jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Bijendra Shrestha | Jetsada Posom | Panmanas Sirisomboon | Bim Prasad Shrestha
Source: Energies (article, 2023-07-13)
Field: Chemistry — Analytical Chemistry

Comprehensive Assessment of Biomass Properties for Energy Usage Using Near-Infrared Spectroscopy and Spectral Multi-Preprocessing Techniques In this study, partial least squares regression (PLSR) models were developed using no preprocessing, traditional preprocessing, multi-preprocessing 5-range, multi-preprocessing 3-range, a genetic algorithm (GA), and a successive projection algorithm (SPA) to assess the higher heating value (HHV) and ultimate analysis of grounded biomass for energy usage by employing near-infrared (NIR) spectroscopy. A novel approach was utilized based on the assumption that using multiple pretreatment methods across different sections in the entire NIR wavenumber range would enhance the performance of the model. The performance of the model obtained from 200 biomass samples for HHV and 120 samples for ultimate analysis were compared, and the best model was selected based on the coefficient of determination of the validation set, root mean square error of prediction, and the ratio of prediction to deviation values. Based on the model performance results, the proposed HHV model from GA-PLSR and the N models from the multi-preprocessing PLSR 5-range could be used for most applications, including research, whereas the C and H models from GA-PLSR and the O model from the multi-preprocessing PLSR 5 range method 5-range air performance and are applicable only for rough screening. The overall findings highlight that the multi-preprocessing 5-range method, which was attempted as a novel approach in this study to develop the PLSR model, demonstrated better accuracy for HHV, C, N, and O, improving these models by 4.1839%, 8.1842%, 3.7587%, and 4.0085%, respectively. Therefore, this method can be considered a reliable and non-destructive alternative method for rapidly assessing biomass properties for energy usage and can also be used effectively in biomass trading. However, due to the smaller number of samples used in the model development, more samples are needed to update the model for robust application. Partial least squares regression ; Preprocessor ; Mean squared error ; Range (aeronautics) ; Near-infrared spectroscopy ; Chemometrics ; Data pre-processing ; Computer science ; Projection (relational algebra) ; Mathematics ; Statistics ; Biological system ; Pattern recognition (psychology) ; Algorithm ; Artificial intelligence ; Materials science ; Machine learning ; Physics ; Optics Spectroscopy and Chemometric Analyses ; Thermochemical Biomass Conversion Processes ; Environmental Impact and Sustainability
Authors: Yi Luo
Source: Precision Chemistry (article, 2023-03-29)
Field: Materials Science — Materials Chemistry

Chemistry in the Era of Artificial Intelligence ADVERTISEMENT RETURN TO ISSUEPREVCommentNEXTChemistry in the Era of Artificial IntelligenceYi Luo*Yi LuoHefei National Research Center for Physical Sciences at the Microscale, University of Science and Technology, Hefei 230026, ChinaHefei National Laboratory, University of Science and Technology, Hefei 230088, China*Email: [email protected]More by Yi LuoCite this: Precis. Chem. 2023, 1, 2, 127–128Publication Date (Web):March 29, 2023Publication History Received21 March 2023Accepted23 March 2023Published online29 March 2023Published inissue 24 April 2023https://pubs.acs.org/doi/10.1021/prechem.3c00038https://doi.org/10.1021/prechem.3c00038article-commentaryACS PublicationsCopyright © 2023 The Author. Co-published by University of Science and Technology of China and American Chemical Society. This publication is licensed under CC-BY-NC-ND 4.0. License Summary*You are free to share (copy and redistribute) this article in any medium or format within the parameters below:Creative Commons (CC): This is a Creative Commons license.Attribution (BY): Credit must be given to the creator.Non-Commercial (NC): Only non-commercial uses of the work are permitted. No Derivatives (ND): Derivative works may be created for non-commercial purposes, but sharing is prohibited. View full license*DisclaimerThis summary highlights only some of the key features and terms of the actual license. It is not a license and has no legal value. Carefully review the actual license before using these materials. This publication is Open Access under the license indicated. Learn MoreArticle Views4204Altmetric-Citations3LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InRedditEmail PDF (922 KB) Get e-Alertsclose Get e-Alerts License ; Library science ; China ; Political science ; Computer science ; World Wide Web ; Law Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Sigitas Vėjelis | Saulius Vaitkus | Arūnas Kremensas | Agnė Kairytė | Jurga Šeputytė-Jucikė
Source: Materials (article, 2023-02-28)
Field: Materials Science — Polymers and Plastics

