jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Seungyong Shin | Jaehyuk Lim | Yun‐Kun Hong | Jihoon Kyhm | A‐Ra Hong | Gumin Kang | Hyungduk Ko | Soong Ju Oh | Ho Seong Jang
Source: Advanced Functional Materials (article, 2025-01-02)
Field: Materials Science — Materials Chemistry

Multicolor Fine‐Tunable Upconversion Luminescence from a Single Nanoparticle for Full‐Color Displays with a Wide Color Gamut Abstract Recently, upconversion nanophosphor (UCNP)‐based displays have received increased attention, with emerging reports on transparent displays utilizing multicolor upconversion luminescence from single UCNPs. For these display devices, color reproducibility is one of the most essential properties and the multicolor‐emitting UCNPs are required to exhibit a wide color gamut (WCG) for enhanced color reproducibility. However, achieving both high brightness and WCG properties simultaneously remains challenging for multicolor‐emitting single UCNPs. Here, bright red/green/blue (RGB) orthogonal luminescent core@sextuple‐shell (C@6S) UCNPs showing a WCG under 800, 980, and 1532 nm near‐infrared (NIR) light is reported. Luminescent properties of various C@6S UCNPs with 6 distinct R‐G‐B configurations are investigated and a green‐emitting core coated with a red‐emitting inner shell and a blue‐emitting outer shell satisfied high brightness and WCG properties. The green‐emitting core‐based C@6S UCNPs showed multicolor tunability with high brightness and the widest color gamut value of 94.2% of the National Television System Committee color space among RGB‐emitting core@multishell UCNPs. Furthermore, potential full‐color displays utilizing the transparent C@6S UCNP‐polydimethylsiloxane composite and NIR light sources are demonstrated. These findings provide the criteria for synthesizing the RGB‐emitting single UCNPs exhibiting high brightness and a WCG and would be beneficial for the realization of true 3D full‐color displays. Gamut ; Materials science ; Brightness ; RGB color model ; Luminescence ; Optoelectronics ; Color space ; Optics ; Computer science ; Artificial intelligence ; Physics Luminescence Properties of Advanced Materials ; Luminescence and Fluorescent Materials ; Advanced Photocatalysis Techniques
Authors: Daniel Franke | Tobias Gräwert | Dmitri I. Svergun
Source: Journal of Applied Crystallography (article, 2025-04-07)
Field: Materials Science — Materials Chemistry

New features in <i>ATSAS-4</i> , a program suite for small-angle scattering data analysis The <i>ATSAS</i> release 4 series introduces substantial architectural updates and enhanced user-friendly tools for analyzing biological macromolecules and nanostructured objects. In particular, highly requested project support for graphical user interfaces (GUIs) has been implemented. Here the state of the main window of each GUI application can be saved as a project and then restored to continue or extend analyses. All GUI applications now share a common framework, enabling various plot annotations and export as figures or text for easy data exchange with other plotting applications. The main interactive data processing and interpretation program, <i>PRIMUS</i>, has been significantly expanded with new features and integrated analysis modules. A new GUI for the program <i>PEAK</i> allows for multiple fitting functions, including Gaussian, Lorentzian and pseudo-Voigt. The online chromatography analysis program <i>CHROMIXS</i> supports multiple frame selections and subtractions, along with a newly included <i>EFAMIX</i> module for resolving partially overlapping components. The new <i>ATSAS</i> application <i>LAUNCHER</i> provides user-friendly wizards to configure and run command-line applications. Wherever feasible, operations like file reading and calculations have been parallelized to take advantage of available local compute resources. Command-line interfaces of many applications have been updated, <i>e.g.</i> with renaming and unification of common options. Improved readers/writers are implemented for Protein Data Bank and macromolecular CIF (mmCIF) file handling. The program <i>MIXTURES</i> now combines the functionalities of <i>BILMIX</i>, <i>LIPMIX</i> and <i>MIXTURE</i>, offering simplified and more versatile analyses for polydisperse systems of different shapes, also with single- or multi-layered organization. Installer packages of <i>ATSAS</i> are available for the latest versions of Windows, frequently used Linux distributions and recent macOS releases, both for Intel and ARM architectures, and are free for academic use. Computer science ; Graphical user interface ; Plot (graphics) ; Upgrade ; Suite ; Installation ; Window (computing) ; Programming language ; Operating system Enzyme Structure and Function ; Advanced Proteomics Techniques and Applications ; Genetics, Bioinformatics, and Biomedical Research
Authors: Kaiwen Chen | Luyao Chen | Xianfu Xiao | Cheng Hao | Haonan Zhang | Tongtong Fu | Wei Shang | Hui Peng | Tianyi Zhan | Jianxiong Lyu | Ning Yan
Source: Materials Horizons (article, 2025-01-01)
Field: Materials Science — Surfaces, Coatings and Films

