jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yunyun Xu | Hairong Xue | Xijuan Li | Xiaoli Fan | Peng Li | Tengfei Zhang | Kun Chang | Tao Wang | Jianping He
Source: Nano Research Energy (article, 2023-02-01)
Field: Materials Science — Materials Chemistry

Application of metal-organic frameworks, covalent organic frameworks and their derivates for the metal-air batteries Metal-organic frameworks (MOFs) and covalent organic frameworks (COFs) as the novel porous materials have the merits of diverse, adjustable functionality, high porosity and surface area, which have great application prospects in the gas storage, separation and catalysis. In addition, their derivates make up for the insufficient of electronic conductivity and chemical stability of MOFs and COFs, and provide a new ideal for accurate control of material structure. Up to now, many efficient electrocatalysts have been designed based on MOFs, COFs and their derivates for O<sub>2</sub> reduction/evolution reactions (ORR/OER) and CO<sub>2</sub> reduction/evolution reactions (CO<sub>2</sub>RR/CO<sub>2</sub>ER) in the metal-air batteries. In this review, the latest development of MOFs, COFs and their derivates in the metal-air batteries is summarized, and we discuss the structural characteristics of these materials and their corresponding mechanisms of action. By comprehensively reviewing the advantages, challenges and prospects of MOFs and COFs, we hope that the organic framework materials will shed more profound insights into the development of electrocatalysis and energy storage in the future. Metal-organic framework ; Nanotechnology ; Electrocatalyst ; Materials science ; Porosity ; Covalent bond ; Energy storage ; Metal ; Chemistry ; Electrochemistry ; Electrode ; Organic chemistry ; Metallurgy Covalent Organic Framework Applications ; Metal-Organic Frameworks: Synthesis and Applications ; Advanced Photocatalysis Techniques
Authors: Li Li | Wei‐Sheng Huang | Zheng Xu | Li‐Wen Xu
Source: Science China Chemistry (article, 2023-05-04)
Field: Chemistry — Organic Chemistry

Catalytic asymmetric silicon-carbon bond-forming transformations based on Si-H functionalization Enantioselective synthesis ; Hydrosilylation ; Surface modification ; Catalysis ; Reactivity (psychology) ; Silicon ; Chemistry ; Combinatorial chemistry ; Carbene ; Silylation ; Carbon fibers ; Organic chemistry ; Materials science ; Physical chemistry Organoboron and organosilicon chemistry ; Catalytic C–H Functionalization Methods ; Catalytic Cross-Coupling Reactions
Authors: Michael S. Chen | Joonho Lee | Hong‐Zhou Ye | Timothy C. Berkelbach | David R. Reichman | Thomas E. Markland
Source: Journal of Chemical Theory and Computation (article, 2023-02-02)
Field: Materials Science — Materials Chemistry

Data-Efficient Machine Learning Potentials from Transfer Learning of Periodic Correlated Electronic Structure Methods: Liquid Water at AFQMC, CCSD, and CCSD(T) Accuracy Obtaining the atomistic structure and dynamics of disordered condensed-phase systems from first-principles remains one of the forefront challenges of chemical theory. Here we exploit recent advances in periodic electronic structure and provide a data-efficient approach to obtain machine-learned condensed-phase potential energy surfaces using AFQMC, CCSD, and CCSD(T) from a very small number (≤200) of energies by leveraging a transfer learning scheme starting from lower-tier electronic structure methods. We demonstrate the effectiveness of this approach for liquid water by performing both classical and path integral molecular dynamics simulations on these machine-learned potential energy surfaces. By doing this, we uncover the interplay of dynamical electron correlation and nuclear quantum effects across the entire liquid range of water while providing a general strategy for efficiently utilizing periodic correlated electronic structure methods to explore disordered condensed-phase systems. Electronic structure ; Statistical physics ; Computer science ; Liquid water ; Molecular dynamics ; Phase (matter) ; Range (aeronautics) ; Quantum ; Physics ; Chemical physics ; Materials science ; Quantum mechanics ; Thermodynamics Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Quantum, superfluid, helium dynamics
Authors: Fan Zhang | Rui Yang | Diannan Lu
Source: Polymers (review, 2023-04-18)
Field: Materials Science — Polymers and Plastics

