jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Osama E. Gouda | E. I. Abdel Aziz | Matti Lehtonen | Mohamed M. F. Darwish
Source: Energy Science & Engineering (article, 2025-07-06)
Field: Materials Science — Materials Chemistry

Computation of Electric Field on High Voltage Polymeric Insulators Using Actual Model With Optimal Corona Ring Dimensions ABSTRACT This article deals with the calculations of the electric field and the potential distribution of the polymer insulators of the HVTLs with the use of corona rings. The calculations are done when the insulators are stressed by power frequency voltage. The aim of this article is to investigate the effect of the optimal corona ring dimensions on the electric field and the potential distribution when the insulators are dried and wetted with salty pollutants dissolved in water droplets. The electric field of the outdoor polymeric insulator is inspected by the use of FEM software and the COMSOL multiphysics program. The 220 kV (RMS line voltage) actual models of the polymeric insulators are used in the study by the use of the 2D model. From the intensive study, it is concluded that the maximum value of the electric field in the absence of the corona ring reaches 13.89 kV/cm and decreases to 1.63 kV/cm (i.e., about 88% of its initial maximum value), in the case of using the optimal dimensions and location of the corona ring. In addition, the proposed corona ring dimensions to be used in high voltage polymer insulators have proven to be suitable in the case of the polluted insulator by salty pollutants dissolved in water droplets as well as the field reached very close to the E‐field values that are recommended by the EPRI and IEEE specifications. In addition, this study helps in the development of corona ring dimensions to maximize the corona ring impacts on the polymeric insulators with respecting the industrial specifications. Finally, in conclusion, this study provides valuable insights into the optimization of grading rings for high‐voltage polymeric insulators to prevent the failure of the 220 kV polymer insulators. Computation ; Corona (planetary geology) ; Electric field ; Ring (chemistry) ; Corona ring ; Voltage ; High voltage ; Field (mathematics) ; Materials science ; Corona discharge ; Computer science ; Electrical engineering ; Electronic engineering ; Engineering ; Physics ; Algorithm ; Mathematics ; Chemistry High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Dielectric materials and actuators
Authors: Max B. Sauerland | Tugay Karakaya | Morten Bahrt Haulrig | Marianne Bengtson Løvendorf | Trine Schønfeldt | Maja Søberg Udsen | Liv Eidsmo | Hans‐Dietmar Beer | Beatrice Dyring‐Andersen | Mads Gyrd‐Hansen
Source: Journal of Investigative Dermatology (article, 2025-09-04)
Field: Chemistry — Spectroscopy

Scalable Acid-Aided Lysis of Skin Samples Improves Proteome Coverage Liquid chromatography-mass spectrometry is an evolving tool for comprehensive proteomic analyses across tissues. Despite the widespread use of liquid chromatography-mass spectrometry in dermatology, full-thickness human skin remains challenging to analyze. The skin extracellular matrix presents 2 major obstacles: the extensive crosslinking complicates protein extraction, and the high abundance of extracellular matrix proteins can mask lower-abundance proteins, reducing identification numbers. These limitations hinder progress in skin proteomics research. To address these challenges, we adapted the trifluoroacetic acid-based SPEED (Sample Preparation by Easy Extraction and Digestion) method for skin samples. Trifluoroacetic acid does not disrupt most crosslinks, allowing for the removal of abundant crosslinked extracellular matrix proteins. This enhanced proteome coverage, increasing the number of identified protein groups to over 6200 in healthy human skin. The improved sensitivity enabled the use of minimally invasive 2-mm punch biopsies, potentially facilitating greater patient enrolment than commonly used larger punch biopsies. We anticipate that these advancements in sample preparation will pave the way for analysis tools such as machine learning in skin proteomics, which require large datasets with high identification numbers. Furthermore, we extend this approach to tape strip proteomics and identify up to 2300 proteins in healthy skin, providing a cost-effective, scalable, and sensitive alternative to current workflows. Lysis ; Proteome ; Computational biology ; Scalability ; Chemistry ; Computer science ; Biology ; Biochemistry ; Database Advanced Proteomics Techniques and Applications ; RNA and protein synthesis mechanisms ; Click Chemistry and Applications
Authors: Norberto Jose Palange | Tonny Obua | Julius Pyton Sserumaga | Enoch Wembabazi | Mildred Ochwo‐Ssemakula | Ephraim Nuwamanya | Isaac Onziga Dramadri | Moses Matovu | Richard Edema | Phinehas Tukamuhabwa
Source: Discover Applied Sciences (article, 2025-06-15)
Field: Chemistry — Analytical Chemistry

