jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yu Fang (219603) | Xuefeng Bai (143186) | Xin Zhang (35492) | Jian-Rong Li (1419784)
Source: Figshare (article, 2025-11-04)
Field: Chemistry — Inorganic Chemistry

Data-Driven Discoveryof Metal–Organic Frameworksfor Sieving Separation of Quinary C5 Olefin Mixtures C5 olefins are widely used as chemical feedstocks for producing high-performance polymers and pharmaceuticals, and their separation requires complex and energy-intensive processes. Herein, we report the adsorptive separation of a quinary C5 mixture through the data-driven discovery of sieving sorbents. By systematically analyzing 1067 literature sources using large language models, a data set of 345 metal–organic frameworks (MOFs) and their corresponding sieving performance was established. This data set enabled the determination of effective pore sizes for MOFs, which subsequently guided the discovery of ZIF-8 as an exceptional sorbent capable of producing 99.99% pure isoprene from the C5 mixture. In a pilot-scale breakthrough experiment, 350 mL of high-purity isoprene was obtained by using 1.8 kg of shaped MOF. Extending this approach, three additional MOFs (Co-FA, Co(pz-NH<sub>2</sub>)Ni, and Co-gallate) were discovered for the separation of remaining four C5 components. Finally, integrating these four MOFs in a sequential separation process achieved complete isolation of all five C5 compounds (isoprene, 1-pentene, <i>cis</i>-2-pentene, <i>trans</i>-2-pentene, and <i>n</i>-pentane) with high purity and demonstrated remarkable cycling stability over consecutive operations. Quinary ; Isoprene ; Separation process ; Olefin fiber ; Sorbent ; Process (computing) ; Polymer Metal-Organic Frameworks: Synthesis and Applications ; Zeolite Catalysis and Synthesis ; Membrane Separation and Gas Transport
Authors: Felipe Ravelly Alves de Souza | Maristela A. Vicente | Maria de Fátima Pereira dos Santos
Source: Latin American Journal of Energy Research (article, 2025-11-29)
Field: Chemistry — Organic Chemistry

Adulteração de azeite de oliva e eficiência energética: inovações analíticas e perspectivas sustentáveis - uma revisão integrativa Olive oil adulteration represents a growing challenge to product authenticity, food safety, and sustainability. This integrative review analyzed 18 studies published between 2023 and 2025, selected according to PRISMA criteria, focusing on analytical methods applied to fraud detection and their relationship with energy efficiency. Optical, magnetic, chromatographic, and multisensory techniques were evaluated based on sensitivity, energy consumption, waste generation, automation, and equipment life cycle. The NIR and TD‑NMR methods and the multisensor system were the most energy-efficient according to the parameters analyzed, achieving accuracies above 90% and detection limits between 2% and 5%. Conventional techniques, while accurate, require more infrastructure and higher energy input and generate chemical waste. Integration with artificial intelligence was recurrent, enhancing the robustness of predictive models. Despite progress, gaps remain, such as reliance on complex laboratories, the need for industrial validation, and the limited availability of portable devices with high precision. Considering these limitations, future perspectives point to the development of miniaturized sensors and autonomous analytical systems capable of performing local data processing and enabling rapid, accurate, energy-efficient, and sustainable analyses across various production and regulatory contexts. Robustness (evolution) ; Applications of artificial intelligence ; Product (mathematics) ; Energy (signal processing) ; Production (economics) ; Efficient energy use Edible Oils Quality and Analysis ; Spectroscopy and Chemometric Analyses ; Electrochemical sensors and biosensors
Authors: Gwangcheol Ji (22456186) | Jaehyun Seo (22456189) | Suji Im (22456192) | Byeongcheol Choe (22456195) | Myeongjun Kang (22456198) | Hyoungjeen Jeen (1351653) | Sungkyun Park (3548045) | Junegak Joung (22456201) | Jong Mok Ok (10711197)
Source: Figshare (article, 2025-10-18)
Field: Materials Science — Materials Chemistry