Reuse of Textile Waste in the Production of Sound Absorption Boards Textile waste is formed in various stages, from the preparation of raw materials to the utilisation of textile products. One of the sources of textile waste is the production of woollen yarns. During the production of woollen yarns, waste is generated during the mixing, carding, roving, and spinning processes. This waste is disposed of in landfills or cogeneration plants. However, there are many examples of textile waste being recycled and new products being produced. This work deals with acoustic boards made from waste from the production of woollen yarns. This waste was generated in various yarn production processes up to the spinning stage. Due to the parameters, this waste was not suitable for further use in the production of yarns. During the work, the composition of waste from the production of woollen yarns was examined-namely, the amount of fibrous and nonfibrous materials, the composition of impurities, and the parameters of the fibres themselves. It was determined that about 74% of the waste is suitable for the production of acoustic boards. Four series of boards with different densities and different thicknesses were made with waste from the production of woollen yarns. The boards were made in a nonwoven line using carding technology to obtain semi-finished products from the individual layers of combed fibres and thermal treatment of the prepared semi-finished product. The sound absorption coefficients in the sound frequency range between 125 and 2000 Hz were determined for the manufactured boards, and the sound reduction coefficients were calculated. It was found that the acoustic characteristics of soft boards made from woollen yarn waste are very similar to those of classic boards or sound insulation products made from renewable resources. At a board density of 40 kg/m<sup>3</sup>, the value of the sound absorption coefficient varied from 0.4 to 0.9, and the noise reduction coefficient reached 0.65. Textile ; Raw material ; Carding ; Waste material ; Materials science ; Waste management ; Reuse ; Spinning ; Pulp and paper industry ; Composite material ; Engineering Textile materials and evaluations ; Natural Fiber Reinforced Composites ; Material Properties and Processing
Authors: Hafiz Ali Imran | Abdallah Zeggada | Ivan Ianniello | Farid Melgani | Annalisa Polverari | Alice Baroni | Davide Danzi | Rino Goller
Source: Applied Sciences (article, 2023-02-13)
Field: Chemistry — Analytical Chemistry

Low-Cost Handheld Spectrometry for Detecting Flavescence Dorée in Vineyards This study was conducted to evaluate the potential of low-cost hyperspectral sensors for the early detection of Flavescence dorée (FD) from asymptomatic samples prior to symptom development. In total, 180 leaf spectra from 60 randomly selected plants (three leaves per plant) were collected by using two portable mini-spectrometers (Hamamatsu: 340–850 nm and NIRScan: 900–1700 nm) at five vegetative growth stages in a vineyard with grape variety Garganega. High differences in the Hamamatsu spectra of the two groups were found in the VIS-NIR (visible–near infrared) spectral region while very small differences were observed in the NIRScan spectra. We analyzed the spectral data of two sensors by using all bands, features reduced by an ensemble method, and by genetic algorithms (GA) to discriminate the asymptomatic healthy (FD negative) and diseased (FD positive) leaves using five different classifiers. Overall, high classification accuracies were found in case of the Hamamatsu sensor compared to the NIRScan sensor. The feature selection techniques performed better compared to all bands, and the highest classification accuracy of 96% was achieved when GA features of the Hamamatsu sensor were used with the logistic regression (LR) classifier on test samples. A slightly low accuracy of 85% was achieved when the features (selected by the ensemble method) of the Hamamatsu sensor were used with the support vector machine (SVM) classifier by using leave-one-out (LOO) cross-validation on the whole dataset. Results demonstrated that employing a feature selection technique can provide a valid tool for determining the optimal bands that can be used to identify FD disease in the vineyard. However, further validation studies are required, as this study was conducted using a small dataset and from the single grapevine variety. Feature selection ; Support vector machine ; Artificial intelligence ; Pattern recognition (psychology) ; Hyperspectral imaging ; Classifier (UML) ; Vineyard ; Computer science ; Mathematics ; Horticulture ; Biology Spectroscopy and Chemometric Analyses ; Horticultural and Viticultural Research ; Plant Pathogens and Fungal Diseases
Authors: Dinh Van Tuan | Hanan Dery
Source: Physical review. B./Physical review. B (article, 2023-08-14)
Field: Materials Science — Materials Chemistry

