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40 results for “material efficiency”
Dataset of "Mn-doped WSe2 as an efficient electrocatalyst for hydrogen production and as anode material for lithium-ion batteries"
<p>The ongoing energy crisis has made it imperative to develop low-cost, easily fabricated, yet efficient materials. It is highly desirable for these nanomaterials to function effectively in multiple applications. Among transition metal dichalcogenides, tungsten diselenide (WSe2) shows great promise but remains understudied. In this work, we doped WSe2 with Mn using a simple hydrothermal method. The resulting material exhibited excellent electrocatalytic activity for the hydrogen evolution reaction, achieving a low overpotential of –0.28 V vs RHE at -10 mA/cm2, enhanced conductivity, and high stability and durability. Moreover, as an anode material in in lithium-ion batteries, the Mn-doped WSe2 outperformed pristine WSe2, reaching discharge and charge capacities of 1223 and 922 mAh g−1, respectively. Additionally, the Mn-doped material maintained a significantly higher discharge capacity of 201 mAh g−1 compared to intact WSe2, which had 68 mAh g−1 after 150 cycles. This work offers novel insights into designing efficient bifunctional nanomaterials using transition metal dichalcogenides.</p>
Supplementary material: Efficient in vivo screening method for the identification of C4 photosynthesis inhibitors based on cell suspensions of the single-cell C4 plant Bienertia sinuspersici
<p>Data described in Minges et al. (2019) Efficient <em>in vivo</em> screening method for the identification of C<sub>4</sub> photosynthesis inhibitors based on cell suspensions of the single-cell C<sub>4</sub> plant <em>Bienertia sinuspersici</em>. doi: <a href="https://doi.org/10.3389/fpls.2019.01350">10.3389/fpls.2019.01350</a></p> <p> </p>
Database and model code for "Material efficiency and climate change mitigation of passenger vehicles"
<p>This record provides all data points and model code necessary to compute the results presented in P. Wolfram, Q. Tu, N. Heeren, S. Pauliuk, E. Hertwich (2020) "Material efficiency and climate change mitigation of passenger vehicles", published in Journal of Industrial Ecology. All data is described in section 2 of the manuscript. The code can be run in MATLAB. </p>
Supplementary Material for "Route Efficiency Assessment and Review of the Synthesis of β-Nucleosides via N-Glycosylation of Nucleobases"
<p>This is the external Supplementary Material for our publication "Route Efficiency Assessment and Review of the Synthesis of β-Nucleosides via <em>N</em>-Glycosylation of Nucleobases", which has been released as a preprint on <em>ChemRxiv </em>(https://doi.org/10.26434/chemrxiv.12753413.v1). The files in this record are additionally available from <em>ChemRxiv</em>.</p>
Supplementary material for the publication: "Efficient Surrogate Models for Materials Science Simulations: Machine Learning-based Prediction of Microstructure Properties"
<p><span><span><span>This dataset contains supplementary code, images and models for the publication „Efficient Surrogate Models for Materials Science Simulations: Machine Learning-based Prediction of Microstructure Properties“.</span></span></span></p> <p> </p> <p><span><span><span>The content will be updated and additionally linked to the corresponding git repositories.</span></span></span></p>
Dataset of the publication: Design and processing as ultrathin films of a sublimable Iron(II) spin crossover material exhibiting efficient and fast light-induced spin transition
<p>Dataset of the publication: Design and processing as ultrathin films of a sublimable Iron(II) spin crossover material exhibiting efficient and fast light-induced spin transition</p> <p>DOI: 10.1021/acs.chemmater.3c01704</p> <p>M. Gavara-Edo, F. J. Valverde-Muñoz, M. C. Muñoz, S. Elidrissi, F. Marques-Moros, J. Herrero-Martín, K. Znovjyak, M. Seredyuk, J. A. Real, E. Coronado <br><br>Chem. Mater., 35, 22, 9591-9602 (2023)</p>
Supplementary material for the paper: "One-shot procedures for efficient minimum compliance topology optimization"
<p>MATLAB codes and results used in the article: "One-shot procedures for efficient minimum compliance topology optimization", published in Structural and Multidisciplinary Optimization, 2024.</p>
Supplemental Material to "Efficiency of Virucidal Disinfectants on Wood Surfaces in Animal Husbandry"
<p>Data set for individual titre reduction of viruses on wood surfaces treated with a disinfactant in multiple experiments.</p>
Supplementary material for research article "Quantifying the risk mitigation efficiency of changing silvicultural systems under storm risk throughout history"
