jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Linlin Zhao | Yu Han | Xiaojuan Zhang | Zhong‐Yan Cao | Xiaowei Zhao | Yuxia Wang | Yonghong Cai | Yufeng Wu | Ying Xu
Source: International Journal of Molecular Sciences (article, 2024-09-21)
Field: Materials Science — Biomaterials

Synthesis and Mechanism of a Green Scale and Corrosion Inhibitor A new green water treatment agent, a poly(aspartic acid)-modified polymer (PASP/5-AVA), was synthesized using polysuccinimide and 5-aminovaleric acid (5-AVA) in a hybrid system. The structure was characterized, and the scale and corrosion inhibition performance were carried out with standard static scale inhibition and electrochemical methods, respectively. The mechanism was explored using XRD, XPS, SEM, and quantum chemistry calculations. The results indicated that PASP/5-AVA exhibited better scale and corrosion inhibition performance than PASP and maintained efficacy and thermal stability of the scale inhibition effect for a long time. Mechanistic studies indicated that PASP/5-AVA interferes with the normal generation of CaCO<sub>3</sub> and CaSO<sub>4</sub> scales through lattice distortion and dispersion, respectively; the combined effect of an alkaline environment and terminal electron-withdrawing -COOH groups can induce the stable C<sup>-</sup> ionic state formation in -CH<sub>2</sub>- of the extended side chain, thus enhancing its chelating ability for Ca<sup>2+</sup> ions. At the same time, the extension of the side chain length also enhances the adsorption ability of the agent on the metal surface, forming a thick film and delaying the corrosion of the metal surface. This study provides the necessary theoretical reference for the design of green scale and corrosion agents. Corrosion ; Thermal stability ; Electrochemistry ; Polymer ; X-ray photoelectron spectroscopy ; Mechanism (biology) ; Chemical engineering ; Chemistry ; Materials science ; Nuclear chemistry ; Combinatorial chemistry ; Organic chemistry ; Physical chemistry ; Physics Calcium Carbonate Crystallization and Inhibition ; Magnesium Oxide Properties and Applications ; Corrosion Behavior and Inhibition
Authors: Hongyu He | Lulu Li | Ruhan Ya | Hong Liu | Bin Luo | Zhipeng Li | Wenhuai Tian
Source: RSC Advances (article, 2024-01-01)
Field: Materials Science — Polymers and Plastics

