jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Anna Péter-Haraszti | Liliána Györgyi Bakucz | Lilla Alexandra Mészáros | Petra Záhonyi | Edina Szabó
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Chemistry — Analytical Chemistry

Drug Content Determination of Amorphous Solid Dispersion Containing Tablets Using a Non-Destructive and Rapid Uv Imaging Method Amorphous solid ; Dispersion (optics) ; Content (measure theory) ; Materials science ; Drug ; Chromatography ; Biomedical engineering ; Optics ; Chemistry ; Crystallography ; Medicine ; Mathematics ; Physics ; Pharmacology Spectroscopy and Chemometric Analyses ; Analytical Chemistry and Chromatography ; Metabolomics and Mass Spectrometry Studies
Source: International Journal of Progressive Research in Engineering Management and Science (article, 2025-08-14)
Field: Chemistry — Organic Chemistry

In Silico Screening of Azetidinone-Substituted Benzotriazole Derivatives as Promising Anti-Tubercular Agents Targeting CYP125 Enzyme Background: Tuberculosis (TB), caused by Mycobacterium tuberculosis (MTB), remains a major global health challenge, particularly due to the emergence of multidrug-resistant (MDR) and extensively drug-resistant (XDR) strains.There is an urgent need for novel therapeutic agents targeting essential mycobacterial enzymes.Objective: This study aimed to evaluate eight novel azetidinone-substituted benzotriazole derivatives as potential inhibitors of Mtb CYP125, a key enzyme involved in bacterial survival, using computational approaches.Methods: Molecular docking was performed using AutoDock Vina to assess binding affinities against the CYP125 enzyme (PDB ID: 7QKE).ADMET (Absorption, Distribution, Metabolism, Excretion, Toxicity) properties were predicted using computational tools to evaluate drug-likeness and safety profiles. Results:The docking results demonstrated strong binding affinities (-8.8 to -10.1 kcal/mol), outperforming the standard drug rifampicin (-6.7 kcal/mol).Compounds 1, 2, and 8 exhibited the highest binding scores (-10.1, -10.0, and -10.1 kcal/mol, respectively), forming stable interactions with key residues (ARG274, CYS172, TYR208) through hydrogen bonds, -stacking, and hydrophobic contacts.ADMET analysis revealed favorable pharmacokinetics, including high intestinal absorption (87.5-97.5%)and compliance with Lipinski's Rule of Five.Compound 2 was identified as a non-mutagenic lead candidate with optimal logP (2.03) and 96.7% absorption, though its low solubility warrants further optimization. Conclusion:The benzotriazole derivatives, particularly Compounds 1, 2, and 8, show promising potential as anti-TB agents against resistant strains.Future studies should focus on in vitro validation, molecular dynamics simulations, and structural modifications to enhance solubility and reduce toxicity. Benzotriazole ; In silico ; Chemistry ; Combinatorial chemistry ; Enzyme ; Pharmacology ; Medicine ; Biochemistry ; Organic chemistry Synthesis and biological activity ; Synthesis of β-Lactam Compounds ; Multicomponent Synthesis of Heterocycles
Authors: Valentino Jadriško | Irantzu Landa-Garcia | Dino Novko | Borna Radatović | Lei Qin | Nataša Vujičić | Christoph Gadermaier | Andrés Castellanos-Gómez | Yong Xie
Source: Nanoscale (article, 2025-11-25)
Field: Materials Science — Materials Chemistry

Strain control of the electronic structure in WS <sub>2</sub> homobilayers with 0° and 60° stacking angles Two-dimensional transition metal dichalcogenides combine attractive semiconductor properties with exceptionally strong light-matter interaction. Their mechanical robustness allows the modulation of their optical and electronic functionalities <i>via</i> strain. Bilayers introduce the stacking angle as an additional parameter, whose role in the strain response is still elusive. Here, we combine differential reflectance spectroscopy and density functional theory calculations to explore the strain response of WS<sub>2</sub> homobilayers with 0° and 60° stacking angles. The change of the excitonic resonance energies and linewidths depends on the stacking angle, highlighting the scope for the manipulation of the electronic structure of two-dimensional semiconductors <i>via</i> the synergistic harnessing of the strain and angle control parameters. Stacking ; Electronic structure ; Density functional theory ; Semiconductor ; Strain (injury) ; Transition metal 2D Materials and Applications ; Strong Light-Matter Interactions ; Organic and Molecular Conductors Research
Authors: Shalom Maria Larasati | Alfian Miftahurrizki | Athallah Dwi Syahputro | Reinal Sihite
Source: Current Steam and Education Research (article, 2025-12-17)
Field: Chemistry — Analytical Chemistry

