jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Dávid Fertig | Adrian L. Usler | Mathijs Janssen
Source: ArXiv.org (preprint, 2025-12-11)
Field: Chemistry — Physical and Theoretical Chemistry

Basic requirements for potential differences across solid--fluid interfaces At model water--vapor and water--solid interfaces, molecular ordering leads to charge oscillations and, thereby, to a spatially varying electrostatic potential. Atomistic simulations indicate that such ordering leads to an electric potential difference $χ$, the surface potential, of about $-0.5\,\mathrm{V}$ across the first few molecular layers. Here, we calculate surface potentials at interfaces between a simple model fluids and a solid, with Molecular Dynamics simulations. The fluids are made up of either diatomic, dipolar molecules or a single Lennard-Jones particle with a dipole moment. All fluids show some structuring near the interface, but charge oscillations and a non-zero surface potential are present only for asymmetric molecules (unequal diameters of the atoms) or molecules with an off-center dipole. We condense this finding into the criterion that the geometric and dipolar centers of a molecule must differ for the fluid to exhibit a surface potential. Remarkably, while the solid--fluid interaction strength strongly affects the magnitude of charge oscillations, it hardly affects the potential drop $χ$. Further, our results demonstrate that changing the diameter of the smaller atom can flip the sign of the surface potential, thus highlighting the importance of steric effects. Dipole ; Surface (topology) ; Charge (physics) ; Molecule ; Molecular dynamics ; Steric effects ; Surface charge ; Particle (ecology) ; Sign (mathematics) Electrostatics and Colloid Interactions ; Spectroscopy and Quantum Chemical Studies ; Nanopore and Nanochannel Transport Studies
Authors: Zhiqi Hong | Chu Zhang | Wenjian Song | Xiangbo Nie | Hongxia Ye | Yong He
Source: Agriculture (article, 2025-05-25)
Field: Chemistry — Analytical Chemistry

Application of Hyperspectral Imaging for Identification of Melon Seed Variety Using Deep Learning The accurate identification of melon seed varieties is essential for improving seed purity and the overall quality of melon production. In this study, hyperspectral imaging was used to identify six varieties of melon seeds. Both hyperspectral images and RGB images were generated during hyperspectral image acquisition. The spectral features of seeds were extracted from the hyperspectral images. The image features of the corresponding seeds were manually extracted from the RGB images. Five different datasets were formed using the spectral features and RGB images of the seeds, including seed spectral features, manually extracted seed image features, seed images, the fusion of seed spectral features with manually extracted features, and the fusion of seed spectral features with seed images. Logistic Regression (LR), Support Vector Classification (SVC), and Extreme Gradient Boosting (XGBoost) were used to establish classification models using spectral features and the manually extracted image features. Convolutional Neural Network (CNN) models were established using the five datasets. The results indicated that the CNN models achieved good performance in all five datasets, with classification accuracies exceeding 90% for the training, validation, and test sets. Also, CNN using the fused datasets obtained optimal performance, achieving classification accuracies exceeding 97% for the training, validation, and test sets. The results indicated that both spectral features and image features can be used to identify the six varieties of melon seeds, and their fusion of spectral features and image features can improve classification performance. These findings provide an alternative approach for melon seed variety identification, which can also be extended to other seed types. Hyperspectral imaging ; Melon ; Identification (biology) ; Variety (cybernetics) ; Biology ; Artificial intelligence ; Agronomy ; Botany ; Agroforestry ; Computer science ; Horticulture Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Advanced Chemical Sensor Technologies
Authors: Thomas Kurian | Rani Sebastian
Source: Journal of Pharmaceutical Research (article, 2025-11-14)
Field: Chemistry — Organic Chemistry

