jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Xu-Ze Zhang | Rui Shi | Zhong‐Yuan Lu | Hu‐Jun Qian
Source: JACS Au (article, 2024-03-13)
Field: Materials Science — Materials Chemistry

Chemically Specific Systematic Coarse-Grained Polymer Model with Both Consistently Structural and Dynamical Properties The coarse-grained (CG) model serves as a powerful tool for the simulation of polymer systems; its reliability depends on the accurate representation of both structural and dynamical properties. However, strong correlations between structural and dynamical properties on different scales and also a strong memory effect, enforced by chain connectivity between monomers in polymer systems, render developing a chemically specific systematic CG model a formidable task. In this study, we report a systematic CG approach that combines the iterative Boltzmann inversion (IBI) method and the generalized Langevin equation (GLE) dynamics. Structural properties are ensured by using conservative CG potentials derived from the IBI method. To retrieve the correct dynamical properties in the system, we demonstrate that using a combination of a Rouse-type delta function and a time-dependent short-time kernel in the GLE simulation is practically efficient. The former can be used to adjust the long-time diffusion dynamics, and the latter can be reconstructed from an iterative procedure according to the velocity autocorrelation function (ACF) from all-atomistic (AA) simulations. Taking the polystyrene as an example, we show that not only structural properties of radial distribution function, intramolecular bond, and angle distributions can be reproduced but also dynamical properties of mean-square displacement, velocity ACF, and force ACF resulted from our CG model have quantitative agreement with the reference AA model. In addition, reasonable agreements are observed in other collective properties between our GLE-CG model and the AA simulations as well. Statistical physics ; Mean squared displacement ; Molecular dynamics ; Representation (politics) ; Distribution function ; Autocorrelation ; Physics ; Radial distribution function ; Thermodynamics ; Mathematics Material Dynamics and Properties ; Protein Structure and Dynamics ; Block Copolymer Self-Assembly
Authors: Thomas Pickles | Chantal L. Mustoe | Christopher Boyle | Javier Cardona | Cameron J. Brown | Alastair J. Florence
Source: CrystEngComm (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Developing a model-driven workflow for the digital design of small-scale batch cooling crystallisation with the antiviral lamivudine A model-driven workflow that uses digital tools and small-scale experiments to maximise the efficiency in achieving a desired set of crystallisation responses, kinetics and objectives. Lamivudine ; Workflow ; Scale (ratio) ; Crystallization ; Computer science ; Chemistry ; Process engineering ; Combinatorial chemistry ; Virology ; Database ; Engineering ; Medicine ; Physics ; Organic chemistry Crystallization and Solubility Studies ; Innovative Microfluidic and Catalytic Techniques Innovation ; Computational Drug Discovery Methods
Authors: Jinlong Zhang | Hang Liu | Shyam S. Sablani | Qinglin Wu
Source: Materials (article, 2024-06-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Recycling Functional Fillers from Waste Tires for Tailored Polystyrene Composites: Mechanical, Fire Retarding, Electromagnetic Field Shielding, and Acoustic Insulation Properties—A Short Review Polymer waste is currently a big and challenging issue throughout the world. Waste tires represent an important source of polymer waste. Therefore, it is highly desirable to recycle functional fillers from waste tires to develop composite materials for advanced applications. The primary theme of this review involves an overview of developing polystyrene (PS) composites using materials from recycled tires as fillers; waste tire recycling in terms of ground tire rubbers, carbon black, and textile fibers; surface treatments of the fillers to optimize various composite properties; and the mechanical, fire retarding, acoustic, and electromagnetic field (EMI) shielding performances of PS composite materials. The development of composite materials from polystyrene and recycled waste tires provides a novel avenue to achieve reductions in carbon emission goals and closed-loop plastic recycling, which is of significance in the development of circular economics and an environmentally friendly society. Materials science ; Composite material ; Polystyrene ; Composite number ; Carbon black ; Filler (materials) ; Environmentally friendly ; Electromagnetic shielding ; Expanded polystyrene ; Polymer ; Flammability ; Waste management ; Natural rubber ; Engineering Electromagnetic wave absorption materials ; Natural Fiber Reinforced Composites ; Flame retardant materials and properties
Authors: Sulaymon Eshkabilov | Ivan Šimko
Source: Agriculture (article, 2024-05-27)
Field: Chemistry — Analytical Chemistry

