jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Tamás Langó | Katalin Kuffa | Gábor Tóth | Lilla Turiák | László Drahos | Gábor Tusnády
Source: International Journal of Molecular Sciences (article, 2022-12-23)
Field: Chemistry — Spectroscopy

Comprehensive Discovery of the Accessible Primary Amino Group-Containing Segments from Cell Surface Proteins by Fine-Tuning a High-Throughput Biotinylation Method Cell surface proteins, including transmembrane and other surface-anchored proteins, play a key role in several critical cellular processes and have a strong diagnostic value. The development of quick and robust experimental methods remains vital for the accurate and comprehensive characterization of the cell surface subproteome of individual cells. Here we present a high-throughput technique which relies on the biotinylation of the accessible primary amino groups in the extracellular segments of the proteins, using HL60 as a model cell line. Several steps of the method have been thoroughly optimized to capture labeled surface proteins selectively and in larger quantities. These include the following: improving the efficiency of the cell surface biotinylation; reducing the endogen protease activity; applying an optimal amount of affinity column and elution steps for labeled peptide enrichment; and examining the effect of various solid-phase extraction methods, different HPLC gradients, and various tandem mass spectrometry settings. Using the optimized workflow, we identified at least 1700 surface-associated individual labeled peptides (~6000-7000 redundant peptides) from the model cell surface in a single nanoHPLC-MS/MS run. The presented method can provide a comprehensive and specific list of the cell surface available protein segments that could be potential targets in various bioinformatics and molecular biology research. Biotinylation ; Tandem mass spectrometry ; Cell ; Chemistry ; Proteomics ; Computational biology ; Transmembrane protein ; Chromatography ; Biochemistry ; Biology ; Mass spectrometry ; Receptor Advanced Proteomics Techniques and Applications ; Biotin and Related Studies ; Mass Spectrometry Techniques and Applications
Authors: Mary de Conti | Swayandipta Dey | Walmir Pottker | Felipe A. La Porta
Source: ChemRxiv (preprint, 2022-08-02)
Field: Materials Science — Materials Chemistry

An overview into advantages and applications of conventional and unconventional hydro(solvo)thermal approaches for novel advanced materials design The hydro(solvo)thermal approaches due to their simplicity and relatively low cost have attracted great attention to novel advanced materials processing with controlled particle sizes and well-defined morphologies, including the desirable obtaining of diverse metastable phases. Furthermore, in this case, such advanced materials obtained by this strategy generally have a high crystallinity structure as the main characteristic, which is interesting for a wide range of technological applications of high performance. This critical review presents the recent progress and challenges in conventional and unconventional hydro(solvo)thermal synthesis of novel advanced materials with desirable functionality and outstanding physicochemical properties designed using such methods. Finally, in this perspective, we believe that this detailed knowledge of the effect of processing parameters and as they will affect the prepared materials structure-composition-morphology is a key step to providing new chemical insights for the rational advanced materials design. Nanotechnology ; Materials science ; Process engineering ; Biochemical engineering ; Engineering Thermal and Kinetic Analysis ; Thermal Expansion and Ionic Conductivity ; Catalysis and Oxidation Reactions
Authors: Ahmad R. Salih | Zainab A. K. Al-Messri
Source: Iraqi Journal of Science (article, 2022-07-31)
Field: Chemistry — Organic Chemistry

