jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yuncheng Du
Source: Nano Trends (article, 2025-09-25)
Field: Chemistry — Inorganic Chemistry

Transfer learning-based modeling of synthesis-particle size relationships in metal-organic frameworks Metal–organic frameworks (MOFs) are promising materials for applications such as gas storage and drug delivery, where their performance is strongly affected by morphology such as particle size. However, the relationships between synthesis conditions and the resulting particle size are complex and remain poorly understood. This work develops a transfer learning-based modeling framework to quantitatively predict how synthesis parameters affect the particle size of zeolitic imidazolate framework-8 (ZIF-8). Using an extreme gradient boosting algorithm (XGBoost), we first build a baseline predictive model trained on a combined dataset of literature and in-house experimental data. To improve performance, we explore transfer learning that pretrains a model on literature data to fix structural hyperparameters such as the number of trees and then fine-tune decision splits with a weighting scheme to emphasize in-house data. To further address data scarcity, we adopt synthetic data augmentation through local interpolation in synthesis parameter space. New data are generated by interpolating between nearby in-house synthesis conditions, preserving the physical meaning of augmented samples. Our results show that transfer learning and data augmentation significantly improve model accuracy and interpretability. This work demonstrates the potential of transfer learning to bridge heterogeneous data sources and accelerate data-driven materials synthesis optimization. Transfer of learning ; Boosting (machine learning) ; Interpolation (computer graphics) ; Weighting ; Hyperparameter ; Synthetic data ; Transfer function ; Transfer (computing) Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Machine Learning and ELM
Authors: Keerthana Madhuri S*, Kishore Babu M, Surekha G, Nagalakshmi G, Divya Sri G, Amulya K, Chanikya S, Mastan Vali Shaik
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2025-11-04)
Field: Chemistry — Spectroscopy

Analytical Review of Integrated Gas Chromatography (GC) Methods by FTIR and Mass Spectrometry Gas chromatography (GC) remains a vital technique for the separation of volatile and semi-volatile chemicals, particularly when paired with advanced detectors such as mass spectrometry (MS) and Fourier-transform infrared spectroscopy (FTIR). The concepts, setups, and analytical capabilities of GC-MS and GC-FTIR are examined in this paper, with a focus on their complimentary functions in contemporary chemical analysis. After a brief introduction to GC, the topic of GC–MS is covered, covering ionisation methods, mass analysers, and acquisition modes. GC-MS is widely recognised for its quantitative capabilities, large spectrum libraries, and excellent sensitivity. On the other hand, GC-FTIR provides structural fingerprinting and functional-group specificity, which improve isomer discrimination, an area in which MS frequently fails. With an emphasis on workflow integration and analytical efficiency, sample-introduction techniques that operate with both systems—such as headspace sampling, solid-phase microextraction (SPME), stir-bar sorptive extraction (SBSE), and thermal desorption (TD) are investigated. In addition to highlighting applications in food, pharmaceutical, environmental, and forensic investigation, the paper compares the advantages and disadvantages of GC-FTIR with GC-MS. High-resolution separations, AI-driven automation, miniaturisation, green analytical chemistry, and the creation of integrated platforms are all topics covered in future perspectives. For researchers and analysts looking to implement or develop GC-based hyphenated approaches in progressively more complicated analytical contexts, this review is a useful resource Mass spectrometry ; Thermal desorption ; Gas chromatography ; Workflow ; Extraction (chemistry) ; Analytical technique ; Analytical Chemistry (journal) Analytical Chemistry and Chromatography ; Analytical chemistry methods development ; Advanced Chemical Sensor Technologies
Authors: Keerthana Madhuri S*, Kishore Babu M, Surekha G, Nagalakshmi G, Divya Sri G, Amulya K, Chanikya S, Mastan Vali Shaik
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2025-11-04)
Field: Chemistry — Spectroscopy