Investigation of Polymer Aging Mechanisms Using Molecular Simulations: A Review Aging has a serious impact on the properties of functional polymers. Therefore, it is necessary to study the aging mechanism to prolong the service and storage life of polymer-based devices and materials. Due to the limitations of traditional experimental methods, more and more studies have adopted molecular simulations to analyze the intrinsic mechanisms of aging. In this paper, recent advances in molecular simulations of the aging of polymers and their composites are reviewed. The characteristics and applications of commonly used simulation methods in the study of the aging mechanisms (traditional molecular dynamics simulation, quantum mechanics, and reactive molecular dynamics simulation) are outlined. The current simulation research progress of physical aging, aging under mechanical stress, thermal aging, hydrothermal aging, thermo-oxidative aging, electric aging, aging under high-energy particle impact, and radiation aging is introduced in detail. Finally, the current research status of the aging simulations of polymers and their composites is summarized, and the future development trend has been prospected. Materials science ; Polymer ; Accelerated aging ; Molecular dynamics ; Service life ; Nanotechnology ; Composite material ; Chemistry ; Computational chemistry Polymer crystallization and properties ; Carbon Nanotubes in Composites ; Polymer Nanocomposite Synthesis and Irradiation
Authors: Li‐Cheng Xu | Johanna Frey | Xiaoyan Hou | Shuo‐Qing Zhang | Yanyu Li | João C. A. Oliveira | Shu-Wen Li | Lutz Ackermann | Xin Hong
Source: Nature Synthesis (article, 2023-01-30)
Field: Materials Science — Materials Chemistry

Enantioselectivity prediction of pallada-electrocatalysed C–H activation using transition state knowledge in machine learning Workflow ; Chemical space ; Artificial intelligence ; Computer science ; Machine learning ; Olefin fiber ; Enantioselective synthesis ; Chemistry ; Interpretation (philosophy) ; Biochemical engineering ; Catalysis ; Organic chemistry ; Drug discovery ; Database ; Engineering Machine Learning in Materials Science ; Asymmetric Hydrogenation and Catalysis ; Surface Chemistry and Catalysis
Authors: Lei Tao | Jinlong He | Nuwayo Eric Munyaneza | Vikas Varshney | Wei Chen | Guoliang Liu | Ying Li
Source: Chemical Engineering Journal (article, 2023-04-13)
Field: Materials Science — Polymers and Plastics

Discovery of multi-functional polyimides through high-throughput screening using explainable machine learning Polyimide ; Computer science ; Materials science ; Glass transition ; Artificial intelligence ; Machine learning ; Ultimate tensile strength ; Polymer ; Mechanical engineering ; Nanotechnology ; Composite material ; Engineering Synthesis and properties of polymers ; Epoxy Resin Curing Processes ; Polymer Nanocomposites and Properties
Authors: David F. Nippa | Kenneth Atz | Remo Hohler | Alex T. Müller | Andreas Marx | Christian Bartelmus | Georg Wuitschik | Irene Marzuoli | Vera Jost | Jens Wolfard | Martin Binder | Antonia F. Stepan | David B. Konrad | Uwe Grether | Rainer E. Martin | Gisbert Schneider
Source: Nature Chemistry (article, 2023-11-23)
Field: Materials Science — Materials Chemistry

Enabling late-stage drug diversification by high-throughput experimentation with geometric deep learning Late-stage functionalization is an economical approach to optimize the properties of drug candidates. However, the chemical complexity of drug molecules often makes late-stage diversification challenging. To address this problem, a late-stage functionalization platform based on geometric deep learning and high-throughput reaction screening was developed. Considering borylation as a critical step in late-stage functionalization, the computational model predicted reaction yields for diverse reaction conditions with a mean absolute error margin of 4-5%, while the reactivity of novel reactions with known and unknown substrates was classified with a balanced accuracy of 92% and 67%, respectively. The regioselectivity of the major products was accurately captured with a classifier F-score of 67%. When applied to 23 diverse commercial drug molecules, the platform successfully identified numerous opportunities for structural diversification. The influence of steric and electronic information on model performance was quantified, and a comprehensive simple user-friendly reaction format was introduced that proved to be a key enabler for seamlessly integrating deep learning and high-throughput experimentation for late-stage functionalization. Surface modification ; Classifier (UML) ; Chemistry ; Drug discovery ; Artificial intelligence ; Diversification (marketing strategy) ; Regioselectivity ; Computer science ; Combinatorial chemistry ; Machine learning ; Organic chemistry Machine Learning in Materials Science ; Chemical Synthesis and Analysis ; Computational Drug Discovery Methods
Authors: Guoquan Wu | Wallace Tan Gian Yion | Khoa Le Nguyen Quang Dang | Zhe Wu
Source: Process Safety and Environmental Protection (article, 2023-03-06)
Field: Materials Science — Materials Chemistry

