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194 results for “solvent”
Identifying routes for transferring spin polarization from parahydrogen to protic solvents
<p>NMR data and MATLAB codes supporting the publication '<strong>Identifying routes for transferring spin polarization from parahydrogen to protic solvents'</strong>. The NMR_data file enables to reconstruct the PTF profiles, the solvent lifetime calculations, and the molar polarization values for each solution. The SABRE_2D data file contains the 2D-OPSY-COSY sequence for elucidation of the catalyst structure 2a-d. The Codes folder contains the MATLAB codes for construction of the PTF profiles and the solvent lifetime plots. The PDF file contains the specific details on which data is used for which figure. </p> <p> </p> <p> </p>
Molecular dynamics simulation trajectories for the GB99dms implicit solvent force field
<p>Molecular dynamics simulation trajectories used in training and validating the GB99dms implicit solvent protein force field. See the paper:</p> <ul> <li>Greener JG. Differentiable simulation to develop molecular dynamics force fields for disordered proteins, <a href="https://doi.org/10.1039/D3SC05230C" target="_blank" rel="noopener">Chemical Science</a> 15, 4897-4909 (2024)</li> </ul> <p>For more information, including structure files for these trajectories, see https://github.com/greener-group/GB99dms.</p>
Data sets and machine learning models for: Machine learning from quantum chemistry to predict experimental solvent effects on reaction rates
<p>The datasets and final machine learning model files for the manuscript "Machine learning from quantum chemistry to predict experimental solvent effects on reaction rates". Citation should refer directly to the manuscript:</p> <ul> <li>Chung, Y.; Green, W. H. Machine learning from quantum chemistry to predict experimental solvent effects on reaction rates. <em>Chemical Science </em><strong>2024,</strong> doi: <a href="https://doi.org/10.1039/D3SC05353A">10.1039/D3SC05353A</a></li> </ul> <p>To use the machine learning models, please refer to the sample files and instructions on <a href="https://github.com/yunsiechung/chemprop/tree/RxnSolvKSE_ML">https://github.com/yunsiechung/chemprop/tree/RxnSolvKSE_ML</a>. </p> <p>Detailed information can be found in README.md file.</p> <p><br><strong>Details on the files</strong></p> <p>In the pretraining and finetuning set csv files, each column represents:</p> <ol> <li>rxn_smiles: atom-mapped reaction SMILES</li> <li>solvent_smiles: solvent SMILES</li> <li>ddGsolv: solvation free energy of activation of a reaction-solvent pair at 298K in kcal/mol (main prediction target)</li> <li>ddHsolv: solvation enthalpy of activation of a reaction-solvent pair at 298K in kcal/mol (main prediction target)</li> <li>dGsolv_reactant: solvation free energy of reactant(s) at 298K in kcal/mol (additional feature)</li> <li>dGsolv_product: solvation free energy of product(s) at 298K in kcal/mol (additional feature)</li> <li>dHsolv_reactant: solvation enthalpy of reactant(s) at 298K in kcal/mol (additional feature)</li> <li>dHsolv_product: solvation enthalpy of product(s) at 298K in kcal/mol (additional feature)</li> </ol> <p><strong>Data sets under 'RxnSolvKSE_dataset_v1.1.zip'</strong></p> <ul> <li>pretraining_set: contains the dataset used for pre-training <ul> <li>all_data: contains all calculated data <ul> <li>pretraining_rxn_solvent_ddGsolv_ddHsolv_with_features_all.csv: contains both main prediction targets and additional feature for reaction-solvent pairs</li> <li>pretraining_solvent_info.csv: list of all solvents</li> <li>pretraining_unique_rxn.csv: list of all reactions, both forward and reverse directions</li> </ul> </li> <li>chosen_500k_data: contains the chosen 500k data <ul> <li>pretraining_rxn_solvent_ddGsolv_ddHsolv_500k.csv: contains main prediction targets for reaction-solvent pairs</li> <li>pretraining_features_react_prod_dGsolv_dHsolv_500k.csv: contains additional features for reaction-solvent pairs</li> <li>train_test_split: contains the 5-fold random split training and test sets.