Bioinspired wood-based wedge-shaped surface with gradient wettability for enhanced directional liquid transport and fog harvesting Inspired by cactus spine and desert beetle back structures, we developed a wood-based wedge-shaped surface with gradient wettability for efficient and controlled spontaneous directional liquid transport. Utilizing the natural anisotropic and porous structure of wood, the wedge-shaped surface exhibited a continuous gradient wettability after chemical treatments combined with UV-induced modifications. The resulting surface enabled highly efficient directional liquid transport with transport rates reaching up to 8.9 mm s<sup>-1</sup> on horizontal placement and 0.64 mm s<sup>-1</sup> on vertical surfaces against gravity. By integrating geometric curvature and surface energy gradients, the innovative design achieved synergistic Laplace pressure-driven and wettability-driven liquid motions. To further demonstrate its potential for practical application, a fog-driven power device constructed using the gradient wettability wood with cactus spines not only enhanced water harvesting and energy conversion capabilities but also offered an environmentally friendly system. This study expanded the design toolbox for bioinspired liquid management surfaces, offering promising applications in water resource management, energy harvesting, and microfluidic devices. Wetting ; Wedge (geometry) ; Materials science ; Contact angle ; Liquid water ; Nanotechnology ; Composite material ; Optics ; Geology ; Earth science Surface Modification and Superhydrophobicity ; Fluid Dynamics and Heat Transfer ; Pickering emulsions and particle stabilization
Authors: Dongjie Zhou | Jinguo Zhang | Chong Tan | Liyan Li | Qianli Qiu | Zongkun Zhang | Yan Sun | Lei Zhou | Ning Dai | Junhao Chu | Jiaming Hao
Source: Nanophotonics (article, 2025-01-16)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Semimetal–dielectric–metal metasurface for infrared camouflage with high‐performance energy dissipation in non‐atmospheric transparency window The development of novel camouflage technologies is of great significance, exerting an impact on both fundamental science and diverse military and civilian applications. Effective camouflage aims to reduce the recognizability of an object, making it to effortlessly blend with the environment. For infrared camouflage, it necessitates precise control over surface emissivity and temperature to ensure that the target blends effectively with the surrounding infrared background. This study presents a semimetal-dielectric-metal metasurface emitter engineered for the application of infrared camouflage. The metasurface, with a total thickness of only 545 nm, consists of a Bi micro-disk array and a continuous ZnS and Ti film beneath it. Unlike conventional metal-based metasurface design, our approach leverages the unique optical properties of Bi, achieving an average emissivity of 0.91 in the 5-8 μm non-atmospheric transparency window. Experimental results indicate that the metasurface emitter achieves lower radiation and actual temperatures compared to those observed in comparative experiments, highlighting its superior energy dissipation and thermal stability. The metasurface offers advantages such as structural simplicity, cost-effectiveness, angular insensitivity, and deep-subwavelength features, rendering it suitable for a range of applications including military camouflage and anti-counterfeiting, with potential for broad deployment in infrared technologies. Camouflage ; Emissivity ; Infrared ; Infrared window ; Materials science ; Metamaterial ; Opacity ; Optoelectronics ; Optical engineering ; Transparency (behavior) ; Optics ; Nanotechnology ; Computer science ; Physics Metamaterials and Metasurfaces Applications ; Thermal Radiation and Cooling Technologies ; Advanced Antenna and Metasurface Technologies
Authors: Patri Upender | Kuldeep Yadav
Source: Scientific Reports (article, 2025-07-20)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Tunable terahertz biosensing and shielding using a graphene-enhanced dielectric resonator absorber In this paper, we present a compact terahertz (THz) absorber based on a graphene-enhanced dielectric resonator structure, designed to achieve both wideband and narrowband absorption. The proposed absorber demonstrates ultra-wideband absorption from 1.07 to 13.3 THz, achieving a bandwidth of 12.23 THz, along with a sharp narrowband peak at 6.81 THz under specific tuning conditions. Tunability is achieved by adjusting the chemical potential (µc) of graphene, while the structure remains polarization-insensitive and maintains high absorption stability up to incident angles of 70°. Simulation results validated through an equivalent circuit model demonstrate the absorber's strong performance with a maximum sensitivity (S) of 5.11 THz/RIU, figure of merit (FOM) of 601.2 RIU<sup>-1</sup>, and a quality factor (Q) of 1322.5, making it highly effective for biosensing applications, including the detection of chemicals, viruses, and cancerous cells. Additionally, for EM interference shielding, the absorber exhibits an absorption effectiveness exceeding 10 dB, and a reflectance effectiveness within 1 dB, confirming its high efficiency in suppressing reflected waves. These results highlight the absorber's potential for versatile applications in stealth, shielding, and practical biosensing. Terahertz radiation ; Graphene ; Resonator ; Electromagnetic shielding ; Biosensor ; Dielectric ; Optoelectronics ; Materials science ; Terahertz metamaterials ; Nanotechnology ; Optics ; Physics ; Far-infrared laser ; Composite material Metamaterials and Metasurfaces Applications ; Terahertz technology and applications ; Superconducting and THz Device Technology
Authors: Sophia R. Flury | Karla Z. Arellano-Córdova | Edward C. Moran | Alaina Einsig
Source: Monthly Notices of the Royal Astronomical Society (article, 2025-09-24)
Field: Chemistry — Spectroscopy

