jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yu. M. Koval | V. V. Odnosum | T. G. Sych | G. S. Mogylnyy | В. В. Бурцев | A. Yu. Sezonenko
Source: METALLOFIZIKA I NOVEISHIE TEKHNOLOGII (article, 2024-03-04)
Field: Materials Science — Materials Chemistry

The Relationship between Chemical Composition of the Alloy and the Parameters of the Martensitic Transformation in NiTi Alloys Today, shape-memory alloys have already taken their place in various fields of science and technology.The most used alloy is titanium nickel or nitinol.When manufacturing alloys based on NiTi in order to use them for various products, it is necessary to know the conditions, under which it is possible to obtain alloys with previously known parameters of the phase transformation.In the work, the dependence between the Ni/Ti ratio, preliminary thermomechanical treatment (TMT) and the temperature of the beginning of the martensitic transformation (Ms) is obtained. Key words: direct and reverse transformations Nickel titanium ; Materials science ; Metallurgy ; Shape-memory alloy ; Alloy ; Transformation (genetics) ; Diffusionless transformation ; Martensite ; Chemical composition ; Thermodynamics ; Microstructure ; Chemistry Shape Memory Alloy Transformations ; Material Properties and Applications
Authors: Leonardo Galliano | Riccardo Rende | Daniele Coslovich
Source: arXiv (Cornell University) (preprint, 2024-07-03)
Field: Materials Science — Ceramics and Composites

Policy-guided Monte Carlo on general state spaces: Application to glass-forming mixtures Policy-guided Monte Carlo is an adaptive method to simulate classical interacting systems. It adjusts the proposal distribution of the Metropolis-Hastings algorithm to maximize the sampling efficiency, using a formalism inspired by reinforcement learning. In this work, we first extend the policy-guided method to deal with a general state space, comprising, for instance, both discrete and continuous degrees of freedom, and then apply it to a few paradigmatic models of glass-forming mixtures. We assess the efficiency of a set of physically inspired moves whose proposal distributions are optimized through on-policy learning. Compared to conventional Monte Carlo methods, the optimized proposals are two orders of magnitude faster for an additive soft sphere mixture but yield a much more limited speed-up for the well-studied Kob-Andersen model. We discuss the current limitations of the method and suggest possible ways to improve it. Monte Carlo method ; Statistical physics ; State (computer science) ; Materials science ; Computer science ; Mathematics ; Physics ; Algorithm ; Statistics Glass properties and applications
Authors: Takanori Nagasaki | Tomoaki Yamada
Source: Institutional Repositories DataBase (IRDB) (article, 2024-11-07)
Field: Materials Science — Materials Chemistry

Static disorder in perovskite-type proton-conducting oxides BaSn1−xMxO3−x/2–(y/2)H2O (M = Ga, Sc, In, Y, La): a novel approach based on statistical analysis of numerous DFT simulated structures Perovskite-type proton-conducting oxides inherently include defects such as substitutional aliovalent cations, oxide ion vacancies, and interstitial protons. These defects introduce static disorder into their crystal structures. To investigate such disorder in BaSn1−xMxO3−x/2–(y/2)H2O (M = Ga, Sc, In, Y, La), we employ a novel approach based on density functional theory (DFT) simulations. This approach involves the structural optimization of a large number of relatively small, randomly constructed supercells, followed by statistical analysis of their geometric, energetic, and vibrational properties. Our key findings are as follows. The structural disorder becomes more pronounced as the dopant concentration increases, and as the dopant ionic radius increases from Sc to La. The O–H covalent bond lengths, as well as the number densities, of hydrogen atoms display clearly different distributions depending on whether the hydrogen atoms are trapped by aliovalent cations or not. The hydrogen-trapping energies appear to decrease as the geometric similarity of O–H covalent bonds between trapped and untrapped hydrogen atoms increases. Hydrogen atoms simultaneously trapped by more than one dopant atom exhibit considerable stability, potentially hindering proton diffusion at high dopant concentrations. The O–H stretching wavenumber decreases as the O–H covalent bond length increases, showing a very strong and nearly linear correlation between the two. The simulated IR absorption spectra show semi-quantitative agreement with the measured spectra. These new findings demonstrate the effectiveness of the present ‘statistical’ approach for investigating static disorder. Dopant ; Density functional theory ; Ionic bonding ; Covalent bond ; Ionic radius ; Hydrogen bond ; Hydrogen ; Atom (system on chip) ; Bond length Electronic and Structural Properties of Oxides ; Advancements in Solid Oxide Fuel Cells ; Gas Sensing Nanomaterials and Sensors
Authors: J.S.O. O'Brien | Renuka Kadirvelraj | Po‐Sen Tseng | Nolan R. Kemppinen | David Crich | Richard M. Walsh | Zachary A. Wood
Source: Biochemistry (article, 2024-12-16)
Field: Materials Science — Materials Chemistry