Wine Quality Analysis Using Newton Interpolation: Applying Computational Physics in the Food Industry This study aims to analyze wine quality based on computational physics using the Newton Interpolation method. This method is applied to estimate the quality value of wine based on certain values of variables such as sulphates, alcohol, citric acid, and the average of several physicochemical variables. Data were obtained from a red wine dataset commonly used in food product quality studies. Each variable was analyzed separately using strategically selected observation data points, then the interpolation value was calculated to predict the quality of wine at certain variable values. The interpolation results showed that the Newton method was able to produce quality value estimates that were consistent with data trends, such as quality predictions of 4.52 for sulphates = 0.61 and 2.97 for alcohol = 9.2. However, in some cases such as citric acid and the average of the variable, extreme fluctuations appeared in the high-degree divided difference values, indicating the potential for overfitting due to uneven data distribution or outliers. Nevertheless, the Newton interpolation method proved effective in providing initial estimates of wine quality, especially when data is limited. Wine ; Overfitting ; Interpolation (computer graphics) ; Quality (philosophy) ; Variable (mathematics) ; Product (mathematics) ; Value (mathematics) Spectroscopy and Chemometric Analyses ; Fermentation and Sensory Analysis ; Sensory Analysis and Statistical Methods
Authors: Khudayberdiev Mirzaakbar | Tojiboyev Bobomurod | Achilov Baxodir | Alimkulov Nurmukhammad | Ravshanov Anvar
Source: Advanced Computing An International Journal (article, 2025-07-28)
Field: Chemistry — Analytical Chemistry

INTEGRATING FRACTAL DIMENSION AND TIME SERIES ANALYSIS FOR OPTIMIZED HYPERSPECTRAL IMAGE CLASSIFICATION Hyperspectral images contain a huge number of spectral channels, which ensures high accuracy of analysis, but at the same time leads to problems associated with data redundancy, high computational load and decreased classification efficiency. In this paper, a method is proposed to integrate time series complexity analysis and fractal dimension (FD) to effectively reduce the dimensionality of hyperspectral data. Each pixel is considered as a time series characterized by spectral complexity, which allows identifying the most informative parts of the spectrum. Fractal dimension is used to quantify the complexity of spectral features and select significant channels. The proposed approach allows preserving critical information while minimizing losses during dimensionality reduction. To evaluate the effectiveness of the method, a comparison of the classification accuracy using the support vector machine (SVM) algorithm was carried out before and after applying the proposed optimization procedure. Experimental results on real hyperspectral data show significant dimensionality reduction while maintaining or improving classification quality. Hyperspectral imaging ; Fractal dimension ; Dimension (graph theory) ; Series (stratigraphy) ; Fractal ; Computer science ; Fractal analysis ; Image (mathematics) ; Box counting ; Time series ; Pattern recognition (psychology) ; Artificial intelligence ; Remote sensing ; Computer vision ; Data mining ; Mathematics ; Geology ; Machine learning ; Combinatorics ; Mathematical analysis Spectroscopy and Chemometric Analyses ; Remote-Sensing Image Classification ; Neural Networks and Applications
Authors: Maksym Dyshkant | Andriy Stepaniuk
Source: Transactions of Kremenchuk Mykhailo Ostrohradskyi National University (article, 2025-01-01)
Field: Materials Science — General Materials Science

MODERNIZATION STRATEGIES FOR APPARATUSES FOR CAPTURING COMPLEX EMISSIONS , . .. , , ( ), ' (CFD), Modernization theory ; Work (physics) ; Government (linguistics) ; Key (lock) Material Properties and Applications
Authors: Rúben Gariso | Jo�o P. L. Coutinho | Tiago J. Rato | Marco S. Reis
Source: Systems and Control Transactions (article, 2025-06-30)
Field: Chemistry — Analytical Chemistry