Synthesis, Characterization and Evaluation of Some Pyranopyrazole Derivatives as Multifunction Additives for Medium Lubricating Oils The current work involves synthesis of 6-amino-4-(4-hydroxyl-3-methoxyphenyl)-3-methyl-1-phenyl-1,4- dihydropyrano[2,3-c]pyrazole-5-carbonitrile (2) by condensation of 2-(4-hydroxyl-3-methoxybenzylidine) malononitrile (1) with 3-methyl-1-phenyl-2-pyrazolin-5-one. The product 2 was then reacted with various aldehydes to form a series of Schiff base derivatives 3a-h. The reaction of the chosen Schiff base derivatives (3a and 3b) with mercaptoacetic acid gave thiazolidinone derivatives (4a and 4b). The structures of prepared compounds were confirmed using FT-IR, 1H NMR, and 13C NMR spectroscopies. They were evaluated as good copper corrosion inhibitors, anti-rust, and antioxidants. This was done by mixing them with the base medium lubricating oil (type 60 stock) provided by the Iraqi Midland Refineries Company/Al-Daura according to the American Society of Testing and Materials ASTM-D130, ASTM-D665, and Institute of Petroleum's testing method, oxidation stability test (IP-280), respectively. Malononitrile ; Chemistry ; Schiff base ; Organic chemistry ; Proton NMR ; Carbon-13 NMR ; Nuclear chemistry ; Polymer chemistry ; Catalysis Synthesis and biological activity ; Multicomponent Synthesis of Heterocycles
Authors: Samsul Bhakri | Muhammad Ghozali | Edy Cahyono | Evi Triwulandari | Witta Kartika Restu | Nissa Nurfajrin Solihat | A. Heri Iswanto | Petar Antov | Viktor Savov | Seng Hua Lee | Erika Ayu Agustiany | Lubos Kristak | Widya Fatrıasarı
Source: JOURNAL OF RENEWABLE MATERIALS (article, 2022-06-07)
Field: Materials Science — Polymers and Plastics

Development and Characterization of Eco-Friendly Non-Isocyanate Urethane Monomer from Jatropha curcas Oil for Wood Composite Applications The aim of this research work was to evaluate the potential of using renewable natural feedstock, i.e., Jatropha curcas oil (JCO) for the synthesis of non-isocyanate polyurethane (NIPU) resin for wood composite applications. Commercial polyurethane (PU) is synthesized through a polycondensation reaction between isocyanate and polyol. However, utilizing toxic and unsustainable isocyanates for obtaining PU could contribute to negative impacts on the environment and human health. Therefore, the development of PU from eco-friendly and sustainable resources without the isocyanate route is required. In this work, tetra-n-butyl ammonium bromide was used as the activator to open the epoxy ring with 3-Aminopropyltriethoxisylane as a catalyst to yield urethane of JCO (UJCO). The UJCO were characterized by Fourier Transform Infra-Red spectroscopy (FTIR) and their oxirane, and hydroxyl values were measured. The result showed that a decrease in oxirane value was found while the hydroxyl value was increased during the time, confirming that the urethane group was formed. The presence of functional groups in FTIR spectra at wave numbers 1732.08, 1562.34, and 3348.42 cm−1 indicates the functional groups of C = O (urethane carbonyl), –NH, and –OH, respectively confirmed this finding. The potential applications of NIPU in the wood composite were also outlined. Isocyanate ; Polyurethane ; Fourier transform infrared spectroscopy ; Hydroxyl value ; Monomer ; Jatropha curcas ; Environmentally friendly ; Polymer chemistry ; Polyol ; Raw material ; Acid value ; Composite number ; Chemistry ; Organic chemistry ; Materials science ; Chemical engineering ; Composite material ; Polymer ; Botany Polymer composites and self-healing ; Lignin and Wood Chemistry ; biodegradable polymer synthesis and properties
Authors: Xinzhi Ding | Kai Yang | Weiyu Wang | Bin Liu | Xuejin Wang | Jie Zhang | Dayi Li
Source: Energies (article, 2022-04-11)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Ferro-Resonance Analysis of Capacitor Voltage Transformer with Fast Saturation Damper With the increase in capacitor voltage transformer (CVT) operation life, CVT impedance changes, and the short-time switching of overhead lines, it is very easy to cause a transient oscillation accident in which a CVT participates, reduce the insulation level of a CVT, and even induce regional power grid oscillation and easily cause capacitor breakdown, after long-term operation with defects. In this paper, the whole power network, including the generator, power grid, and consumer, is modeled, and the theoretical model of the CVT ferro-resonance connected grid is built. The frequency spectrum analysis, and the measurements of resonance bus voltage and line voltage, were carried out to find the design method of a fast saturation damper (FSD) and its voltage–current characteristics that affect the vibration, based on theory and practice; this paper puts forward effective measures to reduce the resonance effect of a CVT in operation, and avoid the oscillation of the line and even the regional power grid system. Capacitor ; Engineering ; Electrical engineering ; Transient voltage suppressor ; Transformer ; Voltage ; Electrical impedance ; Overvoltage ; Electronic engineering Magnetic Properties and Applications ; High-Voltage Power Transmission Systems ; Thermal Analysis in Power Transmission
Authors: Joyjit Chattoraj | Brahim Hamadicharef | Jian Feng Kong | Mohan Kashyap Pargi | Yingzhi Zeng | Chee Kok Poh | Luwei Chen | Fei Gao | Teck Leong Tan
Source: ChemCatChem (article, 2022-05-24)
Field: Materials Science — Materials Chemistry