Physics-informed machine learning for MPC: Application to a batch crystallization process This work presents a framework for developing physics-informed recurrent neural network (PIRNN) models and PIRNN-based predictive control schemes for batch crystallization processes. The population balance model of the aspirin crystallization process is first developed to describe the formation of crystals through nucleation and growth. Then, the PIRNN modeling scheme is introduced to integrate observational data and mechanistic models for the development of machine learning models. Additionally, the physical constraints on process states are embedded in the machine learning models to prevent physically unreasonable predictions. Subsequently, the PIRNN model that captures the dynamic behavior of the batch crystallization process is utilized in the design of model predictive controller that optimizes the operation of the crystallizer. Through open-loop and closed-loop simulations, it is demonstrated that the PIRNN models using less training data achieve prediction accuracy and closed-loop performance comparable to the purely data-driven model. Model predictive control ; Process (computing) ; Machine learning ; Crystallization ; Nucleation ; Controller (irrigation) ; Artificial neural network ; Computer science ; Artificial intelligence ; Population ; Control engineering ; Open-loop controller ; Control theory (sociology) ; Control (management) ; Engineering ; Physics ; Closed loop Crystallization and Solubility Studies ; Advanced Control Systems Optimization ; Machine Learning in Materials Science
Authors: Yen-Hsiang Huang | Hong Wei | Peter J. Santiago | William John Thrift | Regina Ragan | Sunny C. Jiang
Source: Environmental Science & Technology (article, 2023-03-19)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Sensing Antibiotics in Wastewater Using Surface-Enhanced Raman Scattering Rapid and cost-effective detection of antibiotics in wastewater and through wastewater treatment processes is an important first step in developing effective strategies for their removal. Surface-enhanced Raman scattering (SERS) has the potential for label-free, real-time sensing of antibiotic contamination in the environment. This study reports the testing of two gold nanostructures as SERS substrates for the label-free detection of quinoline, a small-molecular-weight antibiotic that is commonly found in wastewater. The results showed that the self-assembled SERS substrate was able to quantify quinoline spiked in wastewater with a lower limit of detection (LoD) of 5.01 ppb. The SERStrate (commercially available SERS substrate with gold nanopillars) had a similar sensitivity for quinoline quantification in pure water (LoD of 1.15 ppb) but did not perform well for quinoline quantification in wastewater (LoD of 97.5 ppm) due to interferences from non-target molecules in the wastewater. Models constructed based on machine learning algorithms could improve the separation and identification of quinoline Raman spectra from those of interference molecules to some degree, but the selectivity of SERS intensification was more critical to achieve the identification and quantification of the target analyte. The results of this study are a proof-of-concept for SERS applications in label-free sensing of environmental contaminants. Further research is warranted to transform the concept into a practical technology for environmental monitoring. Quinoline ; Wastewater ; Raman scattering ; Analyte ; Substrate (aquarium) ; Detection limit ; Contamination ; Nanopillar ; Raman spectroscopy ; Materials science ; Chemistry ; Nanotechnology ; Chromatography ; Nanostructure ; Environmental science ; Organic chemistry ; Environmental engineering ; Optics Gold and Silver Nanoparticles Synthesis and Applications ; Biosensors and Analytical Detection ; Advanced biosensing and bioanalysis techniques
Authors: Runing Lai | Hao Chen | Zigang Zhou | Zao Yi | Bin Tang | Jing Chen | Yougen Yi | Chaojun Tang | Jianguo Zhang | Tangyou Sun
