jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ahmed H. Bakheit | Hamad M. Alkahtani
Source: Molecules (article, 2023-09-28)
Field: Chemistry — Physical and Theoretical Chemistry

Integrated Structural, Functional, and ADMET Analysis of 2-Methoxy-4,6-diphenylnicotinonitrile: The Convergence of X-ray Diffraction, Molecular Docking, Dynamic Simulations, and Advanced Computational Insights This study systematically investigates the molecular structure and electronic properties of 2-methoxy-4,6-diphenylnicotinonitrile, employing X-ray diffraction (XRD) and sophisticated computational methodologies. XRD findings validate the compound's orthorhombic crystallization in the P21212 space group, composed of a pyridine core flanked by two phenyl rings. Utilizing the three-dimensional Hirshfeld surface, the research decodes the molecule's spatial attributes, further supported by exhaustive statistical assessments. Key interactions, such as π-π stacking and H⋯X contacts, are spotlighted, underscoring their role in the crystal's inherent stability and characteristics. Energy framework computations and density functional theory (DFT) analyses elucidate the prevailing forces in the crystal and reveal geometric optimization facets and molecular reactivity descriptors. Emphasis is given to the exploration of frontier molecular orbitals (FMOs), aromaticity, and π-π stacking capacities. The research culminates in distinguishing electron density distributions, aromatic nuances, and potential reactivity hotspots, providing a holistic view of the compound's structural and electronic landscape. Concurrently, molecular docking investigates its interaction with the lipoprotein-associated phospholipase A2 protein. Notably, the compound showcases significant interactions with the protein's active site. Molecular dynamics simulations reveal the compound's influence on protein stability and flexibility. Although the molecule exhibits strong inhibitory potential against Lp-PLA2, its drug development prospects face challenges related to solubility and interactions with drug transport proteins. Stacking ; Density functional theory ; Molecular dynamics ; Chemistry ; Computational chemistry ; Molecular orbital ; Crystallography ; Docking (animal) ; Molecule ; Orthorhombic crystal system ; Chemical physics ; Structural stability ; Crystal structure Crystallography and molecular interactions ; Synthesis and biological activity ; Crystal structures of chemical compounds
Authors: Priyanka Dey
Source: ACS Measurement Science Au (article, 2023-10-31)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Aiming for Maximized and Reproducible Enhancements in the Obstacle Race of SERS Surface enhanced Raman scattering (SERS), since its discovery in the mid-1970s, has taken on many roles in the world of analytical measurement science. From identifying known and unknown chemicals in mixtures such as pharmaceutical and environmental samples to enabling qualitative and quantitative analysis of biomolecules and biomedical disease markers (or biomarkers), furthermore expanding to tracking nanostructures in vivo for medical diagnosis and therapy. This is because SERS combines the inherent power of Raman scattering capable of molecular species identification, topped with tremendous amplification in the Raman signal intensity when the molecule of interest is positioned near plasmonic nanostructures. The higher the SERS signal amplification, the lower the limit of detection (LOD) that could be achieved for the above applications. Therefore, improving SERS sensing efficiencies is vital. The signal reproducibility and SERS enhancement factor (EF) heavily rely on plasmonic nanostructure design, which has led to tremendous work in the field. But SERS signal and EF reproducibility remain key limitations for its wider market usability. This Review will scrutinize factors, some recognized and some often overlooked, that dictate the SERS signal and are of utmost importance to enable reproducible SERS EFs. Most of the factors pertain to colloidal labeled SERS. Some critically reviewed factors include the nanostructure's surface area as a limiting factor, SERS hot-spots including optimizing the SERS EF within the hot-spot volume and positioning labels, properties of label molecules governing molecule orientation in hot-spots, and resonance effects. A better understanding of these factors will enable improved optimization and control of the experimental SERS, enabling extremely sensitive LODs without overestimating the SERS EFs. These are crucial steps toward identification and reproducible quantification in SERS sensing. Nanotechnology ; Raman scattering ; Plasmon ; Biomolecule ; Nanostructure ; Materials science ; Raman spectroscopy ; Nanoshell ; Surface-enhanced Raman spectroscopy ; SIGNAL (programming language) ; Computer science ; Nanoparticle ; Optoelectronics ; Optics ; Physics Gold and Silver Nanoparticles Synthesis and Applications ; Biosensors and Analytical Detection ; Advanced biosensing and bioanalysis techniques
Authors: Kwangrae Kim | Woohyun Hwang | Seung‐Hyun Victor Oh | Aloysius Soon
Source: npj Computational Materials (article, 2023-08-24)
Field: Materials Science — Materials Chemistry

