jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Mira Gandhi | Shweta Loonkar | Karishma Desai
Source: Journal of Carcinogenesis (article, 2025-01-01)
Field: Materials Science — General Materials Science

Cost-Benefit Analysis of Advanced Adsorbent Beads for Industrial Water Filtration This paper systematically evaluates the performance stability and long-term cost-effectiveness of advanced adsorbent beads in industrial water filtration, focusing on the impact of regeneration methods and incorporation of porosity-enhancing agents. With a PRISMA-guided literature review, the study assesses how acid, base and solvent washing influence adsorption capacity and chemical stability across multiple regeneration cycles. The effect of halloysite nanotubes, biochar, and metal organic frameworks (MOFs) on bead durability and treatment per cost cycle is analyzed. Findings suggest that regeneration efficiency affects both adsorption performance and material longevity, with certain acid and solvent protocols maintaining over 80% of initial capacity after ten cycles. The integration of porosity-enhancers such of biochar and MOFs improves both surface area and resilience, yielding reduced cost per treatment cycle and allowing for scale-up. However, due to variability in bead material properties and incomplete reporting on regeneration chemistry, constrains widespread adoption. The implications of the study highlight the necessity of standardizing regeneration protocols, enhancing material production for industrial scalability, and progressing sustainable manufacturing methods. This study enhances the existing knowledge of adsorbent bead distribution, offering a structure for assessing sustainable and economical water solution treatment technologies. Adsorption ; Regeneration (biology) ; Durability ; Filtration (mathematics) ; Bead ; Solvent ; Water treatment Material Properties and Applications ; Advanced Computational Techniques in Science and Engineering
Authors: Wasurat Bunpheng | Ratchagaraja Dhairiyasamy | Deekshant Varshney | Subhav Singh | Choon Kit Chan
Source: Journal of Composites Science (article, 2025-11-01)
Field: Materials Science — Polymers and Plastics

Reducing Material Footprint Through Hybrid Bio-Synthetic Polymer Composites: Advanced Testing and Predictive Modeling Approaches Hybrid natural/synthetic fiber laminates were examined as a practical process to cut mass, reduce material footprint, and meet structural demands while addressing sustainability targets. Yet direct, like-for-like comparisons generated under a single process and accompanied by durability measurements were limited, leaving design choices uncertain. This study aimed to fabricate and benchmark five representative laminates—C1: flax/epoxy, C2: jute/glass/epoxy, C3: hemp/carbon/epoxy, C4: flax/glass/bio-epoxy, and C5: kenaf/basalt/polyester—under a controlled hot-press schedule with a fixed cavity and verified fiber volume fraction. Panels were characterized using ASTM D3039 tension, ASTM D790 flexure, instrumented impact, 168 h water immersion, and thermogravimetric mass retention. The results were normalized to enable direct multi-criteria comparison, and a model was calibrated to predict tensile strength. C3 delivered the highest strengths (tension ≈ 120 MPa; flexure ≈ 126 MPa), while C5 showed the greatest impact capacity (≈60 kJ/m2). End-of-test water uptake at 168 h was C1 ≈ 3.4%, C2 ≈ 2.6%, C3 ≈ 1.4%, C4 ≈ 2.1%, and C5 ≈ 2.3%. The tensile predictor was fitted to panel means, with an R2 of 0.988, and maintained an R2 of 0.96 under leave-one-configuration-out testing. These results indicated that carbon-containing hybrids played the most critical roles in terms of stiffness, with kenaf/basalt being most suitable for stiffness-critical components at a similar density, and flax/glass with a bio-resin maximized the sustainability score while maintaining adequate strength. Future research should focus on enhancing specific strength at high renewable content through interface treatments, and extended modeling to improve flexure and impact responses. Ultimate tensile strength ; Sustainability ; Thermogravimetric analysis ; Fiber ; Benchmark (surveying) ; Schedule ; Process (computing) ; Footprint Natural Fiber Reinforced Composites ; Mechanical Behavior of Composites ; Calcium Carbonate Crystallization and Inhibition
Authors: Weibin Wu | Ting Tang | Yuxin Duan | Wei Qiu | Lian Duan | Jinhong Lv | Yan Zeng | Jiacheng Guo | Yuanqiang Luo
Source: Agriculture (article, 2025-11-16)
Field: Chemistry — Analytical Chemistry