New ionization models and the shocking nitrogen excess at <i>z</i> &amp;gt; 5 ABSTRACT The new era of galaxy evolution studies hearkened in by James Webb Space Telescope has led to the discovery of $z&amp;gt;5$ galaxies exhibiting excess nitrogen with $\rm \log (N/O)$$\sim$1 dex or more than expected from $\rm \log (N/O)$–$\rm 12+\log (O/H)$ trends in the local Universe. As of yet, the ionization sources of these galaxies have not been thoroughly explored, with radiative shocks left out of the picture. We present a suite of homogeneous excitation models for star-forming galaxies, active galactic nuclei, and radiative shocks, with which we explore possible explanations for the apparent nitrogen excess. We propose new BPT-style diagnostics to classify galaxies at $z&amp;gt;5$, finding that, when combined with O iii] $\lambda \lambda$1660,66 and He ii $\lambda$1640, N iii] $\lambda \lambda$1747–54 / C iii] $\lambda \lambda$1907,09 best selects shock-dominated galaxies while N iv] $\lambda \lambda$1483,86/C iii] $\lambda \lambda$1907,09 best distinguishes between active black holes and star-forming galaxies. From our diagnostics, we find that slow/intermediate radiative shocks ($v=75$–150 $\rm km~s^{-1}$) are most consistent with observed UV emission line flux ratios in nitrogen-bright galaxies. Accounting for the effects of shocks can bring nitrogen estimates into better agreement with abundance patterns observed in the local Universe and may be attributable to Wolf-Rayet populations actively enriching these galaxies with nitrogen and possibly driving winds responsible for these shocks. Galaxy ; Radiative transfer ; Active galactic nucleus ; Ionization ; Nitrogen ; Flux (metallurgy) ; James Webb Space Telescope ; Galaxy formation and evolution ; Universe Mass Spectrometry Techniques and Applications ; Atomic and Molecular Physics ; Advanced Data Storage Technologies
Authors: Cong Lyu | Chuanfang Zhao | Meimei Wang | Jiawen Li | Zhenzhen Cai | Xincun Dou | Baiyi Zu
Source: Aggregate (article, 2025-04-30)
Field: Chemistry — Inorganic Chemistry