</li> </ul> </li> </ul> </li> <li>finetuning_set: contains the dataset used for fine-tuning <ul> <li>all_data: contains all calculated data <ul> <li>finetuning_rxn_solvent_ddGsolv_ddHsolv_with_features_all.csv: constains both main prediction targets and additional features for reaction-solvent pairs. The rxn_key column indicates whether the reaction is bimolecular hydrogen abstraction (bihabs), unimolecular hydrogen migration (intrahabs), or radical addition to a multiple bond (raddition). The 'fwd' and 'rev' each indicate forward and reverse reactions.</li> <li>finetuning_solvent_info.csv: list of all solvents</li> <li>finetuning_unique_rxn.csv: list of all reactions, both forward and reverse directions</li> </ul> </li> <li>chosen_data: contains chosen data <ul> <li>finetuning_rxn_solvent_ddGsolv_ddHsolv_chosen.csv: contains main prediction targets for reaction-solvent pairs</li> <li>finetuning_features_react_prod_dGsolv_dHsolv_chosen.csv: contains additional features for reaction-solvent pairs</li> </ul> </li> </ul> </li> <li>experimental_set: contains the experimental rate constant data used to test the model. The original experimental data can be found at <a href="../record/7747557">https://zenodo.org/record/7747557</a>. <ul> <li> expt_rxn_atom_mapped_smiles.csv: contains the atom-mapped reaction SMILES used for the experimental data.</li> <li>expt_data_collected.xlsx: contains all experimental data and detailed information</li> <li>expt_rxn_solv_smiles_with_features_all.csv: contains the computed additional features for the experimental reaction-solvent pairs.</li> </ul> </li> </ul> <p><strong>Machine learning model files under 'RxnSolvKSE_ML_model_files.zip'</strong></p> <ul> <li>Contains the Chemprop machine learning model files for predicting ddGsolv and ddHsolv for a reaction-solvent pair. It takes atom-mapped reaction SMILES and solvent SMILES as inputs.</li> <li>To use these ML models, please refer to the sample files and instructions on <a href="https://github.com/yunsiechung/chemprop/tree/RxnSolvKSE_ML">https://github.com/yunsiechung/chemprop/tree/RxnSolvKSE_ML</a></li> </ul>
Efficacy and Safety Study of Fibrin Sealant With 4 IU/mL Thrombin, Vapor Heated, Solvent Detergent Treated (FS VH S/D 4) in Face-Lift Procedures
ClinicalTrials.gov study NCT00708071. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Programmable assembly of mechanically robust and functional polymer–spore biocomposites in organic solvent
Open the record for dataset details and reuse information.
Directing selective solvent presentations at the electrochemical interfaces to enable initially anode-free sodium metal batteries
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A green extraction design for enhancing flavonoid compounds from the Ixora javanica flowers using a deep eutectic solvent
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Deep eutectic solvent-based emulsification liquid-liquid microextraction coupled with HPLC-UV for the analysis of phenoxy acid herbicides in paddy field water samples
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Replica exchange molecular dynamics simulation data of designed β-hairpins (implicit solvent, AMBER ff99SB-ildn-nmr)
<p>Raw REMD simulation data (protein only) of designed β-hairpins. AMBER ff99SB-ildn-nmr and implicit solvent model is used. More details can be found in this paper: </p> <p>Yunhui Ge, Brandon Kier, Niels H. Andersen and Vincent A. Voelz. <a href="https://pubs.acs.org/doi/10.1021/acs.jcim.7b00132"><em>Computational and experimental evaluation of designed beta-cap hairpins using molecular simulations and kinetic network models.</em></a> J. Chem. Inf. Model., 2017, 57 (7), pp 1609–1620</p>
Multiple solvent signal presaturation and decoupling artifact removal in 13C{1H} NMR
<p>Supplementary material to article "Multiple solvent signal presaturation and decoupling artifact removal in 13C{1H} NMR"</p>
A catalytic process for the production of the polar aprotic solvent diformylxylose using supported heteropoly acids
<p><strong><span>Abstract: </span></strong><span>Diformylxylose (DFX) has been proven to be a good green alternative for fossil based polar aprotic solvents. It possesses similar performance to commercial Solvents (DMSO, DMF, NMP) in hydrogenation or cross coupling reactions, and due to its chemical properties DFX can be characterized as a green solvent. In this paper we show how DFX can be produced in a catalytical way by using immobilized heteropoly acids as solid catalysts. We demonstrate the catalyst development and compare it with commercially available solid acid catalysts. Further we applied the new designed catalyst in the acetalization of xylose with paraformaldehyde where we achieved DFX yields of up to 70%. The catalyst can be easily separated from the reaction by filtration and shows good reusability over 4 runs, with constant activity and high DFX yields.</span></p>