Molecular dynamics simulation and experimental verification of the effects of vinyl silicone oil viscosity on the mechanical properties of silicone rubber foam The molecular motion trajectories of silicone rubber foam (SRF) at various vinyl silicone oil viscosities were studied <i>via</i> molecular dynamics (MD) simulation from the perspective of all atomic molecules. The influence of different viscosities of vinyl silicone oil on interaction, compatibility, and aggregation degree of molecules was determined based on the mean square displacement, diffusion coefficient, binding energy, solubility parameter, radial distribution function, and radius of gyration. The mechanical properties of the SRF were also experimentally verified. Results revealed that as the viscosity of vinyl silicone oil increased, the mean square displacement, fractional free volume, diffusion coefficient, and solubility parameter of the system decreased, whereas its larger radius of gyration increased. Moreover, the radial distribution function showed a weaker relative interaction between molecular chains. The calculated binding energy demonstrated that the system had better compatibility at a viscosity of 0.45 Pa s. This study provided a deeper insight into the relation between the viscosity of vinyl silicone oil and mechanical properties of the SRF. As the viscosity of vinyl silicone oil increased, the changing trend in MD simulation results of elastic modulus, shear modulus, bulk modulus, and Poisson's ratio was consistent with the experimental results. The MD simulations can promote theoretical predictions and scientific basis for the design of the SRF with desired performances. Silicone rubber ; Silicone oil ; Materials science ; Composite material ; Silicone ; Viscosity ; Natural rubber ; Molecular dynamics ; Chemistry ; Computational chemistry Polymer Nanocomposites and Properties ; Polymer composites and self-healing ; Polymer Foaming and Composites
Authors: Zixian Li | Nuo Xu | Jing Ren | Haigang Hao | Rui Gao | Xianggui Kong | Hong Yan | Xiao Hua | Yung‐Kang Peng | Shulan Ma | Dermot O’Hare | Yufei Zhao
Source: Chemical Science (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Theory-driven design of cadmium mineralizing layered double hydroxides for environmental remediation The environmental concern posed by toxic heavy metal pollution in soil and water has grown. Ca-based layered double hydroxides (LDHs) have shown exceptional efficacy in eliminating heavy metal cations through the formation of super-stable mineralization structures (SSMS). Nevertheless, it is still unclear how the intricate coordination environment of Ca<sup>2+</sup> in Ca-based LDH materials affects the mineralization performance, which hinders the development and application of Ca-based LDH materials as efficient mineralizers. Herein, we discover that, in comparison to a standard LDH, the mineralization efficiency for Cd<sup>2+</sup> ions may be significantly enhanced in the pentacoordinated structure of defect-containing Ca-5-LDH utilizing both density functional theory (DFT) and <i>ab initio</i> molecular dynamics (AIMD) simulations. Furthermore, the calcination-reconstruction technique can be utilized to successfully produce pentacoordinated Ca-5-LDH. Subsequent investigations verified that Ca-5-LDH exhibited double the mineralization performance (421.5 mg g<sup>-1</sup>) in comparison to the corresponding pristine seven coordinated Ca-7<sub>OH/H<sub>2</sub>O</sub>-LDH (191.2 mg g<sup>-1</sup>). The coordination-relative mineralization mechanism of Ca-based LDH was confirmed by both theoretical calculations and experimental results. The understanding of LDH materials and their possible use in environmental remediation are advanced by this research. Environmental remediation ; Cadmium ; Layered double hydroxides ; Environmental chemistry ; Environmental science ; Soil remediation ; Environmental pollution ; Pollution ; Soil contamination ; Heavy metals ; Chemistry ; Environmental protection ; Materials science ; Soil water ; Soil science ; Contamination ; Inorganic chemistry ; Metallurgy ; Ecology ; Biology Layered Double Hydroxides Synthesis and Applications ; Magnesium Oxide Properties and Applications ; Advanced Photocatalysis Techniques
Authors: Assa Aravindh Sasikala Devi | Vahid Javaheri | Sakari Pallaspuro | Jukka Kömi
Source: Physical Chemistry Chemical Physics (review, 2024-01-01)
Field: Materials Science — Materials Chemistry

The role of density functional theory in decoding the complexities of hydrogen embrittlement in steels Hydrogen (H) is considered as the key element in aiding the initiated green energy transition. To facilitate this, efficient and durable technologies need to be developed for the generation, storage, transportation, and use of H. All these value chain stages require materials that can withstand continuous exposure to H. Once absorbed, H can eventually concentrate to critical levels in a stressed microstructure, inducing specific damage mechanisms and consecutive loss of mechanical properties. This is known as hydrogen embrittlement (HE). Being one of the most significant structural material types, steels are widely used throughout the H value chain. They can suffer from HE, and numerous attempts are made towards understanding and mitigating this complex phenomenon. While originating at a size scale of atoms, HE acts on multiple spatio-temporal scales, and combined efforts of experimental and modelling techniques are needed to deal with it. This perspective is devoted to assimilating the knowledge that can be generated by density functional theory (DFT) methods to understand interactions between H and iron-based materials, and to promote finding solutions to HE in metallic materials in general. We aim to provide a comprehensive understanding of the properties calculated using DFT that can help advance finding novel H-resistant high-strength materials that facilitate the green shift at sufficient performance levels to meet the growing future needs. Hydrogen embrittlement ; Density functional theory ; Embrittlement ; Hydrogen ; Decoding methods ; Materials science ; Chemistry ; Metallurgy ; Computational chemistry ; Computer science ; Physics ; Quantum mechanics ; Algorithm Material Properties and Failure Mechanisms ; Hydrogen embrittlement and corrosion behaviors in metals ; Engineering Diagnostics and Reliability
Authors: Ali Öter | Betül Ersöz | Zeynep Berktaş | Halil İbrahim Bülbül | Elif Orhan | Şeref Sağıroğlu
Source: Physica Scripta (article, 2024-03-18)
Field: Materials Science — Materials Chemistry

