jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jan‐Michael Y. Carrillo | Zhan Chen | Uvinduni I. Premadasa | Christian Steinmetz | E. Bryan Coughlin | Benjamin Doughty | Thomas P. Russell | Bobby G. Sumpter
Source: Nanoscale (article, 2022-11-22)
Field: Chemistry — Organic Chemistry

Assembly of polyelectrolyte star block copolymers at the oil–water interface To understand and resolve adsorption, reconfiguration, and equilibrium conformations of charged star copolymers, we carried out an integrated experimental and coarse-grained molecular dynamics simulation study of the assembly process at the oil-water interface. This is important to guide development of novel surfactants or amphiphiles for chemical transformations and separations. The star block copolymer consisted of arms that are comprised of hydrophilic-hydrophobic block copolymers that are covalently tethered <i>via</i> the hydrophobic blocks to one point. The hydrophobic core represents polystyrene (PS) chains, while the hydrophilic corona represents quaternized poly(2-vinylpyridine) (P2VP) chains. The P2VP is modeled to become protonated when in contact with an acidic aqueous phase, thereby massively increasing the hydrophilicity of this block, and changing the nature of the star at the oil-water interface. This results in a configurational change whereby the chains comprising the hydrophilic corona are significantly stretched into the aqueous phase, while the hydrophobic core remains solubilized in the oil phase. In the simulations, we followed the kinetics of the anchoring and assembly of the star block copolymer at the interface, monitoring the lateral assembly, and the subsequent reconfiguration of the star <i>via</i> changes in the interfacial tension that varies as the degree-of-protonation increases. At low fractions of protonation, the arm cannot fully partition into the aqueous side of the interface and instead interacts with other arms in the oil phase forming a network near the interface. These insights were used to interpret the non-monotonic dependence of pH with the asymptotic interfacial tension from pendant drop tensiometry experiments and spectral signatures of aromatic stretches seen in vibrational sum frequency generation (SFG) spectroscopy. We describe the relationship of interfacial tension to the star assembly <i>via</i> the Frumkin isotherm, which phenomenologically describes anti-cooperativity in adsorbing stars to the interface due to crowding. Although our model explicitly considers long-range electrostatics, the contribution of electrostatics to interfacial tension is small and brought about by strong counterion condensation at the interface. These results provide key insights into resolving the adsorption, reconfiguration, and equilibrium conformations of charged star block copolymers as surfactants. Copolymer ; Aqueous solution ; Polystyrene ; Materials science ; Phase (matter) ; Polyelectrolyte ; Polymer chemistry ; Surface tension ; Amphiphile ; Adsorption ; Chemical engineering ; Protonation ; Chemistry ; Organic chemistry ; Thermodynamics ; Polymer ; Composite material Surfactants and Colloidal Systems ; Electrostatics and Colloid Interactions ; Block Copolymer Self-Assembly
Authors: Abdullah Burak Arslan | Mehmet Çeli̇k
Source: Journal of ETA Maritime Science (article, 2022-08-12)
Field: Materials Science — Materials Chemistry

