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21 results for “solvent effects”
Aquamarine: Quantum-Mechanical Exploration of Conformers and Solvent Effects in Large Drug-like Molecules
<p>Open challenges in computational drug design include the understanding and accurate description of solvent effects as well as collective dispersion interactions for realistic drug-like molecules. Both interactions profoundly influence the conformational stability of drug molecules and, consequently, the determination of other important quantum-mechanical (QM) observables. In this context, we here introduce the Aquamarine (AQM) dataset -- an extensive QM dataset that contains the structural and electronic information -- of 59,786 low-and high-energy conformers of 1,653 molecules containing up to 54 non-hydrogen atoms (including C, N, O, F, P, S and Cl). To gain insights into the solvent effects, we have carried out QM calculations of structures and properties in gas phase and in an aqueous solution modeled with implicit solvent. AQM contains over 40 global (molecular) and local (atom-in-a-molecule) physicochemical properties (including ground-state and response properties) per molecular structure computed at the tightly converged PBE0+MBD level of theory for gas-phase molecules, whereas PBE0+MBD supplemented with the modified Poisson-Boltzmann (MPB) model of water was used for solvated molecules. By treating both molecule-solvent and dispersion interactions, the AQM dataset can help understand the impact of both interactions in structure-property and property-property relationships of realistic drug-like molecules. Therefore, we propose the AQM dataset as a benchmark for current state-of-the-art machine learning methods for property prediction as well as for the <em>de novo</em> generation of large and flexible (solvated) molecules with pharmaceutical and biological relevance.</p>
The effect of solvent on convectively-driven silica particle assembly: Decoupling surface tension,viscosity, and evaporation rate
<p>Dataset associated with 'The effect of solvent on convectively-driven silica particle assembly: Decoupling surface tension, viscosity, and evaporation rate’.</p> <p>The data is based on the figures below, published in the linked article (see the doi).</p> <p><strong>- Figure 1. S</strong>egmented and raw images of dip-coated films. <strong>(images, .TIF)</strong></p> <p><strong>- Figure 2. </strong>Calculated surface coverages <strong>(data, .csv)</strong></p> <p><strong>- Figure 3. </strong>Rheology on SiO<sub>2</sub>-iPrOH-Glycerol mixtures & SEM micrographs of particle films. <strong>(data, .csv; images, .TIF)</strong></p> <p><strong>- Figure 4. </strong>SEM micrographs of silica helices films. <strong>(images, .TIF)</strong></p> <p><strong>- Figure S1. </strong>Measured evaporated masses of each solvent as a function of time. <strong>(data, .csv)</strong></p> <p><strong>- Figure S2. </strong>TEM micrographs of SiO2 seeds and measured particle diameters. <strong>(data, .csv; images, .TIF)</strong></p> <p><strong>- Figure S3. </strong>TEM micrographs of SiO particles and measured particle diameters. <strong>(data, .csv; images, .TIF)</strong></p> <p><strong>- Figure S4. </strong>Calculated solvent fractions as a function of time. <strong>(data, .csv)</strong></p> <p><strong>- Figure S5.</strong> Rheology of i-PrOH-glycerol mixtures.<strong> (data, .csv)</strong></p>
Data for: Solvent effects in hyperpolarization of 15N nuclei in [15N3]metronidazole and [15N3]nimorazole antibiotics via SABRE-SHEATH
<p>Raw 15N and 1H NMR spectra for the article which is under revision at the time posting this dataset.</p> <p>These results are also available as preprint at https://doi.org/10.26434/chemrxiv-2024-6pg8b</p> <p>15N NMR spectra were acquired using SpinSolve Expert Software (Magritek)</p> <p>1H NMR spectra were acquired using TopSpin (Bruker)</p>
FTIR Microscopy for Direct Observation of Conformational Changes on Immobilized ω-Transaminase: Effect of Water Activity and Organic Solvent on Biocatalyst Performance
<p>Enzyme immobilization is a key strategy to expand the scope of enzyme applications and to enable the recycling of biocatalysts, resulting in greener and more cost-efficient processes. The full exploitation of the technology advantages is strictly connected to the optimal selection of the carriers and the rational development of the immobilization protocol. The present study achieved such objectives by investigating the activity of a ω-transaminase in organic solvent (toluene) upon immobilization on commercially controlled porosity glass carriers (EziG™) with diverse porosity and surface functionalization. In addition to more conventional wet-chemistry approaches and confocal microscopy, infrared microspectroscopy and imaging were exploited to highlight the enzyme distribution in a label-free manner and provide details on the immobilized enzyme's conformation with respect to the native form. Contrary to what could be expected, the highest activity of the enzyme in organic solvent was achieved for the immobilization protocol on the most hydrophilic support that more severely affects the enzyme secondary structure, promoting a beta-sheet rich folding. Experimental data show that values of water activity above 0.90 in the reaction system had a positive effect on the efficiency of the transaminase reaction. The present study represents the first example of rational development of immobilization protocols relying on direct observation of the enzyme conformation upon immobilization, shedding light on the mutual interaction between the diverse process parameters and the carrier properties.</p>
Figure 1. Standard curve for gallic acid y in Effect of extraction solvent system on the antimicrobial, antioxidant and total polyphenol content of the bark of Pistacia chinensis
Figure 1. Standard curve for gallic acid y = absorbance, x = concentration of Gallic Acid, R2 = correlation coefficient.
