jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Camille Goudenhooft | Alessia Melelli | Sylvie Durand | Xavier Falourd | Lucie Le-Bot | Loren Morgillo | Sanaa Gaballah | Roberta Cortopassi | Anita Quilès | Darshil U. Shah | Frédéric Jamme | Johnny Beaugrand | Alain Bourmaud
Source: Carbohydrate Polymers (article, 2024-07-20)
Field: Materials Science — Biomaterials

Comparison of kink-band structures and specificities of cell wall polysaccharides in modern and ancient flax fibres Flax (Linum usitatissimum L.) is a plant of industrial importance, its fibres being presently used for high-value textile applications, composite reinforcements as well as natural actuators. Human interest in this fibre-rich plant dates back several millennia, including to Ancient Egypt where flax was used extensively in various quotidian items. While the recent technical developments of flax fibres continue to diversify through scientific research, the historical use of flax also has rich lessons for today. Through careful examination of ancient Egyptian and modern flax fibres, this study aims to conduct a multi-scale characterization from the yarn to the fibre cell wall scale, linking differences in structure and polysaccharide content to the mechanical performance and durability of flax. Here, a multi-scale biochemical study is enriched by scanning electron microscopy and nanomechanical investigations. A key finding is the similarity of cellulose features, crystallinity index and local mechanical performances between ancient and modern fibres. Biochemically speaking, monosaccharides analysis, deep-UV and NMR investigations demonstrate that ancient fibres exhibit less pectins but a similar hemicellulosic content, especially through uronic acids and galactose, suggesting the sensitivity of these non-crystalline components. Linum ; Crystallinity ; Polysaccharide ; Cellulose ; Characterization (materials science) ; Polymer science ; Xylose ; Cell wall ; Monosaccharide ; Materials science ; Chemistry ; Composite material ; Botany ; Biology ; Nanotechnology ; Organic chemistry Advanced Cellulose Research Studies ; Natural Fiber Reinforced Composites ; Polysaccharides and Plant Cell Walls
Authors: Adil Kabylda | Bohayra Mortazavi | Xiaoying Zhuang | Alexandre Tkatchenko
Source: Advanced Functional Materials (article, 2024-12-15)
Field: Materials Science — Materials Chemistry

Mechanical Properties of Nanoporous Graphenes: Transferability of Graph Machine‐Learned Force Fields Compared to Local and Reactive Potentials Abstract Nanoporous and chemically‐bridged graphene nanosheets span a wide chemical space with a broad set of applications in sensing and electronics. Modeling the structure and dynamics of such nanosheets is challenging, as chemical bond making and breaking as well as non‐covalent interactions must be captured accurately and on equal footing. Here it is showed that recent graph‐based machine‐learned force field (MLFF) SO3krates [J. T. Frank et al. , Nat. Commun. 15 , 6539 (2024)] is able to reliably model the dynamics and mechanical response for a broad class of nanoporous graphenes when trained on accurate density functional theory data that includes long‐range many‐body dispersion (MBD) interactions. In contrast, local moment tensor potentials and empirical reactive potentials are much less accurate. It is also found that recent MLFFs trained on solid‐state datasets must be used with care, since even empirical potentials occasionally yield more accurate results. These findings highlight the potential of properly‐trained graph MLFFs in modeling the properties of whole chemical spaces of complex functional materials. Transferability ; Materials science ; Nanoporous ; Nanotechnology ; Force field (fiction) ; Graph ; Composite material ; Artificial intelligence ; Machine learning ; Computer science ; Theoretical computer science Graphene research and applications ; Machine Learning in Materials Science ; Nanoporous metals and alloys
Authors: Akash Banerjee | Mason Hooten | Nour Srouji | Rebecca S. Welch | Joseph P. Shovlin | Meenakshi Dutt
Source: Frontiers in Soft Matter (article, 2024-04-10)
Field: Materials Science — Materials Chemistry