Excitons and trions in monolayer semiconductors with correlated electrons We revisit low-temperature optical spectra of transition-metal dichalcogenide monolayers and point to a possible crystallization of electrons (or holes) at low to moderate charge densities. To calculate the excitonic spectra under such conditions, we introduce the recursion method and compute how the charge density affects the energies, linewidths, and oscillator strengths of exciton and trion complexes. Equally important, we study how excitons and trions in the monolayer evolve when the charge particles gradually transition to a periodic Wigner lattice. The results provide valuable information on the ability to detect whether the particles are ordered through the exciton spectrum. Finally, we calculate the change in exciton energy in cases where the added charge particles have similar and dissimilar quantum numbers (spin and valley) to those of the electron or hole in the exciton. The results of this work shed new light on the important optical properties of monolayer semiconductors. Trion ; Exciton ; Monolayer ; Electron ; Condensed matter physics ; Semiconductor ; Physics ; Oscillator strength ; Spectral line ; Spin (aerodynamics) ; Biexciton ; Materials science ; Quantum mechanics ; Nanotechnology 2D Materials and Applications ; Graphene research and applications ; Molecular Junctions and Nanostructures
Authors: Beth A. Lindquist | Ryan B. Jadrich | Juampablo E. Heras Rivera | Lucia Rondini
Source: Journal of Applied Physics (article, 2023-08-15)
Field: Chemistry — Spectroscopy

Uncertainty quantification for high explosive reactant and product equations of state Equations of state (EOSs) are typically represented as physics-informed models with tunable parameters that are adjusted to replicate calibration data as closely as possible. Uncertainty quantification (UQ) allows for the development of an ensemble of EOS parameters that are consistent with the calibration data instead of a single EOS. In this work, we perform UQ for the reactant and product EOSs for a variety of high explosives (HEs). In doing so, we demonstrate a strategy for dealing with heterogeneous (both experimental and calculated) data. We also use a statistical distance metric to quantify the differences between the various HEs using the UQ results. Explosive material ; Calibration ; Uncertainty quantification ; Replicate ; Metric (unit) ; Experimental data ; Variety (cybernetics) ; Product (mathematics) ; Equation of state ; Statistical physics ; Computer science ; Thermodynamics ; Chemistry ; Physics ; Mathematics ; Statistics ; Machine learning ; Artificial intelligence ; Engineering Mass Spectrometry Techniques and Applications ; Nuclear Engineering Thermal-Hydraulics ; Probabilistic and Robust Engineering Design
Authors: Mohammad Yazdani-Asrami | Alireza Sadeghi | Wenjuan Song
Source: Journal of Superconductivity and Novel Magnetism (article, 2023-01-13)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Ultra-fast Surrogate Model for Magnetic Field Computation of a Superconducting Magnet Using Multi-layer Artificial Neural Networks Abstract Due to the inherent nonlinear and sophisticated nature of superconducting wires/tapes, magnetic field computation of superconducting magnets by means of finite element methods (FEMs) is a time-consuming and complicated procedure. Although Legendre series method (LSM) was proposed as an alternative of FEMs, LSMs are not still fast enough. In current research, a surrogate model based on multi-layer artificial neural networks (ANNs) was introduced for the first time to dramatically reduce the computation time of a magnetic resonance imaging (MRI) magnet. To do this, firstly, the data related to the magnetic field were extracted based on LSM simulations for around 5000 different coil geometries. After that, the geometries of coils were used as inputs to a semi-deep learning ANN-based model in MATLAB software package. The minimum magnetic field in diameter spherical volume, maximum and minimum of total magnetic field were considered as outputs of the model, known as field indices. Then, ANN model was trained to calculate these field indices for any coil geometry. By doing so, magnetic field indices were estimated with a high accuracy based on the target values and also with extremely higher speed, comparing to FEM and LSM. Results showed that it takes 15 to 17 s for the proposed model to calculate the field indices for 750 different geometries whereas it takes for LSM-based model about 4 h. Magnet ; Magnetic field ; Artificial neural network ; Computation ; Electromagnetic coil ; Field (mathematics) ; MATLAB ; Superconducting magnet ; Computer science ; Surrogate model ; Computational physics ; Algorithm ; Superconductivity ; Physics ; Condensed matter physics ; Mathematics ; Artificial intelligence ; Machine learning Magnetic Properties and Applications ; Non-Destructive Testing Techniques ; Computational Physics and Python Applications
Authors: Yefei He | Luping Liu | Jing Liu‐Zeng | Weijia Wu | Hong Zhou | Bohan Zhuang
Source: arXiv (Cornell University) (preprint, 2023-05-18)
Field: Materials Science — Materials Chemistry

