce crystallization. Thin films fabricated by chemical vapor deposition, exfoliation, and solution processing methods. Provided by the PHEMTRONICS project (Deliverable 2.7).</p>
Using thin films of phase-change material for active tuning of terahertz waves scattering on dielectric cylinders
<p>The uploaded files contain the data generated by MATLAB and used to plot a part of the figures, and a sample code.</p> <p>Research supported by Narodowe Centrum Nauki, project no UMO-2020/39/I/ST3/02413.</p>
Thermally mediated transmission-mode deflection of terahertz waves by lamellar metagratings containing a phase-change material
<p>The data generated by MATLAB, whcih were used to plot a part of the figures. </p> <p>Research supported by Narodowe Centrum Nauki, project no UMO-2020/39/I/ST3/02413.</p>
Dataset on Physics-Based Indicators for Optimizing Phase Change Material Effectiveness in Building Design
<p>This research dataset includes the results as well as the EnrgyPlus models developed to investigate and validate newly proposed indicators to quantify the effectiveness of phase change materials in buildings.</p>
Dataset of optimal cement-based panels enhanced with microencapsulated phase change material for EnergyPlus simulations
<p>This dataset includes:<br> - A series of EnergyPlus models of the BESTEST - Case 900 - from ANSI/ASHRAE Standard 140-2011 for the original (Baseline_Case900) and three enhanced designs (Opt-1, Opt-2, Opt-5) by using a cement-based panel containing microencapsulated phase change material.<br> - All the optimal solutions (parameters and corresponding performance) in XLSX format, which were obtained for two multiobjective optimization studies of the thermophysical properties (ParetoFront_CaseA and ParetoFront_CaseB).<br> - The typical meteorological year (TMY) of Sofia city employed to obtain the results, which is freely provided by Climate.One.Building.Org repository (https://climate.onebuilding.org/)</p>
Dataset of EnergyPlus models to evaluate the impact of modeling the hysteresis phenomenon of phase change materials on the building energy performance
<p>This dataset is the research data generated to evaluate the impact of modeling the hysteresis phenomenon of phase change materials (PCM) on the building performance simulation, which includes:<br> - A series of EnergyPlus models representing the medium office of the Prototype Building Models developed by DOE. These are the original model without PCM (Baseline), and four models with different PCM modeling approaches (melting-curve, solidification-curve, mean-curve, hysteresis-model).<br> - The typical meteorological year (TMY) for Frankfurt city that was used to obtain the results, which is freely provided by Climate.One.Building.Org repository (https://climate.onebuilding.org/).</p>
Dataset of multi-objective optimization results for a new latent energy storage approach in buildings based on several phase change materials with different melting temperatures
<p>This dataset comprises the multi-objective optimization results obtained for a new latent energy storage approach based on several phase change materials (PCMs) with different melting temperatures in buildings. The results were obtained for a small office building in eight climate-representative locations according to the ASHRAE 169-2020 climate classification and within the WMO Region VI (Europe).</p> <p>The dataset contains:</p> <p>- The EnergyPlus baseline models employed as a case study for each climate.</p> <p>- The Pareto fronts obtained after the multi-objective optimization in each climate.</p> <p>- The EnergyPlus models for the best designs achieved on the Pareto fronts in terms of annual total load reductions.</p>
The Data Set for the Publication "Laboratory Testing of Small Scale Solar Facade Module with Phase Change Material and Adjustable Insulation Layer"
<p>In the scope of the ambitious EU goals of carbon neutrality in 2050, building energy efficiency is one of the crucial segments. To ensure a faster energy transition process, innovations are needed at various built environment-related sectors - starting from building components up to urban level energy management advancements.</p> <p>Phase change material (PCM) enriched building components allow to shift the existing paradigm - to make a switch from the static building components to dynamic ones able to take an active part in building energy balance by ensuring energy storage in the building thermal envelope.</p> <p>The data set presented here supports the paper "Laboratory Testing of Small Scale Solar Facade Module with Phase Change Material and Adjustable Insulation Layer". The design of the façade module, experimental setup, used equipment, the plan of the experiment, and obtained results are described in the paper. The provided data set provides heat-flux and average temperature (in PCM) measurements.</p>
