jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Babita Subhash Gawate
Source: Journal of Information Systems Engineering & Management (article, 2025-03-27)
Field: Chemistry — Spectroscopy

Advancements in Supercritical Fluid Extraction for Enhancing Bioavailability of Food-Derived Polyphenols Introduction: Polyphenols are bioactive compounds found in plant-based foods, known for their antioxidant and anti-inflammatory properties. Despite their numerous health benefits, their low bioavailability due to poor solubility and rapid metabolism limits their effectiveness. Conventional extraction methods often result in low yields and degradation of bioactive compounds. Supercritical Fluid Extraction (SFE) has emerged as a green, efficient technology to extract polyphenols while preserving their structural integrity. Objectives: This study aims to optimize SFE conditions—including pressure, temperature, and co-solvent selection—to maximize polyphenol recovery. Additionally, nanoencapsulation techniques such as liposomes and polymeric nanoparticles are explored to enhance solubility, stability, and absorption of extracted polyphenols. Methods: SFE was conducted under varying conditions of pressure (100–400 bar), temperature (35–80°C), and co-solvent (ethanol, methanol, water) to identify the optimal extraction parameters. Extracted polyphenols were characterized using HPLC, FTIR, and UV-Vis spectroscopy. To improve bioavailability, nanoencapsulation techniques—including liposomal encapsulation and polymeric nanoparticles—were employed. In-vitro digestion models were used to evaluate the stability and absorption of encapsulated polyphenols. Results: Optimized SFE conditions (300 bar pressure, 50°C, and ethanol as a co-solvent) resulted in the highest polyphenol recovery and purity compared to conventional methods. Nanoencapsulation enhanced solubility (2.3-fold) and antioxidant retention (1.8-fold), significantly improving bioavailability. In-vitro studies confirmed that encapsulated polyphenols exhibited greater stability and intestinal absorption than non-encapsulated counterparts. Conclusions: This study highlights SFE as a sustainable extraction method that improves polyphenol yield while maintaining bioactivity. The integration of nanoencapsulation further enhances solubility and bioavailability, making polyphenols more effective for functional food and nutraceutical applications. Future research should focus on scalability, cost-effectiveness, and clinical validation of these techniques for commercial applications. Bioavailability ; Supercritical fluid extraction ; Polyphenol ; Supercritical fluid ; Extraction (chemistry) ; Chemistry ; Chromatography ; Food science ; Biochemical engineering ; Environmental science ; Organic chemistry ; Biology ; Pharmacology ; Engineering Analytical Chemistry and Chromatography ; Fermentation and Sensory Analysis ; Microbial Metabolic Engineering and Bioproduction
Authors: Lu‐Ping Chi | Xiaolu Li | Anton F. Ketzel | Armando Navarro‐Vázquez | Caspar Schattenberg | Xiao‐Ming Li | Xin Li | Han Sun | Bin‐Gui Wang
Source: Marine Life Science & Technology (article, 2025-05-21)
Field: Chemistry — Spectroscopy

Impact of dispersion correction in DFT-enhanced anisotropic NMR for stereochemical elucidation of flexible marine natural products The online version contains supplementary material available at 10.1007/s42995-025-00294-w. Dispersion (optics) ; Anisotropy ; Density functional theory ; Computational chemistry ; Stereochemistry ; Chemistry ; Physics ; Quantum mechanics Molecular spectroscopy and chirality ; NMR spectroscopy and applications ; Advanced NMR Techniques and Applications
Authors: Chuansheng Huang | Wensong Jiang | Zai Luo | Wei Xuan | Yang Li | Shuyi Feng | Siyuan Zhang | Zilu Zhang
Source: IET Science Measurement & Technology (article, 2025-01-01)
Field: Materials Science — Polymers and Plastics