PTQD: Accurate Post-Training Quantization for Diffusion Models Diffusion models have recently dominated image synthesis tasks. However, the iterative denoising process is expensive in computations at inference time, making diffusion models less practical for low-latency and scalable real-world applications. Post-training quantization (PTQ) of diffusion models can significantly reduce the model size and accelerate the sampling process without re-training. Nonetheless, applying existing PTQ methods directly to low-bit diffusion models can significantly impair the quality of generated samples. Specifically, for each denoising step, quantization noise leads to deviations in the estimated mean and mismatches with the predetermined variance schedule. As the sampling process proceeds, the quantization noise may accumulate, resulting in a low signal-to-noise ratio (SNR) during the later denoising steps. To address these challenges, we propose a unified formulation for the quantization noise and diffusion perturbed noise in the quantized denoising process. Specifically, we first disentangle the quantization noise into its correlated and residual uncorrelated parts regarding its full-precision counterpart. The correlated part can be easily corrected by estimating the correlation coefficient. For the uncorrelated part, we subtract the bias from the quantized results to correct the mean deviation and calibrate the denoising variance schedule to absorb the excess variance resulting from quantization. Moreover, we introduce a mixed-precision scheme for selecting the optimal bitwidth for each denoising step. Extensive experiments demonstrate that our method outperforms previous post-training quantized diffusion models, with only a 0.06 increase in FID score compared to full-precision LDM-4 on ImageNet 256x256, while saving 19.9x bit operations. Code is available at https://github.com/ziplab/PTQD. Quantization (signal processing) ; Noise reduction ; Algorithm ; Computer science ; Uncorrelated ; Mathematics ; Artificial intelligence ; Statistics Machine Learning in Materials Science ; Cell Image Analysis Techniques ; Medical Image Segmentation Techniques
Authors: Fatemeh Safari | Cheka Kehelpannala | Azadeh Safarchi | Amani Batarseh | Fatemeh Vafaee
Source: Cancers (review, 2023-05-16)
Field: Chemistry — Spectroscopy