Exactly Restricting the Phenyl Ring Rotation in Metal‐Organic Framework for Ultra‐Sensitive and Specific Ratiometric Fluorescent Sensing of Sarin ABSTRACT To realize highly sensitive and specific sensing toward sarin, an Eu(III) based metal‐organic framework (Eu‐CTTB‐MOF), encompassing a π‐conjugated organic ligand H 4 CTTB (4,4′,4″,4‴‐(9H‐carbazole‐1,3,6,8‐tetrayl)tetrabenzoic acid) was explored for ratiometric fluorescent sensing. An unprecedented specific recognition of nerve‐agent sarin mimic diethyl chlorophosphate (DCP) in the presence of HCl interferent and a low limit of detection (LOD, 20.97 nM) were achieved. This excellent detection performance is driven by the dual hydrogen bonding and hydrophobic interaction between the CTTB organic ligand and DCP, which would cause a dramatic change in the molecular configuration of the CTTB ligand. Density functional theory (DFT) calculations further verify the recognition of DCP by Eu‐CTTB‐MOF could suppress the rotations of the aromatic rings in CTTB ligand, significantly reducing the nonradiative decay pathways and subsequently enhancing the fluorescent intensity of the CTTB ligand. Especially, the Eu‐CTTB‐MOF enables the immediate response to DCP vapor and excellent specificity towards DCP even in the presence of 18 types of interferents, including HCl vapor, structural analogs, and volatile organic solvent, and a gas detector with accurate detection of DCP in simulated scenarios, positioning the designed MOF as a promising sensing material for practical scenarios. We expect that the present sensing strategy will shine a light on the development of brand‐new sensing materials for on‐site detection applications. Sarin ; Fluorescence ; Ring (chemistry) ; Rotation (mathematics) ; Metal ; Nanotechnology ; Materials science ; Chemistry ; Photochemistry ; Physics ; Organic chemistry ; Computer science ; Quantum mechanics ; Artificial intelligence Metal-Organic Frameworks: Synthesis and Applications ; Molecular Sensors and Ion Detection ; Luminescence and Fluorescent Materials
Authors: Tao Yao | Yang Yang | Jianghao Cai | Rui Liu | Zhang-Chao Dong | Xinyi Tang | Xuqiang Shao | Zhijun Gao | Guangyao An | Weijie Yang
Source: Journal of Magnesium and Alloys (article, 2025-10-22)
Field: Materials Science — Materials Chemistry

From LLM to Agent: A large-language-model-driven machine learning framework for catalyst design of MgH2 dehydrogenation • AI framework automates MgH 2 catalyst data extraction from literature. • LLM to Agent approach accelerates MgH 2 catalyst discovery and design. • Machine learning predicts MgH 2 dehydrogenation with high accuracy. • Cat-Advisor provides actionable catalyst design recommendations. • Open database and AI tools advance hydrogen storage materials research. Magnesium hydride (MgH 2 ), a promising high-capacity hydrogen storage material, is hindered by slow dehydrogenation kinetics. AI-driven catalyst discovery to address this is often hampered by the laborious extraction of data from unstructured literature. To overcome this, we introduce a transformative “LLM to Agent” framework that synergistically integrates Large Language Models (LLMs) for automated data curation with Machine Learning (ML) for predictive design. We automatically constructed a comprehensive database of 809 MgH 2 catalysts (6555 data rows) with high fidelity and an ∼40-fold acceleration over manual methods. The resulting ML models achieved high accuracy (average R² > 0.91) in predicting dehydrogenation temperature and activation energy, subsequently guiding a Genetic Algorithm (GA) in an exploratory inverse design that autonomously uncovered key design principles for high-performance catalysts. Encouragingly, a strong alignment was found between these AI-discovered principles and the design strategies of recently reported, state-of-the-art experimental systems, providing substantial evidence for the validity of our approach. The framework culminates in Cat-Advisor, a novel, domain-adapted multi-agent system. Cat-Advisor translates ML predictions and retrieval-augmented knowledge into actionable design guidance, demonstrating capabilities that surpass those of general-purpose LLMs in this specialized domain. This work delivers a practical AI toolkit for accelerated materials discovery and advances the emerging Agent-based paradigm for designing next-generation energy technologies. Dehydrogenation ; Hydrogen storage ; Analytics ; Magnesium hydride ; Transformative learning ; Key (lock) ; Catalysis Hydrogen Storage and Materials ; Hybrid Renewable Energy Systems ; Superconductivity in MgB2 and Alloys
Authors: Yuang Shi | Nakul Rampal | Chengbin Zhao | Dongrong Joe Fu | Christian Borgs | Jennifer Chayes | Omar M. Yaghi
Source: Digital Discovery (article, 2025-01-01)
Field: Chemistry — Inorganic Chemistry