Data archive for "Femtosecond X-ray diffraction reveals a liquid-liquid phase transition in phase-change materials"
<p>This archive contains the raw experimentat data used for the data analysis in the article "Femtosecond X-ray diffraction reveals a liquid-liquid phase transition in phase-change materials". It furthermore includes the script(s) to transform raw diffraction images into structure factors and the data shown in the figures in ascii format. For the ab-initio molecular dynamics simulations, the atomic trajectories are included as well.</p>
Trained Potentials for Article "Computationally Efficient Machine-Learned Model for GST Phase Change Materials via Direct and Indirect Learning"
<p>We provide 8 files here to get started using our trained potentials:</p> <p>1) *.yaml files for each trained potential. These are the outputs of the PACE training process.</p> <p>2) *.yace files for each trained potential. These are read by LAMMPS to use the trained potential. They can be obtained from the *.yaml files using the command line command: "pace_yaml2yace *.yaml".</p> <p>3) GST_config.data -- a starting configuration of GST to be read by LAMMPS. This configuration contains 504 atoms at density 5.85 g/cm^3.</p> <p>4) sample.inp -- a sample LAMMPS input file using the trained potentials. This currently uses "ACE-Indir2.yace" to run the starting configuration "GST_config.data" for 10 ps at 1200 K. When run, it outputs a log file "test.log" and a dump file "test.dump". The choice of trained potential can be changed in the "pair_coeff" section.</p>
Examining the Influence of Focal Point Location on Heat Transfer Dynamics in Phase Change Material: A Dataset Publication
<p>Dataset including experimental data from small-scale solar facade module that consists of several components: Fresnel lens; cone-shaped air gap; aerogel (semi-transparent) as insulation layer; PCM container. Variations of the experimental settings in test setups are different cone diameter (2, 3 and 4 cm) and insulation layer thickness is (3,5 and 7cm). Nine different compositions were tested in laboratory to evaluate the ompact of focal point location. The focal point has been described with three different values that reflect its location on the inner and outer surfaces and in the middle of the PCM container. The corresponding figures can be found in the paper The Effect of Fresnel Lens Focal Point Location on Heat Transfer in Phase Change Material (PCM) Enhanced Dynamic Solar Facade.</p> <p>This study has been supported by Fundamental and Applied Research project ‘Smart building EnVElope with solaR Energy STorage (EVEREST)’, project No. lzp-2019/1-0363, funded by the Latvian Council of Science.</p> <p> </p>
Supporting data for: Optical properties of electrochemically gated La1-xSrxCoO3-δ as a topotactic phase-change material
<p>The data included here contain the information necessary to recreate the figures in a manuscript titled "<em>Optical Properties of Electrochemically Gated La<sub>1-x</sub>Sr<sub>x</sub>CoO<sub>3-δ</sub> as a Topotactic Phase-Change Material</em>". The data files include scanning transmission electron microscopy (STEM) images of electrochemically gated La<sub>1-x</sub>Sr<sub>x</sub>CoO<sub>3-δ</sub> (LSCO) films, finite-difference time-domain (FDTD)-simulated electric field and reflectance data for LSCO-based metasurfaces, transfer-matrix model reflectance data for LSCO films on gold substrates, complex refractive index data for LSCO films before and after electrochemical gating, electronic resistivity data for LSCO films before and after electrochemical gating, source-drain current measurements of LSCO films during electrochemical gating, and X-ray diffraction data for LSCO films before and after electrochemical gating.</p>
Supporting data for: Optical properties of electrochemically gated La1-xSrxCoO3-δ as a topotactic phase-change material
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Thermal performance enhancement of phase change material using aluminum-mesh grid foil for lithium-capacitor modules
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Experimental and numerical thermal analysis of a lithium-ion battery module based on a novel liquid cooling plate embedded with phase change material