Biomarker Reproducibility Challenge: A Review of Non-Nucleotide Biomarker Discovery Protocols from Body Fluids in Breast Cancer Diagnosis Breast cancer has now become the most commonly diagnosed cancer, accounting for one in eight cancer diagnoses worldwide. Non-invasive diagnostic biomarkers and associated tests are superlative candidates to complement or improve current approaches for screening, early diagnosis, or prognosis of breast cancer. Biomarkers detected from body fluids such as blood (serum/plasma), urine, saliva, nipple aspiration fluid, and tears can detect breast cancer at its early stages in a minimally invasive way. The advancements in high-throughput molecular profiling (omics) technologies have opened an unprecedented opportunity for unbiased biomarker detection. However, the irreproducibility of biomarkers and discrepancies of reported markers have remained a major roadblock to clinical implementation, demanding the investigation of contributing factors and the development of standardised biomarker discovery pipelines. A typical biomarker discovery workflow includes pre-analytical, analytical, and post-analytical phases, from sample collection to model development. Variations introduced during these steps impact the data quality and the reproducibility of the findings. Here, we present a comprehensive review of methodological variations in biomarker discovery studies in breast cancer, with a focus on non-nucleotide biomarkers (i.e., proteins, lipids, and metabolites), highlighting the pre-analytical to post-analytical variables, which may affect the accurate identification of biomarkers from body fluids. Biomarker discovery ; Biomarker ; Breast cancer ; Cancer biomarkers ; Medicine ; Cancer ; Body fluid ; Bioinformatics ; Proteomics ; Internal medicine ; Biology Advanced Proteomics Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Molecular Biology Techniques and Applications
Authors: Benjamin F. Cravatt | Evert Njomen | Rachel Hayward | Kristen E. DeMeester | Daisuke Ogasawara | Melissa M. Dix | Tracey Nguyen | Paige Ashby | Gabriel M. Simon | Stuart L. Schreiber | Bruno Melillo
Source: ChemRxiv (preprint, 2023-06-04)
Field: Chemistry — Organic Chemistry

Comprehensive Mapping of Electrophilic Small Molecule-Protein Interactions in Human Cells Covalent chemistry is a versatile approach for expanding the ligandability of the human proteome. Activity-based protein profiling (ABPP) can infer the specific residues modified by electrophilic compounds through competition with broadly reactive probes. Nonetheless, the extent to which such residue-directed ABPP platforms fully assess the protein targets of electrophilic compounds in human cells remains unclear. Here, we introduce a complementary approach that directly identifies proteins showing stereoselective reactivity with focused libraries of stereochemically-defined, alkynylated electrophilic compounds. Integration of protein- and cysteine-directed ABPP data from compound-treated human cancer cells revealed generally well-correlated target maps and highlighted specific features, such as protein size and the proteotypicity of cysteine-containing peptides, that help to explain gaps in each ABPP platform. The integrated ABPP strategy furnished stereoselective, high-engagement covalent ligands for > 300 structurally and functionally diverse human proteins, including compounds that modulate enzymes by canonical (active-site cysteine) and non-canonical (isotype-restricted and non-catalytic cysteines) mechanisms. Cysteine ; Electrophile ; Chemistry ; Proteome ; Small molecule ; Human proteome project ; Computational biology ; Biochemistry ; Combinatorial chemistry ; Enzyme ; Biology ; Proteomics ; Gene ; Catalysis Click Chemistry and Applications ; Chemical Synthesis and Analysis ; Peptidase Inhibition and Analysis
Authors: Yanira Méndez | Aldrin V. Vasco | Galway Ivey | Ana Laura Dias | Peter Gierth | Bárbara B. Sousa | Claudio D. Navo | Ángel Torres-Mozas | Tiago Rodrigues | Gonzalo Jiménez‐Osés | Gonçalo J. L. Bernardes
Source: Angewandte Chemie International Edition (article, 2023-09-08)
Field: Chemistry — Organic Chemistry

Merging the Isonitrile‐Tetrazine (4+1) Cycloaddition and the Ugi Four‐Component Reaction into a Single Multicomponent Process Multicomponent reactions are of utmost importance at generating a unique, wide, and complex chemical space. Herein we describe a novel multicomponent approach based on the combination of the isonitrile-tetrazine (4+1) cycloaddition and the Ugi four-component reaction to generate pyrazole amide derivatives. The scope of the reaction as well as mechanistic insights governing the 4H-pyrazol-4-imine tautomerization are provided. This multicomponent process provides access to a new chemical space of pyrazole amide derivatives and offers a tool for peptide modification and stapling. Tetrazine ; Cycloaddition ; Tautomer ; Chemistry ; Combinatorial chemistry ; Amide ; Ugi reaction ; Chemical space ; Pyrazole ; Component (thermodynamics) ; Imine ; Computational chemistry ; Isocyanide ; Organic chemistry ; Drug discovery ; Thermodynamics ; Catalysis ; Physics Multicomponent Synthesis of Heterocycles ; Click Chemistry and Applications ; Synthesis and biological activity