Molecular Docking Study of Five Aromatic and Heteroaromatic Scaffolds for Assessing the Anticancer Activity by Comparison Against Imatinib Heterocyclic compounds are important in biology and chemistry. Their therapeutic, chemical, and physical characteristics are distinct. A computer simulation method called molecular docking is used to identify potential drug candidates that can attach to a protein's active site. This study examined the anticancer properties of five heterocyclic compounds using molecular docking with PyRX 0.8 and MZ dock software. The primary focus was on the co-crystallized human cyclin-dependent kinase 2 (cdk2) complex with the oncoprotein PDGFRA 6JQI and receptor 3EZV, CDK-2 with indazole inhibitor nine bound at its active site. The toxicity of the lead molecule was also assessed using PROTOX 0.3 software. SWISS ADME software computed bioavailability and confirmed the results. According to the findings, ligands like ethyl 2,3-dihydro-1h-indole-2-carboxylate, (2S,3R,4R,5S,6R)-2-[4-chloro-3-[(4-ethoxyphenyl) methyl] phenyl]-6-methylsulfanyloxane-3,4,5-triol were identified as promising candidates and were validated against the well-known drug imatinib. The SWISS ADME data showed that the selected lead compounds had favourable ADME profiles. Despite the need for more research, these results imply that the lead compounds might be valuable additions to medicine. The safety and effectiveness of these heterocyclic compounds as possible therapeutic agents require more investigation, especially in vivo studies. Keywords: In-silico, Cancer, Heteroaromatic, Aromatic, MZ-DOCK ADME ; Indazole ; Docking (animal) ; Drug ; In vivo ; Imatinib ; Drug discovery ; Bioavailability ; Virtual screening Synthesis and biological activity ; Chronic Myeloid Leukemia Treatments ; Diverse Scientific Research Studies
Authors: Vignesh Sathyasaleen | John A. Morgan | Brett M. Savoie
Source: ChemRxiv (preprint, 2025-02-20)
Field: Chemistry — Spectroscopy

Kinetics Overcome Thermodynamics in Primitive Analogs of the Reverse Tricarboxylic Acid Cycle The reverse/reductive tricarboxylic acid (rTCA) cycle is a metabolic pathway that facilitates CO2 fixation in certain anaerobic bacteria and archaea. Its presence in phylogenetically ancient organisms has led to hypotheses about its role in the early evolution of CO2 fixation pathways. While the thermodynamics of the pathway is well studied, the kinetic feasibility of uncatalyzed rTCA cycle reactions remains uncertain. In this study, we report the first complete mechanistic and kinetic characterizations of the uncatalyzed rTCA cycle and its side reactions. A primitive uncatalyzed rTCA reaction network is elucidated for carbon dioxide fixation that includes all transition states and water-catalyzed reaction channels. The thermodynamics and kinetics of competing off-cycle reactions and potential cycles that are parallel to the uncatalyzed rTCA cycle are also investigated using a newly developed chemical reaction network exploration method. While previous work examined overall thermodynamics and possible pathways, our work focuses on kinetic bottlenecks, which guides where in nature to search for primordial catalysts (clays, minerals, etc.) that could lower the major transition state barriers. This exploration reveals that the uncatalyzed rTCA pathway lies in a reaction neighborhood that is thermodynamically favored but kinetically infeasible owing to competitive intramolecular side-reactions and the absence of favorable parallel cycles. The kinetic modeling also provides intermediates that would accumulate in an uncatalyzed environment serving as prebiotic signposts. Kinetics ; Tricarboxylic acid ; Citric acid cycle ; Chemistry ; Thermodynamics ; Biochemistry ; Metabolism ; Physics Analytical Chemistry and Chromatography ; Thermodynamic properties of mixtures ; Process Optimization and Integration
Authors: Abdulaziz Hassan Al Khzem | Mansour S. Alturki
Source: International Journal of Innovative Research and Scientific Studies (article, 2025-11-07)
Field: Chemistry — Organic Chemistry