Exploring anharmonic lattice dynamics and dielectric relations in niobate perovskites from first-principles self-consistent phonon calculations Abstract Group I niobates (KNbO 3 and NaNbO 3 ) are promising lead-free alternatives for high-performance energy storage applications. Despite their potential, their complex phase transitions arising from temperature-dependent phonon softening and anharmonic effects on dielectric properties remain poorly explored. In this study, we employ density-functional theory (DFT) and self-consistent phonon (SCP) calculations to investigate finite-temperature phonons in cubic niobate perovskites. To include explicit anharmonic vibrational effects, SCP frequencies are shifted by the bubble self-energy correction within the quasiparticle (QP) approximation, providing precise descriptions of phonon softening in these strongly anharmonic solids. We further calculate the static dielectric constant of KNbO 3 and NaNbO 3 as a function of temperature using the Lyddane-Sachs-Teller (LST) relation and QP-corrected phonon dispersions. Our theoretical results align with experimental data, offering reliable temperature-dependent phonon dispersions while considering anharmonic self-energies and thermal expansion effects, enhancing our understanding of the complex relations between lattice vibrations and phase transitions in these anharmonic oxides. Anharmonicity ; Phonon ; Condensed matter physics ; Dielectric ; Softening ; Phase transition ; Quasiparticle ; Materials science ; Lattice (music) ; Density functional theory ; Physics ; Quantum mechanics Ferroelectric and Piezoelectric Materials ; Microwave Dielectric Ceramics Synthesis ; Multiferroics and related materials
Authors: Haotian Zhang | Shengming Li | Jintu Zhang | Zhe Wang | Jike Wang | Dejun Jiang | Zhiwen Bian | Yixue Zhang | Yafeng Deng | Jianfei Song | Yu Kang | Tingjun Hou
Source: Chemical Science (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

SDEGen: learning to evolve molecular conformations from thermodynamic noise for conformation generation Generation of representative conformations for small molecules is a fundamental task in cheminformatics and computer-aided drug discovery, but capturing the complex distribution of conformations that contains multiple low energy minima is still a great challenge. Deep generative modeling, aiming to learn complex data distributions, is a promising approach to tackle the conformation generation problem. Here, inspired by stochastic dynamics and recent advances in generative modeling, we developed SDEGen, a novel conformation generation model based on stochastic differential equations. Compared with existing conformation generation methods, it enjoys the following advantages: (1) high model capacity to capture multimodal conformation distribution, thereby searching for multiple low-energy conformations of a molecule quickly, (2) higher conformation generation efficiency, almost ten times faster than the state-of-the-art score-based model, ConfGF, and (3) a clear physical interpretation to learn how a molecule evolves in a stochastic dynamics system starting from noise and eventually relaxing to the conformation that falls in low energy minima. Extensive experiments demonstrate that SDEGen has surpassed existing methods in different tasks for conformation generation, interatomic distance distribution prediction, and thermodynamic property estimation, showing great potential for real-world applications. Noise (video) ; Chemistry ; Computer science ; Artificial intelligence Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Mingxuan Li | Ya-Bo Shi | Jiu-ba Zhang | Xin Wan | Jun Fang | Yi Wu | Rao Fu | Haijun Yu | Lin Li | Lianlin Su | De Ji | Tulin Lu | Zhenhua Bian
Source: Food Chemistry X (article, 2023-11-20)
Field: Chemistry — Analytical Chemistry