Study on the Detection Model of Tea Red Scab Severity Class Using Hyperspectral Imaging Technology Tea red scab, a contagious disease affecting tea plants, can infect both buds and mature leaves. This study developed discrimination models to assess the severity of this disease using RGB and hyperspectral images. The models were constructed from a total of 1188 samples collected in May 2024. The results demonstrated that the model based on hyperspectral Imaging (HSI) data significantly outperformed the RGB-based model. Four spectral preprocessing methods were applied, among which the combination of SNV, SG, and FD (SNV-SG-FD) proved to be the most effective. To better capture long-range dependencies among spectral bands, a hybrid architecture integrating a Gated Recurrent Unit (GRU) with a one-dimensional convolutional neural network (1D-CNN), termed CNN-GRU, was proposed. This hybrid model was compared against standalone CNN and GRU benchmarks. The hyperparameters of the CNN-GRU model were optimized using the Newton-Raphson-based optimizer (NRBO) algorithm. The proposed NRBO-optimized SNV-SG-FD-CNN-GRU model achieved superior performance, with accuracy, precision, recall, and F1-score reaching 92.94%, 92.54%, 92.42%, and 92.43%, respectively. Significant improvements were observed across all evaluation metrics compared to the single-model alternatives, confirming the effectiveness of both the hybrid architecture and the optimization strategy. Hyperspectral imaging ; Preprocessor ; Convolutional neural network ; Pattern recognition (psychology) ; Hyperparameter ; RGB color model ; HSL and HSV Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Remote Sensing in Agriculture
Authors: Wu, Zhen-Wei
Source: arXiv (Cornell University) (article, 2025-12-04)
Field: Materials Science — Materials Chemistry

A Kinetic Criterion for Stokes-Einstein Relation Breakdown Based on Effective Collisional Geometry Here we propose a kinetic framework for interpreting the Stokes-Einstein (SE) relation breakdown in supercooled liquids by introducing an effective collision diameter, $d_{\mathrm{eff}}$, derived from transport data. Numerical simulation of a model CuZr alloy reveal that $d_{\mathrm{eff}}$ increases upon cooling but saturates near the first peak of the radial distribution function just before SE breakdown. This saturation defines a geometric upper bound for the collisional cross-section beyond which further slowdown is governed by cooperative, heterogeneous motion rather than local collisional transport. Our analysis yields a compact criterion for SE breakdown in a mean-field perspective and provides physically interpretable inputs for future data-driven models of glassy dynamics. Kinetic energy ; Collision ; Supercooling ; Function (biology) ; Saturation (graph theory) ; Distribution function ; Motion (physics) ; Distribution (mathematics) ; Perspective (graphical) Material Dynamics and Properties ; Solidification and crystal growth phenomena ; Advanced Physical and Chemical Molecular Interactions
Authors: Amir Bralin | N. Sanjay Rebello
Source:  (article, 2025-10-28)
Field: Materials Science — Materials Chemistry

AI Reasoning Models for Problem Solving in Physics Reasoning models are the new generation of Large Language Models (LLMs) capable of complex problem solving.Their reliability in solving introductory physics problems was tested by evaluating a sample of n = 5 solutions generated by one such model-OpenAI's o3-mini-per each problem from 20 chapters of a standard undergraduate textbook.In total, N = 408 problems were given to the model and N n = 2, 040 generated solutions examined.The model successfully solved 94% of the problems posed, excelling at the beginning topics in mechanics but struggling with the later ones such as waves and thermodynamics. Reliability (semiconductor) ; Sample (material) ; Automated reasoning ; Calculus (dental) ; Simple (philosophy) ; Mathematical logic Machine Learning in Materials Science ; Intelligent Tutoring Systems and Adaptive Learning ; Model Reduction and Neural Networks
Authors: Ryosuke Shimazu | Chee Siang Leow | Prawit Buayai | Xiaoyang Mao | Wan‐Young Chung | Hiromitsu Nishizaki
Source: The Visual Computer (article, 2025-05-26)
Field: Chemistry — Analytical Chemistry