Investigation of the Effect of CeO2 Nanoparticle Addition in Diesel Fuel on Engine Performance and Emissions In this study, the effects of the additive on engine performance and emissions were investigated by adding cerium oxide (CeO 2 ) nanoparticles (NPs) into diesel fuel. The use of CeO 2 NPs as an additive increased the lower calorific value (LHV) of the fuel while decreasing its viscosity and density. As a result of the experiments, an increase of 8.99% in engine torque was obtained in DCe100 fuel which 100 ppm CeO 2 NPs were added compared to diesel (D0) fuel. The increase in the LHV had a positive effect on the specific fuel consumption. The use of CeO 2 NPs resulted in an increase in brake thermal efficiency (BTE) due to the increased ending temperature of combustion. A 5.44% increase was obtained in DCe100 fuel compared to D0 fuel in terms of BTE. With an increase in the amount of CeO 2 , carbon dioxide (CO), hydrocarbon (HC), and smoke emissions were reduced. Compared to D0 fuel, the lowest values were obtained with the DCe100 fuel. CO emissions were reduced by 18.27%, HC emissions by 30.12%, and soot emissions by 21.63%. However, nitrogen oxides (NO X ) emissions increased with rising CeO 2 amount and in-cylinder temperature. The highest NO X value in DCe100 fuel was obtained with an increase of 6.65% compared to D0 fuel. Diesel fuel ; Diesel engine ; Automotive engineering ; Environmental science ; Waste management ; Process engineering ; Engineering Catalytic Processes in Materials Science ; Biodiesel Production and Applications ; Advanced Combustion Engine Technologies
Authors: Ildar Shaikhiev | Natalia V. Kraysman | С. В. Свергузова | P Yeow | S Wong | T Hadibarata | A Thakur | R Singh | R Pullela | V Pundir | S Chong | I Shaikhiev | S Sverguzova | N Kraysman | Onion | P Oladoye | R Chakraborty | A Asthana | A Singh | B Jain | A Susan | H Hamad | S Idrus | J Bayuo | M Rwiza | K Mtei | K Adegoke | O Adesina | O Okon-Akan | O Adegoke | A Olabintan | O Ajala | H Olagoke | N Maxakato | O Bello | C Sinyeue | T Garioud | M Lemestre | M Meyer | F Brgier | V Chaleix | V Sol | N Lebouvier | E Meez | A Rahdar | G Kyzas | S Jena | S Martini | S Afroze | K Roni | M Setiawati | D Kharismadewi | A Sen | H Pereira | M Olivella | I Villaescusa | Md Ahmaruzzaman | K Elsherif | A El-Dali | A Alkarewi | A Ewlad-Ahmed | A Treban | K Banerjee | E Fomina | R Galimova | K Shaykhieva | Zh Sapronova | J Da Silva | J Alves | W Galdino | R Moreira | H Jos | R De Sena | S Andersen | N Kasiri | M Fathi | P Subhedar | P Ray | P Gogate | C Okutucu | G Duman | S Ucar | I Yasa | J Yanik | E Arjeh | H-R Akhavan
Source: Biointerface Research in Applied Chemistry (article, 2022-10-31)
Field: Chemistry — Spectroscopy

Review of Pistachio (Pistacia) Shell Use to Remove Pollutants from Aqua Media The paper summarizes literature data on using crushed pistachio shells (Pistacia vera L.) as sorption materials to remove ions of various metals, dyes, and antibiotics from aqueous media. It provides brief information on the amount of pistachio processing waste, its chemical composition, and its recycling methods. It gives the adsorption process parameters and the values of pistachio shell sorption parameters for the studied pollutants. It was shown that pistachio shells' sorption characteristics for various pollutants could be increased by chemical modification with various chemical reagents. The isotherms of pollutant adsorption with pistachio shells were found to be, in most cases, more accurately described by the Langmuir model, and the process kinetics to follow in most cases, the pseudo-second-order model. It is shown that the pistachio shell is a good precursor for activated carbons production, which can also be used for the adsorption of various pollutants from aqueous media. Sorption ; Adsorption ; Pollutant ; Chemistry ; Pistacia ; Aqueous solution ; Chemical engineering ; Reagent ; Shell (structure) ; Environmental chemistry ; Materials science ; Organic chemistry ; Composite material ; Botany Adsorption, diffusion, and thermodynamic properties of materials ; Water Quality Monitoring and Analysis ; Adsorption and biosorption for pollutant removal
Authors: Balázs Varbai | Patrik Bolyhos | Dávid Miklós Kemény | Kornél Májlinger
Source: Periodica Polytechnica Mechanical Engineering (article, 2022-10-06)
Field: Materials Science — Metals and Alloys

Microstructure and Corrosion Properties of Austenitic and Duplex Stainless Steel Dissimilar Joints In several applications duplex stainless steels should be joint welded to conventional austenitic stainless steels. In this research LDX 2101 lean duplex stainless steel sheets were welded to conventional 304 austenitic stainless steels, using gas tungsten arc welding. For the welded joints three different welding rods were used: ER 308L, ER 309LSi, and ER 2209. For gas shielding two different shielding gases were used: argon and argon +2% nitrogen. It was found that the nitrogen addition to the shielding gas promoted austenite formation in the weld metal. It was also found Schaeffler-diagram modified by Outokumpu showed a very good estimation to the ferrite content and chemical composition of the weld metal. The ferrite content estimated by the Outokumpu-diagram, showed a close correlation to measured ferrite contents, the highest error was 30%. In case of the chemical composition of the weld metal, the Cr- and Ni-contents were estimated with a maximum of 15% error. In terms of the corrosion resistance, the best pitting corrosion resistance was achieved using the 308L welding rod with argon shielding gas, where the weight loss was 1.6% after the 24 hours immersion test. Materials science ; Metallurgy ; Welding ; Shielding gas ; Ferrite (magnet) ; Austenite ; Gas tungsten arc welding ; Austenitic stainless steel ; Corrosion ; Microstructure ; Argon ; Arc welding ; Composite material Hydrogen embrittlement and corrosion behaviors in metals ; Welding Techniques and Residual Stresses ; Microstructure and Mechanical Properties of Steels
Authors: Henriette Capel | K. Anton Feenstra | Sanne Abeln
Source: Scientific Reports (article, 2022-06-21)
Field: Materials Science — Materials Chemistry