Assessing Contents of Sugars, Vitamins, and Nutrients in Baby Leaf Lettuce from Hyperspectral Data with Machine Learning Models Lettuce (Lactuca sativa) is a leafy vegetable that provides a valuable source of phytonutrients for a healthy human diet. The assessment of plant growth and composition is vital for determining crop yield and overall quality; however, classical laboratory analyses are slow and costly. Therefore, new, less expensive, more rapid, and non-destructive approaches are being developed, including those based on (hyper)spectral reflectance. Additionally, it is important to determine how plant phenotypes respond to fertilizer treatments and whether these differences in response can be detected from analyses of hyperspectral image data. In the current study, we demonstrate the suitability of hyperspectral imaging in combination with machine learning models to estimate the content of chlorophyll (SPAD), anthocyanins (ACI), glucose, fructose, sucrose, vitamin C, β-carotene, nitrogen (N), phosphorus (P), potassium (K), dry matter content, and plant fresh weight. Five classification and regression machine learning models were implemented, showing high accuracy in classifying the lettuces based on the applied fertilizers treatments and estimating nutrient concentrations. To reduce the input (predictor data, i.e., hyperspectral data) dimension, 13 principal components were identified and applied in the models. The implemented artificial neural network models of the machine learning algorithm demonstrated high accuracy (r = 0.85 to 0.99) in estimating fresh leaf weight, and the contents of chlorophyll, anthocyanins, N, P, K, and β-carotene. The four applied classification models of machine learning demonstrated 100% accuracy in classifying the studied baby leaf lettuces by phenotype when specific fertilizer treatments were applied. Hyperspectral imaging ; Nutrient ; Biology ; Botany ; Food science ; Artificial intelligence ; Computer science ; Ecology Spectroscopy and Chemometric Analyses ; GABA and Rice Research ; Coconut Research and Applications
Authors: Qingxu Li | Wanhuai Zhou | Xuedong Zhang | Hao Li | Mingjie Li | Houjun Liang
Source: Frontiers in Plant Science (article, 2024-01-25)
Field: Chemistry — Analytical Chemistry

Cotton-Net: efficient and accurate rapid detection of impurity content in machine-picked seed cotton using near-infrared spectroscopy Widespread adoption of machine-picked cotton in China, the impurity content of seed cotton has increased significantly. This impurity content holds direct implications for the valuation of seed cotton and exerts a consequential influence on the ensuing quality of processed lint and textiles. Presently, the primary approach for assessing impurity content in seed cotton primarily depends on semi-automated testing instruments, exhibiting suboptimal detection efficiency and not well-suited for the impurity detection requirements during the purchase of seed cotton. To address this challenge, this study introduces a seed cotton near-infrared spectral (NIRS) data acquisition system, facilitating the rapid collection of seed cotton spectral data. Three pretreatment algorithms, namely SG (Savitzky-Golay convolutional smoothing), SNV (Standard Normal Variate Transformation), and Normalization, were applied to preprocess the seed cotton spectral data. Cotton-Net, a one-dimensional convolutional neural network aligned with the distinctive characteristics of the seed cotton spectral data, was developed in order to improve the prediction accuracy of seed cotton impurity content. Ablation experiments were performed, utilizing SELU, ReLU, and Sigmoid functions as activation functions. The experimental outcomes revealed that after normalization, employing SELU as the activation function led to the optimal performance of Cotton-Net, displaying a correlation coefficient of 0.9063 and an RMSE (Root Mean Square Error) of 0.0546. In the context of machine learning modeling, the LSSVM model, developed after Normalization and Random Frog algorithm processing, demonstrated superior performance, achieving a correlation coefficient of 0.8662 and an RMSE of 0.0622. In comparison, the correlation coefficient of Cotton-Net increased by 4.01%. This approach holds significant potential to underpin the subsequent development of rapid detection instruments targeting seed cotton impurities. Normalization (sociology) ; Correlation coefficient ; Artificial intelligence ; Mathematics ; Biological system ; Computer science ; Machine learning ; Algorithm ; Biology Spectroscopy and Chemometric Analyses ; Industrial Vision Systems and Defect Detection ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Valentina Perricone | Ezra Sarmiento | Andrew Nguyen | Nigel C. Hughes | David Kisailus
Source: Communications Materials (article, 2024-10-13)
Field: Materials Science — Biomaterials