Theory‐Guided Machine Learning to Predict the Performance of Noble Metal Catalysts in the Water‐Gas Shift Reaction Abstract Machine learning (ML) has widespread applications in catalyst discovery and reaction optimization. We present a theory‐guided machine learning framework to evaluate the carbon monoxide (CO) conversion performance of noble metal catalysts in water‐gas shift (WGS) reaction. Our study is based on an open source WGS dataset, which we modify significantly to be consistent with the chemical reaction principles. We apply state‐of‐the‐art ML models including artificial neural networks, extreme gradient boosting to predict CO conversion percentage. These models show superior regression performance than the previously reported results in the literature. We further generalize the existing data structure by including physical, chemical and surface chemistry properties as fingerprint features that rationalize the importance of all the input features for CO conversion. We noticed that purely data‐driven ML models frequently violate the thermodynamic equilibrium principle and predict unphysical CO conversion percentage. We address these two problems by developing a custom loss function and an additional activation function in our neural networks architecture. Our proposed theory‐guided ML model displays high accuracy ( R 2 score is 0.95 and root mean square error is 6.87) and physically robust predictions. The model also opens up promising possibilities to improve CO conversion percentage, which were previously unexplored in experiments. Water-gas shift reaction ; Artificial neural network ; Mean squared error ; Catalysis ; Noble metal ; Boosting (machine learning) ; Density functional theory ; Computer science ; Carbon monoxide ; Gradient boosting ; Artificial intelligence ; Chemistry ; Materials science ; Mathematics ; Computational chemistry ; Organic chemistry ; Statistics Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Electrocatalysts for Energy Conversion
Authors: Sapna Sarupria | Steven W. Hall | Jutta Rogal
Source: MRS Bulletin (article, 2022-09-01)
Field: Materials Science — Materials Chemistry

Machine learning for molecular simulations of crystal nucleation and growth Nucleation ; Crystallization ; Molecular dynamics ; Crystal growth ; Sampling (signal processing) ; Crystal (programming language) ; Transformation (genetics) ; Computer science ; Amorphous solid ; Materials science ; Chemical physics ; Nanotechnology ; Chemistry ; Crystallography ; Computational chemistry ; Physics ; Thermodynamics Machine Learning in Materials Science ; Crystallization and Solubility Studies ; Protein Structure and Dynamics
Authors: А. А. Мирзоев | Б. Р. Гельчинский | А. А. Rempel
Source: Doklady Physical Chemistry (review, 2022-05-01)
Field: Materials Science — Materials Chemistry

Neural Network Prediction of Interatomic Interaction in Multielement Substances and High-Entropy Alloys: A Review One of the most exciting tools that have entered the arsenal of modern science and technology in recent years is machine learning, which can efficiently solve problems of approximation of multidimensional functions. There is a rapid growth in the development and application of machine learning in physics and chemistry. This review is devoted to the possibilities of predicting interatomic interactions in multielement substances and high-entropy alloys using artificial intelligence based on neural networks and their active machine learning, which provides a comprehensive overview and analysis of recent research on this topic. The relevance of this direction is due to that the prediction of the structure and properties of materials by means of atomistic quantum mechanical modeling based on density functional theory (DFT) is difficult in many cases because of the rapid increase in computational costs with increasing size in accordance with the size of the object. Machine learning methods make it possible to reproduce real interparticle interaction potentials of the system using the available DFT calculations, and then, on their basis, to model the required properties by the molecular dynamics method on a multiply increased spatiotemporal scale. As a starting point, we introduce machine learning principles, algorithms, descriptors, and databases in materials science. The design of the potential energy surface and interatomic interaction potentials in solid solutions, high-entropy alloys, high-entropy metal compounds with carbon, nitrogen, and oxygen, as well as in bulk amorphous materials, is described. Interatomic potential ; Artificial neural network ; Computer science ; Entropy (arrow of time) ; Artificial intelligence ; Density functional theory ; Statistical physics ; Machine learning ; Molecular dynamics ; Chemistry ; Computational chemistry ; Physics ; Thermodynamics Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; Chemical Thermodynamics and Molecular Structure
Authors: Abhishek S. Bhesania | Parvesh Kamboj | Sai Abhishek Peddakotla | Rakesh Kumar
Source: Computational Materials Science (article, 2022-02-23)
Field: Materials Science — Polymers and Plastics