Microfluidic solvent extraction of poly(vinyl alcohol) droplets: effect of polymer structure on particle and capsule formation
<p>Raw data from the majority of figures of our 2018 Soft Matter Paper:</p> <p>Selected datasets from figures are excluded, owing to them being transformations of the raw data provided in the same figure.</p> <p> </p>
Examining Solvent Effects on the Ultrafast Dynamics of Catechol
<p>The underlying data for the paper "Examining Solvent Effects on the Ultrafast Dynamics of Catechol". This includes geometry optimizations and frequency calculations of catechol in implicit acetonitrile solvent, in an explicit solvent shell, and with two solvent molecules strategically placed. Further to this, it also contains calculated S0 and S1 energies of catechol with varied COH bond angles.</p> <p>Also included is TEAS data for catechol in acetonitrile at 5 mM and 75 mM concentrations, at pump wavelength 267 nm. All results are in delta mOD, all timescales are in ps, and all wavelengths are in nm. The scans labelled "short_scan" only have 4 time delays whereas those labelled "large_scan" contained enough to assemble a kinetic trace. The large scans only include data averaged around 450 nm probe wavelength. The scan labelled "normalised" represents six individual sets of scans spliced together and normalised via consecutive peaks. For more information on this see main body of work. </p>
QM and COSMO-RS calculation results and experimental data for: Computing kinetic solvent effects and liquid phase rate constants using quantum chemistry and COSMO-RS methods
<p>This dataset contains the calculation results and the experimental data compiled from literature for the manuscript "Computing kinetic solvent effects and liquid phase rate constants using quantum chemistry and COSMO-RS methods". Citations should refer directly to the manuscript (Chung, Y.; Green, W. H. Computing kinetic solvent effects and liquid phase rate constants using quantum chemistry and COSMO-RS methods. <em>J. Phys. Chem. A</em> <strong>2023</strong>, 127, 27, 5637–5651. doi: <a href="https://doi.org/10.1021/acs.jpca.3c01825">10.1021/acs.jpca.3c01825</a>).This includes:</p> <ul> <li>expt_data_collected.xlsx: Experimental rate constants of various liquid phase reactions collected from various sources</li> <li>For each levels of theory used for gas-phase quantum chemical calculations and COSMO-RS calculations: <ul> <li>Gas-phase quantum chemical calculation results (output log files) and computed gas phase rate constants</li> <li>COSMO-RS calculation results and computed solvation free energies</li> <li>Predicted liquid phase rate constants and relative rate constants </li> </ul> </li> </ul> <p> </p>
Raw Data to "Computational Investigation of Explicit Solvent Effects and Specific Interactions of Hydroxypyrene Photoacids in Acetone, DMSO, and Water"
<p>This data is a supplement to the publication entitled "Computational Investigation of Explicit Solvent Effects and Specific Interactions of Hydroxypyrene Photoacids in Acetone, DMSO, and Water" in <em>Physical Chemistry Chemical Physics</em> (DOI: 10.1039/D3CP00800B). It contains the structures (as '.xyz' files) and HF/DFT energies from the quantum chemical (QC) calculations using TURBOMOLE (version 7.6).</p> <p>Additional information on the file structure is given in the README file.</p>
Supporting Information for "Solvent modulated specific ion effects: PNIPAM brushes in non-aqueous electrolytes"
<p>This deposition contains the data and analysis (Jupyter notebooks) detailed in <em>Solvent modulated specific ion effects: PNIPAM brushes in non-aqueous electrolytes</em>. All Jupyter notebooks have also been converted into PDF files for ease of viewing.</p> <p> </p> <p>All data and code (notebooks) required to reproduce the analysis can be found within the “supporting_data_analysis.zip” archive. This archive contains three sub-directories:</p> <ul> <li>Ellipsometry <ul> <li>Data directories containing all raw ellipsometry data for the ‘thick’ and ‘thin’ PNIPAM brushes.</li> <li>Spatial maps of the polymer brush used for spectroscopic ellipsometry data analysis: “surface_map.png”.</li> <li>“refellips_Spectroscopic_SL.ipynb” notebook to reproduce the analysis of the hydrated (solid-liquid) ‘thin’ polymer brush. Relevant plotting tools can be found in the <a href="https://github.com/refnx/refellips">refellips repo</a>.</li> <li>“Ellipsometry_logistical_fitting.ipynb” notebook and “water_results.csv” file for the demonstration of the extraction of a thermotransition temperature from an ellipsometry dataset. </li> <li>“refellips_Spectroscopic_SL_VFP.ipynb” notebook to reproduce the analysis of the hydrated (solid-liquid) ‘thick’ polymer brush.</li> </ul> </li> <li>Neutron_reflectometry <ul> <li>Data directory containing all relevant reduced reflectivity profiles from the Platypus reflectometer at ANSTO.</li> <li>“refnx_dry.ipynb” and “refnx_solvent.ipynb” notebooks required to reproduce the analysis pertaining to a dry polymer brush and a solvated brush, respectively.</li> <li>Additional code required to model the hydrated polymer brush and various plotting tools can be in the <a href="https://github.com/igresh/refnxtoolbox">refnxtoolbox repo</a>.</li> </ul> </li> </ul>