Analytical Review of Integrated Gas Chromatography (GC) Methods by FTIR and Mass Spectrometry Gas chromatography (GC) remains a vital technique for the separation of volatile and semi-volatile chemicals, particularly when paired with advanced detectors such as mass spectrometry (MS) and Fourier-transform infrared spectroscopy (FTIR). The concepts, setups, and analytical capabilities of GC-MS and GC-FTIR are examined in this paper, with a focus on their complimentary functions in contemporary chemical analysis. After a brief introduction to GC, the topic of GC–MS is covered, covering ionisation methods, mass analysers, and acquisition modes. GC-MS is widely recognised for its quantitative capabilities, large spectrum libraries, and excellent sensitivity. On the other hand, GC-FTIR provides structural fingerprinting and functional-group specificity, which improve isomer discrimination, an area in which MS frequently fails. With an emphasis on workflow integration and analytical efficiency, sample-introduction techniques that operate with both systems—such as headspace sampling, solid-phase microextraction (SPME), stir-bar sorptive extraction (SBSE), and thermal desorption (TD) are investigated. In addition to highlighting applications in food, pharmaceutical, environmental, and forensic investigation, the paper compares the advantages and disadvantages of GC-FTIR with GC-MS. High-resolution separations, AI-driven automation, miniaturisation, green analytical chemistry, and the creation of integrated platforms are all topics covered in future perspectives. For researchers and analysts looking to implement or develop GC-based hyphenated approaches in progressively more complicated analytical contexts, this review is a useful resource Mass spectrometry ; Thermal desorption ; Gas chromatography ; Workflow ; Extraction (chemistry) ; Analytical technique ; Analytical Chemistry (journal) Analytical Chemistry and Chromatography ; Analytical chemistry methods development ; Mass Spectrometry Techniques and Applications
Authors: Kemeng Du | Baoshi Qiao | Xiaolei Ding | Changjin Huang | Huan Hu
Source: Nanomaterials (article, 2025-12-16)
Field: Materials Science — Materials Chemistry

Bubbles in 2D Materials: Formation Mechanisms, Impacts, and Removal Strategies for Next-Generation Electronic Devices Two-dimensional materials and their van der Waals heterostructures have shown great potential in quantum physics, flexible electronics, and optoelectronic devices. However, interfacial bubbles originated from trapped air, solvent residues, adsorbed molecules and reaction byproducts remain a key limitation to performance. This review provides a comprehensive overview of the formation mechanisms, characteristics, impacts, and optimization strategies related to bubbles in 2D heterostructures. We first summarize common fabrication approaches for constructing 2D heterostructures and discuss the mechanisms of bubble formation together with their physicochemical features. Then, we introduce characterization techniques ranging from macroscopic morphological observation to atomic-scale interfacial analysis, including optical microscopy, atomic force microscopy, transmission electron microscopy, and spectroscopic methods systematically. The effects of bubbles on the mechanical, electrical, thermal, and optical properties of 2D materials are subsequently examined. Finally, we compare key interface optimization strategies-such as thermal annealing, chemical treatments, AFM-based cleaning, electric field-driven approaches, clean assembly and AI-assisted methods. We demonstrate that, although substantial advances have been made in understanding interfacial bubbles, key fundamental challenges persist. Future breakthroughs will require the combined advancement of mechanistic insight, in situ characterization, and process engineering. Moreover, with the rapid adoption of AI and autonomous experimental platforms in materials fabrication and data analysis, AI-enabled process optimization and real-time characterization are emerging as key enablers for achieving high-cleanliness and scalable van der Waals heterostructures. van der Waals force ; Fabrication ; Characterization (materials science) ; Heterojunction ; Key (lock) ; Scalability ; Bubble ; Electronics 2D Materials and Applications ; Graphene research and applications ; Solar-Powered Water Purification Methods
Authors: Peide Cui | Mengxin Liu | Dezheng Wu | F. Li | Quanfen Guo | Yubo Jiang | Xing Zhou
Source: Crystal Research and Technology (article, 2025-12-10)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Characteristics and Trends of Research on some Functional Liquid Crystal Polymers: A Bibliometric Analysis From 1995 to 2025 ABSTRACT Although numerous studies related to liquid crystal polymer (LCP) have been carried out, there is a notable absence of data‐driven evidence highlighting influential journals, leading countries, prominent institutions, and research tendency. To fill this gap, this study employs bibliometric methods to statistically examine the relevant publications. The annual publication output has shown a significant upward trend, with the number of publications in 2024 reaching a historic peak of 68, nearly quadruple the number in 2004. Among the Web of Science categories, “Materials Science Multidisciplinary” accounted for the highest proportion (33.6%). The bibliometric indicators confirm that China, the USA, and the Netherlands are leading countries, while Eindhoven University of Technology and Fudan University signified their prominent positions with the H‐index of 20 and 19, respectively. The frequency of keyword occurrences over different periods suggests that the research on LCP has evolved from “basic structure and performance” to “device fabrication”, followed by “stimuli‐responsive actuation”, and ultimately to “chiral photonic multifunctional integration”. The findings of this analysis are intended to help delineate the intellectual structure of the field, trace the evolution of research themes, and offer valuable insights and potential directions for future research. Web of science ; Bibliometrics ; TRACE (psycholinguistics) ; Citation analysis ; Trend analysis Liquid Crystal Research Advancements ; Advanced Materials and Mechanics ; Synthesis and Properties of Aromatic Compounds
Authors: Kevin Heng | James Owen | Tian Meng
Source:  (preprint, 2025-07-09)
Field: Chemistry — Inorganic Chemistry

The Gradient of Mean Molecular Weight Across the Radius Valley Photo-evaporation shapes the observed radii of small exoplanets and constrains the underlying distributions of atmospheric and core masses. However, the diversity of atmospheric chemistries corresponding to these distributions remains unelucidated. We develop a first-principles carbon-hydrogen-oxygen-sulfur-silicon (CHOSSi) outgassing model that accounts for non-ideal gas behavior (via fugacities) at high pressures, as well as the tendency for water and hydrogen to dissolve in melt (via solubility laws). We use data-driven radius valley constraints to establish the relationship between the atmospheric surface pressures and melt temperatures of sub-Neptunes. Sub-Neptunes with less massive rocky cores retain less of their primordial hydrogen envelopes, which leads to less heat retention and diminished melt temperatures at the surfaces of these cores. Lower melt temperatures lead thermodynamically to the dominance of carbon-, oxygen-, sulfur- and silicon-bearing molecules over molecular hydrogen, which naturally produce a diversity of mean molecular weights. Our geochemical outgassing calculations robustly predict a gradient of mean molecular weight across the radius valley, where the strength of this gradient is primarily driven by the oxygen fugacity of the molten cores and not by the carbon enrichment (or "metallicity") of the atmosphere. Smaller sub-Neptunes are predicted to have less hydrogen-dominated atmospheres. The precise relationship between the observed and outgassed chemistries requires an understanding of how convection near the core interacts with large-scale atmospheric circulation (driven by stellar heating) near the photosphere, as well as the influence of photochemistry. RADIUS ; Geology ; Mathematics ; Computer science Zeolite Catalysis and Synthesis
Authors: Priyanka K. M | Shinto Varghese | N. Raghavendra Naveen
Source: Recent Advances in Drug Delivery and Formulation (article, 2025-10-04)
Field: Materials Science — Biomaterials

Revolutionizing Hyperlipidaemia Treatment: Magnetic Nanoparticle-Based Delivery Systems Intramuscular magnetic field-driven therapies are a novel means for drug delivery, and, specifically, for the treatment of hyperlipidaemia. With this paradigm shift, the drug delivery system is intended to overcome the limitations of conventional systemic therapies and deliver the drug with precision to the site of action. Magnetic Drug Delivery Systems (MDDS) take advantage of specific properties of magnetic nanoparticles (MNPs) to increase drug localisation and penetration within tissues using external magnetic fields, that is, ensuring targeted delivery of therapeutic agents to the target tissues in a controlled and efficient manner. In this review, MDDS was applied to hyperlipidaemia management, including Orlistat-enhanced magnetic systems for lipid-lowering therapy. Green chemistry advances, biomimetic coatings and intelligent carriers are discussed in the synthesis and design of magnetic nanoparticles. Computational models, in vitro techniques, and animal studies that represent preclinical innovations are explored to demonstrate the translational potential of these systems. Long-term nanoparticle stability and biocompatibility are given special attention, and ethical, regulatory and safety concerns are critically analysed. Finally, this review explored the potential of next-generation technologies like magnetoelectric nanoparticles, AI-driven magnetic field modulation, and integration with wearable health technology to illuminate a new path towards personalised and targeted therapy. Targeted drug delivery ; Magnetic nanoparticles ; Drug delivery ; Drug ; Biocompatibility ; Nanomedicine ; Drug discovery Nanoparticle-Based Drug Delivery ; HIV-related health complications and treatments ; Pharmacological Effects and Toxicity Studies
Authors: Я.В. ШЕСТЕРИКОВА | М.Ю. ВЕПРЖИЦКИЙ | А.С. ФЕДЯЕВ
Source: Экономика и предпринимательство (article, 2025-03-14)
Field: Materials Science — Materials Chemistry

АНАЛИЗ ВЛИЯНИЯ ИСКУССТВЕННОГО ИНТЕЛЛЕКТА НА ПРОЕКТИРОВАНИЕ ЗДАНИЙ И СООРУЖЕНИЙ Искусственный интеллект используется в проектировании зданий и сооружений для оптимизации процессов, а также улучшения качества принятых решений. Он позволяет ускорено обрабатывать данные о материалах, структурных нагрузках и других параметрах, что способствует более точному анализу и принятию обоснованных проектных решений. В статье отражены примеры использования искусственного интеллекта в проектировании зданий и сооружений, такие как анализ, помощь в поиске оптимальных параметров конструктивных элементов здания, учитывающих различные факторы, создание виртуальных моделей здания и самое полезное это предсказывание результатов прочности конструкции, энергопотребления или стоимости строительства, что позволяет принимать обоснованные решения на различных этапах проектирования. Исследование направлено на выявление преимуществ использования искусственного интеллекта при проектировании зданий. Artificial intelligence is used in the design of buildings and structures to optimize processes and improve the quality of decisions made. It enables faster processing of data on materials, structural loads, and other parameters, leading to more accurate analysis and informed design decisions. The article highlights examples of artificial intelligence usage in building and structure design, such as analysis, assistance in finding optimal parameters of building structural elements considering various factors, creating virtual building models, and most importantly, predicting the strength, energy consumption, or construction cost, allowing for informed decisions at different stages of design. The research aims to identify the advantages of using artificial intelligence in building design. Political science Structural mechanics and materials ; Advanced Theoretical and Applied Studies in Material Sciences and Geometry ; Real estate and construction management
Authors: Min Young Cho | S. Asaithambi | Hee-jeong Kim | Byung Do Lee | Woon Bae Park | Kee‐Sun Sohn
Source: Advanced Optical Materials (article, 2025-05-01)
Field: Materials Science — Materials Chemistry

Discovering a Novel Cyan‐Light‐Emitting Thiosilicate Phosphor, Li <sub>2</sub> BaSiS <sub>4</sub> :Eu <sup>2+</sup> , via Experimental Active Learning (Advanced Optical Materials 14/2025) Active Learning In article 2403239, Woon Bae Park, Kee-Sun Sohn, an co-workers present the discovery of a novel cyan-light-emitting phosphor, Li2BaSiS4:Eu2+, using an AI-driven active learning approach. The material emits cyan-blue light and shows strong potential for LED applications. By combining experimental and computational techniques, the study efficiently explores large compositional spaces to identify promising materials. The phosphor demonstrates stable luminescent properties, making it suitable for next-generation lighting and display technologies. Cyan ; Phosphor ; Materials science ; Optical materials ; Blue light ; Optoelectronics ; Luminescence ; Optics ; Physics Luminescence Properties of Advanced Materials ; Inorganic Chemistry and Materials ; Radiation Detection and Scintillator Technologies
Authors: Robert Rauschen | Dean Thomas | Leroy Cronin
Source: Angewandte Chemie (article, 2025-11-20)
Field: Chemistry — Spectroscopy

Utilizing Similarity Measures to Map Chemical Reactivity Abstract The development of fully autonomous chemical synthesis platforms requires robust, real‐time assessment of reactivity that does not rely on prior mechanistic knowledge. Existing methods often depend on predefined reaction models or chemical intuition, limiting their generalizability and adaptability. To address this challenge, we introduce a chemically agnostic approach that quantifies reactivity dynamics by applying similarity metrics to the full informational content of in‐situ spectroscopic data. Using NMR, UV/Vis, IR, and EPR spectroscopy, we demonstrate that spectral similarity trajectories can reliably indicate reaction progress, detect kinetic features such as autocatalysis, and resolve complex behaviours including oscillations. Across diverse reaction classes, this method enabled estimated end‐point detection and kinetic profiling without reaction‐specific tuning. For example, the formation of lophine was followed by NMR at different reaction temperatures revealing Arrhenius‐type kinetics. In a different experiment, the Belousov Zhabotinsky reaction was monitored by UV/Vis and the chemical oscillation with a periodicity of 7.25 s was captured. These results establish a generalizable framework for real‐time, data‐driven reactivity monitoring, representing a critical step toward autonomous synthesis guided by multidimensional spectroscopic feedback. Generalizability theory ; Reactivity (psychology) ; Similarity (geometry) ; Limiting ; Chemical reaction ; Kinetic energy Molecular spectroscopy and chirality ; Cyclization and Aryne Chemistry ; Machine Learning in Materials Science
Authors: Amina Ben Ayed | Mina Namvari
Source: Sabanci University (article, 2025-11-01)
Field: Materials Science — Materials Chemistry

Design strategies in developing MXene-based anti-icing/deicing coatings: toward energy-efficient and durable solutions Graphene ; Oxide ; Polydimethylsiloxane ; Carbon nanotube ; Leverage (statistics) ; Thermal stability ; Polymer ; Photovoltaic system ; Carbon fibers MXene and MAX Phase Materials ; Surface Modification and Superhydrophobicity ; Advanced Sensor and Energy Harvesting Materials
Authors: <p>Meng Wang</p>
Source: Academic Journal of Materials & Chemistry (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Recent advances in energy storage performance of barium titanate-based relaxor ferroelectric ceramics Dielectric energy storage materials have drawn universal research attention for their potential application in a great number of electrical and electronic devices. They are becoming increasingly viewed as core components for the next-generation AI and IT circuits. This study briefly reviews the four dominant families of energy storage ceramic materials, and their respective underlying mechanisms and current research development. At the same time, extensive research has been conducted on the energy storage performance of relaxor ferroelectric (RFE) ceramics derived from barium titanate (BaTiO3, BT). Given the limited energy storage capability of pure BT, four primary enhancement strategies have been explored: Doping modification, re-shell structure, core-shell structure, and grain engineering are comprehensively detailed based on their application for enhanced dielectric energy storage performance. By integrating artificial intelligence techniques, BT-based RFE ceramic development has vast potential for high-speed development. Energy storage ; Ceramic ; Barium titanate ; Dielectric ; Ferroelectric ceramics ; Energy (signal processing) ; Ferroelectricity Ferroelectric and Piezoelectric Materials ; Dielectric materials and actuators ; Ferroelectric and Negative Capacitance Devices