In-depth analysis of reaction kinetics parameters of phenolic resin using molecular dynamics and unsupervised machine learning approach Kinetics ; Molecular dynamics ; Microscale chemistry ; Polymer ; Chemical kinetics ; Activation energy ; Thermodynamics ; Materials science ; Chemistry ; Chemical physics ; Physical chemistry ; Computational chemistry ; Composite material ; Physics Polymer crystallization and properties ; Epoxy Resin Curing Processes ; Mechanical Behavior of Composites
Authors: Harry Handoko Halim | Yoshitada Morikawa
Source: ACS Physical Chemistry Au (article, 2022-06-15)
Field: Materials Science — Materials Chemistry

Elucidation of Cu–Zn Surface Alloying on Cu(997) by Machine-Learning Molecular Dynamics The Cu-Zn surface alloy has been extensively involved in the investigation of the true active site of Cu/ZnO/Al<sub>2</sub>O<sub>3</sub>, the industrial catalyst for methanol synthesis which remains under controversy. The challenge lies in capturing the interplay between the surface and reaction under operating conditions, which can be overcome given that the explicit dynamics of the system is known. To provide a better understanding of the dynamic of Cu-Zn surface at the atomic level, the structure and the formation process of the Cu-Zn surface alloy on Cu(997) were investigated by machine-learning molecular dynamics (MD). Gaussian process regression aided with on-the-fly learning was employed to build the force field used in the MD. The simulation reveals atomistic details of the alloying process, that is, the incorporation of deposited Zn adatoms to the Cu substrate. The surface alloying is found to start at upper and lower terraces near the step edge, which emphasize the role of steps and kinks in the alloying. The incorporation of Zn at the middle terrace was found at the later stage of the simulation. The rationalization of alloying behavior was performed based on statistics and barriers of various elementary events that occur during the simulation. It was observed that the alloying scheme at the upper terrace is dominated by the confinement of Zn step adatoms by other adatoms, highlighting the importance of step fluctuations in the alloying process. On the other hand, the alloying scheme at the lower terrace is dominated by direct exchange between the Zn step adatom and the Cu atom underneath. The alloying at the middle terrace is dominated by the wave deposition mechanism and deep confinement of Zn adatoms. The short propagation of alloyed Zn in the middle terrace was observed to proceed by means of indirect exchange instead of local exchange as proposed in the previous scanning tunneling microscopy (STM) observation. The comparison of migration rate and activation energies to the result of STM observation is also made. We have found that at a certain distance from the surface, the STM tip significantly affects the elementary events such as vacancy formation and direct exchange. Materials science ; Alloy ; Terrace (agriculture) ; Molecular dynamics ; Chemical physics ; Copper ; Metallurgy ; Chemistry ; Computational chemistry Machine Learning in Materials Science ; nanoparticles nucleation surface interactions ; Block Copolymer Self-Assembly
Authors: Qin Zhu | Yuming Gu | Xinzhu Wang | Chenyang Zhang | Jing Ma
Source: Langmuir (article, 2022-03-25)
Field: Chemistry — Inorganic Chemistry