A perspective on coarse-graining methodologies for biomolecules: resolving self-assembly over extended spatiotemporal scales The process of self-assembly of biomolecules underlies the formation of macromolecular assemblies, biomolecular materials and protein folding, and thereby is critical in many disciplines and related applications. This process typically spans numerous spatiotemporal scales and hence, is well suited for scientific interrogation via coarse-grained (CG) models used in conjunction with a suitable computational approach. This perspective provides a discussion on different coarse-graining approaches which have been used to develop CG models that resolve the process of self-assembly of biomolecules. Granularity ; Perspective (graphical) ; Biomolecule ; Computer science ; Biological system ; Nanotechnology ; Data science ; Artificial intelligence ; Materials science ; Biology Block Copolymer Self-Assembly ; Polymer Surface Interaction Studies ; Pickering emulsions and particle stabilization
Authors: Warren R. L. Cairns | Owen T. Butler | Olga Cavoura | Christine M. Davidson | José Luis Todolí Torró | Marcus von der Au
Source: Journal of Analytical Atomic Spectrometry (review, 2024-12-11)
Field: Chemistry — Analytical Chemistry

Atomic spectrometry update – a review of advances in environmental analysis This review covers advances in the analysis of air, water, plants, soils and geological materials by a range of atomic spectrometric techniques, including atomic emission, absorption, fluorescence and mass spectrometry. Atomic absorption spectroscopy ; Mass spectrometry ; Atomic spectroscopy ; Environmental chemistry ; Analytical Chemistry (journal) ; Chemistry ; Environmental analysis ; Spectroscopy ; Chromatography ; Physics Analytical chemistry methods development ; Heavy metals in environment ; Mercury impact and mitigation studies
Authors: Brinda Vallat | Benjamin Webb | John Westbrook | Thomas D. Goddard | Christian A. Hanke | Andrea Graziadei | Ezra Peisach | Arthur O. Zalevsky | Jared Sagendorf | Hongsuda Tangmunarunkit | Serban Voinea | Monica Sekharan | Jianliang Yu | Alexandre M. J. J. Bonvin | Frank DiMaio | Gerhard Hummer | Jens Meiler | Emad Tajkhorshid | Thomas E. Ferrin | Catherine L. Lawson | Alexander Leitner | Juri Rappsilber | Claus A. M. Seidel | Cy M. Jeffries | S.K. Burley | Jeffrey C. Hoch | Genji Kurisu | Kyle L. Morris | Ardan Patwardhan | Sameer Velankar | Torsten Schwede | Jill Trewhella | Carl Kesselman | Helen M. Berman | Andrej Săli
Source: Journal of Molecular Biology (article, 2024-03-18)
Field: Materials Science — Materials Chemistry

IHMCIF: An Extension of the PDBx/mmCIF Data Standard for Integrative Structure Determination Methods IHMCIF (github.com/ihmwg/IHMCIF) is a data information framework that supports archiving and disseminating macromolecular structures determined by integrative or hybrid modeling (IHM), and making them Findable, Accessible, Interoperable, and Reusable (FAIR). IHMCIF is an extension of the Protein Data Bank Exchange/macromolecular Crystallographic Information Framework (PDBx/mmCIF) that serves as the framework for the Protein Data Bank (PDB) to archive experimentally determined atomic structures of biological macromolecules and their complexes with one another and small molecule ligands (e.g., enzyme cofactors and drugs). IHMCIF serves as the foundational data standard for the PDB-Dev prototype system, developed for archiving and disseminating integrative structures. It utilizes a flexible data representation to describe integrative structures that span multiple spatiotemporal scales and structural states with definitions for restraints from a variety of experimental methods contributing to integrative structural biology. The IHMCIF extension was created with the benefit of considerable community input and recommendations gathered by the Worldwide Protein Data Bank (wwPDB) Task Force for Integrative or Hybrid Methods (wwpdb.org/task/hybrid). Herein, we describe the development of IHMCIF to support evolving methodologies and ongoing advancements in integrative structural biology. Ultimately, IHMCIF will facilitate the unification of PDB-Dev data and tools with the PDB archive so that integrative structures can be archived and disseminated through PDB. Protein Data Bank (RCSB PDB) ; Protein Data Bank ; Dissemination ; Computer science ; Interoperability ; Representation (politics) ; Extension (predicate logic) ; Data science ; Data mining ; Information retrieval ; Protein structure ; World Wide Web ; Chemistry ; Programming language Enzyme Structure and Function ; Protein Structure and Dynamics ; Microbial Metabolic Engineering and Bioproduction
Authors: Chuan Peng | Yuling Zhu | Kaibo Zhang | Yiwei Wang | Yi Zheng | Yang Liu | Weili Fu | Hong Tan | Qiang Fu | Mingming Ding
Source: Nature Communications (article, 2024-11-21)
Field: Materials Science — Materials Chemistry