Artificial intelligence-driven near-infrared spectrophotometry model for rapid quantification of anti-nutritional factors in soybean (Glycine max.) Anti-nutritional factors can impact soybean nutrient bioavailability when consumed by monogastric animals. However, conventional methods available for quantifying anti-nutritional factors such as phytate and trypsin inhibitors in feeds are laboratory-intensive, time-consuming, expensive, and error-prone. This study developed near-infrared spectrophotometry (NIRS)-based models to quantify phytate and trypsin inhibitors in soybean. Thus, a set of 190 soybean genotypes assayed through conventional wet chemistry was used as a reference for model development and cross-validation. Using a benchtop NIR instrument (DS2500), spectra readings between 400 and 2500 nm were taken from each soybean sample. Mean values for phytate and total trypsin inhibitors (TTI) were 1.77 mg g−1 (SD = 1.23) and 0.89 mg g−1 (SD = 0.24), respectively. Predictive models were developed through partial least squares (PLS) and random forest (RF) regressions. The random forest models outperformed partial least squares regression with the best predictive performance of R2test = 0.97; RPD = 5.95 and R2test = 0.96; RPD = 3.62 for phytate and TTI, respectively. The high R2 and RPD values demonstrate the model's strong predictive capability and accuracy, suggesting that the NIRS-based models can effectively quantify phytate and TTI in soybean. Thus, breeders can efficiently select for low-anti-nutritional genotypes and accelerate the development of nutritionally beneficial legumes while reducing soybean processing costs. NIRS offers a promising alternative to traditional phenotyping methods due to its speed, simplicity, environmental friendliness, and cost-effectiveness. Its integration into breeding programs can streamline the screening process, especially in early selection stages. Infrared Spectrophotometry ; Glycine ; Spectrophotometry ; Chemistry ; Chromatography ; Biochemistry ; Amino acid Spectroscopy and Chemometric Analyses ; Food composition and properties ; GABA and Rice Research
Authors: Akbar Romadhon | Mohamad Solahudin | Nanik Purwanti | Slamet Widodo
Source: Jurnal Keteknikan Pertanian (article, 2025-06-30)
Field: Chemistry — Analytical Chemistry

Classification of Arabica Coffee Beans Based on Starters Type with Honey Processing Using Multi-channel Spectral Sensor Coffee is one of important agricultural commodities in Indonesia, contributing as an income source for farmers and a major export revenue. The specialty coffee industry has begun to utilize microorganisms (starter) in the fermentation process, including the honey process, to obtain a distinctive flavour. However, the use of various starters in this process produces coffee bean with similar color, making it difficult to determine the authenticity of the type of starter used. This research aims to classify arabica coffee beans processed with different types of starters using multi-channel spectral sensor to ensure product quality and authenticity. This research used arabica coffee beans, in the form of green beans, processed with three types of starters, namely Saccharomyces cerevisiae, Lactobacillus sp, and Rhizopus oryzae. Multi-channel spectral sensor was used to acquire the spectra data of coffee sample processed with different starters. The data was then analysed using multivariate analysis based on Partial Least Square – Discriminant Analysis (PLS-DA). In the calibration stage, PLS-DA model built using de-trending pre-treatment was able to predict the type of starter very well, with accuracy, sensitivity, specificity, and precision values, reaching 97%, 95%, 96%, 95%, respectively. This result is also confirmed during validation stage where the built PLS-DA model could predict the type of starter with accuracy, sensitivity, specificity, and precision values, reaching 100%. Arabica coffee ; Channel (broadcasting) ; Horticulture ; Environmental science ; Computer science ; Biology ; Telecommunications Spectroscopy and Chemometric Analyses
Authors: Philipp O. Tsvetkov | Rémi Eyraud | Stéphane Ayache | Viktoriia E. Baksheeva | Antonella Bertucci | Alice Mogenet | Pascale Tomasini | Bernadette de Rauglaudre | Laëtitia Dahan | Caroline Gaudy‐Marqueste | Shanmugha Sri Siva Kalidindi | Christophe Buffat | Caroline Dehais | Alexandre Astier | L’Houcine Ouafik | Svetlana Gorokhova | Émeline Tabouret | François Devred
Source: medRxiv (preprint, 2025-03-27)
Field: Chemistry — Spectroscopy

