jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Kai Yu | Mingming Zhong | Wenjing Zhu | Arif Rashid | Rongwei Han | Muhammad Safiullah Virk | Kaiwen Duan | Yongjun Zhao | Xiaofeng Ren
Source: Foods (review, 2025-01-24)
Field: Chemistry — Analytical Chemistry

Advances in Computer Vision and Spectroscopy Techniques for Non-Destructive Quality Assessment of Citrus Fruits: A Comprehensive Review <i>Citrus</i> fruits, classified under the Rutaceae family and <i>Citrus</i> genus, are valued for their high nutritional content, attributed to their rich array of natural bioactive compounds. To ensure both quality and nutritional value, precise non-destructive testing methods are crucial. Among these, computer vision and spectroscopy technologies have emerged as key tools. This review examines the principles and applications of computer vision technologies-including traditional computer vision, hyperspectral, and multispectral imaging-as well as various spectroscopy techniques, such as infrared, Raman, fluorescence, terahertz, and nuclear magnetic resonance spectroscopy. Additionally, data fusion methods that integrate these technologies are discussed. The review explores innovative uses of these approaches in <i>Citrus</i> quality inspection and grading, damage detection, adulteration identification, and traceability assessment. Each technology offers distinct characteristics and advantages tailored to the specific testing requirements in <i>Citrus</i> production. Through data fusion, these technologies can be synergistically combined, enhancing the accuracy and depth of <i>Citrus</i> quality assessments. Future advancements in this field will likely focus on optimizing data fusion algorithms, selecting effective preprocessing and feature extraction techniques, and developing portable, on-site detection devices. These innovations will drive the <i>Citrus</i> industry toward increased intelligence and precision in quality control. Hyperspectral imaging ; Multispectral image ; Computer science ; Sensor fusion ; Artificial intelligence ; Data quality ; Quality (philosophy) ; Traceability ; Systems engineering ; Engineering ; Physics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Identification and Quantification in Food
Authors: Maarten L. Hekkelman | Daniel Álvarez Salmoral | Anastassis Perrakis | Robbie P. Joosten
Source: Protein Science (article, 2025-07-17)
Field: Materials Science — Materials Chemistry