Rapid evaluation of Ziziphi Spinosae Semen and its adulterants based on the combination of FT-NIR and multivariate algorithms Ziziphi Spinosae Semen (ZSS) is a valued seed renowned for its sedative and sleep-enhancing properties. However, the price increase has been accompanied by adulteration. In this study, chromaticity analysis and Fourier transform near-infrared (FT-NIR) combined with multivariate algorithms were employed to identify the adulteration and quantitatively predict the adulteration ratio. The findings suggested that the utilization of chromaticity extractor was insufficient for identification of adulteration ratio. The raw spectrum of ZMS and HAS adulterants extracted by FT-NIR was processed by SNV + CARS and 1d + SG + ICO respectively, the average accuracy of machine learning classification model was improved from 77.06 % to 97.58 %. Furthermore, the <i>R<sup>2</sup></i> values of the calibration and prediction set of the two quantitative prediction regression models of adulteration ratio are greater than 0.99, demonstrating excellent linearity and predictive accuracy. Overall, this study demonstrated that FT-NIR combined with multivariate algorithms provided a significant approach to addressing the growing issue of ZSS adulteration. Multivariate statistics ; Near-infrared spectroscopy ; Mathematics ; Extractor ; Artificial intelligence ; Adulterant ; Pattern recognition (psychology) ; Computer science ; Statistics ; Chromatography ; Chemistry ; Engineering Spectroscopy and Chemometric Analyses ; Ziziphus Jujuba Studies and Applications ; Phytochemicals and Antioxidant Activities
Authors: Jose-M. Bueno-Barrachina | Yiyao Ye-Lin | Félix Nieto-del-Amor | Vicente Fuster-Roig
Source: Engineering Applications of Artificial Intelligence (article, 2023-01-05)
Field: Materials Science — Materials Chemistry

Inception 1D-convolutional neural network for accurate prediction of electrical insulator leakage current from environmental data during its normal operation using long-term recording Contamination flashover remains one of the biggest challenges for power grid designers and maintenance engineers. Insulator leakage current contains relevant information about their state so that continuous monitoring is considered the most effective way to prevent contamination flashover. In this work, we attempted to accurately predict insulator leakage current in real time during normal operations based on environmental data using long-term recordings. We first confirmed that the history of environmental data also contained relevant information to predict leakage current by conditional Granger analysis and determined that 20 was the optimal number of previous samples for this purpose. We then compared the performance of typical regression models and convolutional neural network (CNN), when using both current and the last 21 samples as input features. We confirmed that the model with the last 21 samples might perform significantly better. Input features pre-processing by cascaded inception architecture was fundamental to capture the complex dynamic interaction between environmental data and leakage current and significantly improved the model performance. CNN based on inception architecture performed much better, achieving an average R2 of 0.94 ± 0.03. The proposed model could be used to predict leakage current in both porcelain insulators with or without coatings and silicone composite insulators. Our results pave the way for creating an on-line pre-warning system adapted to individual installations, can anticipate the negative consequences of weather and/or pollution deposits and is useful for designing a strategic high-voltage electrical insulator preventive maintenance plan for preventing contamination flashover and thus increase power grid reliability and resilience. Computer science ; Insulator (electricity) ; Convolutional neural network ; Leakage (economics) ; Arc flash ; Real-time computing ; Grid ; Simulation ; Environmental science ; Artificial intelligence ; Electrical engineering ; Engineering High voltage insulation and dielectric phenomena ; Infrastructure Maintenance and Monitoring ; Power Transformer Diagnostics and Insulation
Authors: Daniel Roberto Sáenz-García | Andreu Figuerola | Gemma Turnes Palomino | Luz O. Leal | Carlos Palomino Cabello
Source: Inorganic Chemistry (article, 2023-11-18)
Field: Chemistry — Analytical Chemistry

Thiol-Functionalized MIL-100(Fe)/Device for the Removal of Heavy Metals in Water The preparation of a functional device based on a functionalized MIL-100(Fe) metal-organic framework for the solid-phase extraction of heavy metals is reported. By a simple and easy straightforward grafting procedure, a thiol-functionalized MIL-100(Fe) material (MIL-100(Fe)-SH) with a S/Fe ratio of 0.80 and a surface area of 840 m<sup>2</sup> g<sup>-1</sup> was obtained. MIL-100(Fe)-SH exhibited a higher Hg(II) extraction (96 ± 5%) than that of MIL-100(Fe) (78 ± 4%) due to the interaction between thiol groups and Hg(II) ions. For practical applications, the obtained MIL-100(Fe)-SH was integrated by a simple method to a 3D printed support based on a matrix of interconnected cubes using poly(vinylidene fluoride) as binder, obtaining a functional device that simultaneously acts as stirrer and sorbent. The developed device showed high efficiency for the removal of Hg(II), good reusability, and excellent performance for the simultaneous preconcentration and further detection and quantification of Hg(II), Pb(II), and As(V) in tap, well, and lake water samples. Chemistry ; Sorbent ; Thiol ; Extraction (chemistry) ; Metal ; Tap water ; Solid phase extraction ; Matrix (chemical analysis) ; Metal-organic framework ; Metal ions in aqueous solution ; Inorganic chemistry ; Nuclear chemistry ; Chromatography ; Adsorption ; Organic chemistry Analytical chemistry methods development ; Electrochemical Analysis and Applications ; Analytical Chemistry and Sensors
Authors: H.Y. Liu | Zipeng Zhang | Chenyu Wu | Kang Su | Xiaonan Kan
Source: Micromachines (review, 2023-06-08)
Field: Materials Science — Surfaces, Coatings and Films