Cryo-EM Structure of Recombinantly Expressed hUGDH Unveils a Hidden, Alternative Allosteric Inhibitor Human UDP-glucose dehydrogenase (hUGDH) catalyzes the oxidation of UDP-glucose into UDP-glucuronic acid, an essential substrate in the Phase II metabolism of drugs. hUGDH is a hexamer that exists in an equilibrium between an active (E) state and an inactive (E<sup>Ω</sup>) state, with the latter being stabilized by the binding of the allosteric inhibitor UDP-xylose (UDP-Xyl). The allosteric transition between E<sup>Ω</sup> and E is slow and can be observed as a lag in progress curves. Previous analysis of the lag suggested that unliganded hUGDH exists mainly as E<sup>Ω</sup>, but two unique crystal forms suggest that the enzyme favors the E state. Resolving this discrepancy is necessary to fully understand the allosteric mechanism of hUGDH. Here, we used cryo-EM to show that recombinant hUGDH expressed in <i>Escherichia coli</i> copurifies with UDP-4-keto-xylose (UX4O), which mimics the UDP-Xyl inhibitor and favors the E<sup>Ω</sup> state. Cryo-EM studies show that removing UX4O from hUGDH shifts the ensemble to favor the E state. This shift is consistent with progress curve analysis, which shows the absence of a lag for unliganded hUGDH. Inhibition studies show that hUGDH has similar affinities for UDP-Xyl and UX4O. The discovery that UX4O inhibits allosteric hUGDH suggests that UX4O may be the physiologically relevant inhibitor of allosteric UGDHs in bacteria that do not make UDP-Xyl. Allosteric regulation ; Chemistry ; Random hexamer ; Biochemistry ; Enzyme ; Stereochemistry Enzyme Structure and Function ; Protein Structure and Dynamics ; Diet, Metabolism, and Disease
Authors: Frederik Kamper Jørgensen | Mickaël G. Delcey | Erik D. Hedegård
Source: arXiv (Cornell University) (preprint, 2024-05-19)
Field: Chemistry — Spectroscopy

Perspective: Multi-configurational methods in bio-inorganic chemistry Transition metal ions play crucial roles in the structure and function of numerous proteins, contributing to essential biological processes such as catalysis, electron transfer, and oxygen binding. However, accurately modeling the electronic structure and properties of metalloproteins poses significant challenges due to the complex nature of their electronic configurations and strong correlation effects. Multiconfigurational quantum chemistry methods are, in principle, the most appropriate tools for addressing these challenges, offering the capability to capture the inherent multi-reference character and strong electron correlation present in bio-inorganic systems. Yet their computational cost has long hindered wider adoption, making methods such as Density Functional Theory (DFT) the method of choice. However, advancements over the past decade have substantially alleviated this limitation, rendering multiconfigurational quantum chemistry methods more accessible and applicable to a wider range of bio-inorganic systems. In this perspective, we discuss some of these developments and how they have already been used to answer some of the most important questions in bio-inorganic chemistry. We also comment on ongoing developments in the field and how the future of the field may evolve. Perspective (graphical) ; Chemistry ; Biochemical engineering ; Computational chemistry ; Management science ; Computer science ; Engineering ; Artificial intelligence Molecular spectroscopy and chirality
Authors: Matt Nagle | Hannah Conroy Broderick | Adrián Buganza Tepole | Michael Fop | Aisling Ní Annaidh
Source: Research Square (preprint, 2024-04-18)
Field: Materials Science — Polymers and Plastics