An artificial intelligence model estimation for functionalized graphene quantum dot-based diode characteristics Abstract The use of artificial intelligence (AI) approaches, one of the most significant technological advances of the 21st century, to determine the diode parameters that can be obtained from Schottky diode characterization allows data to be collected, processed, analyzed, and results obtained faster than ever before, with high accuracy. It also supports the development of a selection and modeling tool for future studies and, most importantly, facilitates modeling implementation with faster and fewer experimental results. In this context, this study presents a machine learning model to predict diode parameters from current–voltage (I-V) measurements of polyethyleneimine-functionalized graphene quantum dots (GQDs)-based Schottky hybrid diode. This study used K-Nearest Neighbor, Random Forest (RF), Multilayered Perceptron, and Support Vector Machine algorithms. In research, the lowest model error of each model was compared, and the performance of the models obtained was evaluated. In addition, out of 30 diodes on the fabricated structure, the diode with the best rectification ratio (RR) was identified within a few seconds using machine learning, verified to be the same as the diode selected by the researchers, and the optimal estimation of RR and ideality factor was made from the diode parameters using the thermionic emission method. The experimental results were compared with machine learning models. Among these algorithms, the RF algorithm performed best with a mean square error value of 4.1 E-05 and an R-squared value of 0.999998. The success of RF depends on the characteristics of the dataset used, its size, and data distribution. The success rate of the RF algorithm is more successful in the 200 data sets used in this study. RF reduces overfitting by taking the average of multiple decision trees and is less sensitive to noise and outliers in the data. The results obtained will allow the development of a selection and modeling tool for future studies and, most importantly, facilitate modeling faster and with fewer experimental results, enabling the integration of AI into science. Graphene ; Quantum dot ; Materials science ; Diode ; Quantum ; Optoelectronics ; Nanotechnology ; Physics ; Quantum mechanics Carbon and Quantum Dots Applications ; Quantum Dots Synthesis And Properties ; Graphene research and applications
Authors: Jinxin Chen | Cheng Zhang | Lunzhao Yi | Fengmin Duan | Ying Gu | Kun Ge | Xuejing Fan
Source: Food Chemistry X (article, 2024-11-01)
Field: Materials Science — Materials Chemistry

Design and fabrication of self-calibration colorimetric/fluorescence/SERS tri-modal optical sensor for highly rapid and accurate detection of mercury ions in foods The improvement of detection accuracy without loss of rapidity and sensitivity by optical sensors in complex food analysis is still full of challenges owing to the matrix interference. Herein, a novel and simple self-calibration colorimetric/fluorescence/surface-enhanced Raman spectroscopy (SERS) tri-modal optical sensor based on aminated Rhodamine 6G (R6G-NH<sub>2</sub>) was developed for highly rapid, sensitive, and accurate detection of Hg<sup>2+</sup> in food samples. The high recognition specificity of R6G-NH<sub>2</sub> for Hg<sup>2+</sup> can be achieved through the metal chelation interaction between Hg<sup>2+</sup> and -NH<sub>2</sub>, -COOH groups in R6G-NH<sub>2</sub> with formation of R6G-NH<sub>2</sub>-Hg<sup>2+</sup>-R6G-NH<sub>2</sub> complex. The DFT and FDTD simulations were adopted to confirm the theoretical feasibility in Hg<sup>2+</sup> detection by tri-modal optical. Under the optimum conditions, the analytical method based on self-calibration tri-modal optical sensor for Hg<sup>2+</sup> detection was established with promising properties (rapidity, linearity, linear range, LOD, and LOQ), providing a strategy in rapid, selective, sensitive, and accurate detection for food safety. Mercury (programming language) ; Fabrication ; Fluorescence ; Ion ; Calibration ; Modal ; Optical sensing ; Materials science ; Optoelectronics ; Chemistry ; Nanotechnology ; Optics ; Computer science ; Organic chemistry ; Physics ; Polymer chemistry Advanced Nanomaterials in Catalysis ; Molecular Sensors and Ion Detection ; Advanced biosensing and bioanalysis techniques
Authors: Dongsheng Wen | Victoria Tucker | Michael S. Titus
Source: npj Computational Materials (article, 2024-09-08)
Field: Materials Science — Materials Chemistry

