jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Juan Ouyang | Ze Zhang | Minghua Shangguan | HaoZhang Huang | Yuxuan Qiu | SongBin Liu | Junjun Tan | Xinyu Ye | Bo Zhou
Source: Advanced Physics Research (article, 2025-12-26)
Field: Materials Science — Materials Chemistry

Functional Interface Engineering Enables Selective Multicolor Upconversion in NaErF <sub>4</sub> @NaYF <sub>4</sub> Core–Shell Nanocrystals Abstract Lanthanide‐doped upconversion nanoparticles (UCNPs) have attracted extensive interest in broad frontier fields such as nanophotonics and biomedicine. However, low luminescence intensity, severe nonradiative energy loss, and limited color‐tuning capability still urgently need to be addressed. Herein, a functional interface engineering regulation strategy is proposed to achieve a significant enhancement in upconversion luminescence intensity and multicolor output from the NaErF 4 @NaYF 4 core–shell nanocrystals. It is found that adjusting the amount of CF 3 COONa in the precursor during the shell growth is able to modulate lattice defect densities at the interface and thereby tune the energy migration paths and non‐radiative transition processes of Er 3+ in the core. This also results in a multicolor tuning from green to yellow, orange, and red under 980 nm excitation, and the overall upconversion luminescence intensity is significantly enhanced by 17.1 times. Moreover, the interface engineering strategy is also applicable to 808 and 1540 nm excitations and the UCNPs with other shells (e.g., NaLuF 4 and NaGdF 4 ). These results provide new insights into the photochromic tuning of highly doped UCNPs and exhibit great promise in multiple anti‐counterfeiting, information security, and biosensing and bioimaging. Photon upconversion ; Luminescence ; Nanocrystal ; Nanophotonics ; Interface (matter) ; Nanoparticle ; Doping ; Graphene Luminescence Properties of Advanced Materials ; Inorganic Fluorides and Related Compounds ; Radiation Detection and Scintillator Technologies
Authors: Melih Oztepe | Demet Coşkun | Ferda Arı
Source: Open Medicine (article, 2025-01-01)
Field: Chemistry — Organic Chemistry

Anticancer activity mechanism of novelly synthesized and characterized benzofuran ring-linked 3-nitrophenyl chalcone derivative on colon cancer cells Cancer is the second biggest cause of death after cardiovascular disorders and its incidence is rising significantly. One out of every ten cancer-related deaths is caused by colon cancer. The increasing incidence calls for creating focused therapeutic strategies with fewer adverse effects than traditional clinical techniques like radiation, chemotherapy, and immunotherapy. In this study, we evaluated the anticancer effects and mechanisms of a synthesized and characterized benzofuran ring-linked 3-nitrophenyl chalcone derivative, [1-(2-benzofuranyl)-3-(3-nitrophenyl)-2-propen-1-one], on colon cancer cells (HCT-116 and HT-29) as well as healthy colon cells (CCD-18Co). Cell viability analyses using the sulforhodamine B assay demonstrated that the IC₅₀ values after 48 h of treatment were 1.71 µM for HCT-116, 7.76 µM for HT-29, and higher than 10 µM for CCD-18Co cells. These results indicate a selective cytotoxic effect on cancer cells an essential criterion for evaluating anticancer compounds. Triple fluorescence staining, flow cytometry caspase 3/7 activity, along with protein expression analyses, confirmed that the compound induces apoptosis in both cancer cell lines. At IC₅₀ values, the derivative activated <i>DR-4</i>-mediated apoptosis at the membrane and <i>BCL-2</i>-mediated apoptosis intracellularly. Moreover, treatment with 12.5 µM of the compound for 24 h, corresponding to a cell cycle time, statistically significantly arrested the cell cycle at the G0/G1 phase. In addition, it inhibited cell migration and colony formation in a dose-dependent manner, starting from values as low as 1.56 µM. Additionally, the binding affinity of the derivative with target proteins was determined using artificial intelligence-assisted molecular modeling analysis. Collectively, these findings highlight the potential of this 3-nitrophenyl chalcone derivative as a promising candidate for the development of novel therapeutic agents against colon cancer. Apoptosis ; Chalcone ; Colorectal cancer ; Flow cytometry ; Cell cycle ; Sulforhodamine B ; Cytotoxic T cell ; Viability assay ; Cell Synthesis and biological activity ; Genomics, phytochemicals, and oxidative stress ; Synthesis and Characterization of Heterocyclic Compounds
Authors: Piyush Kumar Soni | Amit Tiwari | S. N. V. J. Devi Kosuru | S. Dhandapani | R. Suresh Kumar | Khemraj Deshmukh | B. Srinivasulu | Rajeev Shrivastava | C. Durga Prasad | Nimona Hailu
Source: Journal of Materials Science Materials in Engineering (article, 2025-10-30)
Field: Materials Science — Materials Chemistry