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Dataset related to article: Equivariant graph neural network interatomic potential for Green-Kubo thermal conductivity in phase change materials
<p>This repository contains the dataset to train and test the GeTe Machine Learning Interatomic Potential (MLIP). The computational details are given in the manuscript. </p>
Research data for "Device-scale atomistic modelling of phase-change memory materials"
<p>This is a dataset related to the publication "Device-scale atomistic modelling of phase-change memory materials".</p> <p>Two folders have been provided for (1) the production data shown in this work, and (2) GAP models trained in this work:</p> <p>(1) The production data have been categorised according to the main text figures:</p> <ul> <li>"reference_database": Reference databases (i.e., training structures) of three GAP models discussed in this work. The structure data are provided in (extended) XYZ format, as labelled using either the PBEsol or the PBE functional. <ul> <li>"main_GST-GAP-22_PBEsol": the main GST-GAP-22 database, which was fitted using a two-step iterative training protocol. The resulting GAP model was used to obtain the results shown in the main text. The reference database is visualised in Fig. 1 of the main text. </li> <li>"refitted_GST-GAP-22_PBE": this dataset contains the same structures as the original GST-GAP-22 training data, with all structures having been relabelled using the PBE functional.</li> <li>"extended_GST-GAP-22_for_efield_PBEsol": an extension of the GST-GAP-22 database to a new, task-specific application, i.e., electromigration under an external electric field (cf. Extended Data Fig. 4).</li> </ul> </li> </ul> <ul> <li>"crystallization_simulations": three trajectories for the crystallization simulations shown in Fig. 2, of which all were obtained from GAP-MD. <ul> <li>"fig2b_growth_GAP-MD": Growth of Ge<sub>1</sub>Sb<sub>2</sub>Te<sub>4</sub> (1008 atoms).</li> <li>"fig2c_cumulative_set_cycles_GAP-MD": Cumulative set process of Ge<sub>1</sub>Sb<sub>2</sub>Te<sub>4</sub> (1008 atoms).</li> <li>"fig2d_crystallization_12096at_GAP-MD": Crystallization of Ge<sub>1</sub>Sb<sub>2</sub>Te<sub>4</sub> (12096 atoms), in which three crystalline seeds were used.</li> </ul> </li> </ul> <ul> <li>"RESET_mushroom_model": two non-isothermal simulations shown in Fig. 3, obtained from GAP-MD. <ul> <li>"Fig3b_small_pulse": The 70 ps NVE equilibrium process after a small heating pulse was imposed in the focal area, giving an excess kinetic energy of 1,650 eV for the atoms in the focal area.</li> <li>"Fig3d_large_pulse": The 70 ps NVE equilibrium process after a large heating pulse was imposed in the focal area, giving an excess kinetic energy of 3,900 eV for the atoms in the focal area.</li> </ul> </li> </ul> <ul> <li>"RESET_device_scale_simulations": the GAP-MD simulations of the melting and heat dissipation process of a device-scale structural model shown in Fig. 4. <ul> <li>"Fig4b_device_scale<strong>_</strong>heating_10ps": The melting process of the device-scale model over 10 ps.</li> <li>"Fig4c_device_scale<strong>_</strong>cooling_40ps": The heat dissipation process of the device-scale model over another 40 ps.</li> </ul> </li> </ul> <p>(2) The GAP models trained in this work:</p> <ul> <li>"main_GAP_potential": the main GAP model used for the production data of this work, which is fitted based on PBEsol data. The XML identifier of this GAP model is GAP_2022_4_7_480_18_6_12_970.</li> </ul> <ul> <li>"other_GAP_potentials": two derivatives of the original GAP model. <ul> <li>"refitted_GST-GAP-22_PBE": using the same reference structures as the original GAP model but re-labelled using the PBE functional. The XML identifier of this GAP model is GAP_2022_5_7_480_0_58_2_26.</li> <li>"extended_GST-GAP-22_for_efield_PBEsol": An extension of the original GAP model to a new, task-specific application, i.e., electromigration under an external electric field. The XML identifier of this GAP model is GAP_2023_3_19_480_18_14_10_174.</li> </ul> </li> </ul> <p> </p>
Whole Body Cooling Using Phase Changing Material
ClinicalTrials.gov study NCT01138176. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Implementation of Battery Thermal Management System for High-Performance Electromobility Application: A Hybrid Cold Plate and Phase Change Material Approach
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