<scp>DSSP</scp> 4: <scp>FAIR</scp> annotation of protein secondary structure Protein secondary structure annotation is essential for understanding protein architecture, serving as a cornerstone for structural classification, alignment, visualization, and machine learning applications. The Define Secondary Structure of Proteins (DSSP) algorithm has long been the standard for assigning secondary structure elements such as α-helices, β-sheets, and loops in protein models. Here, we introduce DSSP version 4, which recapitulates DSSP functionality in a modern computational framework, extending also to the detection of left-handed κ-helices (Poly-Proline II helices). To align with the Findable, Accessible, Interoperable, Reusable principles, DSSP 4 adopts mmCIF as its primary input and output format, while retaining compatibility with legacy Protein Data Bank (PDB) and DSSP formats. We applied this updated tool to analyze the distribution of secondary structure elements across the PDB, differentiating structures from diverse experimental methods, revealing insights into the prevalence and length of secondary structure elements, including the newly annotated κ-helices. The DSSP 4 software, databank, and server are freely accessible from https://pdb-redo.eu/dssp, ensuring broad utility and interoperability in structural biology research. Chemistry ; Annotation ; Physics ; Biochemistry ; Biology ; Genetics Enzyme Structure and Function ; Genomics and Phylogenetic Studies ; Advanced Proteomics Techniques and Applications
Authors: Guangyang Zhang | Lingling Ma | E.Y. Lin | Zeyu Wang | Jintao Pan | Jidan Yang | Minghui Deng | Yang Wei | Yong Ye | Ning Wang | Yu Wang | Satoshi Aya | Yanqing Lu
Source: Nature Communications (article, 2025-10-24)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Periodically-modulated unipolar and bipolar orders in nematic fluids towards miniaturized nonlinear vectorial optics Complex polar topological configurations are the foundation for exploring exotic phases and nontrivial emergent phenomena in condensed-matter physics. While numerous topological configurations like skyrmions and merons are known to occur spontaneously in various materials, achieving the designability of magnetically- and electrically-polar textures in a controlled manner is a long-standing challenge in domain engineering. Here, we discover an unprecedented mesophase that exhibits unipolar and bipolar orders, where both apolar and polar orientational order parameters are modulated. The polar state enables defect-free polar patterning with designability and controllability on large scales. A case study demonstrates the facile generation of second-harmonic perfect vector beams through photopatterned topological polar liquid crystal superstructures. Remarkably, this single-layer, micrometer-thin-film device attains functionalities comparable to multiple conventional optical components, establishing a foundation for advanced nonlinear vectorial optics at the micrometer scale. Polar ; Controllability ; Nonlinear optics ; Liquid crystal ; Nonlinear system ; Topology (electrical circuits) ; Skyrmion Liquid Crystal Research Advancements ; Orbital Angular Momentum in Optics ; Nonlinear Photonic Systems
Authors: Zhao Chen | Yijie Zou | Ya Yin | Dian‐dian Deng | Yue Yang | Congbin Fan | Shouzhi Pu | Gang Liu
Source: Chemical Science (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Molecular engineering of bis(triphenylamine)-modified fluorene luminophore for immensely enriching anisotropic force-triggered high-contrast tricolor fluorescent molecular switches library The preparation of anisotropic mechanical force-triggered high-contrast tricolor fluorescent solid switches is both significant and extremely challenging. Herein, we report a high-efficiency molecular engineering strategy for developing mechanochromic compounds that exhibit an infrequent yet contrasting three-color fluorescence switching feature. Notably, 14 new symmetric fluorescent molecules by introducing a variety of substituents on either side of the central organic moiety involving 4,4'-((9<i>H</i>-fluoren-9-ylidene)methylene)bis(<i>N</i>,<i>N</i>-diphenylaniline) are successfully obtained, and 10 new fluorogenic compounds have been discovered to exhibit seldom-observed tricolored mechanofluorochromic phenomena. Impressively, among them, 8 luminogens demonstrate exceptionally scarce anisotropic force-triggered high-contrast three-color fluorescence responses, which is a commendable achievement, fully verifying the effectiveness of our molecular engineering strategy presented in this study. Furthermore, based on two newly developed representational mechanically responsive tricolor fluorescence switches, three advanced information anticounterfeiting systems are skillfully constructed. Luminophore ; Triphenylamine ; Contrast (vision) ; Fluorescence ; Fluorene ; High contrast ; Chemistry ; Materials science ; Photochemistry ; Physics ; Optics ; Organic chemistry ; Optoelectronics ; Polymer Luminescence and Fluorescent Materials ; Molecular Sensors and Ion Detection ; Supramolecular Chemistry and Complexes
Authors: Fadhel F. Sead | Yashwantsinh Jadeja | Anjan Kumar | M. M. Rekha | Mayank Kundlas | Suman Saini | Kamal Kishore Joshi | Hadi Noorizadeh
Source: Nanoscale Advances (review, 2025-01-01)
Field: Materials Science — Materials Chemistry

Carbon quantum dots for sustainable energy: enhancing electrocatalytic reactions through structural innovation Carbon quantum dots (CQDs) have emerged as a promising class of nanomaterials due to their unique optical, electrical, and catalytic properties, positioning them as key players in electrocatalytic applications. This review provides a comprehensive and up-to-date analysis of CQDs, focusing on their electrocatalytic behavior in critical reactions such as the oxygen evolution reaction (OER), hydrogen evolution reaction (HER), oxygen reduction reaction (ORR), carbon dioxide reduction reaction (CO<sub>2</sub>RR), bifunctional catalysis and liquid fuel electrooxidation. Distinct from prior studies, this study highlights recent innovations in CQD synthesis, including heteroatom doping and defect engineering, and explores their structural properties-like absorbance, photoluminescence, and electroluminescence-that enhance catalytic performance. We elucidate the electrocatalytic mechanisms (<i>e.g.</i>, reactant adsorption, electron transfer, and intermediate stabilization) and address challenges such as low conductivity and scalability, proposing advanced strategies like hybridization with transition metals. Additionally, this review uniquely emphasizes the potential of CQDs in bifunctional catalysis and environmental applications, offering fresh insights into their role in advancing sustainable energy technologies. Quantum dot ; Carbon quantum dots ; Sustainable energy ; Carbon fibers ; Nanotechnology ; Materials science ; Electrocatalyst ; Sustainable development ; Business ; Chemistry ; Renewable energy ; Electrochemistry ; Electrode ; Engineering ; Political science ; Composite material ; Composite number Carbon and Quantum Dots Applications ; Quantum Dots Synthesis And Properties ; Nanocluster Synthesis and Applications
Authors: Hao Suo | Peihang Zhao | Xin Zhang | Yang Guo | Dongxu Guo | Jiwen Chang | Jiangkun Chen | Panlai Li | Zhijun Wang | Hanlin Wei | Weilin Zheng | Feng Wang
Source: Nature Communications (article, 2025-04-05)
Field: Materials Science — Materials Chemistry