A machine learning approach to predict in vivo skin growth Computer science ; Machine learning ; In vivo ; Artificial intelligence ; Biology ; Biotechnology Textile materials and evaluations
Authors: K. J. Kavitha | Krishna Prasad K
Source: Research Square (preprint, 2024-04-10)
Field: Chemistry — Analytical Chemistry

CNN and LSTM Approach for Soil Nutrient analysis for Sugarcane Crop Crop ; Nutrient ; Soil nutrients ; Agronomy ; Agricultural engineering ; Environmental science ; Agroforestry ; Soil science ; Soil water ; Biology ; Ecology ; Engineering Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI
Authors: Stephanie Kew Yen Nee
Source: International Journal of Progressive Sciences and Technologies (article, 2024-05-30)
Field: Chemistry — Electrochemistry

Resting-State Network Functional Connectivity Analysis: A Multimodal Imaging and Computational Modeling Approach Functional connectivity is referred to as the temporal interaction between different brain regions that are spatially distant from each other. It is commonly examined during resting states by classifying resting-state functional connectivity (rs-FC) patterns and their associated synchronized activity to understand the underlying neural mechanisms that support cognitive processes. With the advancement of cutting-edge neuronal systems, multimodal imaging, and computational advances have served as promising tools for rs-FC studies. Despite having reviewed resting-state networks and their influence on a range of health anomalies in the existing literature, there nevertheless remains a lack of reviews that delve into diverse imaging modalities and computational modeling approaches for rs-FC analysis. Therefore, this systematic review aims to provide an understanding of these technologies that analyze the resting-state network functional connectivity and their correlation to cognitive functions. Through a systematic approach to analyzing studies, the most frequently employed modality was functional magnetic resonance imaging (fMRI) due to its high spatial resolution. Key findings demonstrated that higher functional connectivity levels are often linked to greater Exin ratios, which subsequently affect large-scale functional brain synchronization. Across various computational models, a non-parametric classifier has shown promise for rs-FC analysis in various contexts due to its ability to handle multiple EEG features. Moreover, the multivariate model is identified to have superior performance with a predictive area under the curve (AUC) of ~0.77. Overall, the present study underscores the vitality of using imaging and computational tools in elucidating the intricacies of rs-FC and its effects on individual cognition. Resting state fMRI ; Functional connectivity ; Computer science ; Network analysis ; Artificial intelligence ; Neuroscience ; Psychology ; Physics Electrochemical Analysis and Applications ; Functional Brain Connectivity Studies ; Complex Network Analysis Techniques
Authors: Petter Rosander | Erik Fransson | Nicklas Österbacka | Paul Erhart | Gӧran Wahnström
Source: arXiv (Cornell University) (preprint, 2024-09-24)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Untangling the Raman spectrum of cubic and tetragonal BaZrO$_3$ Raman spectroscopy is a widely used experimental technique to study the vibrational properties of solids. Atomic scale simulations can be used to predict such spectra, but trustworthy studies at finite temperatures are challenging, mainly due to the requirement of accurate and computationally efficient models for the dielectric susceptibility. Here, we have made use of molecular dynamics (MD) simulations together with a density functional theory (DFT) based model for the dielectric susceptibility to determine the Raman spectrum of barium zirconate, BaZrO$_3$ (BZO), a well-studied oxide perovskite. At ambient conditions, where the system is cubic, we find excellent agreement with experimentally measured Raman spectra. Our study establishes that the relatively sharp spectra seen experimentally are due to second-order scattering. At higher pressures, where BZO is tetragonal, all first-order Raman active modes are identified. Additionally, slightly below the phase transition, in the cubic phase, a broad "central Raman peak" appears. The origin of this type of peak is controversial and extensively debated in connection to the dynamics of the halide perovskites. Here, we show that it is also present in a "hard" oxide perovskite, and it originates from the highly overdamped R-tilt mode in the cubic structure. Tetragonal crystal system ; Raman spectroscopy ; Materials science ; Spectrum (functional analysis) ; Crystallography ; Chemistry ; Crystal structure ; Physics ; Optics ; Quantum mechanics Multiferroics and related materials ; Ferroelectric and Piezoelectric Materials ; X-ray Diffraction in Crystallography
Authors: Petr Fridrich | Zbyšek Posel
Source: Polymers (article, 2024-03-06)
Field: Materials Science — Surfaces, Coatings and Films

