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236 results for “surfactants”
S18 | TSCASURF | TSCA Surfactants
<p>This is the collection associated with list S18 TSCASURF on the NORMAN Suspect List Exchange.</p> <p><a href="https://www.norman-network.com/nds/SLE/">https://www.norman-network.com/nds/SLE/</a></p> <p>S18 | TSCASURF | <strong>TSCA Surfactants</strong></p> <p>Surfactant information compiled from TSCA by James Little while at Eastman Chemical. More information <a href="https://littlemsandsailing.wordpress.com/2011/05/01/identification-of-surfactants-in-commercial-products-by-mass-spectrometry/">here</a>.</p> <p>Nov 14 update: added CSVs from major sheets of interest. 27 Jun. 2025 update: added merged CSV. </p>
Bolaform Surfactant-Induced Au Nanoparticle Assemblies for Reliable Solution-Based Surface-Enhanced Raman Scattering Detection
<p>Related publication: García-Lojo, D; Méndez-Merino, D; Pérez-Juste, I; Acuña, A; García-Río, L; Rodríguez-Patón, A; Pastoriza-Santos, I; Pérez-Juste, J. Bolaform surfactant-induced Au nanoparticle assemblies for reliable solution-based SERS detection. Adv.Mater. Technol. 2022, 2101726. <a href="https://doi.org/10.1002/admt.202101726">https://doi.org/10.1002/admt.202101726</a></p> <p> </p> <p> </p> <p>Abstract:</p> <p>Solution-based surface-enhanced Raman scattering (SERS) detection typically involves the aggregation of citrate-stabilized Au nanoparticles into colloidal assemblies. Although this sensing methodology offers excellent prospects for sensitivity, portability, and speed, it is still challenging to control the assembly process by a salting-out effect, which affects the reproducibility of the assemblies and, therefore, the reliability of the analysis. This work presents an alternative approach that uses a bolaform surfactant, B<sub>20</sub>, to induce the plasmonic assembly. The decrease of the surface charge and the bridging effect, both promoted by the adsorption of B<sub>20</sub>, are hypothesized as the key points governing the assembly. Furthermore, molecular dynamic simulations supported the bridging effect of the B<sub>20</sub> by showing the preferential bridging of surfactant monomers between two adjacent Au(111) slabs. The colloidal assemblies showed excellent SERS capabilities towards the rapid, on-site detection and quantification of beta-blockers and analgesic drugs in the nanomolar regime, with a portable Raman device. Interestingly, the application of state-of-the-art convolutional neural networks, such as ResNet, allows a 100% accuracy in classifying the concentration of different binary mixtures. Finally, the colloidal approach was successfully implemented in a millifluidic chip allowing the automation of the whole process, as well as improving the performance of the sensor in terms of speed, reliability, and reusability without affecting its sensitivity.</p>
S7 | EAWAGSURF | Eawag Surfactants Suspect List
<p>This is the collection associated with list S7 EAWAGSURF on the NORMAN Suspect List Exchange.</p> <p><a href="https://www.norman-network.com/nds/SLE/">https://www.norman-network.com/nds/SLE/</a></p> <p>Updated 21/11/2019 to contain representative explicit structures for species observed in the 2014 study. Note that for some species multiple isomers are possible; only one representative has been added per formula. Structures created using RChemMass (<a href="https://github.com/schymane/RChemMass/">https://github.com/schymane/RChemMass/</a>)</p> <p>S7: EAWAGSURF: <strong>Eawag Surfactants Suspect List </strong></p> <p>Suspect formulas: <a href="https://www.norman-network.com/sites/default/files/files/suspectListExchange/Surfactant_Suspects_Schymanski_etal_2014.csv">CSV</a>, <a href="https://www.norman-network.com/sites/default/files/files/suspectListExchange/Surfactant_Suspects_Schymanski_etal_2014.xlsx">XLSX</a></p> <p>CompTox <a href="https://comptox.epa.gov/dashboard/chemical_lists/eawagsurf">EAWAGSURF List</a></p> <p>Schymanski <em>et al</em>. 2014. DOI: <a href="http://pubs.acs.org/doi/abs/10.1021/es4044374">10.1021/es4044374</a></p> <p> </p>