Ionization constants (pKa) in pharmaceutical chemistry: Experimental and computational determination in drug discovery and development The ionization constant (pKa) is a major factor in the determination of the chemical and physical properties of drug molecules, which, in turn, affect their distribution, absorption and interaction with the biological targets as well as their pharmacokinetic fate. The present review is an attempt to shed light on the pKa significance in the contemporary drug discovery and medicinal chemistry, pointing its use in rational molecular design and lead optimization. This paper critiques both the experimental and computed methods of pKa determination. It focuses on the key analytical techniques, particularly, UV-visible spectroscopy and NMR spectroscopy. Furthermore, it reviews the recent advances in the in silico prediction platforms and software that are used for high-throughput pKa estimation in the modern screening pipeline. The exponentially increasing number of chemical libraries for screening has made the computational pKa prediction methods indispensable, especially in the early drug discovery phase, where they offer scalable and cost-efficient solutions. It is the accuracy of the experimental validation that still plays a significant role, particularly when it comes to structurally complex molecules. The NMR spectroscopy adds unique advantages to the resolution of individual ionization states in the multi-functional systems, as demonstrated by the case of aminoglycoside antibiotics. The pKa accurate determination and prediction is the basis for drug-like properties optimization and for the compounds' smooth transition throughout the stages of discovery and development. The combination of computational and experimental approaches results in the most reliable ionization profiles. The understanding of pKa behavior allows medicinal chemists to create more effective and less toxic drug candidates by the optimization of solubility, permeability, and target interactions. The use of advanced analytical and computational tools supports efficient compound triaging, accelerating the development of therapeutically relevant molecules. Drug discovery ; Ionization ; In silico ; Nuclear magnetic resonance spectroscopy ; Drug development ; Resolution (logic) ; Drug ; Scalability Free Radicals and Antioxidants ; Computational Drug Discovery Methods ; Organic Chemistry Cycloaddition Reactions
Authors: V. S. Angulakshmi | N. Anusuya | Radhakrishnan Nithya | S. Easterin Shejina | S. Kalaiselvan
Source: Discover Applied Sciences (article, 2025-10-15)
Field: Materials Science — Biomaterials

Eco-smart biopolymer films with Cassia auriculata gum and anthocyanins for pH-responsive fruit freshness monitoring Synthesizing pH-sensitive films for real-time monitoring of freshness is essential for effective food quality assessment. In this study, pH-sensitive films were fabricated by incorporating Ixora coccinea anthocyanin (IC-ATH) into a κ-carrageenan/carboxymethyl cellulose (CA/CMC) matrix using solution casting, both with and without the inclusion of Cassia auriculata gum (CAG) to monitor the freshness of Fragaria ananassa (strawberry). The film properties, including thickness, moisture content, color, mechanical properties, swelling property, as well as pH and biodegradability were evaluated.The CAG film exhibited a thickness of 0.30 mm, a tensile strength of 15.50 MPa, and an elongation at break of 15%, all of which surpassed the corresponding values observed in the CA film.The films exhibited exceptional pH sensitivity, showing clear color transitions across the pH range of 2.0 to 11.0. The inclusion of CAG improved the film’s crystallinity, antioxidant properties and even the moisture content in them. Unwrapped strawberries (control) showed bacterial growth within 3 days and experienced 55% weight loss during storage. In contrast, strawberries wrapped with CA/CMC/IC films exhibited bacterial growth only after 5 days and showed a reduced weight loss of 43%. Notably, CAG/CMC/IC films further delayed bacterial growth until 7 days and minimized weight loss to 24%. This innovative approach underscores the potential of CAG/CMC/IC films as a reliable, eco-friendly solution for the real-time freshnessof Fragaria ananassa, marking significant advancements in smart packaging technology. Fragaria ; Biopolymer ; Anthocyanin ; Ultimate tensile strength ; Moisture ; Bacterial growth ; Bacterial cellulose ; Cassia Nanocomposite Films for Food Packaging ; Postharvest Quality and Shelf Life Management ; Plant Physiology and Cultivation Studies
Authors: Nicolas Hayer | Thomas Specht | Justus Arweiler | Dominik Gond | Hans Hasse | Fabian Jirasek
Source:  (article, 2025-10-01)
Field: Materials Science — Materials Chemistry

Prediction of Activity Coefficients by Similarity-based Imputation Using Quantum-chemical Descriptors In this work, we introduce a novel approach for predicting thermodynamic properties of binary mixtures, which we call the similarity-based method (SBM). The method is based on quantifying the pairwise similarity of components, which we achieve by comparing quantum-chemical descriptors of the components, namely σ-profiles. The basic idea behind the approach is that mixtures with similar pairs of components will have similar thermodynamic properties. The SBM is trained on a matrix that contains some data for a given property for different binary mixtures; the missing entries are then predicted by the SBM. As an example, we consider the prediction of isothermal activity coefficients at infinite dilution (γ∞ij ) and show that the SBM outperforms the well-established physical methods modified UNIFAC (Dortmund) and COSMO-SAC-dsp. In this case, the matrix is only sparsely occupied, and it is shown that the SBM works also if only a limited number of data for similar mixtures is available. The SBM idea can be transferred to any mixture property and is a powerful tool for generating essential data for many applications. Pairwise comparison ; Imputation (statistics) ; Activity coefficient ; Property (philosophy) ; Binary number ; Missing data ; Binary data ; Similarity (geometry) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Metabolomics and Mass Spectrometry Studies
Authors: Marko Kopic | Benjamin Mihaljec
Source: Pomorstvo (article, 2025-09-25)
Field: Materials Science — Materials Chemistry