Discovery of Electronic Structure and Interfacial Interaction Features in Catalytic Activity The selective transformation of inert bonds (C-H, C-O, C-C, C-F, etc.) via various catalysts is one of the most challenging areas, with applications in organic synthesis, materials science, and biological and pharmaceutical chemistry. The catalytic performance of homogeneous and heterogeneous catalysts can be rationally controlled in two ways: (i) electronic structure modulation of the active site, such as the metal center, ligands, and coordination modes, to improve the catalytic activity and stability and (ii) tuning intermolecular or interfacial interactions to promoting the reaction kinetics by accelerating the transmission of electrons between the catalyst and solvents or support. The rational design of catalysts based on adjustable features, such as metal (monometallic or bimetallic) active sites, crystal phase, ligands, solvents, and supports for inert bond activation under mild conditions remains a challenge. This Perspective summarizes the features of electronic structures, interfacial interactions, and their effects on molecular catalysis, metal-organic frameworks (MOFs), and natural mineral catalysis. The discovery of efficient catalysts could be promoted using machine-learning methods with high-performance descriptors. More attention should be paid to high-throughput quantum-chemical computations and experiments, automatic searches of chemical reaction pathways, and efficient machine-learning or deep-learning methods to accelerate catalyst design and synthesis in the future. Catalysis ; Bimetallic strip ; Chemistry ; Rational design ; Heterogeneous catalysis ; Electronic effect ; Combinatorial chemistry ; Catalytic cycle ; Inert ; Nanotechnology ; Materials science ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Asymmetric Hydrogenation and Catalysis
Authors: A. Gallo‐Bueno | Marine Reynaud | Montse Casas‐Cabanas | Javier Carrasco
Source: Energy and AI (article, 2022-04-08)
Field: Materials Science — Materials Chemistry

Unsupervised machine learning to classify crystal structures according to their structural distortion: A case study on Li-argyrodite solid-state electrolytes High-throughput approaches in computational materials discovery often yield a combinatorial explosion that makes the exhaustive rendering of complete structural and chemical spaces impractical. A common bottleneck when screening new compounds with archetypal crystal structures is the lack of fast and reliable decision-making schemes to quantitatively classify the computed candidates as inliers or outliers (too distorted structures). Machine learning-aided workflows can solve this problem and make geometrical optimization procedures more efficient. However, for this to occur, there is still a lack of appropriate combinations of suitable geometrical descriptors and accurate unsupervised models which are capable of accurately differentiating between systems with subtle structural changes. Here, considering as a case study the compositional screening of cubic Li-argyrodites solid electrolytes, we tackle this problem head on. We find that Steinhardt order parameters are very accurate descriptors of the cubic argyrodite structure to train a range of common unsupervised outlier detection models. And, most importantly, the approach enables us to automatically classify crystal structures with uncertainty control. The resulting models can then be used to screen computed structures with respect to an user-defined error threshold and discard too distorted structures during geometrical optimization procedures. Implemented as a decision node in computer-aided materials discovery workflows, this approach can be employed to perform autonomous high-throughput screening methods and make the use of computational and data storage resources more efficient. Computer science ; Workflow ; Artificial intelligence ; Bottleneck ; Outlier ; Rendering (computer graphics) ; Unsupervised learning ; Machine learning ; Database Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Geochemistry and Geologic Mapping
Authors: Wei Wu | Shuo Wang | Qiang Sun
Source: Advanced Theory and Simulations (article, 2022-01-11)
Field: Materials Science — Materials Chemistry

Topological Quantum Cathode Materials for Fast Charging Li‐Ion Battery Identified by Machine Learning and First Principles Calculation Abstract Fast charging electrode materials require excellent ion conductivity as well as high and stable electrical conductivity. However, the main commercial cathode materials are semiconducting transition metal oxides suffering from low electrical conductivity, so cathode materials with good electrical conductivity are highly desirable. It is well‐known that topological quantum materials (TQMs) can exhibit robust electrical conductivity, thus providing a promising solution to this problem. The key question becomes which TQMs can have high ionic conductivity. Herein, such topological quantum cathode materials for fast charging Li‐ion battery are identified by using machine learning combined with first‐principle calculation, where the supervised regression models are trained for diffusion energy barrier prediction. Among 7385 TQMs, 20 materials are found to have a diffusion energy barrier less than 1.0 eV. Especially, LiMnAs exhibits a reversible capacity of 195.9 mAh g −1 , higher than that of commercial cathodes while with comparable diffusion energy barrier. Furthermore, LiMnAs shows high interface stability with selected solid lithium metal oxides electrolytes. This study not only opens a new path to developing novel cathode materials, but also expands the applications of TQMs. Cathode ; Battery (electricity) ; Materials science ; Conductivity ; Electrolyte ; Ion ; Diffusion ; Topology (electrical circuits) ; Lithium-ion battery ; Diffusion barrier ; Chemical physics ; Optoelectronics ; Electrode ; Nanotechnology ; Chemistry ; Electrical engineering ; Thermodynamics ; Physics ; Physical chemistry ; Engineering Graphene research and applications ; Topological Materials and Phenomena ; Advancements in Battery Materials