AI-Driven Plasma Denaturation Profiling for Multi-Cancer Detection Abstract Many cancers cannot be detected early due to lack of effective disease biomarkers, leading to poor prognosis. We applied an existing biophysical technology nanoDSF in a novel way to answer this unmet biomedical need. We developed a breakthrough digital biomarker method for cancer detection based on AI-classification of plasma denaturation profiles (PDPs) obtained by nanoDSF technology. PDPs from 300 plasma samples from patients with melanoma, brain, digestive or lung cancers were automatically distinguished from ‘healthy’ profiles with an accuracy of 94%. Moreover, our method was able to distinguish different types of cancers from each other with an accuracy of 80%, making it an effective way to help cancer diagnosis and monitoring. Our technology thus paves the way for a long-sought multi-cancer early detection (MCED) test that is blood-based, cost-effective and easy-to-implement in any clinical setting. Profiling (computer programming) ; Computational biology ; Chemistry ; Computer science ; Biology ; Programming language Mass Spectrometry Techniques and Applications
Authors: Ernest Oduro‐Kwateng | Musab A. M. Ali | Ibrahim Oluwatobi Kehinde | L. Nagaprasada Rao | Mahmoud E. S. Soliman
Source: BMC Methods (article, 2025-05-01)
Field: Chemistry — Organic Chemistry

De Novo Rational design of peptide-based covalent inhibitors via mapping of complementary binding site residues – technical protocol and case study on KRASG12C and BTK481C Targeting undruggable proteins and challenging binding sites, such as protein–protein interaction (PPI) interfaces and allosteric pockets, using small-molecule inhibitors is often infeasible. Peptide-based irreversible inhibitors are promising emerging strategies for the treatment of such targets. However, there is currently no systematic in silico protocol for the rational design of peptide-based covalent inhibitors. We developed a streamlined computational framework for the de novo design of peptide-based irreversible inhibitors. Key considerations include peptide sequence optimization for binding, selection of electrophilic warheads, peptide folding, target specificity, and pharmacokinetic and toxicity profiles. Binding affinities were estimated using covalent molecular dynamics (MDcov) simulations and thermodynamic binding free energy calculations. Using KRASG12C, a strategic drug target traditionally considered undruggable, as a case study, the protocol identified top-hit peptide inhibitors (RVKDX, HVKXR, and XLKDH) with binding free energies (BFEs) of -48.84, -48.93, and -48.67 kcal/mol, respectively. These values are comparable to sotorasib (-50.63 kcal/mol) and lower than adagrasib (-71.73 kcal/mol), both FDA-approved KRASG12C inhibitors. Benchmarking against BTK481C using zanubrutinib, an FDA-approved therapeutic agent for B-cell malignancies, further validated the protocol. Peptide inhibitors XDYMA, XDYVL, and QDWXL demonstrated BFEs of -83.40, -76.69, and -62.40 kcal/mol, outperforming zanubrutinib (-57.00 kcal/mol), acalabrutinib (-54.19 kcal/mol), and ibrutinib (-55.09 kcal/mol). These findings underscore the robustness and adaptability of our protocol, offering a systematic, multifaceted approach that can be integrated into drug discovery workflows to design novel peptide-based irreversible inhibitors. Rational design ; Chemistry ; Peptide ; Binding site ; Combinatorial chemistry ; Computational biology ; Stereochemistry ; Biochemistry ; Nanotechnology ; Biology ; Materials science Click Chemistry and Applications ; Peptidase Inhibition and Analysis ; Chemical Synthesis and Analysis
Authors: Н. Д. Денисов | Andrey Orekhov | Johan Verbeeck
Source: The European Physical Journal Applied Physics (article, 2025-01-01)
Field: Materials Science — Surfaces, Coatings and Films