Linear and non-linear convolutional approaches and XAI for spectral data: classification of waste lubricant oils Waste lubricant oil (WLO) is a hazardous residual that requires proper management, being WLO regeneration the preferred approach. However, regeneration is only viable if the WLO does not coagulate in the equipment. Otherwise, the process needs to be shut down for cleaning and maintenance. To mitigate this risk, a laboratory test is currently used to assess the WLO coagulation potential before it enters the process. This laboratory test is, however, time-consuming, presents several safety risks, and is subjective. To expedite decision-making, process analytics technology (PAT) and machine learning were used to develop a model to classify WLOs according to their coagulation potential. Three approaches were followed, spanning linear and non-linear models. The first approach (benchmark) uses partial least squares for discriminant analysis (PLS-DA) and interval PLS combined with standard chemometric preprocessing techniques (27 model variants). The second approach uses wavelet transforms to decompose the spectra and PLS-DA for feature selection (10 model variants). Finally, the third approach uses convolutional neural networks (CNN) to estimate the optimal filter for feature extraction (1 model variant). These models were trained on real industrial data. The results show that the three modelling approaches can attain high accuracy, with an average of 91%. The spectral filtering using wavelet transforms proved to be a viable option to reduce the number of models to explore compared to the benchmark approach. The CNN was also able to streamline the preprocessing selection by implicitly estimating the optimal filter. The models also provided physical insight into the WLO coagulation phenomenon. Lubricant ; Computer science ; Pattern recognition (psychology) ; Artificial intelligence ; Statistics ; Mathematics ; Materials science ; Composite material Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Water Quality Monitoring and Analysis
Authors: Oliver Kuster | Yannick Augenstein | Hernández, Roberto Narváez | Carsten Rockstuhl | Thomas Jebb Sturges
Source: arXiv (Cornell University) (preprint, 2025-01-10)
Field: Materials Science — Surfaces, Coatings and Films

Inverse Design of 3D Nanophotonic Devices with Structural Integrity Using Auxiliary Thermal Solvers 3D additive manufacturing enables the fabrication of nanophotonic structures with subwavelength features that control light across macroscopic scales. Gradient-based optimization offers an efficient approach to design these complex and non-intuitive structures. However, expanding this methodology from 2D to 3D introduces complexities, such as the need for structural integrity and connectivity. This work introduces a multi-objective optimization method to address these challenges in 3D nanophotonic designs. Our method combines electromagnetic simulations with an auxiliary heat-diffusion solver to ensure continuous material and void connectivity. By modeling material regions as heat sources and boundaries as heat sinks, we optimize the structure to minimize the total temperature, thereby penalizing disconnected regions that cannot dissipate thermal loads. Alongside the optical response, this heat metric becomes part of our objective function. We demonstrate the utility of our algorithm by designing two 3D nanophotonic devices. The first is a focusing element. The second is a waveguide junction, which connects two incoming waveguides for two different wavelengths into two outgoing waveguides, which are rotated by 90° to the incoming waveguides. Our approach offers a design pipeline that generates digital blueprints for fabricable nanophotonic materials, paving the way for practical 3D nanoprinting applications. Nanophotonics ; Inverse ; Structural integrity ; Thermal ; Computer science ; Computer architecture ; Materials science ; Nanotechnology ; Engineering ; Structural engineering ; Physics ; Mathematics Optical Coatings and Gratings ; Photonic Crystals and Applications ; Photonic and Optical Devices
Authors: Yaolin Zhu | Zheng Li | Xin Chen | Jinni Chen | Hongsong Zhang
Source: Autex Research Journal (article, 2025-01-01)
Field: Chemistry — Analytical Chemistry