Supporting data for "Charging Poly(methyl Methacrylate) Latexes in Nonpolar Solvents: Effect of Particle Concentration" (Langmuir, doi:10.1021/acs.langmuir.7b02257)
<p>Raw data:</p> <p>- Electrophoretic mobilities as a function of particle concentration (Volume fraction phi [unitless, volume per volume], mu [m^2/(V s)], error mu [m^2/(V s]).</p> <p>- Small-angle neutron scattering curves (Q [1/Å], I(Q) [1/cm], error I(Q) [1/cm]).</p> <p>- Small-angle X-ray scattering curves (Q [1/Å], I(Q) [unitless, instrument intensity divided by fit scale]).</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>
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>
The Effect of Solvent Usage on Post-treatment Pain
ClinicalTrials.gov study NCT03756363. IPD Sharing: NO. Countries: 1. Publications: 1.
Data from: Effect of reaction solvent on hydroxyapatite synthesis in sol-gel process
Synthesis of hydroxyapatite (HA) through sol–gel process in different solvent systems is reported. Calcium nitrate tetrahydrate (CNTH) and diammonium hydrogen phosphate (DAHP) were used as calcium and phosphorus precursors, respectively. Three different synthesis reactions were carried out by changing the solvent media, while keeping all other process parameters constant. A measure of 0.5 M aqueous DAHP solution was used in all reactions while CNTH was dissolved in distilled water, tetrahydrofuran (THF) and N,N-dimethylformamide (DMF) at a concentration of 0.5 M. Ammonia solution (28–30%) was used to maintain the pH of the reaction mixtures in the 10–12 range. All reactions were carried out at 40 ± 2°C for 4 h. Upon completion of the reactions, products were filtered, washed and calcined at 500°C for 2 h. It was clearly demonstrated through various techniques that the dielectric constant and polarity of the solvent mixture strongly influence the chemical structure and morphological properties of calcium phosphate synthesized. Water-based reaction medium, with highest dielectric constant, mainly produced β-calcium pyrophosphate (β-CPF) with a minor amount of HA. DMF/water system yielded HA as the major phase with a very minor amount of β-CPF. THF/water solvent system with the lowest dielectric constant resulted in the formation of pure HA.
Data from: Effect of reaction solvent on hydroxyapatite synthesis in sol-gel process
Open the record for dataset details and reuse information.
Calcium effect to the Clostridium beijerinckii NCIMB 8052 degenerate strain for the solvent producing recovery
GEO Series GSE81808. Clostridium beijerinckii NCIMB 8052. 6 samples. Type: Expression profiling by array.
A Clinical Trial to Study the Effects of Nanoparticle Based Paclitaxel Drug, Which Does Not Contain the Solvent Cremophor, in Advanced Breast Cancer
ClinicalTrials.gov study NCT00915369. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Compare the Effectiveness of Sports Drink and Water as a Solvent in Polyethylene Glycol for Colonoscopy
ClinicalTrials.gov study NCT05542810. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Effects of thiol substitution in low-transition-temperature mixtures (LTTMs) as solvents for metal oxides
<p>This study deals with an investigation of how substitution of an alcohol group by a thiol group in mixtures of choline chloride with a series of bio-sourceable molecules affects the physico-chemical properties of the mixtures and their ability to dissolve metal oxides. All of the thiol mixtures studied showed a higher affinity and selectivity for late transition metals and the physical properties of the mixtures were improved compared to their alcohol analogues (<em>i.e.</em> lower viscosity, wider liquid range). The metal solubility was assessed<em> via </em>determinination of the final concentration of the metal oxides dissolved in thiol mixtures<em> via </em>inductively coupled plasma optical emission spectroscopy (ICP-OES). The thiol function selectively improved the solubilities of the late transition metal oxides (<em>i.e.</em> copper and zinc), which are valuable metals often present as residue in industrial waste. The solubility of iron oxides was much lower than that of the valuable metals, which is a significant benefit in industrial applications. The different solubilization behaviour of metal oxides in the thiol and alcohol mixtures was further investigated<em> via </em>UV-vis absorption and infrared spectroscopy. This study allowed to assess the potential of these low-temperature-transition mixtures for the selective recovery of metals.</p>
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