Edge-Detected 4DSTEM - effective low-dose diffraction data acquisition method for nanopowder samples in an SEM instrument The appearance of direct electron detectors marked a new era for electron diffraction. Their high sensitivity and low noise opens the possibility to extend electron diffraction from transmission electron microscopes (TEM) to lower energies such as those found in commercial scanning electron microscopes (SEM). The lower acceleration voltage does however put constraints on the maximum sample thickness and it is a-priori unclear how useful such a diffraction setup could be. On the other hand, nanoparticles are increasingly appearing in consumer products and could form an attractive class of naturally thin samples to investigate with this setup. In this work we present such a diffraction setup and discuss methods to effectively collect and process diffraction data from dispersed crystalline nanoparticles in a commercial SEM instrument. We discuss ways to drastically reduce acquisition time while at the same time lowering beam damage and contamination issues as well as providing significant data reduction leading to fast processing and modest data storage needs. These approaches are also amenable to TEM and could be especially useful in the case of beam-sensitive objects. Materials science ; Diffraction ; Enhanced Data Rates for GSM Evolution ; Optics ; Biomedical engineering ; Analytical Chemistry (journal) ; Computer science ; Chromatography ; Medicine ; Chemistry ; Physics ; Artificial intelligence Electron and X-Ray Spectroscopy Techniques ; X-ray Spectroscopy and Fluorescence Analysis ; Advanced X-ray Imaging Techniques
Authors: Daeuk Kim | Ronnie Concepcion | Joseph Rey H. Sta. Agueda | Jubert Marquez
Source: Sensors (article, 2025-05-31)
Field: Materials Science — Materials Chemistry

Optimization of Dye and Plasticizer Concentrations in Halochromic Sensor Films for Rapid pH Response Using Bird-Inspired Metaheuristic Algorithms The pH level of a wound environment is a crucial biomarker for monitoring wound healing, particularly in chronic wounds, where alkalinity (pH > 7) is linked to bacterial colonization and infection. This study developed and optimized a halochromic sensor film composed of polyvinyl alcohol (PVA), polyethylene glycol (PEG), and bromothymol blue (BTB) to enable rapid and reliable pH-responsive color transitions. Feature selection using Principal Component Analysis (PCA) and the ReliefF algorithm identified Hue, Saturation, and a as key features influencing pH responsivity. Optimization of BTB (0.01-0.05%) and PEG (6-10%) concentrations was conducted using bird-inspired metaheuristic algorithms, including the Parrot Optimizer (PO), Pelican Optimization Algorithm (POA), and Secretary Bird Optimization Algorithm (SBOA). While final fitness values showed negligible variation (188.595647 for GP-PO, 188.595634 for GP-POA, and 188.595634 for GP-SBOA), GP-PO demonstrated superior convergence and stability, efficiently identifying the optimal formulation (0.02% BTB, 6% PEG). The optimized film achieved a complete color transition within 3-5 min, a 23.15% reduction compared to the non-optimized formulation. Statistical analysis revealed that BTB concentration significantly affected response time (<i>p</i> = 0.01), while PEG concentration had no significant effect (<i>p</i> > 0.05). These findings highlight the potential of halochromic films for real-time, non-invasive pH monitoring in chronic wounds. Plasticizer ; Metaheuristic ; Algorithm ; Optimization algorithm ; Biological system ; Computer science ; Chemical engineering ; Materials science ; Environmental science ; Chemistry ; Mathematical optimization ; Engineering ; Mathematics ; Biology Photochromic and Fluorescence Chemistry ; Analytical Chemistry and Sensors ; Advanced Chemical Sensor Technologies
Authors: Hang Yin | Kebin Qin | Xiaodong He | Dongdong Sun | Chuchu Yang | Guangping Song | Yongting Zheng | Yuchen Yuan | Yuelei Bai
Source: Journal of the American Ceramic Society (article, 2025-05-09)
Field: Materials Science — Materials Chemistry