NIRS identification of cashmere and wool fibers based on spare representation and improved AdaBoost algorithm Abstract Near-infrared (NIR) spectroscopy is essential for distinguishing cashmere from wool. It is fast and non-destructive. Both cashmere and wool contain keratin. Their NIR spectral images are very similar. This makes it hard to tell them apart. This article proposes a method to identify and classify two similar fibers. It uses NIR spectroscopy combined with chemometrics. The chemometrics model mainly uses sparse representation and an improved AdaBoost classifier. The sparse representation is used for feature extraction. It expands the distance between sample spectra. This strategy first standardizes the dataset. It then uses the K-singular value decomposition algorithm to learn a low-dimensional dictionary. Finally, it maps the spectra using the dictionary. This approach creates a low-dimensional positive–negative distribution of sample features. It aims to widen the gap for later classification. Also, due to the many cashmere and wool species, some intra-species gaps are larger than the inter-species ones. This has increased misclassification errors. This article uses AdaBoost to assign weights to samples of different species. It optimizes these weights with many decision tree (DT) classifiers. It then uses the sparrow optimization algorithm. It finds the best number and depth of DTs. In the comparison experiments of this article, the sparse representation can effectively amplify the gap between cashmere and wool than principal component analysis and independent component analysis, and the classification efficiency of AdaBoost is also higher than the classification efficiency of K-nearest neighbors, Random Forest, and other classifiers. The combination of these two algorithms can achieve a classification accuracy of 97.4%, which can effectively classify cashmere and wool fibers. Wool ; AdaBoost ; Spare part ; Identification (biology) ; Computer science ; Algorithm ; Representation (politics) ; Pattern recognition (psychology) ; Artificial intelligence ; Engineering drawing ; Mathematics ; Materials science ; Engineering ; Composite material ; Mechanical engineering ; Support vector machine Spectroscopy and Chemometric Analyses ; Industrial Vision Systems and Defect Detection ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Xin Zhou
Source: Atomic Spectroscopy (article, 2025-02-03)
Field: Chemistry — Analytical Chemistry

Advances in Spectral Imaging for Heavy Metal Non-destructive Detection in Crops Given the increasingly serious threat of heavy metal pollution in farmland to the safety of agricultural products and human health, it is of great significance to develop rapid, non-destructive detection technologies.Traditional chemical detection methods suffer from being destructive, time-consuming and costly.In contrast, spectrum and imaging technology has become the research hotspot due to its advantages of fast and non-destructive.Heavy metal pollution alters the physiological and biochemical characteristics and optical responses of crops.Spectrum and imaging technology can rapidly and non-destructively obtain the corresponding spectral and spatial information, providing a way for pollution screening and quantitative and qualitative analysis.This paper systematically reviews the application of spectral imaging techniques such as visible/near-infrared spectroscopy, hyperspectral imaging, fluorescence hyperspectral imaging, laser-induced breakdown spectroscopy, and Raman spectroscopy in this field, and identifies that these technologies existing limitations and challenges.Future research should focus on multitechnology integration, model universality improvement, and portable device development.Through algorithm optimization, cost control and the extension of the industrial chain, the in-depth application of spectrum and imaging technology should be promoted.This article aims to provide theoretical references and practical guidance for the research and application of rapid non-destructive detection technology for heavy metals in crops. Spectral imaging ; Hyperspectral imaging ; Spectral analysis ; Metal ; Imaging spectroscopy ; Heavy metals Spectroscopy and Chemometric Analyses ; Soil Geostatistics and Mapping ; Remote Sensing in Agriculture
Authors: Mojtaba Javdani | Sina Shakouri | Maysam Mohammadzadeh-Shirazi
Source: Scientific Reports (article, 2025-07-01)
Field: Chemistry — Analytical Chemistry