Biomimetic Superhydrophobic Materials through 3D Printing: Progress and Challenges Superhydrophobicity, a unique natural phenomenon observed in organisms such as lotus leaves and desert beetles, has inspired extensive research on biomimetic materials. Two main superhydrophobic effects have been identified: the "lotus leaf effect" and the "rose petal effect", both showing water contact angles larger than 150°, but with differing contact angle hysteresis values. In recent years, numerous strategies have been developed to fabricate superhydrophobic materials, among which 3D printing has garnered significant attention due to its rapid, low-cost, and precise construction of complex materials in a facile way. In this minireview, we provide a comprehensive overview of biomimetic superhydrophobic materials fabricated through 3D printing, focusing on wetting regimes, fabrication techniques, including printing of diverse micro/nanostructures, post-modification, and bulk material printing, and applications ranging from liquid manipulation and oil/water separation to drag reduction. Additionally, we discuss the challenges and future research directions in this burgeoning field. Nanotechnology ; 3D printing ; Biomimetic materials ; Biomimetics ; Materials science ; Engineering ; Composite material Surface Modification and Superhydrophobicity ; Electrospun Nanofibers in Biomedical Applications ; Electrohydrodynamics and Fluid Dynamics
Authors: Mercedes E. Paoletti | Óscar Mogollón Gutiérrez | Sergio Moreno‐Álvarez | José Carlos Sancho Núñez | Juan M. Haut
Source: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (article, 2023-01-01)
Field: Chemistry — Analytical Chemistry

A Comprehensive Survey of Imbalance Correction Techniques for Hyperspectral Data Classification Land-cover classification is an important topic for remotely sensed hyperspectral (HS) data exploitation. In this regard, HS classifiers have to face important challenges, such as the high spectral redundancy, as well as noise, present in the data, and the fact that obtaining accurate labeled training data for supervised classification is expensive and time-consuming. As a result, the availability of large amounts of training samples, needed to alleviate the so-called Hughes phenomenon, is often unfeasible in practice. The class-imbalance problem, which results from the uneven distribution of labeled samples per class, is also a very challenging factor for HS classifiers. In this article, a comprehensive review of oversampling techniques is provided, which mitigate the aforementioned issues by generating new samples for the minority classes. More specifically, this article pursues a twofold objective. First, it reviews the most relevant oversampling methods that can be adopted according to the nature of HS data. Second, it provides a comprehensive experimental study and comparison, which are useful to derive practical conclusions about the performance of oversampling techniques in different HS image-based applications. Hyperspectral imaging ; Computer science ; Statistical classification ; Remote sensing ; Artificial intelligence ; Data mining ; Pattern recognition (psychology) ; Geology Spectroscopy and Chemometric Analyses ; Imbalanced Data Classification Techniques ; Currency Recognition and Detection
Authors: Nicky Rahmana Putra | Dwila Nur Rizkiyah | Mohd Azizi Che Yunus | Ahmad Hazim Abdul Aziz | Ahmad Shah Hizam Md Yasir | Irianto Irianto | Jumakir Jumakir | Waluyo Waluyo | Suparwoto Suparwoto | Lailatul Qomariyah
Source: Molecules (review, 2023-05-25)
Field: Chemistry — Inorganic Chemistry