Multi-task learning to leverage partially annotated data for PPI interface prediction Protein protein interactions (PPI) are crucial for protein functioning, nevertheless predicting residues in PPI interfaces from the protein sequence remains a challenging problem. In addition, structure-based functional annotations, such as the PPI interface annotations, are scarce: only for about one-third of all protein structures residue-based PPI interface annotations are available. If we want to use a deep learning strategy, we have to overcome the problem of limited data availability. Here we use a multi-task learning strategy that can handle missing data. We start with the multi-task model architecture, and adapted it to carefully handle missing data in the cost function. As related learning tasks we include prediction of secondary structure, solvent accessibility, and buried residue. Our results show that the multi-task learning strategy significantly outperforms single task approaches. Moreover, only the multi-task strategy is able to effectively learn over a dataset extended with structural feature data, without additional PPI annotations. The multi-task setup becomes even more important, if the fraction of PPI annotations becomes very small: the multi-task learner trained on only one-eighth of the PPI annotations-with data extension-reaches the same performances as the single-task learner on all PPI annotations. Thus, we show that the multi-task learning strategy can be beneficial for a small training dataset where the protein's functional properties of interest are only partially annotated. Leverage (statistics) ; Computer science ; Task (project management) ; Interface (matter) ; Machine learning ; Artificial intelligence ; Parallel computing Machine Learning in Materials Science ; Software Engineering Research ; Web Data Mining and Analysis
Authors: Yangyang Wang | Hong-Ju He | Shengqi Jiang | Hanjun Ma
Source: International journal of agricultural and biological engineering (article, 2022-01-01)
Field: Chemistry — Analytical Chemistry

Nondestructive determination of IMP content in chilled chicken based on hyperspectral data combined with chemometrics This study was conducted to investigate the potential of hyperspectral imaging technique (900-1700 nm) for nondestructive determination of inosinic acid (IMP) in chicken. Hyperspectral images of chicken flesh samples were acquired, and their mean spectra within the images were extracted. The quantitative relationship between the mean spectra and reference IMP value was fitted by partial least squares (PLS) regression algorithm. A PLS model (MAS-PLS) built with moving average smoothing (MAS) spectra showed better performance in predicting IMP content, leading to correlation coefficients (RP) of 0.951, root mean square error (RMSEP) of 0.046 mg/g, and residual predictive deviation (RPD) of 3.152. Regression coefficient (RC), successive projections algorithm (SPA), stepwise, competitive adaptive reweighted sampling (CARS), and uninformative variable elimination (UVE) were used to select the optimal wavelengths to optimize the MAS-PLS model. Based on the 18 optimal wavelengths (907.14, 917.02, 918.67, 926.90, 930.20, 936.78, 956.54, 1004.28, 1135.89, 1211.56, 1302.07, 1367.94, 1397.60, 1488.31, 1680.17, 1683.49, 1686.80 and 1695.10 nm) selected from MAS spectra by SPA, the MAS-SPA-PLS model was built with RP of 0.920, RMSEP of 0.056 mg/g and RPD of 3.220, which was similar to the MAS-PLS model. The overall study indicated that hyperspectral imaging in the 900-1700 nm range combined with PLS and SPA could be used to predict the IMP content in chicken flesh. Keywords: near-infrared hyperspectral imaging, chicken, inosinic acid, partial least squares, successive projections algorithm DOI: 10.25165/j.ijabe.20221501.6612 Citation: Wang Y Y, He H J, Jiang S Q, Ma H J. Nondestructive determination of IMP content in chilled chicken based on hyperspectral data combined with chemometrics. Int J Agric & Biol Eng, 2022; 15(1): 277–284. Partial least squares regression ; Hyperspectral imaging ; Chemometrics ; Coefficient of determination ; Mathematics ; Residual ; Correlation coefficient ; Content (measure theory) ; Mean squared error ; Analytical Chemistry (journal) ; Chemistry ; Chromatography ; Artificial intelligence ; Statistics ; Computer science ; Algorithm Spectroscopy and Chemometric Analyses
Authors: Eugenia Voytik | Patricia Skowronek | Wen‐Feng Zeng | Maria C. Tanzer | Andreas‐David Brunner | Marvin Thielert | Maximilian T. Strauss | Sander Willems | Matthias Mann
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2022-07-13)
Field: Chemistry — Spectroscopy