Experimental assessment of chemical solvents for asphaltic sludge removal to mitigate formation damage in oil reservoirs The reaction between acid and crude oil during oil well acidizing results in the formation of asphaltic sludge, a protonated complex enriched with H⁺ ions that forms within oil reservoirs. Its intricate structure and limited solubility make removal challenging, distinguishing it from asphaltene precipitation. This study aims to address the research gap regarding the effectiveness of solvents in asphaltic sludge removal. Three solvents, namely xylene, tetrahydrofuran (THF), and a kerosene-diesel mixture, were tested to evaluate their dissolution efficiency compared with the solubility of different asphaltene types. The results indicated that THF and xylene were the most effective solvents, with solubility rates of 70% and 60%, respectively. In contrast, the kerosene-diesel mixture exhibited nearly 0% solubility due to the absence of aliphatic and nonpolar compounds in the sludge required for effective dissolution. Analysis of influencing parameters revealed that increasing solvent volume to 20 mL/g improved solubility by 20%. In reservoir conditions, rock particle deposition is likely. The presence of 0.5 g of rock powders reduced THF and xylene solubility to 30% and 39%, respectively, due to H⁺ ion deactivation. Increasing contact time to 60 min and mixing speed to 1800 RPM further improved solubility by up to 10% and 20%, respectively. Solubility ; Dissolution ; Kerosene ; Asphaltene ; Xylene ; Solvent ; Chemistry ; Diesel fuel ; Chemical engineering ; Oil sludge ; Hildebrand solubility parameter ; Organic chemistry ; Pulp and paper industry ; Toluene Petroleum Processing and Analysis ; Hydrocarbon exploration and reservoir analysis ; Enhanced Oil Recovery Techniques
Authors: A.G. Molokanov | Boris Krysiuk | Wujie Zhou
Source: ITM Web of Conferences (article, 2025-01-01)
Field: Chemistry — Organic Chemistry

Application of information technologies, including quantum chemistry methods, in creating high-efficiency fuel compositions This paper discusses the methodology for creating new fuels. The development of a laboratory fuel sample includes preparing technical requirements, selecting components, theoretically assessing key characteristics, experimental investigation of components properties, mathematical modeling, and performing optimization studies. The approach utilizes models to calculate thermophysical and thermochemical properties, critical parameters, combustion characteristics, as well as models for thermal degradation, oxidation, and combustion. A model for octane thermolysis was developed using quantum chemistry methods CBS and DLPNO-CCSD(T)/aug-cc-pVTZ. Chemistry ; Nanotechnology ; Computer science ; Engineering physics ; Materials science ; Engineering Chemical Thermodynamics and Molecular Structure ; Thermal and Kinetic Analysis ; Petroleum Processing and Analysis
Authors: Xuecheng Liu | Xiujuan Hou | Shijin Zhang | Ming Chen
Source: Research Square (preprint, 2025-05-05)
Field: Materials Science — Metals and Alloys

Model Reconstruction of AWJ Separation Speed Driven by Cutting Mechanisms and Cutting Data Separation (statistics) ; Computer science ; Mechanical engineering ; Engineering ; Machine learning Hydrogen embrittlement and corrosion behaviors in metals ; Non-Destructive Testing Techniques ; Drilling and Well Engineering
Authors: Indra Purnama | Suratno Suratno | Anisa Mutamima | Anjar Windarsih | Dita Fitriani | Vonny Indah Sari | Riskina Juwita
Source: Springer Link (Chiba Institute of Technology) (article, 2025-12-16)
Field: Chemistry — Analytical Chemistry

Discrimination of Rice Varieties Using Attenuated Total Reflectance-Fourier Transform Infrared (ATR-FTIR) Spectroscopy Combined with Chemometric Methods Ensuring the authenticity and quality of rice varieties is crucial for protecting market value and maintaining consumer trust. This study evaluates the potential of Attenuated Total Reflectance-Fourier Transform Infrared (ATR-FTIR) spectroscopy combined with chemometric methods to accurately discriminate three Indonesian rice varieties: Inpari42, Pandan Wangi, and Logawa. Spectral data collected in the mid-infrared region (4000−600 cm−1) were analyzed using Partial Least Squares-Discriminant Analysis (PLS-DA), sparse PLS-DA (sPLS-DA), and Hierarchical Cluster Analysis (HCA). While PLS-DA provided clear varietal separation, minor overlaps were observed, which were effectively resolved by sPLS-DA through the identification of key spectral variables in carbohydrate, protein, and lipid regions. HCA further validated the classification, forming distinct clusters for each variety. This study highlights ATR-FTIR spectroscopy as a rapid, non-destructive, and reliable tool for rice variety discrimination, with promising applications for global rice quality control and authentication. Chemometrics ; Principal component analysis ; Spectroscopy ; Near-infrared spectroscopy ; Infrared spectroscopy ; Mid infrared ; Pattern recognition (psychology) ; Infrared Spectroscopy and Chemometric Analyses ; GABA and Rice Research ; Remote Sensing in Agriculture