Acid dissociation constants in selected dipolar non-hydrogen-bond-donor solvents
<p>This compilation includes more than <strong>9000</strong> p<em>K</em><sub>a</sub><strong> </strong>values determined in seven dipolar non-hydrogen-bond-donor solvents (dimethyl sulfoxide, acetonitrile, <em>N,N</em>-dimethylformamide, pyridine, acetone, propylene carbonate, tetrahydrofuran) for close to <strong>5000</strong> acids collected from around <strong>800</strong> original works published during the last sixty years. The data have been critically evaluated on the basis of defined quality criteria and depending on situation, kept as they were originally published, marked as doubtful/unreliable (2700 values) or corrected (around 2400 values).</p> <p>To enable automated processing and mining, the data are presented as an XLSX file, together with structural codes, compound class qualifiers and comments.</p> <p><strong>All citations should refer to the manuscript: </strong></p> <blockquote> <p>Ivo Leito, Ivari Kaljurand, Mare Piirsalu, Sofja Tshepelevitsh, Jonathan Wenyuan Zheng, Martí Rosés, Jean-François Gal. Acid Dissociation Constants in Selected Dipolar non-Hydrogen-Bond-Donor Solvents. <em>Pure Appl. Chem</em>. <strong>2025 </strong>https://doi.org/10.1515/pac-2024-0276</p> </blockquote>
Deep Eutectic Solvents: The Thruster De-cloggers
<p>• Led proposal development for utilizing Deep Eutectic Solvents (DES) to address fuel line obstructions<br>in rocket propulsion systems.</p> <p>• Conducted extensive analysis to optimize DES mixture for non-toxicity, low melting and freezing<br>points, and compatibility with liquid fuel, aiming to enhance thruster performance.</p> <p>• Designed proposal framework including small-scale simulations to project effectiveness of DES in<br>mitigating fuel obstruction and improving propellant flow into thrusters.</p> <p>• Proposed thorough testing plan for modified liquid propellant with DES to reduce clogging risks and<br>enhance mission success.</p>
Physicochemical properties (viscosity, electrical conductivity) of betaine based deep eutectic solvents
<p>New deep eutectic solvents (DES) based on betaine as a hydrogen bond acceptor and urea, lactic acid, glycerol, 1,2-propanediol, and xylitol as hydrogen bond donors have been prepared. Viscosity and electrical conductivity were investigated. </p>
DMSO-TMP-ACN-H2O Co-solvent Bayesian Optimization with Reproducibility and Gas Analysis via OEMS Data
<p>The zipped files contain the data collected and used for the Bayesian optimization (BO) of Coulombic efficiency (and discharge capacity) from the exploration of 4 co-solvents (dimethyl sulfoxide, trimethyl phosphate, acetonitrile, and water) and 2 salts (lithium perchlorate and LiTFSI).</p> <p>The cycling data and the BO clients are contained in BayesianOptimization.zip.</p> <p>The gas analysis data via online electrochemical mass spectrometry (OEMS) are contained in OEMS_data.zip.</p> <p>The cycling data of select repeats from the BO are contained in Reproducibility_data.zip.</p> <p>These are the raw datafiles. Preprocessing and analysis is not included.</p>
Dataset of "Influence of the used solvent on the efficiency of the extraction of cathode materials for direct recycling"