Redox-switchable multicolor luminescent polymers for theragnosis of osteoarthritis Nonaromatic and nonconjugated fluorescent materials have garnered increasing attention in recent years. However, most non-classical chromophores are derived from electro-rich nitrogen and oxygen atoms, which suffer from short emission wavelengths, low efficiency, limited responsiveness, and obscure luminescence mechanisms. Here we present an emission mechanism in bioactive polycysteine, an aliphatic polymer that displays polymerization- and aggregation-induced emission, high quantum yield, and multicolor emission properties. We show that the hydrogen atoms bonded to the sulfur atoms play a crucial role in luminescence. This enables reversible modulation of polymer fluorescence under reducing and oxidizing conditions, facilitating specific imaging and quantitative detection of redox species in cells and in vivo. Furthermore, the polymer exhibits better anti-inflammatory and antioxidative activities compared to first-line clinical antioxidants, offering a promising platform for in vivo theragnosis of diseases such as osteoarthritis. Fluorescence ; Aggregation-induced emission ; Luminescence ; Chromophore ; Photochemistry ; Polymer ; In vivo ; Quantum yield ; Polymerization ; Oxidizing agent ; Redox ; Materials science ; Nanotechnology ; Chemistry ; Combinatorial chemistry ; Organic chemistry ; Optoelectronics ; Biology Luminescence and Fluorescent Materials ; Click Chemistry and Applications ; Molecular Sensors and Ion Detection
Authors: A. Mousavi | Raziyeh Pourdarbani | Sajad Sabzi | Dorrin Sotoudeh | Mehrab Moradzadeh | Ginés Garcı́a-Mateos | Shohreh Kasaei | Mohammad Hossein Rohban
Source: Horticulturae (article, 2024-01-01)
Field: Chemistry — Analytical Chemistry

Classification of Healthy and Frozen Pomegranates Using Hyperspectral Imaging and Deep Learning Pomegranate is a temperature-sensitive fruit during postharvest storage. If exposed to cold temperatures above its freezing point for a long time, it will suffer from cold stress. Failure to pay attention to the symptoms that may occur during storage will result in significant damage. Identifying pomegranates susceptible to cold damage in a timely manner requires considerable skill, time and cost. Therefore, non-destructive and real-time methods offer great benefits for commercial producers. To this end, the purpose of this study is the non-destructive identification of healthy frozen pomegranates. First, healthy pomegranates were collected, and hyperspectral images were acquired using a hyperspectral camera. Then, to ensure that enough frozen pomegranates were collected for model training, all samples were kept in cold storage at 0 °C for two months. They were then transferred to the laboratory and hyperspectral images were taken from all of them again. The dataset consisted of frozen and healthy images of pomegranates in a ratio of 4:6. The data was divided into three categories, training, validation and test, each containing 1/3 of the data. Since there is a class imbalance in the training data, it was necessary to increase the data of the frozen class by the amount of its difference with the healthy class. Deep learning networks with ResNeXt, RegNetX, RegNetY, EfficientNetV2, VisionTransformer and SwinTransformer architectures were used for data analysis. The results showed that the accuracies of all models were above 99%. In addition, the accuracy values of RegNetX and EfficientNetV2 models are close to one, which means that the number of false positives is very small. In general, due to the higher accuracy of EfficientNetV2 model, as well as its relatively high precision and recall compared to other models, the F1 score of this model is also higher than the others with a value of 0.9995. Hyperspectral imaging ; Cold storage ; Postharvest ; Artificial intelligence ; Chemistry ; Mathematics ; Computer science ; Horticulture ; Biology Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Date Palm Research Studies
Authors: Xiaoying He | Mengxian Yu | Jian‐Peng Han | Jie Jiang | Qingzhu Jia | Qiang Wang | Zheng‐Hong Luo | Fangyou Yan | Yin‐Ning Zhou
Source: AIChE Journal (article, 2024-03-04)
Field: Materials Science — Biomaterials