<p>This public repository contains mainly datasets generated and analyzed during the current study, closely linked to the research article "Quantifying the risk mitigation efficiency of changing silvicultural systems under storm risk throughout history". Furthermore, the repository contains additional figures and deep dives on the methodological background the research article was built on.</p>
Dopant Engineering for Spiro-OMeTAD Hole-Transporting Materials towards Efficient Perovskite Solar Cells
<p>Optoelectronic, photovoltaic, and supplementary characterization data for “Dopant Engineering for Spiro-OMeTAD Hole-Transporting Materials towards Efficient Perovskite Solar Cells”, DOI:10.1002/adfm.202102124</p> <ul> <li>CV.zip: Data (cyclic voltammograms) described in Figures S3–S5 in Origin (*opj) file format.</li> <li>EPR.zip: Data (EPR spectra) described in Figure 5, Figure S2, and Table S2 in Origin (*opj) file format.</li> <li>Optical.zip: Data (UV-vis, PL, and TRPL spectra) described in Figure 4 and Table S1 in Origin (*opj) file format.</li> <li>PV.zip: Data (photovoltaic characteristics) described in the Figures 2–3, Table 1, Figure S1, and Figure S6 in Origin (*opj) file format.</li> </ul>
Dataset for "Effect of benzothiadiazole-based π-spacers on fine-tuning of optoelectronic properties of oligothiophene-core donor materials for efficient organic solar cells: a DFT study"
<p># Data and code for "Effect of benzothiadiazole-based π-spacers on fine-tuning of optoelectronic properties of oligothiophene-core donor materials for efficient organic solar cells: a DFT study."</p><p>## Contents</p><p>* data-{type}/*: reproducible data</p><p>* job.job : example slurm script</p><p> </p><p>## Description of the data</p><p>The data are organized in subdirectories *data-{type}/{system}/* corresponding to the considered molecules and simulation type:</p><p>* data-gs: Ground state calculations</p><p>* data-td: TD-DFT calculations</p><p>The contents of each subdirectory are:</p><p>* data-gs/{system}/structure.xyz: physical atomic structure</p><p>* data-td/{system}/td-dft/td_uvvis.txt: photoabsorption spectrum</p><p>The spectrum plots in the article correspond to the first (x values) and second (y values) columns of the spectrum files.</p><p> </p><p>## Reproduction of the data</p><p>The data were produced using Gaussian version g16.A.01</p><p>The calculation of the data of a system consists of the following steps:</p><p>1. Ground-state (gs) calculation:</p><p> * Prepare the input file for the gs by adjusting the parameters of the ground state calculations:</p><p> * "# opt b3lyp/6-311+g(d,p) scrf=(smd,solvent=chloroform) geom=connectivity empiricaldispersion=gd3bj out=wfn"</p><p> * out = wfn keyword to create a wfn file of the ground state that will be used for EDD and RDG investigations</p><p> * Submit the job.job file for the gs calculation as appropriate for the particular input file of the system</p><p> * The optimized sturctures are visualised using GaussView</p><p>2. Time-propagation calculation:</p><p> * Requires finished ground-state calculation</p><p> * Set up the TD-DFT calculation parameters as necessary:</p><p> * "# td=(nstates=6) wb97xd/6-311+g(d,p) scrf=(smd,solvent=chloroform) guess=read density out=wfn"</p><p> * density out = wfn keywords to create a wfn file of the excited state that will be used for EDD investigation</p><p> * Submit the job.job file for TD-DFT calculation as appropriate for the particular system</p><p> * The photoabsoption specta are visualised using GaussView</p><p>3. RDG calculation:</p><p> * Put the .wfn file of the gs calculation in the command window of the open source Multiwfn software and follow the sturcture in Section 3.23.1 in the manual</p><p>4. DOS calculation:</p><p> * The dos curves are plotted starting from the .fchk of the ground state geometry, select the atoms index corresponding to the diffrents subpart of the studied molecules (donor, acceptor, pi-spacer)</p><p> * Put the .fchk file of the gs calculation in the command window of the open source Multiwfn software and follow the structure in Section 4.10.1 in the manual</p><p> * the output generates .chk file which is transformed to .fchk file : formchk .chk .fch</p><p>5. TDM calculations:</p><p> * Requires finished ground-state calculation</p><p> * Set up the TD-DFT calculaton parameters as necessary</p><p> * "# td=(nstates=6) wb97xd/6-311+g(d,p) scrf=(smd,solvent=chloroform) guess=read density transition=1 iop(6/8=3) out=wfn"</p><p> * Put the .fchk file of the gs calculation in the command window of the open source Multiwfn software and follow the sturcture in Section 4.18.8 in the manual</p><p>6. EDD calculation:</p><p> * Edd plots are plotted based on the es.wfn and gs.wfn following Section 4.18.1 in the manual</p><p> </p>
Code and Data for "Routescore: Punching the Ticket to More Efficient Materials Development"
<p>Code and dataset for the manuscript "Routescore: Punching the Ticket to More Efficient Materials Development" by Seifrid, <em>et al.</em></p>