Phase Behavior of Polydisperse Y-Shaped Polymer Brushes under Good Solvent Conditions Y-shaped polymer brushes represent a special class of binary mixed polymer brushes, in which a combination of different homopolymers leads to unique phase behavior. While most theoretical and simulation studies use monodisperse models, experimental systems are always polydisperse. This discrepancy hampers linking theoretical and experimental results. In this theoretical study, we employed dissipative particle dynamics to study the influence of polydispersity on the phase behavior of Y-shaped brushes grafted to flat surfaces under good solvent conditions. Polydispersity was kept within experimentally achievable values and was modeled via Schulz-Zimm distribution. In total, 10 systems were considered, thus covering the phase behavior of monodisperse, partially polydisperse and fully polydisperse systems. Using such generic representation of real polymers, we observed a rippled structure and aggregates in monodisperse systems. In addition, polydisperse brushes formed a stable perforated layer not observed previously in monodisperse studies, and influenced the stability of the remaining phases. Although the perforated layer was experimentally observed under good solvent conditions and in the melt state, further confirmation of its presence in systems under good solvent conditions required mapping real polymers onto mesoscale models that reflected, for example, different polymer rigidity, and excluded volume effects or direct influence of the surface, just to mention a few parameters. Finally, in this work, we show that mesoscale modeling successfully describes polydisperse models, which opens the way for rapid exploring of complex systems such as polydisperse Y-shaped brushes in selective or bad solvents or under non-equilibrium conditions. Dispersity ; Dissipative particle dynamics ; Polymer ; Materials science ; Phase (matter) ; Solvent ; Thermodynamics ; Polymer chemistry ; Chemical physics ; Composite material ; Chemistry ; Organic chemistry ; Physics Polymer Surface Interaction Studies ; Pickering emulsions and particle stabilization ; Blood properties and coagulation
Authors: Valeriia Burlakova | Тетяна Мітченко
Source: WATER AND WATER PURIFICATION TECHNOLOGIES SCIENTIFIC AND TECHNICAL NEWS (review, 2024-12-29)
Field: Chemistry — Analytical Chemistry

A COMPREHENSIVE REVIEW OF THE CURRENT STATE OF ORGANIC EMERGING CONTAMINANTS MANAGEMENT IN DRINKING WATER: REGULATORY LANDSCAPE, PROPERTIES, HEALTH IMPACTS, TREATMENT METHODS This study provides a comprehensive assessment of the growing challenge posed by organic emerging contaminants in drinking water systems. A comparative analysis is conducted on key pollutants, including per- and polyfluoroalkyl substances, pesticides, bisphenol A, nonylphenol, 1,4-dioxane, and β-estradiol, focusing on their occurrence, physicochemical characteristics, environmental persistence, and health impacts on both ecosystems and human populations. These substances frequently exhibit similar traits such as hydrophobicity, low volatility, and resistance to conventional water treatment processes, thereby significantly complicating their effective removal using standard technologies. Regulatory frameworks in the European Union, the United States, China, and Ukraine are critically examined, with attention to both recent regulatory advancements and persistent gaps that hinder uniform international control. In addition, a detailed assessment of current water treatment technologies, including adsorption, ion exchange, and membrane filtration, which demonstrate a fairly high efficiency in pollutant removal under optimized operational conditions, is provided. However, challenges remain related to the regeneration of spent sorbents, membrane fouling, operational costs, and safe waste management. Oxidative methods, such as ozonation, UV irradiation, and advanced oxidation processes, are effective for the destruction of organic micropollutants, although the formation of potentially hazardous by-products, such as aldehydes, carboxylic acids, or halogenated organics, requires further water treatment. The effective removal of organic micropollutants from water requires the integration of strategies for both physical removal and chemical or biological degradation. Degradation technologies, such as incineration, electrochemical degradation, supercritical water oxidation, and biodegradation, demonstrate varying efficiency and levels of environmental sustainability and are often limited by high energy consumption, high operational costs, or incomplete mineralization of pollutants. The findings highlight the pressing need for further enhancement of international regulations, the development of cost-effective, energy-efficient, and sustainable advanced treatment technologies, and the adoption of integrated water management strategies to ensure the long-term protection of public health and drinking water resources globally. Current (fluid) ; Environmental planning ; Environmental science ; State (computer science) ; Environmental resource management ; Business ; Environmental health ; Computer science ; Engineering ; Medicine Dye analysis and toxicity ; Water Quality Monitoring and Analysis
Authors: Tongzhao Wang | Isaac Lun | Liang Xia | Yongji Wang | Song Pan
Source: Coatings (article, 2024-12-13)
Field: Chemistry — Organic Chemistry