Leveraging data‐driven strategy for accelerating the discovery of polyesters with targeted glass transition temperatures Abstract To overcome the limitations of empirical synthesis and expedite the discovery of new polymers, this work aims to develop a data‐driven strategy for profoundly aiding in the design and screening of novel polyester materials. Initially, we collected 695 polyesters with their associated glass transition temperatures ( T g s) to develop a quantitative structure–property relationship (QSPR) model. The model underwent rigorous validation (i.e., external validation, internal validation, Y ‐random, and application domain analysis) to demonstrate its robust predictive capabilities and high stability. Subsequently, by employing an in‐silico retrosynthesis strategy, over 95,000 virtual polyesters were designed, largely expanding the available space for polyester material family. External assessments were performed, highlighting good extrapolation ability of the QSPR model. Furthermore, we experimentally synthesized 10 designed polyesters with predicted T g s covering a large temperature range from −42.52 to 103.61°C, and characterization results gave an average absolute error of 17.40°C relative to the predicted ones. It is believed that such data‐driven approach can drive future product development of polymer industry. Polyester ; Glass transition ; Transition (genetics) ; Nanotechnology ; Chemistry ; Process engineering ; Materials science ; Engineering ; Polymer ; Organic chemistry biodegradable polymer synthesis and properties ; Polymer crystallization and properties ; Chemistry and Chemical Engineering
Authors: K. Nepal | Chinonso Ugwumadu | Kashi N. Subedi | Keerti Kappagantula | D. A. Drabold
Source: Applied Physics Letters (article, 2024-02-26)
Field: Materials Science — Materials Chemistry

Physical origin of enhanced electrical conduction in aluminum-graphene composites The electronic and transport properties of aluminum-graphene composite materials were investigated using the ab initio plane wave density functional theory. The interfacial structure is reported for several configurations. In some cases, the face-centered aluminum (111) surface relaxes in a nearly ideal registry with graphene, resulting in a remarkably continuous interface structure. The Kubo–Greenwood formula and space-projected conductivity were employed to study electronic conduction in aluminum single- and double-layer graphene-aluminum composite models. The electronic density of states at the Fermi level is enhanced by the graphene for certain aluminum–graphene interfaces, thus improving electronic conductivity. In double-layer graphene composites, conductivity varies non-monotonically with temperature, showing an increase between 300 and 400 K at short aluminum-graphene distances, unlike the consistent decrease in single-layer composites. Graphene ; Materials science ; Composite material ; Composite number ; Aluminium ; Density functional theory ; Electrical resistivity and conductivity ; Graphene nanoribbons ; Fermi level ; Conductivity ; Graphene foam ; Ab initio ; Electronic structure ; Thermal conduction ; Graphene oxide paper ; Condensed matter physics ; Electron ; Nanotechnology ; Computational chemistry ; Chemistry Graphene research and applications ; Surface and Thin Film Phenomena ; Thermal properties of materials
Authors: Zhilu Liu | Wei Li | Song Li
Source: RSC Applied Interfaces (article, 2024-11-18)
Field: Materials Science — Materials Chemistry

High-efficiency prediction of water adsorption performance of porous adsorbents by lattice grand canonical Monte Carlo molecular simulation A lattice GCMC method (LGCMC) was attempted to accurately and rapidly simulate the water adsorption performance of adsorbents using a coarse-grained Molinero water (mW) model. Monte Carlo method ; Adsorption ; Porosity ; Grand canonical ensemble ; Statistical physics ; Materials science ; Lattice (music) ; Thermodynamics ; Physics ; Chemistry ; Physical chemistry ; Mathematics ; Statistics ; Composite material Catalytic Processes in Materials Science ; Mathematical Approximation and Integration ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Steven M. Baksa | Simon Gelin | Seda Oturak | R. Jackson Spurling | Alireza Sepehrinezhad | Leonard Jacques | Susan Trolier‐McKinstry | Adri C. T. van Duin | Jon‐Paul Maria | Andrew M. Rappe | Ismaïla Dabo
Source: Advanced Electronic Materials (article, 2024-11-04)
Field: Materials Science — Materials Chemistry