Bayesian optimization acquisition functions for accelerated search of cluster expansion convex hull of multi-component alloys Abstract Atomistic simulations are crucial for predicting material properties and understanding phase stability, essential for materials selection and development. However, the high computational cost of density functional theory calculations challenges the design of materials with complex structures and composition. This study introduces new data acquisition strategies using Bayesian-Gaussian optimization that efficiently integrate the geometry of the convex hull to optimize the yield of batch experiments. We developed uncertainty-based acquisition functions to prioritize the computation tasks of configurations of multi-component alloys, enhancing our ability to identify the ground-state line. Our methods were validated across diverse materials systems including Co-Ni alloys, Zr-O compounds, Ni-Al-Cr ternary alloys, and a planar defect system in intermetallic (Ni 1− x , Co x ) 3 Al. Compared to traditional genetic algorithms, our strategies reduce training parameters and user interaction, cutting the number of experiments needed to accurately determine the ground-state line by over 30%. These approaches can be expanded to multi-component systems and integrated with cost functions to further optimize experimental designs. Hull ; Convex hull ; Component (thermodynamics) ; Cluster (spacecraft) ; Bayesian probability ; Mathematical optimization ; Computer science ; Regular polygon ; Mathematics ; Applied mathematics ; Artificial intelligence ; Materials science ; Physics ; Geometry ; Composite material Machine Learning in Materials Science ; Additive Manufacturing Materials and Processes ; X-ray Diffraction in Crystallography
Authors: Jerret Ross | Brian Belgodere | Samuel C. Hoffman | Vijil Chenthamarakshan | Navratil, Jiri | Youssef Mroueh | Payel Das
Source: arXiv (Cornell University) (preprint, 2024-04-04)
Field: Chemistry — Organic Chemistry

GP-MoLFormer: A Foundation Model For Molecular Generation Transformer-based models trained on large and general purpose datasets consisting of molecular strings have recently emerged as a powerful tool for successfully modeling various structure-property relations. Inspired by this success, we extend the paradigm of training chemical language transformers on large-scale chemical datasets to generative tasks in this work. Specifically, we propose GP-MoLFormer, an autoregressive molecular string generator that is trained on more than 1.1B (billion) chemical SMILES. GP-MoLFormer uses a 46.8M parameter transformer decoder model with linear attention and rotary positional encodings as the base architecture. GP-MoLFormer's utility is evaluated and compared with that of existing baselines on three different tasks: de novo generation, scaffold-constrained molecular decoration, and unconstrained property-guided optimization. While the first two are handled with no additional training, we propose a parameter-efficient fine-tuning method for the last task, which uses property-ordered molecular pairs as input. We call this new approach pair-tuning. Our results show GP-MoLFormer performs better or comparable with baselines across all three tasks, demonstrating its general utility for a variety of molecular generation tasks. We further report strong memorization of training data in GP-MoLFormer generations, which has so far remained unexplored for chemical language models. Our analyses reveal that training data memorization and novelty in generations are impacted by the quality and scale of the training data; duplication bias in training data can enhance memorization at the cost of lowering novelty. We further establish a scaling law relating inference compute and novelty in generations. Foundation (evidence) ; History ; Archaeology Nanomaterials for catalytic reactions ; Chemical Synthesis and Analysis
Authors: Satvik Tripathi | K. R. Gabriel | Pushpendra Kumar Tripathi | Edward Kim
Source: Chemical Biology & Drug Design (review, 2024-06-01)
Field: Materials Science — Materials Chemistry