Overcoming contrast reversals in focused probe ptychography of thick materials: an optimal pipeline for efficiently determining local atomic structure in materials science
<p>Files concerning the publication "Overcoming contrast reversals in focused probe ptychography of thick materials: an optimal pipeline for efficiently determining local atomic structure in materials science"(arxiv:2205.13308 )</p>
Certifying Parity Reasoning Efficiently Using Pseudo-Boolean Proofs --- Supplemental Material
<p>Supplemental material for the extended version of the paper "Certifying Parity Reasoning Efficiently Using Pseudo-Boolean Proofs".</p>
Dataset of paper "Photoelectrochemical disinfection efficiency of WO3-based photoanodes: Development of multifunctional photoelectrocatalytic materials"
<p>Dataset of paper "Photoelectrochemical disinfection efficiency of WO3-based photoanodes: Development of multifunctional photoelectrocatalytic materials"</p> <ul> <li><span><span>Linear Sweep Voltammetry data for five photoanodes, a) nanostructured WO<sub>3</sub>/BiVO<sub>4</sub>, b) nanostructured WO<sub>3</sub>, c) flat WO<sub>3</sub>/BiVO<sub>4</sub>, d) flat WO<sub>3</sub>, and e) flat BiVO<sub>4</sub></span></span></li> <li><span><span>Kinetic constant for the partial oxidation of methanol to formaldehyde data for five photoanodes, nanostructured WO<sub>3</sub>/BiVO<sub>4</sub>, nanostructured WO<sub>3</sub>, flat WO<sub>3</sub>/BiVO<sub>4</sub>, flat WO<sub>3</sub>, and flat BiVO<sub>4</sub></span></span></li> <li><span><span>Kinetic constant for the inactivation of MS2 data for five photoanodes, nanostructured WO<sub>3</sub>/BiVO<sub>4</sub>, nanostructured WO<sub>3</sub>, flat WO<sub>3</sub>/BiVO<sub>4</sub>, flat WO<sub>3</sub>, and flat BiVO<sub>4</sub></span></span></li> <li><span><span>Kinetic constant for the inactivation of <em>E. coli</em> data for five photoanodes, nanostructured WO<sub>3</sub>/BiVO<sub>4</sub>, nanostructured WO<sub>3</sub>, flat WO<sub>3</sub>/BiVO<sub>4</sub>, flat WO<sub>3</sub>, and flat BiVO<sub>4</sub></span></span></li> </ul>
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>
Applicability of new sustainable and efficient alginate-based composites for critical raw materials recovery: General composites fabrication optimization and adsorption performance evaluation
<p>This dataset contains the raw data for the publication "Applicability of new sustainable and efficient alginate-based composites for critical raw materials recovery: General composites fabrication optimization and adsorption performance evaluation" by Fila et al, published in Chemical Engineering Journal. The upload includes raw data of physicochemical characterizations of calcium alginate and its composites, i.e. alginate-biochar and alginate-clinoptilolite, including BET, SEM, TG, XPS and XRD analyses. </p>
Material Efficiency Policy and Nationally Determined Contributions
<p>This is a table containing the data used to create Table 4, Number of G20 countries that include energy or material efficiency commitments in 2nd round of National Determined Contributions in the paper, Policy for material efficiency: Enabling new climate change mitigation strategies by Lifset et al.</p>
Supplementary Material for "Efficient biocatalytic synthesis of dihalogenated purine nucleoside analogues applying thermodynamic calculations"
<p>This is the supplementary material for our publication "Efficient biocatalytic synthesis of dihalogenated purine nucleoside analogues applying thermodynamic calculations". We wish to thank all authors for their contribution and have uploaded this dataset in their name.</p> <p>We apologize that these files are not publicly available. Please contact Dr. Anke Kurreck for access to raw and analyzed data.</p> <p>Naturally, this work builds on previous publications. Specifically, we have borrowed data from our recent work on the thermodynamic control of nucleoside phosphorolysis (10.1002/adsc.201901230; the full dataset can be found at 10.5281/zenodo.3568858) and employed equations and methodology described in our piece on yield optimization of nucleoside transgylcosylations (10.1002/cbic.201900740; the external supplementary information can be accessed at 10.5281/zenodo.3565561 and 10.5281/zenodo.3522588).</p>
Supplementary Material for the paper "Efficient Strategies for CEGAR-based Model Checking"
<p>Supplementary material for the paper "Efficient Strategies for CEGAR-based Model Checking" by Akos Hajdu and Zoltan Micskei. The material includes a detailed report (report.html) and all artifacts to replicate our measurements and analysis (artifact.tar.gz).</p> <p><em>Version 1.2.0 corresponds to the final, published paper. This version (1.2.1) is a minor update that added two new plots.</em></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.