An analytical approach to study the mechanical and temperature resistance of Cr2C3 reinforced with cobalt-based advanced composite coatings on Inconel 713 substrate Abstract This research presents a wide mathematical framework for modeling, developing, and optimizing the pressure vessels in hydrogen storage tanks using state-of-the-art solid materials. The emphasis is on metal hybrid storage tanks because these systems have been extensively studied from an experimental and theoretical perspective in the literature, and if the current R&amp;D efforts are successful in bringing the required technology to market, they should offer several advantages. It is found that better cooling is essential during the hydrogen filling process of the storage tank in order to shorten the time required for hydrogen storage. CoMoCrSi + Cr 2 C 3 material comprised the inner layer of the pressure vessel, while Inconel 713 made up the exterior layer. Their thicknesses were 10 mm and 8 mm, respectively. The pressure vessel’s response to various conditions could be assessed through static structural analysis in Ansys Workbench. This particular study aimed at investigating whether a square sample had met the requirements of storing hydrogen gas. Promising results indicate that the multi-layered design used for the pressure vessel is well-suited for hydrogen storage. This may be deduced from its ability to withstand pressure and maintain structural integrity, and this exceeds what other cylinder materials can do. Through sophisticated modeling tools and advanced materials science, this project demonstrates how improvements in hydrogen storage technology can contribute to sustainable energy development. Inconel ; Hydrogen storage ; Hydrogen ; Cylinder ; Deformation (meteorology) ; Composite number ; Storage tank ; Current (fluid) Hydrogen Storage and Materials ; MXene and MAX Phase Materials ; High Entropy Alloys Studies
Authors: Nora Beier | Thomas Gatter | Jakob L. Andersen | Peter F. Stadler
Source: Research Square (preprint, 2025-06-13)
Field: Materials Science — Materials Chemistry

Computing Double-Pushout GraphTransformation Rules and Atom-to-Atom Mapsfrom KEGG RCLASS Data Atom (system on chip) ; KEGG ; Computer science ; Information retrieval ; Data mining ; Chemistry ; Parallel computing Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Electrochemical Analysis and Applications
Authors: Mateo Vergara-Roa | Rogelio Manuel Alvarado-Martinez | Angelica Chappe-Chappe | Rossember Edén Cárdenas-Torres | Cristian Rincón-Guio | María Paz Saavedra-Saavedra | Carlos Francisco Trujillo-Trujillo | Julián David Aristizabal-Yusty
Source: Revista Colombiana de Ciencias Químico Farmacéuticas (article, 2025-09-25)
Field: Materials Science — Materials Chemistry

Evaluation of AI models for predicting drug solubility in cosolvent systems The low solubility of drugs presents a significant challenge in pharmaceutical development. This study evaluates the ability of five artificial intelligence models — Artificial Neural Network (ANN), Random Forest, K-Nearest Neighbors (KNN), Linear Regression, and XGBoost — to predict drug solubility in cosolvent systems. The study uses a dataset that integrates the physicochemical properties of the solute and solvent. The objective was to identify the most accurate model and analyze the relative importance of the predictor variables. The results demonstrate a clear hierarchy in model performance. Tree-based algorithms XGBoost and Random Forest showed exceptional predictive power with coefficients of determination (R²) of 0.9737 and 0.9752, respectively, and the lowest mean squared errors. The ANN model also demonstrated robust performance (R² = 0.9585), whereas the linear regression model was unable to capture the nonlinear relationships inherent in the system (R² = 0.4463). Feature importance analysis revealed significant differences: tree models prioritized the drug's molar mass, while ANN attributed considerable importance to the cosolvent system's properties, such as the solvents' molar mass and volume. These differences highlight the complexity of solubility as a multifactorial phenomenon. We conclude that ensemble models, particularly XGBoost, are highly effective and accurate tools for in silico solubility prediction. Additionally, we find that a comparative modeling approach is crucial for gaining a holistic understanding of the physicochemical interactions that govern the process. Solubility ; Random forest ; Artificial neural network ; Linear regression ; Nonlinear system ; Regression ; Predictive modelling ; Quantitative structure–activity relationship Crystallization and Solubility Studies ; Drug Solubulity and Delivery Systems ; Computational Drug Discovery Methods
Authors: M.I. Hakimi bin Ismail | S.S. Syed Bakar | S. Yahud | Sutichai Chaisitsak
Source: Archives of Metallurgy and Materials (article, 2025-12-22)
Field: Materials Science — Biomaterials

Impact of Dimethylformamide/Acetone Ratios and Stirring Time on Morphology and Electrical Properties of Electrospun PVDF Fibers This study examines the impact of Dimethylformamide, DMF/Acetone ratio (60/40, 70/30 and 100/0) and stirring time (16 hours and 24 hours) on Polyvinylidene Fluoride electrospun fibers. Total of six samples were prepared at 12% solution concentration were electrospun at 20 kV, 10 cm tip-to-collector distance, and 1 mL/h flow rate. Morphological analysis showed that a 70/30 ratio produced finer fibers with fewer beads than 60/40 ratio, while prolonged stirring increased fiber diameter. Electrical analysis confirmed the lowest resistance and highest conductivity for these fibers. The 70/30 ratio and 16-hour stirring time technique optimized fiber structure and electrical performance for advanced applications. Polyvinylidene fluoride ; Morphology (biology) ; Electrical resistivity and conductivity ; Electrospinning ; Fiber ; Aspect ratio (aeronautics) ; Conductivity Electrospun Nanofibers in Biomedical Applications ; Advanced Sensor and Energy Harvesting Materials ; Surface Modification and Superhydrophobicity
Authors: Piotr Zapotoczny | Patricia A. Graczyk
Source: Technical Sciences (article, 2025-06-10)
Field: Chemistry — Analytical Chemistry

Using deep learning algorithms and a flatbed scanner to assess rice quality This article presents the results of research on the application of deep learning techniques in the automatic assessment of rice grain quality. A measurement methodology and a computer program were developed, which uses a deep learning model to identify individual grains in an image and detect impurities for further qualitative analysis. The program was implemented in Python using the OpenCV 4, Numpy, and Ultralytics YOLO libraries. The study used a flatbed scanner, enabling the identification of approximately 3,000 objects in a single scanner ruler pass. Scanner ; Artificial intelligence ; Computer science ; Algorithm ; Quality (philosophy) ; Computer vision ; Pattern recognition (psychology) ; Physics Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI
Authors: С.А. Гученко | O.B. Seldyugaev | В. М. Фомин | D. A. Afanasyev
Source: Eurasian Physical Technical Journal (article, 2025-06-30)
Field: Materials Science — Materials Chemistry

PREDICTION OF CORROSION RESISTANCE OF MAGNALIAS In the presented work the factors influencing the strength and corrosion rate in seawater of magnalium aluminum-magnesium alloy were considered. It was shown that in unsaturated solid alpha solutions the main form of magnesium in the aluminium structure was the electronic compound Mg2Al4 entering the rhombic subsystem of aluminium cells. The calculation showed that the more aluminium cells contain Mg2Al4 group as a rhombic subsystem, the higher the strength of these alloys. The mechanism of destruction by halogen ions of pure aluminium cells and aluminium cells containing the Mg2Al4 group was determined by the PM3 method. Destruction of both types of cells occurred by the same scenario, through the detachment by halogen ions of the central atom in the upper edge of cells bordering. It was shown that in seawater the corrosion rate of aluminium cells containing Mg2Al4 group was higher than the corrosion rate of pure aluminium cells. A mathematical model has been developed that allows calculating the change in the corrosion rate of magnesium alloys with low magnesium mass fraction (up to 3.5 % magnesium inclusive) in seawater. The model allowed to take into account the change of influence of the mass fraction of the main alloying elements (Mg, Cr, Mn, Zn, Ti, Cu) on the corrosion rate. Change of corrosion rate in seawater of three aluminium-magnesium alloys variants was calculated using this model. These aluminium alloy variants include a magnesium content of 2.6 mass % with higher levels of the alloying elements Cr and Mn. Corrosion ; Resistance (ecology) ; Materials science ; Metallurgy ; Biology ; Agronomy Corrosion Behavior and Inhibition ; Hydrogen embrittlement and corrosion behaviors in metals ; Welding Techniques and Residual Stresses
Authors: Srivani Mallepelli
Source: Journal of Pharmaceutical Research & Reports (article, 2025-03-24)
Field: Chemistry — Spectroscopy

Advancements in Two-Dimensional Liquid Chromatography for Biological Samples Enhancing Peak Capacity and Speed through High-Temperature Techniques The need for improved separation power in Liquid Chromatography (LC), particularly for complex multi-constituent biological samples, is becoming increasingly critical. Recent advancements have led to a growing interest in Two-Dimensional Liquid Chromatography (2DLC), which offers significant advantages over traditional One-Dimensional Liquid Chromatography (1DLC) by enabling higher peak capacities and resolution. This review explores the potential of 2DLC, highlighting its ability to enhance separation by combining orthogonal separations and offering peak capacity that, under ideal conditions, is the product of the peak capacities of both dimensions. Despite its impressive separation power, comprehensive 2DLC is hindered by long analysis times, often extending several hours. Recent innovations in high-temperature LC techniques have addressed this challenge by drastically reducing the time required for the Second Dimension of 2DLC to approximately 20 seconds, enabling rapid separations with improved peak capacities exceeding 2000. These developments have led to significant reductions in analysis time, allowing complex biological samples with over 200 detectable peaks to be analyzed in under 30 minutes. This shift toward fast, high-temperature LC is transforming the capabilities of 2DLC, making it an invaluable tool for the analysis of biological samples. Additionally, the review touches on complementary techniques like NMR spin relaxation spectroscopy, which aids in characterizing protein dynamics, offering further insights into protein conformational changes on the picosecond-nanosecond timescale. Chromatography ; Materials science ; Environmental science ; Process engineering ; Chemistry ; Engineering Analytical Chemistry and Chromatography ; Microbial Metabolic Engineering and Bioproduction ; Chromatography in Natural Products
Authors: Suphatchakorn Limhengha | Supattarachai Sudsawat
Source: Journal of Imaging (article, 2025-11-07)
Field: Materials Science — Polymers and Plastics

Real-Time Colorimetric Imaging System for Automated Quality Classification of Natural Rubber Using Yellowness Index Analysis Natural rubber quality assessment traditionally relies on subjective visual inspection, leading to inconsistent grading and processing inefficiencies. This study presents a colorimetric imaging system integrating 48-megapixel image acquisition with automated colorimetric analysis for objective rubber classification. Five rubber grades-white crepe, STR5, STR5L, RSS3, and RSS5-were analyzed using standardized 25 × 25 mm<sup>2</sup> specimens under controlled environmental conditions (25 ± 2 °C, 50 ± 5% relative humidity, 3200 K illumination). The image processing pipeline employed color space transformations from RGB through CIE1931 XYZ to CIELAB coordinates, with yellowness index calculation following ASTM E313-20 standards. The classification algorithm achieved 100% accuracy across 100 validation specimens under controlled laboratory conditions, with a processing time of 1.01 ± 0.09 s per specimen. Statistical validation via one-way ANOVA confirmed measurement reliability (<i>p</i> > 0.05) with yellowness index values ranging from 8.52 ± 0.52 for white crepe to 72.15 ± 7.47 for RSS3. Image quality metrics demonstrated a signal-to-noise ratio exceeding 35 dB and a spatial uniformity coefficient of variation below 5%. The system provides 12-fold throughput improvement over manual inspection, offering objective quality assessment suitable for industrial implementation, though field validation under diverse conditions remains necessary. RGB color model ; Visual inspection ; Color space ; Natural rubber ; Image processing ; Image quality ; Colorimetry ; Reliability (semiconductor) Polymer Nanocomposites and Properties ; Plant biochemistry and biosynthesis ; Plant-Derived Bioactive Compounds
Authors: Juan José Moreno-Labella | Milagrosa González Fernández de Castro | Víctor Saiz Sevilla | M. Panizo-Laiz | Yolanda Martín Álvarez
Source: Materials (article, 2025-07-20)
Field: Materials Science — Materials Chemistry

Machine Learning-Based Prediction of Time Required to Reach the Melting Temperature of Metals in Domestic Microwaves Using Dimensionless Modeling and XGBoost A novel and cost-effective methodology is introduced for the precise prediction of the melting time of metals and alloys in a 700 W domestic microwave oven, using a hybrid SiC-graphite susceptor to ensure efficient heating without direct interaction with microwaves. The study includes experimental trials with multiple alloys (Sn-Bi, Zn, Zamak, and Al-Si, among others) and variable masses, whose results made it possible to construct a dimensionless model, trained with XGBoost on easily measurable thermophysical properties (specific heat, density, thermal conductivity, mass, and melting temperature). The model achieves high accuracy, with a relative error below 5%, and metrics of MAE = 4.8 s, RMSE = 6.1 s, and R<sup>2</sup> = 0.9996. The generalization of the model to different microwave powers (600-1100 W) is also validated through analytical adjustment, without the need for additional experiments. The proposal is implemented as a Python application with a graphical interface, suitable for any academic or teaching laboratory, and its performance is compared with classical models. This approach effectively contributes to the democratization of thermal testing of metals in educational and research settings with limited resources, providing thermodynamic rigor and advanced artificial intelligence tools. Dimensionless quantity ; Microwave ; Microwave heating ; Melting temperature ; Materials science ; Environmental science ; Metallurgy ; Composite material ; Thermodynamics ; Computer science ; Physics ; Telecommunications Machine Learning in Materials Science
Authors: Yiming Huang | Jinhui Chen | J. Jia | Lu-Meng Liu | Y. G. | C. Zhang
Source: arXiv (Cornell University) (preprint, 2025-01-02)
Field: Materials Science — Materials Chemistry

Validation and extrapolation of atomic mass with physics-informed fully connected neural network Machine learning offers a powerful framework for validating and predicting atomic mass. We compare three improved neural network methods for representation and extrapolation for atomic mass prediction. The powerful method, adopting a macroscopic-microscopic approach and treating complex nuclear effects as output labels, achieves superior accuracy in AME2020, yielding a much lower root-mean-square deviation of 0.122 MeV in the test set, significantly lower than alternative methods. It also exhibits a better extrapolation performance when predicting AME2020 from AME2016, with a root-mean-square deviation of 0.191 MeV. We further conduct sensitivity analyses against the model inputs to verify interpretable alignment beyond statistical metrics. Incorporating theoretical predictions of magic numbers and masses, our fully connected neural networks reproduce key nuclear phenomena including nucleon pairing correlation and magic number effects. The extrapolation capability of the framework is discussed and the accuracy of predicting new mass measurements for isotope chains has also been tested. Extrapolation ; Artificial neural network ; Physics ; Statistical physics ; Computer science ; Theoretical physics ; Artificial intelligence ; Mathematics ; Statistics Machine Learning in Materials Science ; Nuclear Physics and Applications
Authors: Cailum M. K. Stienstra | Jin-Il Song | David Healey | Gennady Voronov | Abhishek Patel | Venkat R. Macherla | Christoph A. Krettler | Pieter C. Dorrestein | Daniel Domingo‐Fernándéz
Source: ChemRxiv (preprint, 2025-09-16)
Field: Chemistry — Spectroscopy

Structure Characterization with NMR Molecular Networking Nuclear Magnetic Resonance (NMR) is among the most widely used techniques for structure determination, yet automated workflows remain underdeveloped compared to mass spectrometry. In this work, we introduce NMR molecular networking and apply it to Heteronuclear Single Quantum Coherence (HSQC) spectra, a key 2D-NMR experiment for structure elucidation. We adapt core principles of MS² networking—transitivity across multiple spectra, dereplication, and annotation propagation—to NMR-driven workflows. First, we develop a modified Hungarian distance metric for HSQC peak matching. Benchmarks show that using this metric, traditional spectral lookup with this score recovers ~70-80% of available structural similarity, but efficiency does not improve when increasing the size of the spectral library. Second, we establish NMR molecular networking using HSQC spectra to propagate annotations and dereplicate compounds. Case studies of experimental natural product spectra demonstrate that annotation transitivity within networks accelerates and improves identification of unknowns. Third, we introduce algorithmic molecular networking, which integrates graph topology metrics to correct inefficient rankings and reduce false positives. Together, these approaches define the first generalizable framework for NMR molecular networking, enabling scalable, high-throughput annotation for natural product discovery and drug development. Heteronuclear single quantum coherence spectroscopy ; Workflow ; Heteronuclear molecule ; Annotation ; Key (lock) ; Characterization (materials science) ; Coherence (philosophical gambling strategy) ; Metric (unit) Molecular spectroscopy and chirality