Kinetic Study of Infrared Heat Curing of Thermosetting Polymers Infrared (IR) radiation curing technology has a high potential to improve the curing process of thermosetting polymers. To investigate the IR curing reaction mechanism, the present study explores the curing kinetics of glycidyl methacrylate (GMA)/dodecanedioic acid (DDDA) powder coatings subjected to IR radiation. Fourier-transformed infrared (FT-IR) spectroscopy is employed to record the concentration of epoxide groups with respect to time under different temperature conditions, with the reaction conducted under IR radiation. The resulting data are then fitted by the Levenberg–Marquardt algorithm using MATLAB software to obtain the kinetic parameters, namely the rate constant (k), catalytic constants (n, m), manifestation activation energy (E), and the pre-exponential factor (A) of the curing reaction. Additionally, this study proposes a new concept: the ‘photo-thermal synergistic effect’ of infrared curing and its evaluation criteria using a dimensionless quantity. Incredibly, this index integrates the impact of IR curing technology on two aspects: the curing process and the properties of the cured product. Overall, this study deepens our understanding of the IR curing reaction mechanism and provides a reference for the application of this technology in practical engineering. Thermosetting polymer ; Curing (chemistry) ; Infrared ; Kinetic energy ; Polymer ; Materials science ; Polymer chemistry ; Composite material ; Polymer science ; Physics ; Optics Photopolymerization techniques and applications ; Epoxy Resin Curing Processes ; Synthesis and properties of polymers
Authors: Jinyoung Kim | Sean D. Lawley | Jinsu Kim
Source: arXiv (Cornell University) (preprint, 2024-08-27)
Field: Materials Science — Materials Chemistry

A reaction network model of microscale liquid-liquid phase separation reveals effects of spatial dimension Proteins can form droplets via liquid-liquid phase separation (LLPS) in cells. Recent experiments demonstrate that LLPS is qualitatively different on two-dimensional (2d) surfaces compared to three-dimensional (3d) solutions. In this paper, we use mathematical modeling to investigate the causes of the discrepancies between LLPS in 2d versus 3d. We model the number of proteins and droplets inducing LLPS by continuous-time Markov chains and use chemical reaction network theory to analyze the model. To reflect the influence of space dimension, droplet formation and dissociation rates are determined using the first hitting times of diffusing proteins. We first show that our stochastic model reproduces the appropriate phase diagram and is consistent with the relevant thermodynamic constraints. After further analyzing the model, we find that it predicts that the space dimension induces qualitatively different features of LLPS which are consistent with recent experiments. While it has been claimed that the differences between 2d and 3d LLPS stems mainly from different diffusion coefficients, our analysis is independent of the diffusion coefficients of the proteins since we use the stationary model behavior. Therefore, our results give new hypotheses about how space dimension affects LLPS. Microscale chemistry ; Dimension (graph theory) ; Separation (statistics) ; Liquid liquid ; Phase (matter) ; Liquid phase ; Materials science ; Chromatography ; Physics ; Thermodynamics ; Chemistry ; Mathematics ; Statistics Pickering emulsions and particle stabilization ; Innovative Microfluidic and Catalytic Techniques Innovation