Output data for "Comparison of six approaches to predicting droplet activation of surface active aerosol. Part 2: strong surfactants" by Vepsäläinen et al. (2023)
<p>Output data of the models used in "Comparison of six approaches to predicting droplet activation of surface active aerosol. Part 2: strong surfactants" by Vepsäläinen et al. (2023).</p><p>Output data is included for 50 nm particles containing sodium myristate (c14na) and myristic acid (myristica), mixed with NaCl (nacl) in different surfactant mass fractions. Data about the critical points is also included for particles containing sodium myristate for particle size range 50-200 nm.</p><p>Plotters have been provided for the following:</p><ul><li>Part2_plotter_50_200_nm: Plots the critical supersaturations, diameters, and the relative change in cloud droplet concentrations for dry particles with 50-200 nm diameters containing c14na</li><li>Part2_plotter_50nm: Plots the Köhler curves, surface tension and partitioning factors for 50 nm particles containing c14na</li><li>Part2_plotter_50nm_myristica: Plots the Köhler curves and surface tensions for 50 nm particles containing myristica and also plots c14na for comparison (separate output files for the compounds and c14na data here is different than for the Part2_plotter_50nm plotter)</li></ul><p>Each plotter needs the user to set the location where the output files are stored. </p><p>In addition, a function is included:</p><ul><li>relative_change_in_cloud_droplet_number_conc: This function is called in "Part2_plotter_50_200_nm" and calculates the relative change in cloud droplet number concentration from the critical supersaturations.</li></ul>
Effect of Surfactants on 1,2-Dichloroethane-in-Water Droplet Impacts at Electrified Liquid-Liquid Interface
<p>The data set for the submited publication "Effect of Surfactants on 1,2-Dichloroethane-in-Water Droplet Impacts at Electrified Liquid-Liquid Interface". </p>
S8 | ATHENSSUS | University of Athens Surfactants and Suspects List
<p>This is the collection associated with list S8 ATHENSSUS on the NORMAN Suspect List Exchange.</p> <p><a href="https://www.norman-network.com/?q=suspect-list-exchange">https://www.norman-network.com/?q=suspect-list-exchange</a></p> <p>S8ATHENSSUS<strong>University of Athens Surfactants and Suspects List </strong></p> <p>Gago Ferrero <em>et al </em><a href="https://www.norman-network.com/sites/default/files/files/suspectListExchange/031017Update/GagoFerrero_etal_2015_SuspectsNontargets_wDTXSIDs.csv">CSV</a>, <a href="https://www.norman-network.com/sites/default/files/files/suspectListExchange/031017Update/GagoFerrero_etal_2015_SuspectsNontargets_wDTXSIDs.xlsx">XLSX</a> (3/10/2017)</p> <p>CompTox <a href="https://comptox.epa.gov/dashboard/chemical_lists/athenssus">ATHENSSUS List</a></p> <p><a href="https://www.norman-network.com/sites/default/files/files/suspectListExchange/UniAthens_SuspectAndSurfactants_InChIKeys.txt">UniAthens InChIKeys</a> (28/01/2016)</p> <p>Gago-Ferrero <em>et </em><em>al</em>. 2015. DOI: <a href="http://pubs.acs.org/doi/abs/10.1021/acs.est.5b03454">10.1021/acs.est.5b03454</a></p>
S23 | EIUBASURF | Surfactant Suspect List from EI and UBA
<p>This is the collection associated with list S23 EIUBASURF on the NORMAN Suspect List Exchange.</p> <p><a href="https://www.norman-network.com/?q=suspect-list-exchange">https://www.norman-network.com/?q=suspect-list-exchange</a></p> <p>S23</p> <p>EIUBASURF</p> <p><strong>Surfactant Suspect List from EI and UBA</strong></p> <p>Surfactant List <a href="https://www.norman-network.com/sites/default/files/files/suspectListExchange/190618Update/SurfactantSuspects_EI_UBA_15032018_wDTXSIDs.xlsx">XLSX</a> (19/06/2018)<br> Surfactant List <a href="https://www.norman-network.com/sites/default/files/files/suspectListExchange/190618Update/SurfactantSuspects_EI_UBA_15032018_wDTXSIDs.csv">CSV</a> (19/06/2018)<br> CompTox <a href="https://comptox.epa.gov/dashboard/chemical_lists/eiubasurf">EIUBASURF List</a> </p> <p>EI UBA Surfactant <a href="https://www.norman-network.com/sites/default/files/files/suspectListExchange/190618Update/SurfactantSuspects_EI_UBA_15032018_InChIKeys.txt">InChIKeys</a> (19/06/2018)</p> <p>A compiled list of eco-labeled surfactants from Environmental Institute (EI, SK) and the German Federal Environmental Agency (UBA, DE) assigning chemical structures to UVCB chemicals based on names and prior knowledge. Provided by Nikiforos Alygizakis, EI.</p>