<p>Lithium-ion batteries are playing an increasingly significant role in modern mobility and energy storage. Their increasing quantity and the limited amount of material resources available on earth will place great demands on the most efficient recycling in the future. In this paper, we focused on the first stage of the so-called direct recycling, i.e. the extraction of electrode material from the current collector, using six types of solvents in combination with three types of the most commonly used cathode materials in lithium-ion batteries. Three selected lithium-ion batteries were aged for 500 cycles and disassembled for the next step - cathode material extraction. The extraction was carried out by a method that is least energy intensive, cost effective and preserves the structural stability of the cathode materials after extraction. Of the used solvents, Dimethylacetamide (DMAC) was found to be the most effective solvent for the extraction. At the same time, Dimethylsulfoxide (DMSO) was the most effective solvent in terms of solvents considered to be biologically harmless. The study also included an analysis of the degradation of the cathode materials after their extraction from the current collector. It was found that all materials remained stable even one month after the extraction.</p>
Multivariate sodalite zeolitic imidazolate frameworks: a direct solvent-free synthesis
<p>Relevant data for publication with DOI: <a title="Link to landing page via DOI" href="https://doi.org/10.1039/D1SC04779E">10.1039/D1SC04779E</a></p>
Experimental and computational data of "Nanoconfinement-induced electrochemical ion-solvent cointercalation in pillared titanate host materials"
<p>Electrochemical ion-solvent cointercalation reactions are an avenue to reach improved kinetics compared to the corresponding intercalation of desolvated ions. Here, we demonstrate the impact of different structural pillar molecules on the electrochemical Li<sup>+</sup> intercalation mechanism in expanded hydrogen titanate (HTO) electrode materials. We show that interlayer-expansion of HTO with organic pillars can enable cointercalation reactions. Their electrochemical reversibility is drastically improved when non-cross-linking pillars are employed that expand and separate the host material’s individual layers, underlining the impact of the electrochemo-mechanics of the nanoconfined interlayer space. This pillared HTO structure results in an increased Li<sup>+</sup> storage capacity and reversibility compared to pristine HTO. We derive structural models of the pillared HTO host materials based on combined experiments and theoretical calculations, and employ electrochemical operando experiments to unambiguously demonstrate the nanoconfinement-induced cointercalation mechanism in pillared HTO electrode materials. The work demonstrates the potential of nanoconfined pillar molecules to modify host materials and enable highly reversible cointercalation reactions with improved capacity and kinetics.</p>
SolProp Dataset for: Predicting Solubility Limits of Organic Solutes for a Wide Range of Solvents and Temperatures
<p>The datasets and supplementary materials for the manuscript "Predicting Solubility Limits of Organic Solutes<br> for a Wide Range of Solvents and Temperatures" by Florence H. Vermeire, Yunsie Chung, and William H. Green. <strong>Citations should refer directly to the manuscript (</strong><a href="https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdoi.org%2F10.1021%2Fjacs.2c01768&data=05%7C01%7CFlorence.Vermeire%40ugent.be%7C31f767a36cf547f91aef08da48bb3fcd%7Cd7811cdeecef496c8f91a1786241b99c%7C1%7C0%7C637902266316761522%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=1CKXFXvTtqOGDSBpSw8AeKCt25%2B27CC%2FNU7Ery276D0%3D&reserved=0">https://doi.org/10.1021/jacs.2c01768</a><strong>).</strong></p> <p>The preprint version of of the manuscript is available at: https://doi.org/10.26434/chemrxiv-2022-92hl1</p> <p>The SolProp data collection contains thermodynamic properties related to the dissolution of organic solutes in organic solvents and water. Thermodynamic properties included are solvation free energy at 298K (dGsolv), solvation enthalpy at 298K (dHsolv), Abraham Solute Parameters (E,S,A,B,L), aqueous solid solubility at 298K (logSaq), and solid solubility in organic solvents at various temperatures (logS). The dataset is limited to neutral solutes and solvents.</p> <p>Detailed information can be found in the "ReadMe.txt" file.</p>
Supporting molecular simulations data for "A universal co-solvent dilution strategy enables facile and cost-effective fabrication of perovskite photovoltaics"
<p>Supplementary data for "A universal co-solvent dilution strategy enables facile and cost-effective fabrication of perovskite photovoltaics"</p>
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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.