Environmental Ageing of Structural Materials in Shipbuilding and Marine Engineering – A Review The mechanical properties of structural materials in shipbuilding and marine engineering are significantly affected by environmental stressors such as corrosive environments, UV radiation, and chemical treatments, all leading to material degradation. This review examines nearly 1,000 studies published between 2015 and 2025 to reveal key trends in the research on the impact of environmental factors on material degradation. The review has two parts. The bibliometric review maps the structure of academic research and identifies patterns and trends, while the critical review provides research gaps and future research directions. Specifically, the study highlights that UV-C radiation accelerates surface embrittlement and strength loss in polymers, while chlorination promotes localized corrosion in stainless steels, notably AISI 316. A critical gap was identified in the limited research on combined environmental stressors, despite their real-world relevance in marine environments. The findings highlight the need for a multidisciplinary study of the interplay of various environmental effects on marine materials and structures. Also, there is a certain lack of long-term studies in the natural marine environment. Additionally, attention should be paid to the behavior of modern materials (composites, additively manufactured materials) to allow their faster uptake in the maritime industry. Shipbuilding ; Embrittlement ; Multidisciplinary approach ; Environmental impact assessment ; Engineering research ; Structural integrity ; Environmental effect ; Relevance (law) Corrosion Behavior and Inhibition
Authors: Yujie Xie | Xixiang Zhu | Jinpeng Li | Haomiao Yu | Kai Wang | Zhenmei He | Yukang Lu | Yongchao Xie | Henan Li | Zheng Chen | Hanlin Hu | Hui Zhong | Xiaoxian Zhang | Yumeng Shi | Aiwei Tang
Source: Nano Research (article, 2025-12-31)
Field: Materials Science — Materials Chemistry

Machine-learning assisted filterless color imaging with donor–acceptor ratio engineered self-driven organic photodetectors Miniaturized optical wavelength-sensing devices based on solution-processed organic materials hold great promise for integration into portable and wearable technologies. Yet, the realization of self-powered compact wavelength sensors remains elusive. Here, we report a self-powered wavelength sensor built from broadband photodetectors featuring a meticulously engineered PM6: L8-BO active layer. By systematically varying the donor-acceptor stoichiometries and implementing these blends in nano-scale active layers (50 nm and 100 nm) that modulate the internal optical field distribution, we tailor the spectral responsivity of individual sensor units, yielding distinct wavelength-dependent optoelectronic signatures. An array of these wavelength-discriminating units enables quantitative discrimination and identification of incident light wavelengths. The device accurately resolves wavelengths from 380 nm to 850 nm with a resolution of better than ～1 nm, determined through the photocurrent ratio mapping of the four photodetector elements. As a proof of concept, we demonstrate the device’s capability in wavelength recognition and full-color imaging, underscoring its potential for compact, self-powered, and versatile optical sensing platforms. Photodetector ; Responsivity ; Photocurrent ; Wavelength ; Broadband ; Photoconductivity ; Image sensor ; Photodiode ; Photoresistor Luminescence and Fluorescent Materials ; Advanced Sensor and Energy Harvesting Materials ; Organic Electronics and Photovoltaics
Authors: Ty Hanna | T - Chandni
Source: International Journal on Science and Technology (article, 2025-04-13)
Field: Chemistry — Analytical Chemistry