Heteroaggregation and Homoaggregation of Latex Particles in the Presence of Alkyl Sulfate Surfactants
<p>The dataset for the publication "Heteroaggregation and Homoaggregation of Latex Particles in the Presence of Alkyl Sulfate Surfactants". DOI: 10.3390/colloids4040052.</p> <p>Files containing data have .dat extension and are in text format</p>
Simulation trajectories for the article "Molecular conformation and bilayer pores in a nonionic surfactant lamellar phase studies with 13C-1H solid-state NMR and molecular dynamics simulations"
<p>Simulation trajectories for the article "Molecular conformation and bilayer pores in a nonionic surfactant lamellar phase studies with 1H-13C solid-state NMR and molecular dynamics simulations" Langmuir 2014, 30 (2), pp 461–469 http://dx.doi.org/10.1021/la404684r</p> <p>System: 80 wt% C12E5, T=298K</p> <p>Other files available: http://dx.doi.org/10.6084/m9.figshare.861071</p>
Dataset From: Surfactant mediated particle aggregation in nonpolar solvents
<p>The dataset for the publication "Surfactant mediated particle aggregation in nonpolar solvents". DOI: 10.1039/c9cp01985e</p> <p>Files containing data have .TXT extension and are in text format.</p>
Progeny Project NAMD data for MW1 and MW2 surfactants
<p>Data of the excitation energy relaxation in MW1 and MW2 surfactants of Progeny project from non-adiabatic molecular dynamics simulations</p> <p>See Readme.txt for more details.</p> <p> </p> <p> </p>
Progeny Project DFT and NAMD data for periodic 2D surfactant
<p>Crystalline unit cell, band structure, and electron-hole recombination dynamics from non-adiabatic molecular dynamics for the periodic 2D surfactant of the Progeny.</p> <p>See Readme.txt for more details.</p>
Surfactant Transport on Evolving Surfaces - Solutions of Space-Time Trace Finite Element Methods visualized.
<p>Videos of numerical experiments in the article "An accurate and robust Eulerian finite element method for partial differential equations on evolving surfaces" by H. Sass and A. Reusken. Surfactant transport on evolving surfaces with high curvatures and topological singularities is illustrated.</p>
Dataset for the manuscript: Trap-and-Track for Characterizing Surfactants at Interfaces
<p>This repository includes datasets supporting our manuscript that will be submitted to Molecules – Special issue "Surfactants with Specific Molecular Architecture as Building Blocks for Nanocarriers."</p> <ul> <li><strong>'rawdata.zip'</strong>: Recordings of trapped particle motions, estimated trajectories, and calculated mean squared displacements (MSD). Each recording has a identification number (e.g., 1, 2, 3, etc.); the corresponding trajectories and MSDs have file names '(ID#)_traj.csv' and '(ID#)_msd.csv', respectively. </li> <li><strong>'results_summary+figures.opju'</strong>: This is a Origin file summarizing the raw data and containing data figures in the manuscript.</li> </ul> <p>Typical particle recording has about 30 s duration. The MSDs are calculated for τ up to 30 seconds. For data analysis, MSDs up to τ = 10 s were used in order to avoid errors occurring at marginal τ.</p> <p>The data with cetyltrimethylammonium chloride (<em>CTAC</em>) can be found in a separate repository: </p> <p>Kim, Jeonghyeon, & Martin, Olivier J. F. (2021). Dataset for the Manuscript: Surfactants Control Optical Trapping Near a Glass Wall [Data set]. Zenodo. https://doi.org/10.5281/zenodo.5557074</p>
Data and Code for: Phospholipase-catalyzed Degradation Drives Domain Morphology and Rheology Transitions in Model Lung Surfactant Monolayers
Open the record for dataset details and reuse information.