AlphaViz: Visualization and validation of critical proteomics data directly at the raw data level ABSTRACT Although current mass spectrometry (MS)-based proteomics identifies and quantifies thousands of proteins and (modified) peptides, only a minority of them are subjected to in-depth downstream analysis. With the advent of automated processing workflows, biologically or clinically important results within a study are rarely validated by visualization of the underlying raw information. Current tools are often not integrated into the overall analysis nor readily extendable with new approaches. To remedy this, we developed AlphaViz, an open-source Python package to superimpose output from common analysis workflows on the raw data for easy visualization and validation of protein and peptide identifications. AlphaViz takes advantage of recent breakthroughs in the deep learning-assisted prediction of experimental peptide properties to allow manual assessment of the expected versus measured peptide result. We focused on the visualization of the 4-dimensional data cuboid provided by Bruker TimsTOF instruments, where the ion mobility dimension, besides intensity and retention time, can be predicted and used for verification. We illustrate how AlphaViz can quickly validate or invalidate peptide identifications regardless of the score given to them by automated workflows. Furthermore, we provide a ‘predict mode’ that can locate peptides present in the raw data but not reported by the search engine. This is illustrated the recovery of missing values from experimental replicates. Applied to phosphoproteomics, we show how key signaling nodes can be validated to enhance confidence for downstream interpretation or follow-up experiments. AlphaViz follows standards for open-source software development and features an easy-to-install graphical user interface for end-users and a modular Python package for bioinformaticians. Validation of critical proteomics results should now become a standard feature in MS-based proteomics. Visualization ; Python (programming language) ; Computer science ; Workflow ; Graphical user interface ; Raw data ; Data mining ; Software ; Data visualization ; Downstream (manufacturing) ; Database ; Engineering ; Programming language Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Edgar López‐López | Carlos M. Cerda‐García‐Rojas | José L. Medina‐Franco
Source: Molecular Informatics (article, 2022-09-30)
Field: Chemistry — Organic Chemistry

Consensus Virtual Screening Protocol Towards the Identification of Small Molecules Interacting with the Colchicine Binding Site of the Tubulin‐microtubule System Modification of the tubulin-microtubule (Tub-Mts) system has generated effective strategies for developing different treatments for cancer. A huge amount of clinical data about inhibitors of the tubulin-microtubule system have supported and validated the studies on this pharmacological target. However, many tubulin-microtubule inhibitors have been developed from representative and common scaffolds that cover a small region of the chemical space with limited structural innovation. The main goal of this study is to develop the first consensus virtual screening protocol for natural products (ligand- and structure-based drug design methods) tuned for the identification of new potential inhibitors of the Tub-Mts system. A combined strategy that involves molecular similarity, molecular docking, pharmacophore modeling, and in silico ADMET prediction has been employed to prioritize the selections of potential inhibitors of the Tub-Mts system. Five compounds were selected and further studied using molecular dynamics and binding energy predictions to characterize their possible binding mechanisms. Their structures correspond to 5-[2-(4-hydroxy-3-methoxyphenyl) ethyl]-2,3-dimethoxyphenol (1), 9,10-dihydro-3,4-dimethoxy-2,7-phenanthrenediol (2), 2-(3,4-dimethoxyphenyl)-5,7-dihydroxy-6-methoxy-4H-1-benzopyran-4-one (3), 13,14-epoxyparvifoline-4',5',6'-trimethoxybenzoate (4), and phenylmethyl 6-hydroxy-2,3-dimethoxybenzoate (5). Compounds 1-3 have been associated with literature reports that confirm their activity against several cancer cell lines, thus supporting the utility of this protocol. Pharmacophore ; Virtual screening ; In silico ; Tubulin ; Microtubule ; Computational biology ; Docking (animal) ; Small molecule ; Chemistry ; Binding site ; Drug discovery ; Biology ; Stereochemistry ; Biochemistry ; Cell biology ; Medicine Click Chemistry and Applications ; Computational Drug Discovery Methods ; Synthesis and biological activity
Authors: B. L. Milman | И. К. Журкович
Source: Journal of Analytical Chemistry (article, 2022-05-01)
Field: Chemistry — Spectroscopy