Large language models reshaping molecular biology and drug development The utilization of large language models (LLMs) has become a significant advancement in the domains of medicine and clinical informatics, providing a revolutionary potential for scientific breakthroughs and customized therapies. LLM models are trained on large datasets and exhibit the capacity to comprehend and analyze intricate biological data, encompassing genomic sequences, protein structures, and clinical health records. With the utilization of their comprehension of the language of biology, they possess the ability to reveal concealed patterns and insights that may evade human researchers. LLMs have been shown to positively impact various aspects of molecular biology, including the following: genomic analysis, drug development, precision medicine, biomarker development, experimental design, collaborative research, and accessibility to specialized expertise. However, it is imperative to acknowledge and tackle the obstacles and ethical implications involved. The careful consideration of data bias and generalization, data privacy and security, explainability and interpretability, and ethical concerns around responsible application is vital. The successful resolution of these obstacles will enable us to fully utilize the capabilities of LLMs, leading to substantial progress in the fields of molecular biology and pharmaceutical research. This progression also has the ability to bolster influential impacts for both the individual and the broader community. Personalized medicine ; Informatics ; Drug development ; Data science ; Interpretability ; Precision medicine ; Engineering ethics ; Comprehension ; Computer science ; Computational biology ; Biology ; Bioinformatics ; Political science ; Drug ; Artificial intelligence ; Pharmacology ; Genetics ; Engineering Machine Learning in Materials Science ; Topic Modeling ; Genomics and Rare Diseases
Authors: Shi Huang | Kang Xiang | Jiawei Mi
Source: Journal of Material Science and Technology (article, 2024-04-13)
Field: Materials Science — Materials Chemistry

Recent advances in synchrotron X-ray studies of the atomic structures of metal alloys in liquid state Research into the atomic structures of metal materials in the liquid state, their dynamic evolution versus temperature until the onset of crystal nucleation has been a central research topic in condensed matter physics and materials science for well over a century. Unfortunately, research and basic understanding of the atomic structures of liquid metals are far less than those in the solid state of the same compositions. This review serves as a condensed collection of the most important research literature published so far in this field, providing a critical and focused review of the historical research development and progress in this field since the 1920s. In particular, the development of powerful synchrotron X-ray sources and the associated experimental techniques for studying in-situ the atomic structures of different metallic systems. The key findings made in numerous pure metals and metal alloy systems are critically reviewed and discussed with a focus on the results and new understandings of structural heterogeneities found in bulk liquids, surface of liquids, and at liquid-solid interfaces. The possible future directions of research and development on the most advanced experimental and modeling techniques are envisaged and briefly discussed as well. Synchrotron ; Nucleation ; Nanotechnology ; Liquid metal ; State of matter ; Engineering physics ; Field (mathematics) ; Materials science ; Solid-state ; Chemistry ; Chemical physics ; Physics ; Metallurgy ; Condensed matter physics ; Nuclear physics ; Thermodynamics Material Dynamics and Properties ; nanoparticles nucleation surface interactions ; Iron oxide chemistry and applications
Authors: Adam A. L. Michalchuk∞
Source: Chemical Communications (review, 2024-01-01)
Field: Chemistry — Physical and Theoretical Chemistry

On the physical processes of mechanochemically induced transformations in molecular solids Initiating or sustaining physical and chemical transformations with mechanical force - mechanochemistry - provides an opportunity for more sustainable chemical processes, and access to new chemical reactivity. These transformations, however, do not always adhere to 'conventional' chemical wisdom, making them difficult to design and rationalise. This challenge is exacerbated by the fact that not all mechanochemical transformations are equal, with mechanical force playing a different role in different types of processes. In this review we discuss some of the different roles mechanical force can play in mechanochemical transformations, set primarily against the author's own research. We classify mechanochemical reactions broadly as those (1) where mechanical energy is for mixing, (2) where mechanical energy alters the stability of the reagent, and (3) where mechanical energy directly excites the solid. Finally, we demonstrate how - while useful - these classifications have fuzzy boundaries and concepts from across them are needed to understand many mechanochemical reactions. Mechanochemistry ; Chemistry ; Chemical physics ; Materials science ; Chemical engineering ; Nanotechnology ; Computational chemistry ; Engineering Crystallography and molecular interactions ; Chemical Thermodynamics and Molecular Structure ; Thermal and Kinetic Analysis