LSA‐HELM: A Boosted Configuration to Optimize the Reconstruction Model of Dynamic Force on Aviation Composite Skin ABSTRACT The reconstruction of impact forces is critical for structural health monitoring of aero‐composite wings. However, due to its complex structure with a limited sensor array, the impact force cannot be accurately determined simply by using an equal‐weight transfer function. Meanwhile, the introduction of complex models can improve the accuracy of reconstruction but also increase the computational complexity and running time. To address this issue, a method combining lightweight spatiotemporal attention mechanism and extreme learning machine (ELM) (LSA‐HELM) is proposed. By introducing a lightweight spatiotemporal attention mechanism, the input data are weighted to capture key features effectively. Then, the mapping relationship between impact force and strain array is constructed by using Hermite polynomials as the ELM of activation function. The suggested method is verified on an aircraft composite plate. The experimental results show that the peak relative error (PRE) is 4.62% for LSA‐HELM, 11.03% for Bayesian, 13.33% for convolutional neural network (CNN), 7.31% for Tiny1DCNN and 9.82% for transformer. It shows under the condition of limited sample number and scarce data features, the proposed method has obvious advantages in terms of reconstruction accuracy and real‐time performance and is superior to other methods based on machine learning and traditional analysis methods. Convolutional neural network ; Hermite polynomials ; Extreme learning machine ; Artificial neural network ; Structural health monitoring ; Mechanism (biology) ; Key (lock) ; Hermite interpolation Textile materials and evaluations ; Acoustic Wave Phenomena Research ; Mechanical Behavior of Composites
Authors: Letícia de Oliveira Carneiro | Nairiane dos Santos Bilhalva | E A Oliveira Filho | Dthenifer Cordeiro Santana | Larissa Pereira Ribeiro Teodoro | Paulo Eduardo Teodoro | Paulo Carteri Coradi
Source: Foods (article, 2025-12-24)
Field: Chemistry — Analytical Chemistry

Method of Characterization and Classification of the Physicochemical Quality of Polished White Rice Grains Using VIS/NIR/SWIR Techniques and Machine Learning Models for Lot Segregation and Commercialization in Storage and Processing Units The quality of rice depends on physical, nutritional, and sensory attributes. However, in industrial practice, quality is predominantly based on physical characteristics evaluated by the conventional method for categorizing commercial atches. In this context, the present study aimed to characterize the physical quality and proximate composition and to classify commercial batches of polished white rice using machine learning (ML) algorithms based on spectral data. Individual samples (healthy grains and physical defects) and samples from commercial batches (Type 1 to Type 5 and Off-Type) were analyzed and prepared in accordance with current legislation. Spectral data were obtained using NIR and hyperspectral measurements covering the VIS/NIR/SWIR regions, and proximate composition was determined for moisture (MOI), starch (ST), protein (PRO), lipids (LIP), fiber (FIB), and ash (ASH). Multivariate analyses and ML classification models were applied to evaluate differences among grain types and commercial categories and to assess the discriminatory capacity of spectral information. The results showed that including physicochemical attributes to evaluate the quality of commercial batches simplifies the commercial categories currently used. For spectral behavior, batches classified as Type 1 and Type 2 showed low reflectance in the NIR and SWIR regions, suggesting greater interaction of radiant energy with compounds associated with nutritional and sensory quality. The MLP, LGBM, CAT, XGB and RF models performed best for the classification of commercial white polished rice batches, with metrics above 95%. The SWIR region, especially the 2173 nm spectral point, demonstrated high discriminatory power. In conclusion, the application of machine learning models based on VIS/NIR/SWIR spectroscopy proved highly efficient for classifying commercial batches of polished white rice, integrating physical and physicochemical attributes of the grains. Hyperspectral imaging ; Commercialization ; Principal component analysis ; Predictive modelling ; Quality (philosophy) ; Grain quality ; Moisture ; Characterization (materials science) Spectroscopy and Chemometric Analyses ; Food composition and properties ; GABA and Rice Research
Authors: Shuanglong Yi | Xueli Ren | Jie Liu | Luodan Yu
Source: Medical Materials Research (article, 2025-04-02)
Field: Materials Science — Biomaterials

Drug Delivery Systems for Enhancing Cancer Chemotherapy Cancer chemotherapy remains one of the most effective treatment strategies, but its clinical success is often limited by challenges such as poor drug bioavailability, non-specific toxicity, and drug resistance. Drug delivery systems (DDSs) have emerged as a promising solution to overcome these barriers, offering enhanced efficacy and reduced side effects. For instance, liposomal doxorubicin (Doxil®) has significantly improved treatment outcomes in triple-negative breast cancer (TNBC) by reducing cardiotoxicity, while albumin-bound paclitaxel (Abraxane®) enhances drug solubility and tumor targeting in glioblastoma. This review focuses on the classification of DDSs, drug loading methods, and surface functionalization strategies, which enable targeted drug delivery, controlled release, and improved cellular uptake. Additionally, we explore the integration of stimuli-responsive systems that can release chemotherapeutic agents in situ in response to endogenous or exogenous stimuli. The potential of multifunctional DDSs to combine chemotherapy with radiotherapy, phototherapy, ultrasound therapy, immunotherapy, and imaging is also discussed. Despite promising results, the clinical translation of these systems faces challenges, including manufacturing scalability, regulatory approval, and safety concerns. Future directions for the development of more efficient and personalized DDSs for cancer treatment are also proposed. Paclitaxel ; Drug delivery ; Drug ; Chemotherapy ; Doxorubicin ; Targeted drug delivery ; Cancer ; Cancer treatment Nanoparticle-Based Drug Delivery ; RNA Interference and Gene Delivery ; Nanoplatforms for cancer theranostics
Authors: Abdul M Gbaj
Source: Journal of Surgical Case Reports and Images (article, 2025-03-20)
Field: Materials Science — Biomaterials

Design And In-Silico Evaluation of Pyridine-4-Carbohydrazide Derivatives for Potential Therapeutic Applications The emergence of drug-resistant and novel diseases underscores the urgency for innovative therapeutic interventions. Drug repositioning and computational approaches offer an efficient pathway to accelerate drug discovery and development. This study leverages these techniques in designing and evaluating derivatives based on the FDA-approved compound, pyridine-4-carbohydrazide, to assess how structural modifications impact therapeutic potential. Methods: The derivatives were designed using a chemical library of small molecules containing imine functional groups, built upon pyridine-4-carbohydrazide scaffolds (INH01-INH19). Computational tools, including Molinspiration Cheminformatics, way2drug, and the pkCSM platform, were utilized to evaluate each derivative's physicochemical properties, drug-likeness, bioactivity scores, potential biological activities, and ADME (Absorption, Distribution, Metabolism, Excretion) profiles. Results: Most derivatives demonstrated enhanced physicochemical characteristics, adhering to both Lipinski’s Rule of Five and Veber’s Rule. Bioactivity scores varied with moderate to inactive interactions across six target classes, ranked as follows: enzyme inhibitors, kinase inhibitors, G-protein-coupled receptors, protease inhibitors, nuclear receptors, and ion channel modulators. Notably, derivatives INH03, INH09, INH14, and INH19 exhibited high predicted activity in multiple therapeutic areas, indicating potential applications in antibacterial, antiviral, antiprotozoal, anti-inflammatory and anticancer treatments. Moreover, structural modifications in these derivatives positively influenced ADME profiles compared to pyridine-4-carbohydrazide, though certain compounds presented challenges, such as limited solubility, P-glycoprotein interactions and CYP450 inhibition. Conclusions: These Schiff base derivatives stand out as promising candidates for further drug development, underscoring the importance of computational strategies in optimizing drug discovery and design. Carbohydrazide ; In silico ; Computer science ; Combinatorial chemistry ; Computational biology ; Chemistry ; Biology ; Medicinal chemistry ; Biochemistry Nanoparticle-Based Drug Delivery ; Graphene and Nanomaterials Applications ; Cancer Research and Treatment
Authors: Tengteng Niu | Zhi Zhang | Qiyuan Yi | Ying Yang
Source: High Voltage (article, 2025-10-01)
Field: Materials Science — Materials Chemistry

Interface Engineering for Synergistic Achievement of Enhanced Thermal Conductivity and Suppressed Dielectric Loss of Epoxy/Boron Nitride Composite ABSTRACT Interface design for synergistic improvement of the thermal conductivity and dielectric properties of dielectrics is urgently needed but still challenging for the next generation of electronic and electrical equipment development. Herein, the authors report a strategy to screen structural units for the interface design of polymer dielectrics working under high‐frequency and high‐voltage conditions. Thermal conductivity, dielectric loss, the highest occupied molecular orbital (HOMO) and the lowest unoccupied molecular orbital (LUMO) gap are employed as key parameters to be considered simultaneously in the interface design for the screening. The authors find that a rigid ring with hydroxyl groups at symmetric sites can suppress the steric hindrance of neighbouring hydrogen bonds, leading to a better phonon spectrum matching and more efficient suppression of molecular chain segment motion, which ensures the dielectric insulating performance and thermal conduction performance simultaneously. Typically, alkyl polyglucoside (APG) was selected as the optimal modifier to demonstrate the interface designing principle experimentally, exhibiting improved thermal conductivity and suppressed dielectric loss, ultimately resulting in a 4.98‐fold increase in the high‐frequency breakdown time. This study provides important insights into decisive structural factors necessary to achieve dielectrics with excellent insulating properties under high‐frequency applications. Dielectric ; Thermal conductivity ; Thermal conduction ; Composite number ; Nitride ; Thermal ; Interface (matter) ; Phonon Thermal properties of materials ; Dielectric materials and actuators ; High voltage insulation and dielectric phenomena
Authors: Magdaléna Kadlěčíková | I. Hotový | Satya Vijaya Kumar Nune | I. Kostič | Michaela Sojková | V. Řeháček | D. Gregušová
Source: Crystals (article, 2025-11-05)
Field: Materials Science — Materials Chemistry

Raman Spectral Analysis of Sputtered and Sulfurized Nanostructured WS2 Films This study presents the Raman spectral characteristics and selected electrical parameter measurements of WS2 films deposited by magnetron sputtering on sapphire and subsequently sulfurized. The analysis of the Raman spectra focuses on the positions and shifts of the E12g and A1g vibrational modes. The effects of different sputtering times on WS2 films and the corresponding activation energy values were also investigated. From both physical and experimental perspectives, the Raman spectral features of WS2 films were found to depend on the laser excitation wavelengths (532 nm and 632.8 nm) as well as on possible crystallographic defects and variations in the excitation point position. These defects have a significant influence on both the Raman spectra and the activation energies of the studied samples. The calculated activation energies (~ 0.15–0.19 eV) of the conduction charge carriers correlate with shallow defect-related energy levels indicated by the Raman characteristics. Raman spectroscopy ; Sapphire ; Excitation ; Sputter deposition ; Sputtering ; Raman scattering ; Analytical Chemistry (journal) ; Spectral line 2D Materials and Applications ; Chalcogenide Semiconductor Thin Films ; Advanced Materials and Semiconductor Technologies
Authors: Jianghai Chen | Jie Ling | Nana Lei | Lingqiao Li
Source: Sensors (article, 2025-06-27)
Field: Chemistry — Analytical Chemistry

BDSER-InceptionNet: A Novel Method for Near-Infrared Spectroscopy Model Transfer Based on Deep Learning and Balanced Distribution Adaptation Near-Infrared Spectroscopy (NIRS) analysis technology faces numerous challenges in industrial applications. Firstly, the generalization capability of models is significantly affected by instrumental heterogeneity, environmental interference, and sample diversity. Traditional modeling methods exhibit certain limitations in handling these factors, making it difficult to achieve effective adaptation across different scenarios. Specifically, data distribution shifts and mismatches in multi-scale features hinder the transferability of models across different crop varieties or instruments from different manufacturers. As a result, the large amount of previously accumulated NIRS and reference data cannot be effectively utilized in modeling for new instruments or new varieties, thereby limiting improvements in modeling efficiency and prediction accuracy. To address these limitations, this study proposes a novel transfer learning framework integrating multi-scale network architecture with Balanced Distribution Adaptation (BDA) to enhance cross-instrument compatibility. The key contributions include: (1) RX-Inception multi-scale structure: Combines Xception's depthwise separable convolution with ResNet's residual connections to strengthen global-local feature coupling. (2) Squeeze-and-Excitation (SE) attention: Dynamically recalibrates spectral band weights to enhance discriminative feature representation. (3) Systematic evaluation of six transfer strategies: Comparative analysis of their impacts on model adaptation performance. Experimental results on open corn and pharmaceutical datasets demonstrate that BDSER-InceptionNet achieves state-of-the-art performance on primary instruments. Notably, the proposed Method 6 successfully enables NIRS model sharing from primary to secondary instruments, effectively mitigating spectral discrepancies and significantly improving transfer efficacy. Computer science ; Discriminative model ; Transfer of learning ; Residual ; Adaptation (eye) ; Artificial intelligence ; Machine learning ; Data mining ; Feature (linguistics) ; Robustness (evolution) ; Pattern recognition (psychology) ; Algorithm Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Water Quality Monitoring and Analysis
Authors: Arnaud Nguembang Fadja | Armel Gabin Fameni Tagni | Sain Rigobert | Marcellin Atemkeng
Source: Data in Brief (article, 2025-01-30)
Field: Chemistry — Analytical Chemistry

A dataset of annotated African plum images from Cameroon for AI-based quality assessment This paper presents a dataset of 4507 annotated images of African plums collected across diverse regions in Cameroon, marking the first dataset specifically designed for AI-driven quality assessment of this fruit. The dataset is categorized into six quality grades: unaffected, bruised, cracked, rotten, spotted, and unripe. These categories represent varying degrees of plum quality, from optimal condition to various defects and ripeness levels. Captured under natural lighting using a consistent smartphone setup, the images were meticulously labeled by agricultural experts, ensuring high annotation accuracy. This resource is valuable for developing and testing computer vision, deep learning-based recognition systems and object detection models in agriculture, enabling automated evaluation of plum quality for commercialization. By offering a comprehensive, culturally relevant dataset focused on a traditionally underrepresented crop, this work supports advancements in precision agriculture, particularly in developing regions. Potential applications include AI-based tools for real-time sorting, defect detection, and quality assurance in the supply chain. Quality assessment ; Quality (philosophy) ; Computer science ; Research article ; Information retrieval ; Library science ; Engineering ; Evaluation methods Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Remote-Sensing Image Classification
Authors: Evgen V. Govor | Sofia Dymura | Oleksandr Viniichuk | Vasyl Naumchyk | Anton V. Zhemera | Dmytro S. Radchenko | Oleksandr O. Grygorenko
Source: ChemRxiv (article, 2025-09-15)
Field: Chemistry — Organic Chemistry

Generating Multimillion Chemical Space Based on the Ugi Four-Сenter Three-Component Reaction with Oxocarboxylic Acids The scope of the Ugi four-center three-component reaction involving oxocarboxylic acids, primary amines, and isonitriles under parallel synthesis conditions was studied thoroughly. Special attention was paid to the limitations of each starting material; in particular, the relative reactivity of various oxocarboxylic acid types was established. For a model validation library of 1,000 members, an experimental synthesis success rate of 88% was achieved. The obtained results and established trends were used to generate a 363-million synthetically tractable virtual chemical space of gamma- and delta-lactams. The distribution of physicochemical properties within this chemical space revealed that 43% of its members complied with the Lipinski rule-of-five, and a significant fraction of members (21.5 million) were lead-like. Furthermore, the chemical space showed low similarity to already existing compound collections and was enriched with disk-like molecules. Comparison with the ChEMBL database revealed that over 100 representatives generated had biological activity, with some exhibiting potency in the low nanomolar range. Chemical space ; chEMBL ; Space (punctuation) ; Reactivity (psychology) ; Chemical reaction ; Chemical synthesis ; Scope (computer science) ; Virtual screening Multicomponent Synthesis of Heterocycles ; Synthesis of Organic Compounds ; Synthesis and biological activity