Present-Day Practice of Non-Target Chemical Analysis Abstract We review the main techniques, procedures, and information products used in non-target analysis (NTA) to reveal the composition of substances. Sampling and sample preparation methods are preferable that ensure the extraction of analytes from test samples in a wide range of analyte properties with the most negligible loss. The necessary techniques of analysis are versions of chromatography–high-resolution tandem mass spectrometry (HRMS), yielding individual characteristics of analytes (mass spectra, retention properties) to accurately identify them. The prioritization of the analytical strategy discards unnecessary measurements and thereby increases the performance of the NTA. Chemical databases, collections of reference mass spectra and retention characteristics, algorithms, and software for processing HRMS data are indispensable in NTA. Analyte ; Chemistry ; Mass spectrometry ; Chromatography ; Sample preparation ; Resolution (logic) ; Sampling (signal processing) ; Analytical Chemistry (journal) ; Artificial intelligence ; Computer science ; Filter (signal processing) Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Analytical Chemistry and Chromatography
Authors: Tatiana Osheter | Charles Linder | Zeev Wiesman
Source: Biosensors (review, 2022-04-09)
Field: Chemistry — Analytical Chemistry

Time Domain (TD) Proton NMR Analysis of the Oxidative Safety and Quality of Lipid-Rich Foods Food safety monitoring is highly important due to the generation of unhealthy components within many food products during harvesting, processing, storage, transportation and cooking. Current technologies for food safety analysis often require sample extraction and the modification of the complex chemical and morphological structures of foods, and are either time consuming, have insufficient component resolution or require costly and complex instrumentation. In addition to the detection of unhealthy chemical toxins and microbes, food safety needs further developments in (a) monitoring the optimal nutritional compositions in many different food categories and (b) minimizing the potential chemical changes of food components into unhealthy products at different stages from food production until digestion. Here, we review an efficient methodology for overcoming the present analytical limitations of monitoring a food's composition, with an emphasis on oxidized food components, such as polyunsaturated fatty acids, in complex structures, including food emulsions, using compact instruments for simple real-time analysis. An intelligent low-field proton NMR as a time domain (TD) NMR relaxation sensor technology for the monitoring of T<sub>2</sub> (spin-spin) and T<sub>1</sub> (spin-lattice) energy relaxation times is reviewed to support decision-making by producers, retailers and consumers in regard to food safety and nutritional value during production, shipping, storage and consumption. Food safety ; Biochemical engineering ; Food quality ; Food processing ; Food industry ; Environmental science ; Food science ; Process engineering ; Computer science ; Biotechnology ; Risk analysis (engineering) ; Chemistry ; Business ; Engineering ; Biology Spectroscopy and Chemometric Analyses ; Metabolomics and Mass Spectrometry Studies ; Advanced Chemical Sensor Technologies
Authors: Kai Trepte | Johannes Voss
Source: Journal of Computational Chemistry (article, 2022-04-27)
Field: Materials Science — Materials Chemistry

Data‐driven and constrained optimization of semi‐local exchange and nonlocal correlation functionals for materials and surface chemistry Reliable predictions of surface chemical reaction energetics require an accurate description of both chemisorption and physisorption. Here, we present an empirical approach to simultaneously optimize semi-local exchange and nonlocal correlation of a density functional approximation to improve these energetics. A combination of reference data for solid bulk, surface, and gas-phase chemistry and physical exchange-correlation model constraints leads to the VCML-rVV10 exchange-correlation functional. Owing to the variety of training data, the applicability of VCML-rVV10 extends beyond surface chemistry simulations. It provides optimized gas phase reaction energetics and an accurate description of bulk lattice constants and elastic properties. Physisorption ; Chemisorption ; Energetics ; Gas phase ; Surface (topology) ; Chemistry ; Density functional theory ; Thermodynamics ; Statistical physics ; Chemical physics ; Physical chemistry ; Computational chemistry ; Physics ; Adsorption ; Mathematics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Advanced Thermoelectric Materials and Devices