Statistical analysis for the strength and thermal shock behavior of MoAlB Abstract The statistical distribution for the strength as well as thermal shock behavior of high‐purity MoAlB, fabricated via self‐propagating high‐temperature combustion synthesis with hot pressing (SHS/HP), was investigated. Among the statistical distribution models, the three‐parameter Weibull (3P‐Weibull) method exhibited the best fit for the flexural strength of MoAlB, with the primary error in the low‐stress region. Notably, the 3P‐Weibull modulus of MoAlB fell between those of typical metals and ceramics, aligning with the trends in fracture toughness and thermal conductivity. Its critical thermal shock temperature ranges from 100°C to 200°C. The introduced pores did not only enhance thermal shock resistance across the entire temperature range, but also result in an abnormal strength recovery above 1000°C, which suggests that pores play a significant role in the abnormal strength recovery after water quenching, in addition to the established explanations such as the formation of oxide layer and grain kinking. Thermal shock ; Materials science ; Statistical analysis ; Shock (circulatory) ; Composite material ; Mechanics ; Mathematics ; Physics ; Statistics ; Medicine MXene and MAX Phase Materials ; Boron and Carbon Nanomaterials Research ; Advanced ceramic materials synthesis
Authors: S. R. Ouyang | Sijia Lv | Bin Li
Source: Agriculture (article, 2025-05-06)
Field: Chemistry — Analytical Chemistry

Detection of Water Content of Watermelon Seeds Based on Hyperspectral Reflection Combined with Transmission Imaging Watermelon is a widely cultivated fruit and vegetable that is native to Africa and has become one of the world’s important summer fruits. Watermelon seed vigor has a critical impact on watermelon planting and yield, and seed water content is a key factor in maintaining vigor during seed storage and germination. In this study, reflectance and transmittance spectral data from hyperspectral imaging were fused to improve the detection accuracy of moisture content in watermelon seeds. First, watermelon seed samples with different water content gradients were prepared by dividing all 456 selected watermelon seeds into 10 groups and drying them in a drying oven at 60 °C for 0, 3, 5, 10, 15, 20, 25, 30, 40, and 50 min. Reflectance and transmission spectra of 456 watermelon seeds were collected by a hyperspectral imaging system, and the single spectral data were subsequently used to build PLSR and LSSVR models for quantitative analysis of watermelon seed moisture content. Model performance is enhanced by Competitive Adaptive Reweighted Sampling (CARS), Unrelated Variable Elimination (UVE), and primary and intermediate data fusion methods. Primary data fusion improves model predictions compared to single models based on reflectance and transmission spectra. The intermediate data fusion of the feature spectral data of reflectance and transmittance selected by the CARS algorithm improves the prediction effect of the model more obviously, in which the model with the best prediction accuracy is Raw-CRAS-LSSVR, whose RP2 and RMSEP are 0.9149 and 0.0144, respectively, which improves the prediction effect of the model built by a single full-spectrum datum by 5.72%. This study demonstrates that hyperspectral reflectance and transmission imaging techniques combined with data fusion can effectively detect watermelon seed moisture content quickly and with high accuracy. Hyperspectral imaging ; Transmission (telecommunications) ; Reflection (computer programming) ; Content (measure theory) ; Remote sensing ; Biology ; Horticulture ; Computer science ; Mathematics ; Geography ; Telecommunications Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Remote Sensing and Land Use
Authors: Laura Vanessa Alvarez Marin | Zahra Naghizadeh | Tawsif Raman | María Soledad Peresin
Source: European Journal of Wood and Wood Products (article, 2025-12-29)
Field: Materials Science — Polymers and Plastics

Optimization of material and extrusion parameters for wood flour–epoxy composites with potential additive-manufacturing applications in construction Abstract The sustainable utilization of wood waste is critical for reducing environmental impact and promoting more resource-efficient construction materials. This study investigates the effects of wood flour particle size, wood content, and extrusion parameters—specifically extrusion rate—on the fabrication of wood flour–epoxy composites designed for extrusion-based 3D printing. Through a factorial experimental design, the effects of these parameters on mechanical, physical, and fire-resistant properties were systematically evaluated. Characterization included flexural and compressive strength tests, water absorption, dimensional stability, and fire resistance assessments. Statistical analyses revealed that the epoxy-to-wood ratio is the most influential factor, with a 55:45 ratio yielding optimal results enhanced mechanical strength, improved dimensional stability, and superior fire resistance, while minimizing surface defects. These findings highlight the importance of precise extrusion parameter optimization to produce high-performance composites that incorporate renewable wood resources. The results provide valuable insights for developing partially bio-based materials capable of reducing reliance on petroleum-derived polymers, supporting improved sustainability and performance in construction applications. Extrusion ; Flexural strength ; Raw material ; Factorial experiment ; Wood flour ; Compressive strength ; Response surface methodology ; Engineered wood Natural Fiber Reinforced Composites ; Flame retardant materials and properties ; Lignin and Wood Chemistry