jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: D Sai Pavani | Sowmiya A Sowmiya | S. Sasikala | Usharani N Usharani
Source: Current Trends in Biotechnology and Pharmacy (article, 2025-12-18)
Field: Materials Science — Materials Chemistry

Next-Generation Materials in Biophysical Chemistry: Nano and Bio Innovations Nanomaterials are revolutionizing biophysical chemistry through their applications in targeted drug delivery, diagnostics, and biosensing; however, a detailed understanding of their interactions with biological macromolecules is essential for effective therapeutic development. Ubiquitin (PDB ID: 1UBQ), a conserved 76- amino-acid protein central to protein degradation and intracellular signalling, serves as an ideal model for studying nano– bio interactions. In this study, silicon dioxide (SiO₂), sourced from PubChem as a representative nanomaterial-inspired ligand, was docked with ubiquitin to explore potential binding mechanisms. The protein structure was retrieved from the Protein Data Bank, and ligand optimization and docking were performed using AutoDock Vina, with blind docking employed to survey the entire protein surface. Analysis of docking scores, hydrogen bonds, and hydrophobic interaction profiles revealed moderate-affinity binding pockets involving β-sheet surfaces and flexible loop regions, suggesting that SiO₂ can interact with key polar and charged residues to stabilize or modulate protein function. Linking these interactions to practical applications, the SiO₂–ubiquitin contacts at the β-sheet regions could potentially enhance serum stability, increase circulation half-life and improving drug delivery efficiency. Furthermore, the interactions within flexible loop regions might facilitate endosomal escape, enabling targeted release in intracellular environments. These findings highlight the potential of silicon dioxide-based nanomaterials for biomedical applications such as targeted therapeutics, protein stabilization, and biosensor development, and provide a computational foundation for future experimental validation of nanomaterial– protein interactions. Docking (animal) ; Nanomaterials ; Protein–protein interaction ; In silico ; Drug discovery ; Drug delivery ; Nanomedicine ; AutoDock ; Targeted drug delivery Nanoparticles: synthesis and applications ; Graphene and Nanomaterials Applications ; Diatoms and Algae Research
Authors: Firman Tempola
Source: Journal of Wireless Mobile Networks Ubiquitous Computing and Dependable Applications (article, 2025-06-30)
Field: Chemistry — Analytical Chemistry

Enhancing Cloves Quality Classification Based on Ensemble Features Selection Based on the Indonesian National Standard (SNI) 01-3392-1994, clove quality in Indonesia is categorized into three distinct classes (Quality 1, 2, and 3). However, quality determination presents significant challenges due to the similar morphological characteristics across classes and limited available data, necessitating manual feature extraction. This manual process often yields irrelevant features, highlighting the need for robust feature selection methods. This study demonstrates that ensemble feature selection significantly enhances the performance of conventional machine learning models (K-NN and Naïve Bayes) in SNI-compliant clove quality classification. The proposed approach employs five distinct feature selection methods to assign importance scores to features, with the final selected features representing those consistently identified across all methods. Experimental results indicate notable improvements, including a 2.66% accuracy increase in K-NN classification and reduced computational time across all tested models. These findings substantiate the effectiveness of ensemble feature selection for optimizing conventional machine learning applications in agricultural quality assessment. Feature selection ; Artificial intelligence ; Computer science ; Ensemble learning ; Machine learning ; Selection (genetic algorithm) ; Pattern recognition (psychology) ; Feature (linguistics) ; Naive Bayes classifier ; Random forest ; Quality (philosophy) ; Feature extraction ; Data mining ; Support vector machine Spectroscopy and Chemometric Analyses
Authors: S Rajani
Source: Journal of Information Systems Engineering & Management (article, 2025-04-13)
Field: Chemistry — Analytical Chemistry