Strain Fluctuations Unlock Ferroelectricity in Wurtzites Abstract Ferroelectrics are of practical interest for non‐volatile data storage due to their reorientable, crystallographically defined polarization. Yet efforts to integrate conventional ferroelectrics into ultrathin memories have been frustrated by film‐thickness limitations, which impede polarization reversal under low applied voltage. Wurtzite materials, including magnesium‐substituted zinc oxide (Zn,Mg)O, have been shown to exhibit scalable ferroelectricity as thin films. In this work, the origins of ferroelectricity in (Zn,Mg)O are explained, showing that large strain fluctuations emerge locally in (Zn,Mg)O and can reduce local barriers to ferroelectric switching by more than 40%. Concurrent experimental and computational evidence of these effects are provided by demonstrating polarization switching in ZnO/(Zn,Mg)O/ZnO heterostructures featuring built‐in interfacial strain gradients. These results open up an avenue to develop scalable ferroelectrics by controlling strain fluctuations atomistically. Materials science ; Ferroelectricity ; Strain (injury) ; Nanotechnology ; Condensed matter physics ; Optoelectronics ; Physics ; Dielectric Ferroelectric and Piezoelectric Materials ; Acoustic Wave Resonator Technologies ; Ferroelectric and Negative Capacitance Devices
Authors: Chungfeng Zhang | Mingli Chen | Yongjun Zhang | Wenyang Deng | Yu Gong | Di Zhang
Source: IET Generation Transmission & Distribution (article, 2024-01-27)
Field: Materials Science — Materials Chemistry

Partial discharge pattern recognition algorithm of overhead covered conductors based on feature optimization and bidirectional LSTM‐GRU Abstract Recognition of partial discharge (PD) patterns is essential for insulation diagnosis of covered conductors in overhead lines. Current research has not sufficiently addressed the complex background noise in real environments, and most detection methods depend primarily on feature engineering or deep learning, suggesting potential for improvement in accuracy and efficiency. This has led the authors to propose a PD pattern recognition algorithm that integrates feature selection and deep learning. This algorithm incorporates the design of a discrete wavelet denoising function specifically tailored to the characteristics of PD for data preprocessing. It employs Bayesian optimization algorithms and light gradient boosting machines for characterizing corona discharge features. Furthermore, it develops multi‐scale clustering features and phase‐resolved features for feature fusion, and constructs insightful features based on the light gradient boosting machine. Finally, a novel deep learning model is formulated, demonstrating exceptional detection performance for early faults in covered conductors. Experimental results show that this algorithm attains an Matthews correlation coefficient score of 0.814, a 13.2% improvement over the baseline algorithm's 0.719, and a speed increase of 39.18%. The final accuracy amounts to 97.85%. This algorithm demonstrates exceptional performance in detecting early insulation faults in conductors. Artificial intelligence ; Computer science ; Algorithm ; Boosting (machine learning) ; Partial discharge ; Pattern recognition (psychology) ; Overhead (engineering) ; Machine learning ; Engineering ; Voltage High voltage insulation and dielectric phenomena ; Thermal Analysis in Power Transmission ; Power Transformer Diagnostics and Insulation
Authors: Serhii Zaruba | Michaela Ovšonková | Patrycja Makoś‐Chełstowska | Vasiľ Andruch
Source: Frontiers in Chemistry (article, 2024-06-11)
Field: Chemistry — Analytical Chemistry

A closer look at how the dispersive liquid–liquid microextraction method works. Investigation of the effect of solvent mixture composition on the quality and stability of the cloudy state The dispersive liquid-liquid microextraction (DLLME) is one of the most popular miniaturized extraction procedures. In this paper, the degree of dispersion and dispersion stability were studied with the aim to assess the correlations of these parameters with efficiency for the selected analytical application. The dependence between the degree of dispersion (cloudy state quality) and its stability obtained by various emulsification procedures, such as solvent-assisted emulsification (using various dispersive solvents) and mechanical emulsification (using auxiliary energies), is investigated and discussed. It was found out that the degree of dispersion depends on the type of emulsification procedure and decreases in the series: solvent-assisted (SA-) = ultrasound-assisted (UA-) > air-assisted (AA-) > vortex-assisted (VA-) emulsification. The emulsion stability depends on the degree of dispersion and there were 1810 and 2070 s for the most effective emulsification procedures, such us solvent-assisted and ultrasound-assisted emulsification, respectively. A comparison between the sensitivity of the analytical methods (using spectrophotometric determination of the anionic surfactants) and the degree of dispersion have been made. The sensitivity of the methods was ranked as follows: DLLME > UA-LLME > VA-LLME > AA-LLME. Dispersion (optics) ; Solvent ; Liquid liquid ; Chemistry ; Chromatography ; Emulsion ; Degree (music) ; Dispersion stability ; Analytical Chemistry (journal) ; Extraction (chemistry) ; Pulmonary surfactant ; Organic chemistry Analytical chemistry methods development ; Forensic Fingerprint Detection Methods ; Radioactive element chemistry and processing