Comparison of LLMs in extracting synthesis conditions and generating Q&amp;A datasets for metal–organic frameworks Spoiler alert: Claude and Gemini did better than GPT-4 in extracting information from chemistry literature. Metal-organic framework ; Chemistry ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Zeolite Catalysis and Synthesis
Authors: Negin Orouji | Jeffrey A. Bennett | Richard B. Canty | Long Qi | Shijing Sun | Prabal Kumar Majumdar | Chong Liu | Núria López | Neil M. Schweitzer | John R. Kitchin | Hongliang Xin | Milad Abolhasani
Source: Nature Catalysis (article, 2025-11-05)
Field: Materials Science — Materials Chemistry

Autonomous catalysis research with human–AI–robot collaboration Workflow ; Robotics ; Transformative learning ; Applications of artificial intelligence ; Scalability ; Perspective (graphical) ; Robot Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Mahyar Rajabi Kochi | Negareh Mahboubi | Aseem Partap Singh Gill | Seyed Mohamad Moosavi
Source: Chemical Science (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Adaptive representation of molecules and materials in Bayesian optimization Bayesian optimization (BO) is increasingly used in molecular optimization and in guiding self-driving laboratories for automated materials discovery. A crucial aspect of BO is how molecules and materials are represented as feature vectors, where both the completeness and compactness of these representations can influence the efficiency of the optimization process. Traditionally, a fixed representation is chosen by expert chemists or applying data-driven feature selection methods on available labeled datasets. However, when dealing with novel optimization tasks, prior knowledge or large datasets are often unavailable, and relying on these even can introduce bias into the search process. In this work, we demonstrate a Feature Adaptive Bayesian Optimization (FABO) framework, which integrates feature selection in the Bayesian optimization process with Gaussian processes to dynamically adapt material representations throughout the optimization cycles. We demonstrate the effectiveness of this adaptive approach across several molecular optimization tasks, including the discovery of high-performing metal-organic frameworks (MOFs) in three distinct tasks, each involving unique property distributions and requiring a distinct representation. Our results show that the adaptive nature of the representation leads to outperforming random search baseline and scenarios where prior knowledge of the feature space is available. Notably, for known optimization tasks, FABO automatically identifies representations that are aligned with human chemical intuition, validating its utility for optimization tasks where such insights are not available in advance. Lastly, we show how a suboptimal representation, <i>e.g.</i>, when missing key features, can adversely impact BO performance, highlighting the importance of starting from a full feature set and adapt it to different tasks. Our findings highlight FABO as a robust approach for navigating large, complex materials search spaces in automated discovery campaigns. Bayesian optimization ; Representation (politics) ; Bayesian probability ; Computer science ; Molecule ; Chemistry ; Theoretical computer science ; Nanotechnology ; Artificial intelligence ; Materials science ; Organic chemistry ; Political science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Machine Learning and Algorithms
Authors: Cheng Li | Jianjun Ma | Des Gibson | Yijun Yan | Muhammad Haroon | Mian Bi
Source: International Journal of Hydrogen Energy (article, 2025-03-21)
Field: Materials Science — Materials Chemistry

Enhancing energy materials with data-driven methods: A roadmap to long-term hydrogen energy sustainability using machine learning Sustainability ; Term (time) ; Energy (signal processing) ; Hydrogen fuel ; Computer science ; Hydrogen ; Sustainability science ; Environmental science ; Social sustainability ; Chemistry ; Physics Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals ; Hybrid Renewable Energy Systems