The convergent design evolution of multiscale biomineralized structures in extinct and extant organisms Evolution has generated a sophisticated convergence of material components, ultrastructural designs, and fabrication processes in response to similar selective pressures across a diverse array of extinct and extant species. This review explores three key convergent design strategies: struts for lightweight structures with load-bearing efficiency, sutures for increased flexibility and stress management, and helicoids for impact resistance and fracture toughness. Through this examination, the review sheds light on how evolution can inspire innovative engineering approaches and technologies through the adoption of aspects of natural design. We foresee natural evolutive processes of construction as the informative harbingers of new, advanced, ecologically aware, and energy-efficient modes of human fabrication. The evolutionary process has created natural systems with structures that impart high mechanical performance, providing guidance for biomimetics. Here, the role played by three convergent design strategies – struts, sutures and helicoids – is discussed, spanning their occurrence in nature through to applications. Extant taxon ; Convergent evolution ; Evolutionary biology ; Biology ; Phylogenetics ; Genetics Calcium Carbonate Crystallization and Inhibition ; Cephalopods and Marine Biology ; Paleontology and Stratigraphy of Fossils
Authors: Yunpeng Wei | Huiqiang Hu | Minghua Yuan | Huaxing Xu | Xiaobo Mao | Yuping Zhao | Luqi Huang
Source: Foods (article, 2024-12-21)
Field: Chemistry — Analytical Chemistry

Determination of Bioactive Components in Chrysanthemum Tea (Gongju) Using Hyperspectral Imaging Technique and Chemometrics The bioactive components of chrysanthemum tea are an essential indicator in evaluating its nutritive and commercial values. Combining hyperspectral imaging (HSI) with key wavelength selection and pattern recognition methods, this study developed a novel approach to estimating the content of bioactive components in chrysanthemums, including the total flavonoids (TFs) and chlorogenic acids (TCAs). To determine the informative wavelengths of hyperspectral images, we introduced a variable similarity regularization term into particle swarm optimization (denoted as VSPSO), which can focus on improving the combinatorial performance of key wavelengths and filtering out the features with higher collinearity simultaneously. Moreover, considering the underlying relevance of the phytochemical content and the exterior morphology characteristics, the spatial image features were also extracted. Finally, an ensemble learning model, LightGBM, was established to estimate the TF and TCA contents using the fused features. Experimental results indicated that the proposed VSPSO achieved a superior accuracy, with R<sup>2</sup> scores of 0.9280 and 0.8882 for TF and TCA prediction. Furthermore, after the involvement of spatial image information, the fused spectral-spatial features achieved the optimal model accuracy on LightGBM. The R<sup>2</sup> scores reached 0.9541 and 0.9137, increasing by 0.0308-0.1404 and 0.0181-0.1066 in comparison with classical wavelength-related methods and models. Overall, our research provides a novel method for estimating the bioactive components in chrysanthemum tea accurately and efficiently. These discoveries revealed the potential effectiveness for constructing feature fusion in HSI-based practical applications, such as nutritive value evaluation and heavy metal pollution detection, which will also facilitate the development of quality detection in the food industry. Hyperspectral imaging ; Collinearity ; Pattern recognition (psychology) ; Artificial intelligence ; Chemometrics ; Chlorogenic acid ; Principal component analysis ; Mathematics ; Feature selection ; Computer science ; Biological system ; Machine learning ; Chemistry ; Statistics ; Food science Spectroscopy and Chemometric Analyses ; Remote-Sensing Image Classification ; Advanced Chemical Sensor Technologies
Authors: Duncan A. Greeley | Jacob F. Adams | Péter Kenesei | Ashley D. Spear | John E. Allison
Source: Fatigue & Fracture of Engineering Materials & Structures (article, 2024-01-16)
Field: Materials Science — Biomaterials