Valorization of Peanut Skin as Agricultural Waste Using Various Extraction Methods: A Review Peanuts (Arachis hypogea) can be made into various products, from oil to butter to roasted snack peanuts and candies, all from the kernels. However, the skin is usually thrown away, used as cheap animal feed, or as one of the ingredients in plant fertilizer due to its little value on the market. For the past ten years, studies have been conducted to determine the full extent of the skin’s bioactive substance repertoire and its powerful antioxidant potential. Alternatively, researchers reported that peanut skin could be used and be profitable in a less-intensive extraction technique. Therefore, this review explores the conventional and green extraction of peanut oil, peanut production, peanut physicochemical characteristics, antioxidant activity, and the prospects of valorization of peanut skin. The significance of the valorization of peanut skin is that it contains high antioxidant capacity, catechin, epicatechin resveratrol, and procyanidins, which are also advantageous. It could be exploited in sustainable extraction, notably in the pharmaceutical industries. Peanut oil ; Extraction (chemistry) ; Arachis ; Arachis hypogaea ; Food science ; Biotechnology ; Catechin ; Agriculture ; Chemistry ; Antioxidant ; Raw material ; Polyphenol ; Biology ; Botany ; Organic chemistry Coconut Research and Applications ; Biochemical effects in animals ; Advanced Chemical Sensor Technologies
Authors: Miriam Abele | Etienne V. Doll | Florian Bayer | Chen Meng | Nina Lomp | Klaus Neuhaus | Siegfried Scherer | Bernhard Küster | Christina Ludwig
Source: Molecular & Cellular Proteomics (article, 2023-06-28)
Field: Chemistry — Spectroscopy

Unified Workflow for the Rapid and In-Depth Characterization of Bacterial Proteomes Bacteria are the most abundant and diverse organisms among the kingdoms of life. Due to this excessive variance, finding a unified, comprehensive, and safe workflow for quantitative bacterial proteomics is challenging. In this study, we have systematically evaluated and optimized sample preparation, mass spectrometric data acquisition, and data analysis strategies in bacterial proteomics. We investigated workflow performances on six representative species with highly different physiologic properties to mimic bacterial diversity. The best sample preparation strategy was a cell lysis protocol in 100% trifluoroacetic acid followed by an in-solution digest. Peptides were separated on a 30-minute linear microflow liquid chromatography gradient and analyzed in data-independent acquisition mode. Data analysis was performed with DIA-NN using a predicted spectral library. Performance was evaluated according to the number of identified proteins, quantitative precision, throughput, costs, and biological safety. With this rapid workflow, over 40% of all encoded genes were detected per bacterial species. We demonstrated the general applicability of our workflow on a set of 23 taxonomically and physiologically diverse bacterial species. We could confidently identify over 45,000 proteins in the combined dataset, of which 30,000 have not been experimentally validated. Our work thereby provides a valuable resource for the microbial scientific community. Finally, we grew Escherichia coli and Bacillus cereus in replicates under twelve different cultivation conditions to demonstrate the high-throughput suitability of the workflow. The proteomic workflow we present in this manuscript does not require any specialized equipment or commercial software and can be easily applied by other laboratories to support and accelerate the proteomic exploration of the bacterial kingdom. Workflow ; Proteome ; Computational biology ; Computer science ; Proteomics ; Bioprocess ; Lysis ; Mass spectrometry ; Chromatography ; Data mining ; Chemistry ; Biology ; Bioinformatics ; Database ; Biochemistry ; Gene Advanced Proteomics Techniques and Applications ; Genomics and Phylogenetic Studies ; Bacterial Identification and Susceptibility Testing
Authors: Michela Pia Totaro | Giacomo Squeo | Davide De Angelis | Antonella Pasqualone | Francesco Caponio | Carmine Summo
Source: Journal of Food Composition and Analysis (article, 2023-02-10)
Field: Chemistry — Analytical Chemistry

Application of NIR spectroscopy coupled with DD-SIMCA class modelling for the authentication of pork meat Leverage (statistics) ; Principal component analysis ; Calibration ; Computer science ; Pattern recognition (psychology) ; Food science ; Quality (philosophy) ; Chemometrics ; Artificial intelligence ; Mathematics ; Statistics ; Chemistry ; Machine learning ; Physics Spectroscopy and Chemometric Analyses ; Meat and Animal Product Quality ; Advanced Chemical Sensor Technologies