Achieving High-CrystallinityYSZ Thin-Films on SiUsing Explainable AI and Bayesian Optimization in Resource-ConstrainedSettings The growth of epitaxial yttria-stabilized zirconia (YSZ) thin-films on Si substrates is critical for integrating functional oxides into advanced electronic and energy devices. However, identifying the optimal growth conditions for YSZ thin-films remains challenging because of the complex and interdependent nature of deposition parameters in thin-film growth processes. Herein, we propose an integrated approach that combines Shapley additive explanations (SHAP), which is an explainable artificial intelligence (XAI) method, with Bayesian optimization (BO) to efficiently identify key growth parameters and optimize crystallinity in resource-constrained settings. By performing SHAP analysis of literature-derived data, oxygen partial pressure was identified as the most influential parameter, followed by temperature. Guided by this ranking, BO was employed to minimize the full width at half maximum (FWHM) of the YSZ 002 rocking curve. High-quality epitaxial films with an FWHM of 0.74° were achieved with only nine growth attempts. The optimized films exhibited atomically smooth surfaces (root-mean-square roughness of ∼0.48 nm) and uniform chemical compositions, as confirmed by Raman and energy-dispersive spectroscopy. This study shows the power of integrating XAI and BO for efficient and interpretable process optimization of thin-films. Bayesian optimization ; Yttria-stabilized zirconia ; Process (computing) ; Raman spectroscopy ; Full width at half maximum ; Cubic zirconia ; Epitaxy ; Bayesian probability ; Power (physics) Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Advancements in Solid Oxide Fuel Cells
Authors: Wu, Zhen-Wei
Source: arXiv (Cornell University) (preprint, 2025-12-04)
Field: Materials Science — Materials Chemistry

A Kinetic Criterion for Stokes-Einstein Relation Breakdown Based on Effective Collisional Geometry Here we propose a kinetic framework for interpreting the Stokes-Einstein (SE) relation breakdown in supercooled liquids by introducing an effective collision diameter, $d_{\mathrm{eff}}$, derived from transport data. Numerical simulation of a model CuZr alloy reveal that $d_{\mathrm{eff}}$ increases upon cooling but saturates near the first peak of the radial distribution function just before SE breakdown. This saturation defines a geometric upper bound for the collisional cross-section beyond which further slowdown is governed by cooperative, heterogeneous motion rather than local collisional transport. Our analysis yields a compact criterion for SE breakdown in a mean-field perspective and provides physically interpretable inputs for future data-driven models of glassy dynamics. Kinetic energy ; Collision ; Supercooling ; Function (biology) ; Saturation (graph theory) ; Distribution function ; Motion (physics) ; Distribution (mathematics) ; Perspective (graphical) Material Dynamics and Properties ; Solidification and crystal growth phenomena ; Advanced Physical and Chemical Molecular Interactions
Authors: Gwangcheol Ji | Jae-Hyun Seo | Subin Im | Byeongcheol Choe | Myeongjun Kang | Hyoungjeen Jeen | Sungkyun Park | Junegak Joung | Jong Mok Ok
Source: Crystal Growth & Design (article, 2025-10-18)
Field: Materials Science — Materials Chemistry

Achieving High-Crystallinity YSZ Thin-Films on Si Using Explainable AI and Bayesian Optimization in Resource-Constrained Settings The growth of epitaxial yttria-stabilized zirconia (YSZ) thin-films on Si substrates is critical for integrating functional oxides into advanced electronic and energy devices. However, identifying the optimal growth conditions for YSZ thin-films remains challenging because of the complex and interdependent nature of deposition parameters in thin-film growth processes. Herein, we propose an integrated approach that combines Shapley additive explanations (SHAP), which is an explainable artificial intelligence (XAI) method, with Bayesian optimization (BO) to efficiently identify key growth parameters and optimize crystallinity in resource-constrained settings. By performing SHAP analysis of literature-derived data, oxygen partial pressure was identified as the most influential parameter, followed by temperature. Guided by this ranking, BO was employed to minimize the full width at half maximum (FWHM) of the YSZ 002 rocking curve. High-quality epitaxial films with an FWHM of 0.74° were achieved with only nine growth attempts. The optimized films exhibited atomically smooth surfaces (root-mean-square roughness of ∼0.48 nm) and uniform chemical compositions, as confirmed by Raman and energy-dispersive spectroscopy. This study shows the power of integrating XAI and BO for efficient and interpretable process optimization of thin-films. Yttria-stabilized zirconia ; Bayesian optimization ; Raman spectroscopy ; Epitaxy ; Full width at half maximum ; Process (computing) ; Cubic zirconia ; Crystallinity ; Power (physics) Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Advancements in Semiconductor Devices and Circuit Design
Authors: Tadakatsu Ohkubo
Source: Electrical Engineering in Japan (article, 2025-09-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Development of Permanent Magnets for the Reduction of Heavy Rare Earth Elements ABSTRACT We have investigated the mechanism of coercivity evolution in magnets through multi‐scale microstructural characterization. On the basis of these findings, our goal is to enhance the coercivity of heavy rare‐earth‐lean magnets by optimizing their microstructure. We have developed several new processes and demonstrated improved magnet properties. This review article describes (1) coercivity enhancement by the eutectic alloy diffusion process, (2) the development of light rare earth magnets, and (3) the development of high electrical resistance magnets. Recently, there has been an increasing demand for highly efficient magnet development that not only improves properties but also meets multiple requirements. Accordingly, (4) data‐driven methods are being employed in ongoing magnet development. Coercivity ; Magnet ; Eutectic system ; Rare earth ; Neodymium magnet ; Alloy Magnetic Properties of Alloys ; Magnetic and transport properties of perovskites and related materials ; Magnetic properties of thin films
Authors: Tao Hai | Fahad M. Alhomayani | Pradeep Kumar Singh | Naglaa F. Soliman | Walid El‐Shafai | Abuzar Es’haghi Oskui
Source: Mechanics of Time-Dependent Materials (article, 2025-09-01)
Field: Materials Science — Materials Chemistry

Improving stab resistance of soft body armor composites via modified shear thickening fluids (STFs) Dilatant ; Stab ; Rheology ; Viscosity ; Polyethylene glycol ; Rheometry ; Shear (geology) ; Viscoelasticity High-Velocity Impact and Material Behavior ; Natural Fiber Reinforced Composites ; Tribology and Wear Analysis
Authors: Vishal Chaudhary | Harsh Sable | Pradeep Bhadola
Source: Advances in Physics X (article, 2025-12-31)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Harnessing complex light-matter interactions for point-of-care nano-optical biosensing Recent advancements in nanoscale physics have resulted in a paradigm shift towards point-of-care (POC) complex healthcare diagnostics, enabling real-time biomolecular detection. These innovations are based on manipulating complex light–matter interactions at the nanoscale, where photons couple with plasmons, excitons, phonons, and resonant cavities to transduce biomolecular events into quantifiable optical signals. This review presents a physics-oriented overview and quantitative comparison of POC nano-optical biosensors based on the dominant fundamental light–matter interaction mechanism at the nanoscale. It includes surface plasmon resonance (localized, imaging, and long-range), interferometric methods (plasmon-assisted, dual-beam, and frequency-domain reflectometry), fluorescence and colorimetric assays, along with resonator-enabled architectures including whispering gallery modes, photonic crystals, Raman scattering, and optical coherence tomography. Emerging modalities, including photothermal, chemiluminescence, and nonlinear optical biosensors, are highlighted due to their wide and dynamic detection range, spanning from the micromolar to the challenging attomolar range. Besides, it details the advancements, device-miniaturization strategies, integration with advanced nanomaterials and microfluidics, and persistent challenges such as stability, non-specificity, and signal interference alongside proposed solutions. Finally, it presents future directions, including multi-modal sensing, wearable platforms, and AI-assisted predictive modelling, pointing towards next-generation of physics-enabled POC biosensors that can deliver accessible, accurate, and personalised healthcare. Biosensor ; Photonics ; Surface plasmon resonance ; Interferometry ; Whispering-gallery wave ; Coherence (philosophical gambling strategy) ; Optical sensing ; Nanomaterials Gold and Silver Nanoparticles Synthesis and Applications ; Plasmonic and Surface Plasmon Research ; Biosensors and Analytical Detection
Authors: Lezhang Mao | Furen Zeng
Source: Proceedings of Anticancer Research (article, 2025-06-05)
Field: Materials Science — Biomaterials

Advances in Biomimetic Nanotechnology for Triple-Negative Breast Cancer Therapy This article systematically reviews the application of biomimetic nanotechnology in targeted therapy for triple-negative breast cancer (TNBC). TNBC poses significant challenges for conventional treatments due to the lack of defined therapeutic targets, chemotherapy resistance, and a complex immunosuppressive microenvironment. Biomimetic nanotechnology, by mimicking the functional properties of biological structures (e.g., cell membranes, exosomes), has significantly enhanced drug delivery efficiency, targeting precision, and anti-tumor immune responses. This review focuses on the design strategies of biomimetic nanocarriers (including cell membrane-coated nanoparticles, engineered exosomes, and biomimetic synthetic materials) and their innovative applications in TNBC therapy: (1) Targeted delivery systems that overcome tumor barriers and reduce systemic toxicity; (2) Photothermal therapy combined with immunomodulation for precise treatment and immune activation; (3) Tumor microenvironment regulation (e.g., vascular normalization, pH neutralization, immunosuppression reversal). Studies demonstrate that biomimetic nanotechnology significantly improves TNBC treatment efficacy through multimodal synergistic mechanisms (e.g., chemo-photothermal-immunotherapy). However, challenges such as scalable production, long-term safety, and personalized adaptation remain for clinical translation. Future research should integrate artificial intelligence for optimized design and dynamic imaging technologies to advance biomimetic nanomedicines toward clinical applications. Triple-negative breast cancer ; Breast cancer ; Cancer therapy ; Nanotechnology ; Targeted therapy ; Medicine ; Cancer ; Internal medicine ; Materials science Nanoparticle-Based Drug Delivery ; Nanoplatforms for cancer theranostics ; Graphene and Nanomaterials Applications
Authors: Jun Chen Ng | Farina Muhamad | Pauline Shan Qing Yeoh | Ziyi Han | Zanlin Qiu | Khin Wee Lai | Xiaoxu Zhao
Source: Nanoscale (article, 2025-12-10)
Field: Materials Science — Materials Chemistry

High-throughput thickness analysis of 2D materials enabled by intelligent image segmentation Thickness measurement of two-dimensional (2D) materials is essential due to their thickness-dependent physical and optical properties. However, current thickness characterization techniques, <i>e.g.</i>, Atomic Force Microscopy (AFM), suffer from limitations such as slow scanning, tip-sample artifacts, and low throughput. To address this, an Artificial Intelligence-based pipeline was proposed for estimating the thickness of 2D materials from Optical Microscopy (OM) images, offering a significantly faster and more efficient alternative. OM captures colour contrast due to thin-film interference, explained by Fresnel's law. These colour cues, along with morphological features (area and perimeter), were extracted from the regions of interest (ROIs) segmented using Otsu's thresholding. Several regression models, including Random Forest Regressor (RFR) and a shallow Multi-Layer Perceptron (MLP), were trained on augmented paired OM-AFM data. Both models performed well on representative 2D materials, <i>e.g.</i>, In<sub>2</sub>Se<sub>3</sub>, under threshold-based segmentation, but only the MLP maintained strong accuracy with automated ROI detection using Cellpose, achieving excellent predictive performance (<i>R</i><sup>2</sup> = 0.947, MSE = 34.580 nm<sup>2</sup>, MAE = 4.696 nm, RMSE = 5.881 nm). Statistical analysis validated the model's generalizability across segmentation methods. Shapley Additive Explanations (SHAP) identified red and green intensities as key predictors, aligning with thin-film interference theory. Overall, this AI-based model provides a non-destructive, efficient alternative to AFM, allowing precise and continuous thickness estimation from small datasets with high robustness and generalizability. Segmentation ; Robustness (evolution) ; Generalizability theory ; Multilayer perceptron ; Random forest ; Pattern recognition (psychology) ; Pipeline (software) ; Image segmentation ; Mean squared error 2D Materials and Applications ; Graphene research and applications ; Force Microscopy Techniques and Applications
Authors: Nozimakhon Imomova | Lola Akramova | Bakhrom Tukhtarov
Source:  (article, 2025-12-18)
Field: Materials Science — Materials Chemistry

AI-accelerated discovery of transparent conducting oxides for UV-visible applications Transparent conducting oxides (TCOs) are unique materials that combine electrical conductivity with optical transparency across the ultraviolet (UV) to visible spectrum. These materials are essential for improving the performance, durability, and energy efficiency of solar energy systems and organic optoelectronic devices. The search for new TCOs with enhanced properties remains a dynamic and important challenge in materials science. Advances in computational modeling, high-throughput screening, and data-driven approaches have revolutionized the way researchers design and optimize TCOs. By analyzing large datasets and uncovering hidden patterns in material structures and compositions, these methods enable the discovery of novel TCOs with superior UV-visible transparency and conductivity. Integrating computational predictions with experimental validation accelerates the development of next-generation TCOs, paving the way for faster innovation in photonic and optoelectronic technologies. This research not only advances the field of TCOs but also opens new possibilities for future breakthroughs in transparent electronics. Transparency (behavior) ; Photonics ; Ultraviolet ; Field (mathematics) ; Efficient energy use ; Solar energy conversion Machine Learning in Materials Science ; Nanomaterials and Printing Technologies ; Advanced Sensor and Energy Harvesting Materials
Authors: Matherne, William | Farfan, Eduardo B.
Source: DigitalCommons - Kennesaw State University (Kennesaw State University) (article, 2025-12-08)
Field: Materials Science — Materials Chemistry

Empowering the Next Generation of Nuclear Engineers: Large Language Model-Driven Educational Tools for the Nuclear Fuel Cycle Without the nuclear fuel cycle, nuclear power generation would not be possible. This complex sequence of industrial processes produces the uranium dioxide fuel pellets used in commercial nuclear reactors in the United States. The Nuclear Fuel Cycle course at Kennesaw State University introduces students to each stage of this process, including mining, milling, conversion, enrichment, fuel fabrication, power generation, and the storage and disposal of spent fuel. To help students better comprehend the extensive material, this project focuses on developing concise lectures and chapter summaries that reinforce key concepts and support cumulative learning. Creating comprehensive and accessible educational content that accurately represents the entire fuel cycle demands both time and specialized expertise. To address this challenge, the project explores the use of artificial intelligence, specifically Large Language Models, to generate high-quality, technically accurate lectures and summaries efficiently. All AI-generated content has undergone rigorous review and revision to ensure accuracy and educational value, contributing to a well-structured, multi-chapter curriculum on the nuclear fuel cycle. Chapters and chapter summaries were developed to serve as effective pre-reading materials, review aids, and accelerated learning resources. Students are encouraged to engage with these materials prior to lectures and assigned readings to reinforce foundational concepts and enhance comprehension. In addition to covering the core stages of the nuclear fuel cycle, the chapters and summaries also address key related topics such as low-level versus high-level waste, reprocessing, nuclear nonproliferation, and environmental impacts. This study also aims to improve both student performance and conceptual understanding within the course. As the nuclear industry is projected to expand significantly by 2050 due to global carbon reduction goals and increasing energy demand, these educational tools will help better prepare students for future careers in the field. Nuclear fuel cycle ; Nuclear power ; Spent nuclear fuel ; Nuclear fuel ; Curriculum ; Key (lock) ; Nuclear reactor ; Fissile material Graphite, nuclear technology, radiation studies ; Nuclear reactor physics and engineering ; Photovoltaic Systems and Sustainability
Authors: Zahir Thebault | Fabian S. Sorce | Sonny Ngo | Ambrose C. Taylor
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Modelling thermomechanical properties of thermoset coatings using crosslink density extracted from molecular dynamics simulations via network mapping Thermosetting polymer ; Molecular dynamics ; Piecewise ; Substrate (aquarium) ; Glass transition ; Deformation (meteorology) ; Polyester ; Melamine Material Dynamics and Properties ; Epoxy Resin Curing Processes ; Polymer Nanocomposites and Properties