Quantitative analysis of three‐dimensional fatigue crack path selection in Mg alloy WE43 using high‐energy X‐ray diffraction microscopy Abstract Fatigue short‐cracks in Mg alloys display complex growth behavior due to high plastic anisotropy and crack path dependence on local microstructural features. In this study, the three‐dimensional crystallography of short‐crack paths in Mg alloy WE43 was characterized by mapping near‐field high‐energy X‐ray diffraction microscopy (HEDM) reconstructed grain maps to high‐resolution X‐ray CT reconstructions of the fracture surfaces in the crack initiation and short‐crack growth regions of six ultrasonic fatigue specimens. Crack–grain–boundary intersections were analyzed at 81 locations across the six crack paths. The basal intragranular, non‐basal intragranular, or intergranular character of short‐crack growth following each boundary intersection was correlated to crystallographic and geometric parameters of the trailing and leading grains, three‐dimensional grain boundary plane, and advancing crack front. The results indicate that crack paths are dependent on the combined crystallographic and geometric character of the local microstructure, and crack path prediction can be improved by use of dimensionality reduction on subsets of high‐linear‐correlation microstructural parameters. Materials science ; Crack closure ; Grain boundary ; Paris' law ; Crystallography ; Microstructure ; Fracture mechanics ; Alloy ; Diffraction ; Crack growth resistance curve ; Metallurgy ; Composite material ; Optics ; Physics Magnesium Alloys: Properties and Applications ; Fatigue and fracture mechanics ; Advanced Welding Techniques Analysis
Authors: Jake M. Peterson | Scott T. Becker | Collin A. O’Leary | Puneet Juneja | Yang Yang | Walter N. Moss
Source: Biochemistry (article, 2024-05-10)
Field: Materials Science — Materials Chemistry

Structure of the SARS-CoV-2 Frameshift Stimulatory Element with an Upstream Multibranch Loop The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) frameshift stimulatory element (FSE) is necessary for programmed -1 ribosomal frameshifting (-1 PRF) and optimized viral efficacy. The FSE has an abundance of context-dependent alternate conformations, but two of the structures most crucial to -1 PRF are an attenuator hairpin and a three-stem H-type pseudoknot structure. A crystal structure of the pseudoknot alone features three RNA stems in a helically stacked linear structure, whereas a 6.9 Å cryo-EM structure including the upstream heptameric slippery site resulted in a bend between two stems. Our previous research alluded to an extended upstream multibranch loop that includes both the attenuator hairpin and the slippery site-a conformation not previously modeled. We aim to provide further context to the SARS-CoV-2 FSE via computational and medium resolution cryo-EM approaches, by presenting a 6.1 Å cryo-EM structure featuring a linear pseudoknot structure and a dynamic upstream multibranch loop. Frameshift mutation ; Loop (graph theory) ; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) ; Element (criminal law) ; Upstream (networking) ; Virology ; Physics ; Coronavirus disease 2019 (COVID-19) ; Chemistry ; Biology ; Genetics ; Computer science ; Mutation ; Mathematics ; Medicine ; Combinatorics ; Telecommunications Carbon Nanotubes in Composites ; SARS-CoV-2 and COVID-19 Research ; Bacillus and Francisella bacterial research
Authors: Artur Wodyński | Bryan Lauw | Marc Reimann | Martin Kaupp
Source: Journal of Chemical Theory and Computation (article, 2024-02-27)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Spin-Symmetry Breaking and Hyperfine Couplings in Transition-Metal Complexes Revisited Using Density Functionals Based on the Exact-Exchange Energy Density A small set of mononuclear manganese complexes evaluated previously for their Mn hyperfine couplings (HFCs) has been analyzed using density functionals based on the exact-exchange energy density─in particular, the spin symmetry breaking (SSB) found previously when using hybrid functionals. Employing various strong-correlation corrected local hybrids (scLHs) and strong-correlation corrected range-separated local hybrids (scRSLHs) with or without additional corrections to their local mixing functions (LMFs) to mitigate delocalization errors (DE), the SSB and the associated dipolar HFCs of [Mn(CN)<sub>4</sub>]<sup>2-</sup>, MnO<sub>3</sub>, [Mn(CN)<sub>4</sub>N]<sup>-</sup>, and [Mn(CN)<sub>5</sub>NO]<sup>2-</sup> (the latter with cluster embedding) have been examined. Both strong-correlation (sc)-correction and DE-correction terms help to diminish SSB and correct the dipolar HFCs. The DE corrections are more effective, and the effects of the sc corrections depend on their damping factors. Interestingly, the DE-corrections reduce valence-shell spin polarization (VSSP) and thus SSB by locally enhancing exact-exchange (EXX) admixture near the metal center and thereby diminishing spin-density delocalization onto the ligand atoms. In contrast, sc corrections diminish EXX admixture locally, mostly on specific ligand atoms. This then reduces VSSP and SSB as well. The performance of scLHs and scRSLHs for the isotropic Mn HFCs has also been analyzed, with particular attention to core-shell spin-polarization contributions. Further sc-corrected functionals, such as the KP16/B13 construction and the DM21 deep-neural-network functional, have been examined. Hyperfine structure ; Spin (aerodynamics) ; Symmetry (geometry) ; Symmetry breaking ; Physics ; Spin density ; Condensed matter physics ; Transition metal ; Energy (signal processing) ; Density functional theory ; Hyperfine coupling ; Theoretical physics ; Chemical physics ; Atomic physics ; Quantum mechanics ; Chemistry ; Thermodynamics ; Catalysis ; Mathematics Magnetism in coordination complexes ; Advanced Chemical Physics Studies ; Advanced NMR Techniques and Applications
Authors: Huanyu Li | Meng Pan | Yifan Li | Hao Liang | Manli Cui | Mingzhen Zhang | Mingxin Zhang
Source: Chinese Medical Journal (review, 2024-12-16)
Field: Materials Science — Materials Chemistry

Nanomedicine: The new trend and future of precision medicine for inflammatory bowel disease Nanomedicine is an interdisciplinary area that utilizes nanoscience and technology in the realm of medicine. Rapid advances in science and technology have propelled the medical sector into a new era. The most commonly used nanotechnology in the field of medicine is nanoparticles. Due to their unique physicochemical properties, nanoparticles offer significant benefits of precision medicine for diseases such as inflammatory bowel disease that cannot be effectively treated by existing approaches. Nanomedicine has emerged as a highly active research field, with extensive scientific and technological studies being carried out, as well as growing international competition in the commercialization of this field. The accumulation of expertise in the key technologies relating to nanomedicine would provide strategic advantages in the development of cutting-edge medical techniques. This review presented a comprehensive analysis of the primary uses of nanoparticles in medicine, including recent advances in their application for the diagnosis and treatment of inflammatory bowel disease. Furthermore, we discussed the challenges and possibilities associated with the application of nanoparticles in clinical settings. Nanomedicine ; Commercialization ; Inflammatory bowel disease ; Nanotechnology ; Inflammatory Bowel Diseases ; Medicine ; Precision medicine ; Disease ; Engineering ethics ; Engineering ; Business ; Nanoparticle ; Materials science ; Pathology Advanced Nanomaterials in Catalysis ; Nanoparticles: synthesis and applications ; Biosensors and Analytical Detection
Authors: Fabiana Liendo | Bryan Pichún | Amaya Paz de la Vega | Johisner Penagos | Núria Serrano | José Manuel Dı́az-Cruz | Jaime Pizarro | Rodrigo Segura | Marı́a J. Aguirre
Source: Nanomaterials (article, 2024-09-09)
Field: Chemistry — Electrochemistry

Electrochemical Sensor Based on Glassy Carbon Electrode Modified with Carbon Nanohorns (SWCNH) for Determination of Cr(VI) via Adsorptive Cathodic Stripping Voltammetry (AdCSV) in Tap Water In this study, a new and simple glassy carbon electrode modified with carbon nanohorns (SWCNH/GCE) was used for the determination of Cr(VI) in aqueous matrices via adsorptive cathodic stripping voltammetry (AdCSV). The modified electrode was characterized via field emission scanning electron microscopy and cyclic voltammetry, which revealed a homogeneous distribution of spherical agglomerates of SWCNH on the electrode surface. The modification increased the electrochemically active area from 0.10 cm<sup>2</sup> ± 0.01 (GCE) to 0.16 cm<sup>2</sup> ± 0.01 (SWCNH/GCE). The optimized analytical conditions were as follows: a supporting electrolyte (0.15 mol L<sup>-1</sup> HCl), an accumulation potential of 0.8 V versus Ag/AgCl, and an accumulation time of 240 s. Validation of the analytical methodology was performed, obtaining a linear range between 20 and 100 µg L<sup>-1</sup>, a limit of detection of 3.5 µg L<sup>-1</sup>, and a limit of quantification of 11.6 µg L<sup>-1</sup> with good accuracy and precision. The method was applied to the analysis of spiked tap water samples, and the results were compared using a flame atomic absorption spectrophotometer (FAAS) with no significant statistical differences. Detection limit ; Tap water ; Cathodic stripping voltammetry ; Adsorptive stripping voltammetry ; Cyclic voltammetry ; Analytical Chemistry (journal) ; Electrochemistry ; Voltammetry ; Glassy carbon ; Electrochemical gas sensor ; Stripping (fiber) ; Materials science ; Supporting electrolyte ; Anodic stripping voltammetry ; Electrode ; Carbon fibers ; Chemistry ; Chromatography ; Physical chemistry Electrochemical Analysis and Applications ; Electrochemical sensors and biosensors ; Analytical Chemistry and Sensors