A Comparative Study of Fluorescent Dyes for TLC Detection of Arson Accelerants: Identifying a safer alternative to Rhodamine B Thin Layer Chromatography (TLC) is a most widely used technique in arson investigations and Rhodamine B is the commonly used fluorescent dye in this technique due to its strong fluorescence but it have chemical hazard concerns. This study explores Eosin Yellow, Rose Bengal, Fluorescein Sodium, and Coumarin as potential substitutes, assessing their fluorescence, stability, and detection efficiency. The objective is to find a safer yet equally effective alternative for forensic applications. Arson ; Chromatography ; Rhodamine ; Fluorescence ; Cosmetics ; Chemistry ; Organic chemistry ; Geography ; Archaeology Dye analysis and toxicity ; bioluminescence and chemiluminescence research ; Analytical Chemistry and Sensors
Authors: Otabek Atabaev | Moulay‐Rachid Babaa | Shakhzodbek Samandarov | Asadbek Tajimuratov
Source: Research Square (preprint, 2025-12-11)
Field: Chemistry — Analytical Chemistry

A Concentration-Invariant FTIR Chemometric Workflow with Peak-Sparse Representation and Machine-Learning Classification Principal component analysis ; Robustness (evolution) ; Preprocessor ; Workflow ; Pattern recognition (psychology) ; Chemometrics ; Linear discriminant analysis ; Sample (material) ; Data pre-processing Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Water Quality Monitoring and Analysis
Authors: Chen Chen | Jiuyang Liang | Zhenli Xu | Qianru Zhang
Source: ArXiv.org (article, 2025-12-31)
Field: Materials Science — Materials Chemistry

Random Batch Sum-of-Gaussians Method for Molecular Dynamics of Born-Mayer-Huggins Systems The Born-Mayer-Huggins (BMH) potential, which combines Coulomb interactions with dispersion and short-range exponential repulsion, is widely used for ionic materials such as molten salts. However, large-scale molecular dynamics simulations of BMH systems are often limited by computation, communication, and memory costs. We recently proposed the random batch sum-of-Gaussians (RBSOG) method, which accelerates Coulomb calculations by using a sum-of-Gaussians (SOG) decomposition to split the potential into short- and long-range parts and by applying importance sampling in Fourier space for the long-range part. In this work, we extend the RBSOG to BMH systems and incorporate a random batch list (RBL) scheme to further accelerate the short-range part, yielding a unified framework for efficient simulations with the BMH potential. The combination of the SOG decomposition and the RBL enables an efficient and scalable treatment of both long- and short-range interactions in BMH system, particularly the RBL well handles the medium-range exponential repulsion and dispersion by the random batch neighbor list. Error estimate is provided to show the theoretical convergence of the RBL force. We evaluate the framework on molten NaCl and mixed alkali halide with up to $5\times10^6$ atoms on $2048$ CPU cores. Compared to the Ewald-based particle-particle particle-mesh method and the RBSOG-only method, our method achieves approximately $4\sim10\times$ and $2\times$ speedups while using $1000$ cores, respectively, under the same level of structural and thermodynamic accuracy and with a reduced memory usage. These results demonstrate the attractive performance of our method in accuracy and scalability for MD simulations with long-range interactions. Molecular dynamics ; Scalability ; Exponential function ; Dispersion (optics) ; Coulomb ; Convergence (economics) ; Decomposition Block Copolymer Self-Assembly ; Machine Learning in Materials Science ; Material Dynamics and Properties
Authors: Nguyễn Quang Thanh | Nguyen Ngoc Chien | Huynh Cao Tuan | Phan Thị Mai Hương
Source: Tạp chí Khoa học Lạc Hồng (article, 2025-09-30)
Field: Chemistry — Analytical Chemistry

1. Automated orange quality classification using convolutional neural networks: A deep learning approach for smart agriculture Quality control is the core activity of an agribusiness and food processing industry just to make sure that the customers have access to quality oranges in a reduced wastage system. This study molds a deep learning idea to classify oranges as either good or bad. These images capture critical features such as consistency of color, surface texture, and apparent defects. Brightness adjustments, enhanced contrasts, and even the addition of some noise are among the possible scenes to improve model generalization error performance. The proposed system would give an automated and scalable real-time orange grading system that would gradually reduce the influence of time-based human inspection practices and improve quality. The finding that even a simple CNN without any pre-train models can be used to achieve high accuracy in this classification task indeed, the results provide for deep learning to be effective in fruit sorting, with scope for much else based on larger data sets, as well as real-world deployment potential Deep learning ; Convolutional neural network ; Orange (colour) ; Scalability ; Artificial neural network ; Software deployment ; Classifier (UML) Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI