jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yuwei Geng | Junqi Yin | Eric C. Cyr | Guannan Zhang | Lili Ju
Source: ArXiv.org (preprint, 2025-10-09)
Field: Materials Science — Materials Chemistry

Parallel-in-Time Solution of Allen-Cahn Equations by Integrating Operator Learning into the Parareal Method While recent advances in deep learning have shown promising efficiency gains in solving time-dependent partial differential equations (PDEs), matching the accuracy of conventional numerical solvers still remains a challenge. One strategy to improve the accuracy of deep learning-based solutions for time-dependent PDEs is to use the learned solution as the coarse propagator in the Parareal method and a traditional numerical method as the fine solver. However, successful integration of deep learning into the Parareal method requires consistency between the coarse and fine solvers, particularly for PDEs exhibiting rapid changes such as sharp transitions. To ensure such consistency, we propose to use the convolutional neural networks (CNNs) to learn the fully discrete time-stepping operator defined by the traditional numerical scheme used as the fine solver. We demonstrate the effectiveness of the proposed method in solving the classical and mass-conservative Allen-Cahn (AC) equations. Through iterative updates in the Parareal algorithm, our approach achieves a significant computational speedup compared to traditional fine solvers while converging to high-accuracy solutions. Our results highlight that the proposed Parareal algorithm effectively accelerates simulations, particularly when implemented on multiple GPUs, and converges to the desired accuracy in only a few iterations. Another advantage of our method is that the CNNs model is trained on trajectories-based on random initial conditions, such that the trained model can be used to solve the AC equations with various initial conditions without re-training. This work demonstrates the potential of integrating neural network methods into the parallel-in-time frameworks for efficient and accurate simulations of time-dependent PDEs. Speedup ; Operator (biology) ; Consistency (knowledge bases) ; Matching (statistics) ; Convolutional neural network ; Artificial neural network ; Deep learning ; Scheme (mathematics) Solidification and crystal growth phenomena ; Magnetic properties of thin films
Authors: Dianta Ginting | Ai Nurlela | Dwi Nanto | Mashadi Mashadi | Toto Sudiro | Tony Kristiantoro | Jong-Soo Rhye
Source: Science & Technology Indonesia (article, 2025-10-01)
Field: Materials Science — Materials Chemistry

Significant Reduction in Lattice Thermal Conductivity of (PbTe)0.95 - (PbS)0.05 Thermoelectric Materials Through Liquid Silicon Quenching Thermoelectric materials are game-changers, that have the ability to transform waste heat into electrical energy, making them a potential renewable energy solution to reduce reliance on fossil fuels. The standard metric for evaluating thermoelectric materials is the dimensionless figure of merit, ZT, which is markedly influenced by lattice thermal conductivity (ĸl ). Higher thermal transport through the lattice lowers the ZT value, reducing the material’s efficiency. Therefore, finding ways to decrease ????l is critical for boosting thermoelectric performance. In our research, we explored an innovative approach by applying a quenching technique using liquid silicon to reduce thermal conductivity (ĸT ) due to lattice vibrations. We compared the lattice conductivity (ĸl ) of materials with and without this liquid silicon quenching process. The results were striking: at 300 K, quenching lowers the lattice thermal conductivity by about 40.1 %, and at 800 K, it is still reduced by roughly 24.7%compared with pristine PbTe. Even more impressive, when compared to non-quenched (PbTe)0.95 − (PbS)0.05 alloys, at 300 K, the silicon-quenched sample attains an additional ĸl reduction of roughly 16.1 %, while at 800 K the extra decrease is about 13.0%. These findings highlight that liquid silicon quenching is a highly effective method for lowering ĸl of PbTe thermoelectric materials. This approach paves the way for developing next-generation thermoelectric materials that are more efficient, particularly for eco-friendly waste heat recovery applications. Our work opens new possibilities for sustainable energy innovation. Thermal conductivity ; Thermoelectric effect ; Thermoelectric materials ; Silicon ; Quenching (fluorescence) ; Thermoelectric generator ; Thermal Advanced Thermoelectric Materials and Devices
Authors: Sudip Mandal
Source: International Journal of Scientific Research in Science Engineering and Technology (article, 2025-03-04)
Field: Materials Science — Biomaterials

Advancing Breast Cancer Therapy with Iron Oxide Nanoparticles Breast cancer remains a leading cause of morbidity and mortality among women worldwide, presenting a pressing challenge for global health. The integration of nanotechnology into cancer care offers transformative opportunities to improve diagnostics, therapeutic interventions, and monitoring. Among various nanomaterials, iron oxide nanoparticles (IONPs) stand out for their distinctive physicochemical properties, including superparamagnetism, excellent biocompatibility, and the flexibility for surface functionalization. This article delves into the diverse applications of IONPs in breast cancer care, focusing on breakthroughs in imaging modalities, precision-targeted drug delivery systems, and innovative therapeutic strategies. By analyzing the latest research findings and clinical trial data, we present a detailed exploration of how IONPs can significantly advance the landscape of breast cancer management, paving the way for personalized and effective patient care. Breast cancer ; Iron oxide nanoparticles ; Cancer therapy ; Nanoparticle ; Iron oxide ; Nanotechnology ; Materials science ; Medicine ; Cancer ; Oncology ; Internal medicine ; Metallurgy Nanoparticle-Based Drug Delivery
Authors: Farooq Algaragoolee | Ayad Kareem Khan | Shakir M. Alwan
Source: Turkish computational and theoretical chemistry/Turkish computational and theoretical chemistry : (article, 2025-12-15)
Field: Chemistry — Organic Chemistry

In Silico Molecular Modelling and Dynamic Simulations, ADME Parameters Predictions of new Naproxen Analogues Containing 1,3,4-Oxadiazole as Anti-inflammatory Agents The serious challenges associated with the non-steroidal anti-inflammatory drugs (NSAIDs), especially their unfavorable impacts on the gastrointestinal tract (GIT)and kidneys arise from restraining cyclooxygenase (COX) enzymes. To address this issue and manage the side effects, several investigations were employed by modifying and adjusting the free carboxyl group of NSAIDs to upgrade their safety profile, while, keeping up their anti-inflammatory adequacy. An approach of suggesting new analogues of NSAIDS focusing on COX-2 enzyme devoid of side effects on stomach or kidneys is highly recommended. In this respect, the design of new analogues of naproxen is investigated by insertion of the carboxylic group into a 1,3,4-oxadiazole ring structure. This chemical modification points to hold anti-inflammatory impacts, whereas decreasing the hazard of stomach irritation and ulceration. These analogues were evaluated by means of computational methods, such as molecular docking, virtual screening, ADME (absorption, distribution, metabolism, and excretion) and molecular dynamics. The proposed compounds exhibited encouraging binding affinities to the COX-2 receptor, as demonstrated by molecular docking and molecular dynamic simulations. This may suggest that these compounds have the potential to be effective anti-inflammatory agents. Moreover, in silico ADME appraisals showed favorable pharmacokinetic profiles for the compounds that may suggest safer choices to known NSAIDs. Virtual evaluations of the new naproxen analogues have indicated their anti-inflammatory activity. These evaluations and the promising binding affinities and favorable pharmacokinetic profiles bolster the basis for advance preclinical and clinical ponders to approve the high potential of these compounds. Molecular dynamics simulations provide experiences into the energetic stability of the protein-ligand complex, with RMSD and RMSF investigations demonstrate steady interactions during time. This chemical modification offers a promising approach for the development of safer anti-inflammatory analogues of naproxen. ADME ; Naproxen ; In silico ; Molecular dynamics ; Binding affinities ; Molecular model ; Virtual screening Synthesis and biological activity ; Click Chemistry and Applications ; Synthesis and Characterization of Heterocyclic Compounds
Authors: Yihao Wang | Zaichun Sun | Bingchu Mei
Source: Advanced Functional Materials (article, 2025-11-19)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Intrinsic Ferroelectricity in ɛ‐Ga <sub>2</sub> O <sub>3</sub> : Experimental and Theoretical Insights Abstract The wide‐bandgap ferroelectric semiconductor epsilon gallium oxide (ɛ‐Ga 2 O 3 ) combines exceptional breakdown strength with electrically tunable polarization, enabling advancements in electronics, neuromorphic systems, and optoelectronic devices. Despite recent progress, investigation of ferroelectric behavior in ɛ‐Ga 2 O 3 remains at an early stage with various fundamental questions yet to be addressed. Herein, epitaxial growth of ɛ‐Ga 2 O 3 is achieved via mist chemical vapor deposition (CVD). Second‐harmonic generation (SHG) further reveals their non‐centrosymmetric nature and determines an elevated high Curie temperature of 690 K. The ɛ‐Ga 2 O 3 films display intercorrelated out‐of‐plane and in‐plane ferroelectricity. Theoretical calculations reveal that ferroelectricity originated from the displacement of Ga atoms along the c ‐axis. Leveraging this polarization behavior, a prototype ferroelectric nonvolatile device is developed, where a vertical ɛ‐Ga 2 O 3 memristor showed pronounced resistive switching with on/off ratio of 10 6 . The ɛ‐Ga 2 O 3 memristor‐based computing system, designed for intelligent automotive applications, demonstrates excellent multi‐target recognition capabilities. By deciphering the relationship between crystallographic symmetry and ferroelectric ordering in ɛ‐Ga 2 O 3 , this study would not only deliver experimental verification of its intrinsic ferroelectricity but also represent a pioneering exploration of novel applications based on its unique combination of ultrawide bandgap and ferroelectricity of Ga 2 O 3 . Ferroelectricity ; Non-volatile memory ; Neuromorphic engineering ; Curie temperature ; Semiconductor ; Thin film ; Memristor ; Polarization (electrochemistry) ; Ferroelectric capacitor Ga2O3 and related materials ; Ferroelectric and Piezoelectric Materials ; Ferroelectric and Negative Capacitance Devices
Authors: Sang-Gyu Choi | Sangwook Kim | J.H. Lee | Keun-Soo Kim | Soong‐Keun Hyun
Source: Metals (article, 2025-10-03)
Field: Materials Science — Materials Chemistry

Predictive Modeling of Shear Strength for Lotus-Type Porous Copper Bonded to Alumina This study investigates the shear strength of lotus-type unidirectional porous copper bonded to alumina substrates using the Direct Bonded Copper (DBC) process. Porous copper specimens with various porosities (38.7–50.9%) and pore sizes (150–800 μm) were fabricated and joined to alumina discs. Shear testing revealed that both porosity and pore size significantly affect the interfacial strength. While higher porosity led to reduced shear strength, larger pore sizes enhanced the maximum shear strength owing to increased local contact areas and crack coalescence in the alumina substrate. Fractographic analysis using optical microscopy and SEM-EDS confirmed that failure mainly occurred in the alumina, with local fracture associated with pore distribution and size. To improve strength prediction, a modified model was proposed, reducing the error from 12.3% to 7.5% and increasing the coefficient of determination (R2) from 0.43 to 0.74. These findings highlight the necessity of considering both porosity and pore size when predicting the shear strength of porous copper/alumina DBC joints, and they provide important insights for optimizing metal structures in metal–ceramic bonding for high-performance applications. Porosity ; Copper ; Coalescence (physics) ; Shear (geology) ; Shear strength (soil) ; Metal Metallurgy and Material Science ; Metal Forming Simulation Techniques ; Cellular and Composite Structures
Authors: Hari Krishan Aggarwal | Kuldip Singh
Source: International Journal For Multidisciplinary Research (review, 2025-06-19)
Field: Chemistry — Spectroscopy

ANALYTICAL METHOD VALIDATION FOR ANTI-TUBERCULOSIS DRUGS: A REVIEW OF RIFAMPICIN ISONIAZID AND PYRIDOXINE TB is still a major worldwide health concern, especially in low- and middle-income nations. Accurate dosing, pharmacokinetic profile, and quality control of vital medications including pyridoxine, isoniazid, and rifampicin are crucial to the effectiveness of anti-tuberculosis treatment. For methods used for drug quantification in bulk materials, pharmaceutical formulations, and biological matrices to be reliable, specific, and reproducible, analytical method validation is essential. This study offers a thorough summary of the approved analytical techniques used to determine pyridoxine, isoniazid, and rifampicin, namely High-Performance Liquid Chromatography (HPLC), UV-Visible spectrophotometry, and LC-MS/MS. In compliance with ICH and USP criteria, important validation factors are examined, including accuracy, precision, linearity, sensitivity, specificity, robustness, and system adaptability. Rifampicin needs method development techniques that address matrix interference and degradation because of its instability and low solubility. While pyridoxine, which is used prophylactically to avoid neurotoxicity from isoniazid, requires precise monitoring, particularly in fixed-dose combination therapy, isoniazid, a hydrophilic molecule, presents difficulties in derivatization and UV detection. Common problems include matrix effects in biological materials and drug-drug interactions during co-formulation are also highlighted in the paper. Additionally assessed are recent developments in chromatographic methods, such as UPLC and bioanalytical validations for monitoring therapeutic drugs (TDM). In order to promote strong quality assurance and regulatory compliance in anti-TB pharmacotherapy and enhance treatment results and medication safety, this paper attempts to compile important insights into method development and validation. Isoniazid ; Rifampicin ; Pyridoxine ; Pharmacology ; Medicine ; Tuberculosis Analytical Chemistry and Chromatography ; Analytical Methods in Pharmaceuticals ; Computational Drug Discovery Methods
Authors: Nutan Shukla | Aayushi Chanderiya | Ratnesh Das | Supriya Vyas | Elizaveta Mukhanova | Alexander Soldatov | Samuel B. Adeloju
Source: Advanced NanoBiomed Research (article, 2025-11-04)
Field: Materials Science — Materials Chemistry

Toward Customization of Pickering Emulsion Nanocarriers for Personalized Delivery of Therapeutics in Cancer Treatments: A Review The rapid advancement in nanotechnology over the past three decades has accelerated the development of novel and more potent cancer treatments. In particular, this has led to the development of an increasing number of novel carrier systems for drug delivery, among which Pickering emulsions are a strong contender. This article reviews the development, characterization, and therapeutic efficacy of Pickering emulsion nanocarriers designed specifically for cancer treatment. This approach offers significant benefits for overcoming many of the challenges experienced in conventional drug delivery systems for cancer therapy. The mechanisms of drug release, targeting strategies, and the stability of Pickering emulsions under physiological conditions are examined, along with an evaluation of their therapeutic potential and biocompatibility of these nanocarriers in various cancer models with in vitro and in vivo studies. The ability of Pickering emulsions to improve therapeutic efficacy through encapsulation and protection of hydrophobic drugs is also highlighted, resulting in targeted drug release at the tumor site and minimal side effects. Future development of the nanocarrier systems must address the challenges of achieving large‐scale production, regulatory approval, and translational application. If successfully addressed, it will pave the way for making personalized delivery of therapeutics for cancer treatment a reality. Nanocarriers ; Pickering emulsion ; Drug delivery ; Cancer ; Targeted drug delivery ; Cancer therapy ; Cancer treatment Pickering emulsions and particle stabilization ; Nanoparticle-Based Drug Delivery ; Proteins in Food Systems
Authors: Saif Haider Kayani | Byung Joo Kim
Source: Crystals (article, 2025-11-19)
Field: Materials Science — Materials Chemistry

Microstructure and Mechanical Behavior of Structural Materials: 2nd Edition Metallic materials are the workhorses of structural applications and remain central to modern engineering across energy, transportation, process equipment, and extreme-environment service [...] Microstructure ; Process (computing) ; Manufacturing process ; Deformation (meteorology) Material Selection and Properties ; Aluminum Alloys Composites Properties ; Fatigue and fracture mechanics
Authors: Ping Guo | Yong Li | Yanbao Liu | Yunlong Zou
Source: Processes (article, 2025-09-11)
Field: Materials Science — Materials Chemistry

Development of Double Crosslinked Nano Microspheres and Study on CO2 Drive Blocking Mechanism In this study, a new type of double crosslinked nanospheres (DCNPM-A) was developed to solve the problem of gas channeling caused by fracture development in the process of CO2 oil displacement, and the microsphere system with delayed swelling was successfully synthesized by inverse micro lotion polymerization. The microsphere adopts a dual crosslinking structure of stable crosslinking agent (MBA) and unstable crosslinking agent (UCA), achieving intelligent sealing function of shallow low expansion and deep high temperature triggered secondary expansion. The successful preparation of microspheres was verified by characterization methods such as Zeta potential and SEM, and the effects of reaction temperature, time, initiator and crosslinking agent dosage on microsphere properties were systematically studied. The experimental results show that DCNPM-A microspheres exhibit excellent expansion performance, thermal stability, and acid resistance in acidic, high-temperature, and high mineralization environments. Their expansion ratio can reach 13.5 times, and they can maintain stability for more than 60 days in supercritical CO2 environments. Core displacement experiments have confirmed that the microspheres have the best sealing performance in matrices with a permeability of 10 × 10−3 μm2 and fractures with a width of 0.03 mm. The combination of 0.8 PV injection volume, 0.5 mL·min−1 injection rate, and continuous injection method significantly improved the plugging rate and recovery rate of CO2 flooding. This study provides new technical support for the efficient development of low-permeability fractured reservoirs. Swelling ; Zeta potential ; Nano- ; Thermal expansion ; Nanometre ; Permeability (electromagnetism) Catalytic Processes in Materials Science ; Enhanced Oil Recovery Techniques ; Catalysis and Oxidation Reactions
Authors: Rosario Othen | Steven Macpherson | Christian Möbitz | Thomas Gries
Source: Applied System Innovation (article, 2025-09-12)
Field: Materials Science — Polymers and Plastics

Integrating Machine Learning and Sustainability in Nonwoven Production: A Case Study Using the FOREST Framework The environmental impact of industrial processes, especially regarding CO2 emissions, requires innovative tools to monitor and optimize resource consumption. This study presents a data-driven approach for a carded nonwoven production line, aiming to support integration into the sustainability framework FOREST (Framework for Resource, Energy, Sustainability Treatment). Real process data from a pilot line were pre-processed, analysed, and used to train machine learning models to predict energy consumption across multiple production stages. Using techniques such as recursive feature elimination and SHAP value interpretation, the most influential parameters for each process step were identified. Extra Trees Regression proved to be the most accurate and explainable model across all scenarios. The results allow real-time estimation of the Product Carbon Footprint (PCF) based on process parameters and provide a foundation for energy optimization in nonwoven manufacturing. Sustainability ; Process (computing) ; Carbon footprint ; Energy consumption ; Product (mathematics) ; Production (economics) ; Energy (signal processing) ; Resource (disambiguation) Textile materials and evaluations ; Industrial Vision Systems and Defect Detection
Authors: Aleksandra Orzechowska | M. Wołoszyn
Source: BMC Plant Biology (article, 2025-09-30)
Field: Chemistry — Physical and Theoretical Chemistry

Combined effect of salt stress and high light in plants: from basic statistical approach to machine learning methods Infrared thermal imaging offers a rapid and sensitive approach to assessing temperature changes in plants caused by salt stress, even in the early stages of exposure. Given the increasing prevalence of salt contamination in the environment, it is essential to accurately estimate salinity levels, as the effects strongly depend on salt concentration: moderate salinity elicits a reversible, osmotic driven rise in leaf temperature, whereas higher salinity induces a larger, sustained temperature increase indicative of ion toxicity related stress. We propose a method to evaluate the severity of salt stress in plants exposed to sodium chloride, using a series of thermograms captured through a non-invasive infrared imaging technique under illuminated conditions. Thermal measurements are then used to train machine learning models used to perform multi-class classification to distinguish between four different salt concentrations. To test the proposed method, we cultivated Arabidopsis thaliana plants under controlled conditions. Data collected from the prepared samples were used to assess the accuracy of various approaches and classifiers with lead-one-out cross-validation. This experimental evaluation shows that the optimal performance is achieved when the datasets used for training consist of longer sequences of thermal data provided to models using neural networks. Salinity ; Salt (chemistry) ; Soil salinity ; Stress (linguistics) ; Predictive modelling ; Thermal ; Sodium salt ; Statistical model thermodynamics and calorimetric analyses ; Plant Stress Responses and Tolerance ; Spectroscopy and Chemometric Analyses