Non-invasive estimation of Shine Muscat grape color and sensory evaluation from standard camera images Abstract This study proposes a non-invasive method to estimate both color and sensory attributes of Shine Muscat grapes from standard camera images. First, we focus on color estimation by integrating a Vision Transformer (ViT) feature extractor with interquartile range (IQR)-based outlier removal. Experimental results show that our approach achieves 97.2% accuracy, significantly outperforming Convolutional Neural Network (CNN) models. This improvement underscores the importance of capturing global contextual information to differentiate subtle color variations in grape ripeness. Second, we address human sensory evaluation by collecting questionnaire responses on 13 attributes (e.g., “Sweetness,” “Overall taste rating”), each rated on a five-point scale. Because these ratings tend to cluster around midrange values (labels “2,” “3,” and “4”), we initially limit the dataset to the extreme labels “1” (“lowest grade”) and “5” (“highest grade”) for binary classification. Three attributes—“Overall color,” “Sweetness,” and “Overall taste rating”—exhibit relatively high classification accuracies of 79.9%, 75.1%, and 75.7%, respectively. By contrast, the other 10 attributes reach only 50%–66%, suggesting that subjective variations and limited visual cues pose significant challenges. Overall, the proposed approach demonstrates the feasibility of an image-based system that integrates color estimation and sensory evaluation to support more objective, data-driven harvest timing decisions for Shine Muscat grapes. Artificial intelligence ; Computer vision ; Computer science ; Computer graphics ; Computer graphics (images) ; Estimation ; Sensory system ; Mathematics ; Psychology ; Engineering Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Smart Agriculture and AI
Authors: Felix Odame | David Neglo | Digby F. Warner | Cedric Dzidzor Kodjo Amengor | Richmond Arthur
Source: Pharmaceutical Fronts (article, 2025-11-07)
Field: Chemistry — Organic Chemistry

Benzothiazole and 2,3-dihydro-1,5-benzoxazepine Derivatives Demonstrate Antimicrobial Activity: An Antimicrobial and ADMET Study Antimicrobial resistance continues to be a serious public health threat globally, hence the continuous design of new clinical candidates with novel mechanisms of action. Heterocyclic drugs have been a hotspot in antibiotic research. Benzothiazole and 2,3-dihydro-1,5-benzoxazepine are anticancer derivatives. To follow up on their antimicrobial activity, the current work resynthesized 13 benzothiazole, benzimidazole, benzothiazepine, and 2,3-dihydro-1,5-benzoxazepine derivatives, followed by the evaluation of their antimicrobial and antitubercular activity against methicillin-resistant Staphylococcus aureus (MRSA), Escherichia coli, Klebsiella pneumonia, Bacillus subtilis, Streptococcus mutans, Salmonella typhi, and Mycobacterium tuberculosis. In addition, in silico ADMET studies were performed on the compounds using the ADMET Laboratory 2.0 platform. The compounds were found to be active against all the bacterial strains except against S. mutans and S. typhi. 4-[(E)-2-(2-chlorophenyl)ethenyl]-2,2-dimethyl-2,3-dihydro-1,5-benzoxazepine (3) was found to be the most active against E. coli, 2,2,4-trimethyl-2,3-dihydrobenzoxazepine (12) the most active against MRSA, and 4-[(E)-2-(4-methoxyphenyl)ethenyl]-2,2-dimethyl-2,3-dihydro-1,5-benzoxazepine (6) the most active against Klebsiella pneumoniae. The compounds also showed moderate activity against M. tuberculosis. The ADMET analysis predicted largely drug-like properties of the compounds and their suitability as potential drugs. The synthesized compounds showed good activity against some of the selected organisms and, therefore, could be modified to improve their action as antimicrobial agents. Antimicrobial ; Benzothiazole ; Escherichia coli ; Staphylococcus aureus ; Klebsiella pneumoniae ; Salmonella ; Klebsiella ; In silico Synthesis of Tetrazole Derivatives ; Synthesis and biological activity ; Synthesis and pharmacology of benzodiazepine derivatives
Authors: Purushottam R. Laddha | Prafulla R. Tathe | Gopal R. Sitaphale | Kishor B. Charhate | Vaibhav N. Chavhan | Gaurav J. Jogade | Tanmay Dongaonkar | Meenakshi G. Chitte
Source: International Journal of Environmental Sciences (article, 2025-08-11)
Field: Chemistry — Organic Chemistry

Synthesis and Anticancer Evaluation of Pyridine, Triazole, and Thiazole Compounds Heterocyclic scaffolds are ubiquitous in modern anticancer drug discovery, with pyridine, triazole, and thiazole rings featuring prominently in many potent agents. In this study, we designed and synthesized a new series of compounds incorporating pyridine, 1,2,4-triazole, and thiazole moieties, hypothesizing that these heterocycles would impart significant cytotoxic activity against cancer cells. The synthetic routes yielded two main classes of compounds: 2-pyridone derivatives (dihydropyridine-dicarbonitriles) and thiazolidinone derivatives, with additional analogues for comparison. All compounds were fully characterized by elemental analysis and spectroscopic methods (FT-IR, NMR, MS), confirming their structures. In vitro anticancer activity was evaluated against human lung carcinoma (A549) and breast adenocarcinoma (MCF-7) cell lines using the MTT assay. Several compounds showed remarkable cytotoxic potency, with sub-micromolar to nanomolar IC&lt;sub&gt;50&lt;/sub&gt; values. Notably, two pyridone–based analogues exhibited IC&lt;sub&gt;50&lt;/sub&gt; ≈ 8–15 nM in both cell lines, outperforming the reference drug cisplatin (IC&lt;sub&gt;50&lt;/sub&gt; ≈ 50 µM) and even matching or exceeding doxorubicin’s potency. Thiazole-based derivatives also displayed strong activity in the ~50–120 nM range. The compounds were relatively selective for cancer cells over normal cells, showing high safety indices (e.g. &gt;28-fold). A preliminary structure–activity relationship (SAR) analysis suggests that electron-withdrawing substituents on the phenyl ring (e.g. nitro) enhanced cytotoxicity, whereas electron-donating groups reduced it. Overall, these findings demonstrate the potential of pyridine-, triazole-, and thiazole-containing hybrids as promising anticancer agents. Four lead compounds are identified for further development, and possible mechanisms of action (including EGFR kinase inhibition and pro-apoptotic effects) are proposed based on initial enzyme assays and literature precedents. Thiazole ; Pyridine ; Chemistry ; Triazole ; Combinatorial chemistry ; Stereochemistry ; Medicinal chemistry ; Organic chemistry Synthesis and biological activity
Authors: Sahrul Amri | Muhamad Akrom | Gustina Alfa Trisnapradika
Source: Journal of Applied Informatics and Computing (article, 2025-12-15)
Field: Materials Science — Materials Chemistry

Enhancing the Predictive Accuracy of Corrosion Inhibition Efficiency Using Gradient Boosting with Feature Engineering and Gaussian Mixture Model Prediction The development of Quantitative structure property relationship (QSPR) models for predicting corrosion inhibition efficiency (IE) often faces challenges due to small datasets, which heightens the risk of overfitting and results in less reliable performance assessments. This research creates an entirely leakage-free modeling framework by combining per-fold preprocessing, augmentation of training-only data, and rigorous Leave-One-Out Cross-Validation (LOOCV). A set of 20 pyridazine derivatives was evaluated using 12 quantum-chemical descriptors, including HOMO, LUMO, ΔE, dipole moment, electronegativity, hardness, softness, and the electron-transfer fraction. An initial assessment showed that all baseline models lacking augmentation Gradient Boosting, Random Forest, SVR, and XGBoost demonstrated limited predictive power (R² &lt; 0.20), revealing the dataset's inherently low information complexity.To enhance representation in the feature space, a multi-scale Gaussian Mixture Model (GMM) was used to generate chemically valid synthetic samples, with all components trained solely on the training subset from each LOOCV fold. This strategy consistently improved model performance. The two most successful configurations, XGBoost + GMM v2 and Random Forest + GMM v3, reached R² values of 0.4457 and 0.4108, respectively, along with significant decreases in RMSE, MAE, and MAPE. These findings illustrate that GMM-based generative augmentation effectively captures multicluster structures within the descriptor space while expanding the chemical variability domain in a controlled way.While the resulting R² values remain inadequate for high-precision quantitative predictions, the proposed methodology provides a solid basis for early-stage evaluation of corrosion inhibitors in situations with limited data. Future research will aim to integrate advanced DFT-derived descriptors, molecular graph representations, and tests against larger external datasets to enhance model generalizability. Overfitting ; Mixture model ; Random forest ; Gaussian ; Applicability domain ; Pattern recognition (psychology) ; Generative model ; Kriging ; Gaussian process ; Support vector machine Machine Learning in Materials Science ; Corrosion Behavior and Inhibition ; Computational Drug Discovery Methods
Source: ARPN Journal of Engineering and Applied Sciences (article, 2025-03-21)
Field: Chemistry — Analytical Chemistry

Angiosperm species classification using transfer learning in convolutional neural networks The species composition provides the fundamental individual biological characteristics of an environment and ecosystems. The ability to identify species of individual plants or trees over an assessment plot and a forest stand is necessary for automatic mapping of plant distribution, biological diversity, stand structure, and even for diagnosing the dynamics of a forest stand. The advantage of developing plant mapping techniques utilizing image classification is the ability to identify signs of climate change based on plant phenology. Due to the large number of sequential layers and input data, deep learning takes a long time to train. Due to its capacity to do parallel processing, a powerful computer with a Graphic Processing Unit is the best choice for the training process. To classify angiosperm species, this study used photos from the University of Oxford dataset to represent 10 different angiosperm or flowering plant species. The 800 images used for the training and testing of various convolutional network models are made up of angiosperm species like bluebell, cowslip, crocus, daisy, dandelion, fritillary, iris, snowdrop, sunflower, and windflower. This study employed pre-trained weights from several convolutional neural network models, such as DenseNet 201, InceptionV3, MobileNet, ResNet152V2, ResNet50, and Xception, to classify the angiosperm species. The ResNet50 model had the lowest accuracy, 91.87%, while the Xception model had the greatest accuracy, 97.5%. Transfer of learning ; Convolutional neural network ; Artificial intelligence ; Computer science ; Pattern recognition (psychology) ; Machine learning Spectroscopy and Chemometric Analyses
Authors: Ryosuke Kuwashima | Masato Ishizuka | Takashi Tanikawa | Yoshiyuki Ishida | Daisuke Nakata | Keiji Terao | Yutaka Inoue
Source: International Journal of Functional Nutrition (article, 2025-07-04)
Field: Materials Science — Biomaterials

Evaluation of the effects of ursolic acid/γ‑cyclodextrin or cyclodextrin‑based metal‑organic framework‑1 complexes on reducing cytotoxicity and improving muscle atrophy in C2C12 cells The present study aimed to evaluate the effects of ursolic acid (UA), a compound found in apple peels and other fruits and known for its biological activities on the improvement of muscle atrophy. The present study also examined &gamma;‑cyclodextrin (&gamma;‑CD) and cyclodextrin‑based metal‑organic framework‑1 (CD‑MOF‑1) complexes for their effects on the proliferation of C2C12 myotube cells, which are derived from mouse skeletal muscle. Cell viability under proliferative and differentiation conditions was assessed using the 3‑(4,5‑dimethylthiazol‑2‑yl)‑2,5‑diphenyltetrazolium bromide assay. Cell viability was reduced at 20 &micro;M in intact UA, at 40 &micro;M in UA/&gamma;‑CD, and at 20 &micro;M in UA/CD‑MOF‑1 cells under proliferative conditions. Under differentiation conditions, cell viability was decreased at 20 &micro;M in intact UA, at 40 &micro;M in UA/&gamma;‑CD, and at 10 &micro;M in UA/CD‑MOF‑1. The reduced viability of C2C12 cells under proliferative and differentiation conditions was not observed in UA/&gamma;‑CD concentrations up to and higher than those of intact UA, suggesting that the cytotoxic effect of UA was mitigated. Under both proliferative and differentiation conditions, encapsulation with CD‑MOF‑1 did not reduce the cytotoxic effect of UA, suggesting that UA/CD‑MOF‑1 enhanced the intracellular release of UA. The myotube diameters of the C2C12 cells were measured to evaluate muscle atrophy. The addition of dexamethasone (DEX) decreased the myotube diameter; however, UA + DEX, UA/&gamma;‑CD + DEX, and UA/CD‑MOF‑1 + DEX exhibited no difference compared with the control, suggesting that UA ameliorated muscle atrophy induced by DEX. On the whole, the present study confirms that UA myotubular cells can act even when UA/&gamma;‑CD and UA/CD‑MOF‑1 form inclusion complexes, and that muscle atrophy is sustained in UA myotubular cells. Cyclodextrin ; Cytotoxicity ; Ursolic acid ; Chemistry ; C2C12 ; Biochemistry ; Chromatography ; Myogenesis ; In vitro Nanoparticle-Based Drug Delivery ; Curcumin's Biomedical Applications ; Graphene and Nanomaterials Applications
Authors: Yaroslava Pushkarova | Галина Зайцева | Kaouthar Bouaalam
Source: ADMET & DMPK (article, 2025-09-17)
Field: Chemistry — Spectroscopy

Prediction of metabolism and solubility of tablet-form drugs according to the biopharmaceutical drug disposition classification system The developed models demonstrate robust predictive capability for drug metabolism and solubility classes as defined by the Biopharmaceutical Drug Disposition Classification System, supporting early-stage drug screening based solely on molecular structure. The lower accuracy observed for solubility prediction reflects its complex and multifactorial nature, highlighting the need for further refinement of molecular descriptors. These findings advance the field by providing effective computational tools to predict key biopharmaceutical properties, potentially accelerating the drug development process. Biopharmaceutical ; Drug ; Solubility ; Drug development ; Drug metabolism ; Drug discovery Analytical Chemistry and Chromatography ; Crystallization and Solubility Studies ; Computational Drug Discovery Methods
Authors: Feng Bin | Chuanfei Yao | Jian Feng | Fangwei Liang | Qiuqin Sun
Source: Research Square (preprint, 2025-04-21)
Field: Materials Science — Materials Chemistry

Multi-Scale Simulation of Electromagnetic Wave Excitation by Positive Corona Discharge in SF6 Gas Excitation ; Corona discharge ; Scale (ratio) ; Corona (planetary geology) ; Partial discharge ; Electric discharge in gases ; Physics ; Mechanics ; Acoustics ; Atomic physics ; Materials science ; Computational physics ; Environmental science ; Voltage ; Quantum mechanics High voltage insulation and dielectric phenomena ; Aerosol Filtration and Electrostatic Precipitation ; Plasma Diagnostics and Applications