Hybrid CNN-PSO Approach for Accurate Classification of Medicinal Flowers Accurate classification and identification of flowering medicinal plants hold immense potential for advancing traditional medicine and biodiversity conservation. Currently, experts manually identify medicinal plants based on characteristics such as aroma, flowers, and visual features—a process that is not only time-consuming but also prone to human error and inefficiencies. To address these challenges, this study proposes a robust medicinal flower classification framework composed of three major phases: segmentation, feature extraction, and classification. The segmentation phase utilizes a deep learning-based Detectron2 model to isolate flower regions accurately. Three distinct feature extraction schemes are then applied to capture the color and texture patterns of the segmented flowers. The extracted features are optimized and classified using a Convolutional Neural Network (CNN) enhanced with an Improved Seeding Particle Swarm Optimization (IS-PSO) algorithm. Dominant features are selected using a Particle Swarm Optimization (PSO) technique prior to classification to improve computational efficiency and model accuracy. The proposed method was validated using 14 real-world medicinal flower datasets, all of which hold significant medicinal value. The CNN-IS-PSO model achieved superior performance, with an accuracy of 96.24%, precision of 96.90%, recall of 93.41%, and an F1-score of 94.69%, significantly outperforming recent state-of-the-art models. This method demonstrates the potential to enhance the reliability and accuracy of automated medicinal plant classification. Artificial intelligence ; Pattern recognition (psychology) ; Traditional medicine ; Computer science ; Mathematics ; Medicine Spectroscopy and Chemometric Analyses
Authors: Somayeh Kakasoltani | Hadi Zakeri Khatir | M. K. Salem | Amir Hossein Sari | Mohammad Ali Nekooie
Source: Research Square (preprint, 2025-10-22)
Field: Materials Science — Materials Chemistry

An Experimental Investigation of Pressure and Magnetic Field Effects on Gram-Scale Flash Graphene Synthesis Graphene ; Magnetic field ; Raman spectroscopy ; Reproducibility ; Flash (photography) ; Characterization (materials science) ; Joule heating ; Magnetometer Graphene research and applications ; Graphene and Nanomaterials Applications ; Carbon Nanotubes in Composites
Authors: Johanis M. Johanis M. Ramandey | Suryadi Suryadi | Hans Frits Liborang | Wardhana Wahyu Dharsono
Source: Jurnal Teknik Industri Terintegrasi (article, 2025-04-10)
Field: Materials Science — Materials Chemistry

Pengaruh Pola Anyaman terhadap Sifat Mekanik Serat Kulit Pohon Melinjo untuk Pengembangan Produk Tas Noken This study aims to optimize the tensile strength of woven melinjo tree bark fibers in noken bag products as a superior regional product based on local wisdom. The research method used is an experimental approach with stages of literature study, material selection, determination of test parameters, creation of woven patterns, and tensile strength testing. Melinjo tree bark fibers were woven in three different patterns: 1-1, 1-2, and 2-2. The results showed that the weaving pattern significantly influences the mechanical properties of the fibers. The 2-2 weave pattern performed best in terms of tensile strength, with tensile stresses ranging from 24.8-25.2 N/mm². The 1-1 weave pattern had the highest elasticity and flexibility with 2.8% strain and 2.8% flexibility. The optimal weave pattern depends on the specific application and product requirements. These findings can serve as a basis for future development of melinjo tree bark fiber-based products, particularly noken bags as a superior regional product based on local wisdom. Environmental science Material Selection and Properties ; Natural Products and Applications ; Food and Agricultural Sciences
Authors: Mutee Murshed | Jameel Al-Tamimi | Hind Alzaylaee | Saleh Al-Qurishy | I Balkan | H Doan | G Zengin | N Colak | F Ayaz | A Gren | . Yeilada | E | M Bhatti | H Ismail | W Kayani | N Chaachouay | L Zidane | M Fitzgerald | M Heinrich | A Booker | C Forni | F Facchiano | M Bartoli | S Pieretti | A Facchiano | D D'arcangelo | . Beninati | S | W Huang | Y He | R Jiang | Z Deng | F Long | S Jafri | Z Khalid | M Khan | S Ashraf | N Ahmad | A Karami | . Aslam | M Samdani | M Alam | A Alammari | M Azzam | H Ali | . Alam | P | G Kontoghiorghes | C Kontoghiorghe | M Kurek | N Benaida-Debbache | I Elez Garofuli | K Gali | S Avallone | A Voilley | Y Wach | A Nasr | T Saleem Khan | G.-P Zhu | I Sani | A Abdulhamid | F Bello | S Singh | S Sharma | H Sharma | Y Wang | O Chun | W Song | X Yang | C Li | C Huang
Source: Global NEST Journal (article, 2025-08-19)
Field: Chemistry — Analytical Chemistry

Medicinal Plant Extracts as Potential Green Antioxidants: Their Role in Environmental and Waste Management &lt;p&gt;Herbs are abundant sources of therapeutic compounds with significant potential in the global pharmaceutical industry. This study focused on analyzing the phytochemical composition, specifically phenols, flavonoids, and tannins, and their antioxidant properties in methanolic extracts from four selected medicinal plants: Nerium oleander (NO), and Eucalyptus camaldulensis (EC). The total contents of phenolic compounds, flavonoids, and tannins were quantified using standardized methods. Antioxidant activity was assessed through the DPPH radical scavenging assay. The analysis confirmed the presence of phenols, flavonoids, and tannins in all extracts. Notably, E. camaldulensis exhibited the highest concentrations of total phenolics and flavonoids (5.57 ± 0.22 mg GAE/g and 1.38 ± 0.06 mg QE/g, respectively). All extracts demonstrated strong antioxidant activity against DPPH and ABTS radicals, with IC50 values ranging from 0.55 to 49.43 µg/mL and 0.65 to 13.7 µg/mL, respectively. Additionally, &lt;em&gt;E. camaldulensis&lt;/em&gt; showed significant anticoccidial effects. The findings suggest that these medicinal plant extracts could serve as valuable an antioxidant and anticoccidial agents for drug development.&lt;/p&gt; Chemistry ; Environmental science ; Business Dye analysis and toxicity
Authors: Carmen Barrientos | Miguel Sanz‐Novo | Clara I. Sá‎nchez | Antonio Largo | Pilar Redondo
Source: Pure and Applied Chemistry (article, 2025-06-23)
Field: Chemistry — Spectroscopy

Theoretical insights on the structure and stability of the [C2, H3, P, O] isomeric family Abstract Phosphorus is a crucial biogenic element, yet its astrochemical role remains poorly understood due to its low cosmic abundance and the limited number of detected P-containing molecules in the interstellar medium. Given its significance for prebiotic chemistry, PCO-bearing molecules, such as the phosphorus analogs of isocyanates, are promising candidates for laboratory and interstellar studies. Herein, we present a comprehensive theoretical study on the isomeric landscape of the C 2 H 3 PO system, identifying and characterizing 24 low-lying isomers through high-level quantum chemical calculations. The study employs double-hybrid DFT and coupled-cluster methods to refine energy values and structural parameters, while topological analysis of electronic density characterizes chemical bonding. Vinylphosphinidene oxide (CH 2 CHPO) emerges as the most stable isomer, followed by methylphosphaketene (CH 3 PCO), with oxygen-bound structures playing a crucial role in stability. Comparisons with the C 2 H 3 NO system reveal structural parallels, reinforcing the importance of oxygen-bound species. Cyclic structures were also explored, with three- and four-membered P- and O-heterocycles identified, although they are generally less stable than open-chain isomers. These results provide insights into the chemical behavior and stability of C 2 H 3 PO isomers, which could help future spectroscopic studies and detection efforts in the interstellar medium. Chemistry ; Molecule ; Interstellar medium ; Cluster (spacecraft) ; Computational chemistry ; Astrochemistry ; Chemical physics ; Density functional theory ; Binding energy ; Quantum chemical ; Oxygen ; Organic chemistry ; Atomic physics ; Astrophysics ; Physics Molecular Spectroscopy and Structure ; Advanced Chemical Physics Studies ; Atmospheric chemistry and aerosols
Authors: Didar Swara Salih | Shaida Anwer kakil | Mohammed Issa  Hussein
Source: Eurasian Journal of Science and Engineering (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Radiation Protection Properties of Binary and Tertiary Tellurite Glasses: Comparative Study The theoretical estimation of the radiation shielding property was done for a series of binary and ternary tellurite glasses. TeO2-based glasses were modified with some binary chemicals like Bi2O3, WO3, ZnO, and PbO, along with their ternary combinations including Ta2O5. Their mass attenuation coefficient, effective atomic number, and electron density have been computed to study the efficiency of such glasses in gamma-ray shielding. It has been found that the binary composition of TeO₂ + Bi₂O₃ has greater shielding efficiency than other binary glasses. Among the ternary combinations, the TeO₂ + Bi₂O₃ + PbO glass was confirmed as the most efficient material. This work testifies to the efficiency of the tellurite glasses for their use as promising materials in radiation shielding applications due to the improved performance with the addition of Bi₂O₃ and PbO. A comparative analysis is performed to determine which glass compositions offer the most efficient radiation attenuation. The results aim to guide the design of advanced radiation shielding materials for medical, nuclear, and industrial applications. Ternary operation ; Electromagnetic shielding ; Radiation shielding ; Binary number ; Attenuation ; Radiation ; Work (physics) ; Radiation protection Nuclear materials and radiation effects ; Glass properties and applications ; Radiation Shielding Materials Analysis
Authors: Araf Mahmud | Yingnan Song | Qi Zhou | Chen Huang
Source: Molecular & Cellular Proteomics (article, 2025-12-23)
Field: Chemistry — Spectroscopy

Assessing the Performance of Mass Spectrometry Search Strategies in Identifying Translational Errors Using PDX Proteomics Data Translational errors (TEs) result in a mismatch between mRNA codons and the amino acids (AAs) of the corresponding protein. Unlike DNA mutations or RNA editing, where nucleotide sequences can be used to infer AA substitutions, TEs can only be detected at the protein level. Although high-throughput mass spectrometry (MS) proteomics offers the potential to resolve peptide sequences and could theoretically be used to identify TEs, the feasibility of current MS data analysis approaches for this application remains uncertain. Here, we utilize patient-derived xenograft proteomics data, which include both human and mouse peptides with identifiable cross-species AA variations, as a ground truth for benchmarking TE identification methods. By using high-confidence mouse peptides as surrogates for "TE-containing" peptides, we show that current open search approaches can achieve >65% overall sensitivity and >70% overall precision for high-quality samples. The intersection of different search strategies significantly enhances precision, albeit at the expense of reduced sensitivity. Notably, the evaluation metrics vary significantly across individual AA substitutions, suggesting that caution is warranted when detecting or interpreting specific AA substitutions. Moreover, closed searches targeting predefined AA changes exhibit poor precision, with post-translational modification mislocalization identified as a key bottleneck for this application. Overall, our study provides a first-of-its-kind benchmark for MS-based TE discovery and offers guidance for optimizing MS search strategies. Proteomics ; Bottleneck ; Benchmark (surveying) ; Identification (biology) ; Benchmarking ; Quantitative proteomics ; Mass spectrometry ; Posttranslational modification ; Database search engine Advanced Proteomics Techniques and Applications ; RNA and protein synthesis mechanisms ; Mass Spectrometry Techniques and Applications
Authors: Min Gee Cho | Luis Rangel DaCosta | Katherine Sytwu | Myoung Hwan Oh | Mary Scott
Source: Microscopy and Microanalysis (article, 2025-07-01)
Field: Materials Science — Materials Chemistry

Deep Learning-Driven High-Throughput High-Resolution TEM Analysis of Surface Characteristics during Nanocrystal Growth Nanocrystal ; Materials science ; Throughput ; Nanotechnology ; High resolution ; Surface (topology) ; Chemical engineering ; Computer science ; Remote sensing ; Geology ; Engineering ; Geometry ; Telecommunications Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Quantum Dots Synthesis And Properties
Authors: Puspa Upreti | Delaram Rashadfar | Raffi Sahul | D. L. Abernathy | Joseph P. Heremans | Raphaël P. Hermann | Michael E. Manley
Source: Research Square (preprint, 2025-05-28)
Field: Materials Science — Materials Chemistry

Giant electric-field induced thermal switching controlled by phonon scattering in a relaxor ferroelectric Condensed matter physics ; Electric field ; Phonon ; Ferroelectricity ; Scattering ; Materials science ; Thermal ; Field (mathematics) ; Phonon scattering ; Physics ; Quantum mechanics ; Optoelectronics ; Dielectric ; Mathematics ; Thermodynamics Solid-state spectroscopy and crystallography ; Phase-change materials and chalcogenides ; Ferroelectric and Piezoelectric Materials
Authors: Bing Yang | Xuyang Liu | Dongfang Zhang | Xiaofei Fan | Bo Peng | Jun Zhang
Source: Frontiers in Plant Science (article, 2025-06-03)
Field: Chemistry — Analytical Chemistry

Optimized wavelength selection for eggplant seed vitality classification using information acquisition techniques Eggplant seed vigor is a crucial indicator of its germination rate and seedling growth quality. In response to the need for efficient and nondestructive assessment methods, this study explores the use of hyperspectral imaging combined with advanced feature selection and classification algorithms to evaluate eggplant seed viability. Hyperspectral imaging was employed to collect spectral data from eggplant seeds, covering 360 bands within a wavelength range of 395.24-1008.20 nm. The seeds underwent microwave heating and constant-temperature water bath aging treatments. Data preprocessing involved three techniques: Multiplicative Scatter Correction (MSC), Savitzky-Golay (SG) smoothing, and Standard Normal Variate (SNV) transformation. An Enhanced Information Acquisition Optimization (EIAO) algorithm was proposed for feature selection, which successfully identified a minimal set of 23 key wavelengths. Seed vigor classification models were developed using Extreme Learning Machine (ELM), Random Forest (RF), and Support Vector Machine (SVM).The optimal classification accuracies achieved were 90.0% for ELM, 91.45% for RF, and 90.5% for SVM. The MSC-EIAO-RF model demonstrated the best performance, achieving an accuracy of 91.45%, which is 9.04% higher than the MSC-IAO model (82.41%).Validation on four UCI datasets further confirmed the EIAO algorithm's superiority over conventional feature selection methods. These results verify the robustness and generalizability of hyperspectral imaging combined with EIAO for nondestructive seed viability detection, offering an intelligent and efficient solution for seed quality assessment. Selection (genetic algorithm) ; Vitality ; Biology ; Botany ; Computer science ; Artificial intelligence ; Pattern recognition (psychology) ; Genetics Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Agricultural Practices and Plant Genetics
Authors: Anamika Biswas | D. Roy | Mousumi Dutta | Goutam Paul
Source: International Journal of Pharmaceutical Sciences Review and Research (article, 2025-03-01)
Field: Chemistry — Analytical Chemistry

Metanil Yellow (MY) Impairs Heart Ventricular Function by Promoting Cholinergic and Nitrergic Signalling Pathways Metanil yellow (MY), being a synthetic commercial azo dye, is not approved to be used as a food colour.Despite being non-permitted, it is still used as a food adulterant in unorganized food manufacturing and processing sectors.As a consequence, human exposure to this hazardous chemical occurs through the consumption of MY-contaminated food items resulting in the entry of MY into blood and finally to the heart via systemic circulation.Though the chemical is known for its potentiality to induce toxicity in several organ systems in animal models, its impact on mammalian heart has not been thoroughly reported till date.Therefore, this study has been designed to evaluate the effects of MY on structural and functional aspects of heart ventricular muscle in rat model in vivo.Exposure to MY has been found to significantly (p<0.001)decrease acetylcholinesterase (AChE) activity; significantly (p<0.001) increase nitric oxide synthase (NOS) activity; and concentrations of cytosolic and mitochondrial nitrite in heart ventricular muscle.Moreover, MY exposure has also been found to induce morphological alterations in heart ventricular muscle.So, from the results, it can be postulated that MY suppresses heart ventricular function in rats by compromising the structural integrity and organization of ventricular muscle.These detrimental effects seem to be mediated by changes in the activities of AChE and NOS enzymes, as well as alterations in nitrite concentration within the ventricular muscle and mitochondria. Cholinergic ; Signalling ; Signalling pathways ; Function (biology) ; Chemistry ; Medicine ; Internal medicine ; Cell biology ; Signal transduction ; Biology ; Biochemistry Dye analysis and toxicity ; Electrochemical sensors and biosensors ; Advanced Nanomaterials in Catalysis
Authors: Luis J. Walter | Tristan Bereau
Source: ArXiv.org (preprint, 2025-05-07)
Field: Materials Science — Materials Chemistry

Navigating Chemical Space: Multi-Level Bayesian Optimization with Hierarchical Coarse-Graining Molecular discovery within the vast chemical space remains a significant challenge due to the immense number of possible molecules and limited scalability of conventional screening methods. To approach chemical space exploration more effectively, we have developed an active learning-based method that uses transferable coarse-grained models to compress chemical space into varying levels of resolution. By using multiple representations of chemical space with different coarse-graining resolutions, we balance combinatorial complexity and chemical detail. To identify target compounds, we first transform the discrete molecular spaces into smooth latent representations. We then perform Bayesian optimization within these latent spaces, using molecular dynamics simulations to calculate target free energies of the coarse-grained compounds. This multi-level approach effectively balances exploration and exploitation at lower and higher resolutions, respectively. We demonstrate the effectiveness of our method by optimizing molecules to enhance phase separation in phospholipid bilayers. Our funnel-like strategy not only suggests optimal compounds but also provides insight into relevant neighborhoods in chemical space. We show how this neighborhood information from lower resolutions can guide the optimization at higher resolutions, thereby providing an efficient way to navigate large chemical spaces for free energy-based molecular optimization. Chemical space ; Scalability ; Bayesian optimization ; Space (punctuation) ; Bayesian probability ; Chemical process ; Molecular dynamics Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Nanopore and Nanochannel Transport Studies