Source: Micromachines (article, 2023-09-21)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Design of a Penta-Band Graphene-Based Terahertz Metamaterial Absorber with Fine Sensing Performance This paper presents a new theoretical proposal for a surface plasmon resonance (SPR) terahertz metamaterial absorber with five narrow absorption peaks. The overall structure comprises a sandwich stack consisting of a gold bottom layer, a silica medium, and a single-layer patterned graphene array on top. COMSOL simulation represents that the five absorption peaks under TE polarization are at <i>f</i><sub>I</sub> = 1.99 THz (95.82%), <i>f</i><sub>Ⅱ</sub> = 6.00 THz (98.47%), <i>f</i><sub>Ⅲ</sub> = 7.37 THz (98.72%), <i>f</i><sub>Ⅳ</sub> = 8.47 THz (99.87%), and <i>f</i><sub>V</sub> = 9.38 THz (97.20%), respectively, which is almost consistent with the absorption performance under TM polarization. In contrast to noble metal absorbers, its absorption rates and resonance frequencies can be dynamically regulated by controlling the Fermi level and relaxation time of graphene. In addition, the device can maintain high absorptivity at 0~50° in TE polarization and 0~40° in TM polarization. The maximum refractive index sensitivity can reach <i>S</i><sub>V</sub> = 1.75 THz/RIU, and the maximum figure of merit (FOM) can reach FOM<sub>V</sub> = 12.774 RIU<sup>-1</sup>. In conclusion, our design has the properties of dynamic tunability, polarization independence, wide-incident-angle absorption, and fine refractive index sensitivity. We believe that the device has potential applications in photodetectors, active optoelectronic devices, sensors, and other related fields. Terahertz radiation ; Materials science ; Metamaterial absorber ; Optoelectronics ; Graphene ; Molar absorptivity ; Surface plasmon resonance ; Metamaterial ; Refractive index ; Polarization (electrochemistry) ; Optics ; Absorption (acoustics) ; Figure of merit ; Photodetector ; Tunable metamaterials ; Nanotechnology ; Nanoparticle ; Chemistry ; Physics Metamaterials and Metasurfaces Applications ; Plasmonic and Surface Plasmon Research ; Thermal Radiation and Cooling Technologies
Authors: Cheng Xu | Zhihao Ren | Hong Zhou | Jingkai Zhou | Chong Pei Ho | Nan Wang | Chengkuo Lee
Source: Light Science & Applications (article, 2023-06-25)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Expanding chiral metamaterials for retrieving fingerprints via vibrational circular dichroism Circular dichroism (CD) spectroscopy has been widely demonstrated for detecting chiral molecules. However, the determination of chiral mixtures with various concentrations and enantiomeric ratios can be a challenging task. To solve this problem, we report an enhanced vibrational circular dichroism (VCD) sensing platform based on plasmonic chiral metamaterials, which presents a 6-magnitude signal enhancement with a selectivity of chiral molecules. Guided by coupled-mode theory, we leverage both in-plane and out-of-plane symmetry-breaking structures for chiral metamaterial design enabled by a two-step lithography process, which increases the near-field coupling strengths and varies the ratio between absorption and radiation loss, resulting in improved chiral light-matter interaction and enhanced molecular VCD signals. Besides, we demonstrate the thin-film sensing process of BSA and β-lactoglobulin proteins, which contain secondary structures α-helix and β-sheet and achieve a limit of detection down to zeptomole level. Furthermore, we also, for the first time, explore the potential of enhanced VCD spectroscopy by demonstrating a selective sensing process of chiral mixtures, where the mixing ratio can be successfully differentiated with our proposed chiral metamaterials. Our findings improve the sensing signal of molecules and expand the extractable information, paving the way toward label-free, compact, small-volume chiral molecule detection for stereochemical and clinical diagnosis applications. Circular dichroism ; Metamaterial ; Vibrational circular dichroism ; Materials science ; Plasmon ; Molecule ; Chemistry ; Optoelectronics ; Crystallography ; Organic chemistry Metamaterials and Metasurfaces Applications ; Plasmonic and Surface Plasmon Research ; Orbital Angular Momentum in Optics
Authors: Mario Picciani | Wassim Gabriel | Victor Giurcoiu | Omar Shouman | Firas Hamood | Ludwig Lautenbacher | Cecilia Bang Jensen | Julian Müller | Mostafa Kalhor | Armin Soleymaniniya | Bernhard Küster | Matthew The | Mathias Wilhelm
Source: PROTEOMICS (article, 2023-09-06)
Field: Chemistry — Spectroscopy

Oktoberfest: Open‐source spectral library generation and rescoring pipeline based on Prosit Machine learning (ML) and deep learning (DL) models for peptide property prediction such as Prosit have enabled the creation of high quality in silico reference libraries. These libraries are used in various applications, ranging from data-independent acquisition (DIA) data analysis to data-driven rescoring of search engine results. Here, we present Oktoberfest, an open source Python package of our spectral library generation and rescoring pipeline originally only available online via ProteomicsDB. Oktoberfest is largely search engine agnostic and provides access to online peptide property predictions, promoting the adoption of state-of-the-art ML/DL models in proteomics analysis pipelines. We demonstrate its ability to reproduce and even improve our results from previously published rescoring analyses on two distinct use cases. Oktoberfest is freely available on GitHub (https://github.com/wilhelm-lab/oktoberfest) and can easily be installed locally through the cross-platform PyPI Python package. Python (programming language) ; Computer science ; Open source ; Pipeline (software) ; Artificial intelligence ; Machine learning ; Software ; Programming language Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Machine Learning in Bioinformatics
Authors: Justyna Grabska | Krzysztof B. Beć | Nami Ueno | Christian W. Huck
Source: Foods (review, 2023-05-10)
Field: Chemistry — Analytical Chemistry

Analyzing the Quality Parameters of Apples by Spectroscopy from Vis/NIR to NIR Region: A Comprehensive Review Spectroscopic methods deliver a valuable non-destructive analytical tool that provides simultaneous qualitative and quantitative characterization of various samples. Apples belong to the world's most consumed crops and with the current challenges of climate change and human impacts on the environment, maintaining high-quality apple production has become critical. This review comprehensively analyzes the application of spectroscopy in near-infrared (NIR) and visible (Vis) regions, which not only show particular potential in evaluating the quality parameters of apples but also in optimizing their production and supply routines. This includes the assessment of the external and internal characteristics such as color, size, shape, surface defects, soluble solids content (SSC), total titratable acidity (TA), firmness, starch pattern index (SPI), total dry matter concentration (DM), and nutritional value. The review also summarizes various techniques and approaches used in Vis/NIR studies of apples, such as authenticity, origin, identification, adulteration, and quality control. Optical sensors and associated methods offer a wide suite of solutions readily addressing the main needs of the industry in practical routines as well, e.g., efficient sorting and grading of apples based on sweetness and other quality parameters, facilitating quality control throughout the production and supply chain. This review also evaluates ongoing development trends in the application of handheld and portable instruments operating in the Vis/NIR and NIR spectral regions for apple quality control. The use of these technologies can enhance apple crop quality, maintain competitiveness, and meet the demands of consumers, making them a crucial topic in the apple industry. The focal point of this review is placed on the literature published in the last five years, with the exceptions of seminal works that have played a critical role in shaping the field or representative studies that highlight the progress made in specific areas. Near-infrared spectroscopy ; Quality (philosophy) ; Environmental science ; Agricultural engineering ; Sweetness ; Computer science ; Process engineering ; Biochemical engineering ; Biotechnology ; Food science ; Chemistry ; Engineering ; Biology ; Physics Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Essential Oils and Antimicrobial Activity
Authors: María Bueno Álvez | Fredrik Edfors | Kalle von Feilitzen | Martin Zwahlen | Adil Mardinoğlu | Per‐Henrik Edqvist | Tobias Sjöblom | Emma Lundin | Natallia Rameika | Gunilla Enblad | Henrik Lindman | Martin Höglund | Göran Hesselager | Karin Stålberg | Malin Enblad | Oscar E. Simonson | Michael Häggman | Tomas Axelsson | Mikael Åberg | Jessica Nordlund | Wen Zhong | Max Karlsson | Ulf Gyllensten | Fredrik Pontén | Linn Fagerberg | Mathias Uhlén
Source: Nature Communications (article, 2023-07-18)
Field: Chemistry — Spectroscopy

Next generation pan-cancer blood proteome profiling using proximity extension assay A comprehensive characterization of blood proteome profiles in cancer patients can contribute to a better understanding of the disease etiology, resulting in earlier diagnosis, risk stratification and better monitoring of the different cancer subtypes. Here, we describe the use of next generation protein profiling to explore the proteome signature in blood across patients representing many of the major cancer types. Plasma profiles of 1463 proteins from more than 1400 cancer patients are measured in minute amounts of blood collected at the time of diagnosis and before treatment. An open access Disease Blood Atlas resource allows the exploration of the individual protein profiles in blood collected from the individual cancer patients. We also present studies in which classification models based on machine learning have been used for the identification of a set of proteins associated with each of the analyzed cancers. The implication for cancer precision medicine of next generation plasma profiling is discussed. Proteome ; Blood cancer ; Computational biology ; Cancer ; Blood proteins ; Profiling (computer programming) ; Disease ; Bioinformatics ; Medicine ; Biology ; Pathology ; Computer science ; Internal medicine Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Advanced Biosensing Techniques and Applications