Data from: Spirobifluorene-based polymers of intrinsic microporosity for the adsorption of methylene blue from wastewater: effect of surfactants
<p>Owing to their high surface area and superior adsorption properties, spirobifluorene PIMs namely, PIM-SBF-Me (methyl) and PIM-SBF-tBu (<i>tert</i>-butyl) were used for the first time for the removal of methylene blue (MB) dye from wastewater. Spirobifluorene PIMs are known to have large surface area (can be up to 1100 m2/g) and have been previously used mainly for gas storage applications. Dispersion of the polymers in aqueous solution was challenging due to their extreme hydrophobic nature leading to poor adsorption efficiency of MB. For this reason, cationic (CPC), anionic (SDS) and nonionic (Brij-35) surfactants were utilized and tested with the aim of enhancing the dispersion of the hydrophobic polymers in water and hence improving the adsorption efficiencies of the polymers. The effect of surfactant type and concentration was investigated. All surfactants offered a homogenous dispersion of the polymers in the aqueous dye solution, however, the highest adsorption efficiency was obtained using an anionic surfactant (SDS) and this seems due to the predominance of electrostatic interaction between its molecules and the positively charges dye molecules. Furthermore, the effect of polymer dosage and initial dye concentration on MB adsorption were also considered. The kinetic data for both polymers were well described by pseudo-second-order model, while Langumir model better simulated the adsorption process of MB dye on PIM-SBF-Me and Freundlich model was more suitable for PIM-SBF-tBu. Moreover, the maximum adsorption capacities recorded were 84.0 and 101.0 mg/g for PIM-SBF-Me and PIM-SBF-tBu, respectively. Reusability of both polymers was tested by performing three adsorption cycles and the results substantiate that both polymers can be effectively reused with insignificant loss of their adsorption efficiency (%AE). These preliminary results suggested that incorporation of a surfactant to enhance the dispersion of hydrophobic polymers and adsorption of organic contaminants from wastewater is a simple and cost-effective approach that can be adapted for many other environmental applications.</p>
Simulation trajectories for the article "Molecular conformation and bilayer pores in a nonionic surfactant lamellar phase studies with 13C-1H solid-state NMR and molecular dynamics simulations"
<p>Simulation trajectories for the article "Molecular conformation and bilayer pores in a nonionic surfactant lamellar phase studies with 1H-13C solid-state NMR and molecular dynamics simulations" Langmuir 2014, 30 (2), pp 461–469 http://dx.doi.org/10.1021/la404684r</p> <p>System: 70 wt% C12E5, T=298K</p> <p>Other files available: http://dx.doi.org/10.6084/m9.figshare.861071</p>
Simulation trajectories for the article "Molecular conformation and bilayer pores in a nonionic surfactant lamellar phase studies with 13C-1H solid-state NMR and molecular dynamics simulations"
<p>Simulation trajectories for the article "Molecular conformation and bilayer pores in a nonionic surfactant lamellar phase studies with 1H-13C solid-state NMR and molecular dynamics simulations" Langmuir 2014, 30 (2), pp 461–469 http://dx.doi.org/10.1021/la404684r</p> <p>System: 70 wt% C12E5, T=320K</p> <p>Other files available: http://dx.doi.org/10.6084/m9.figshare.861071</p>
Simulation trajectories for the article "Molecular conformation and bilayer pores in a nonionic surfactant lamellar phase studies with 13C-1H solid-state NMR and molecular dynamics simulations"
<p>Simulation trajectories for the article "Molecular conformation and bilayer pores in a nonionic surfactant lamellar phase studies with 1H-13C solid-state NMR and molecular dynamics simulations" Langmuir 2014, 30 (2), pp 461–469 http://dx.doi.org/10.1021/la404684r</p> <p>System: 70 wt% C12E5, T=333K</p> <p>Other files available: http://dx.doi.org/10.6084/m9.figshare.861071</p>
Simulation trajectories for the article "Molecular conformation and bilayer pores in a nonionic surfactant lamellar phase studies with 13C-1H solid-state NMR and molecular dynamics simulations"
<p>Simulation trajectories for the article "Molecular conformation and bilayer pores in a nonionic surfactant lamellar phase studies with 1H-13C solid-state NMR and molecular dynamics simulations" Langmuir 2014, 30 (2), pp 461–469 http://dx.doi.org/10.1021/la404684r</p> <p>System: 60 wt% C12E5, T=333K</p> <p>Other files available: http://dx.doi.org/10.6084/m9.figshare.861071</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)
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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.