Bright upconversion over extended temperatures enabled by an organic surface layer Lanthanide-doped nanocrystals are promising for photon frequency upconversion with substantial spectrum tunability. However, the utilization of the upconversion process has been constrained by low luminescence efficiency, which may further attenuate at elevated temperatures due to thermal quenching. Herein, we report a versatile strategy to boost upconversion luminescence across a wide temperature range by surface coordination of small organic molecules. Mechanistic investigations affirm that the organic surface layer passivates defects and isolates high-energy surface oscillators, thereby preventing the dissipation of excitation energy. The energy preserving effect becomes more prominent with increasing temperatures, especially in a humid environment. Accordingly, the upconversion emission of NaGdF<sub>4</sub>:Yb<sup>3+</sup>/Tm<sup>3+</sup> nanocrystals is substantially enhanced in the ambient environment after ligand coordination, accompanied by an additional emission augmentation with increasing temperature to 443 K. By leveraging this anomalous optical response to thermal stimuli, we further establish full-color thermochromic upconversion switching for advanced anti-counterfeiting and logic encryption technologies. Photon upconversion ; Layer (electronics) ; Materials science ; Nanotechnology ; Optoelectronics ; Luminescence Luminescence Properties of Advanced Materials ; Atomic and Subatomic Physics Research ; Perovskite Materials and Applications
Authors: Moon Sup Yoon | Jae Min Lee | Min Jeong Jo | Su Jeong Kang | Myeong Kyun Yoo | So Yeon Park | Seok Ho Bong | Chan‐Su Park | Chun‐Woong Park | Jin‐Seok Kim | Sang‐Bae Han | Hye Jin Lee | Dae Hwan Shin
Source: Gels (review, 2025-07-03)
Field: Materials Science — Biomaterials

Dual-Drug Delivery Systems Using Hydrogel–Nanoparticle Composites: Recent Advances and Key Applications Dual-drug delivery systems using hydrogel-nanoparticle composites have emerged as a versatile platform for achieving controlled, targeted, and efficient delivery of two distinct therapeutic agents. This approach combines the high loading capacity and tunable release properties of hydrogels with the enhanced stability and targeting ability of nanoparticles, providing synergistic benefits in various biomedical applications. While significant progress has been made, previous research has primarily focused on single-drug systems or simple co-delivery strategies, often lacking precise spatial and temporal control. This gap underscores the need for more sophisticated composite designs that enable programmable, multi-phase release. This review discusses representative fabrication methods, including physical embedding, covalent integration, and layer-by-layer assembly, to offer insights into practical implementation strategies. Also we present recent studies focusing on key applications-including wound healing, cancer therapy, infection prevention, transplant immunosuppression, and tissue regeneration-with an emphasis on composite design and formulation strategies, types of hydrogels and nanoparticles, and mechanisms of dual-drug release and evaluation. Recent advances in nanoparticle engineering and hydrogel formulation have enabled precise control over drug release and improved therapeutic outcomes. Dual-drug delivery systems using hydrogel-nanoparticle composites present a promising approach for overcoming the limitations of conventional monotherapy and achieving synergistic therapeutic effects. Ongoing research continues to optimize the design, efficacy, and safety of these systems, paving the way for their clinical translation. Drug delivery ; Key (lock) ; Materials science ; Nanoparticle ; Nanotechnology ; Dual (grammatical number) ; Self-healing hydrogels ; Composite material ; Computer science ; Polymer chemistry Nanoparticle-Based Drug Delivery ; Hydrogels: synthesis, properties, applications ; Graphene and Nanomaterials Applications
Authors: Mariia Radova | Wojciech G. Stark | Caroline Allen | Reinhard J. Maurer | Albert P. Bartók
Source: npj Computational Materials (article, 2025-07-18)
Field: Materials Science — Materials Chemistry

Fine-tuning foundation models of materials interatomic potentials with frozen transfer learning Machine-learned interatomic potentials are revolutionising atomistic materials simulations by providing accurate and scalable predictions within the scope covered by the training data. However, generation of an accurate and robust training data set remains a challenge, often requiring thousands of first-principles calculations to achieve high accuracy. Foundation models have started to emerge with the ambition to create universally applicable potentials across a wide range of materials. While foundation models can be robust and transferable, they do not yet achieve the accuracy required to predict reaction barriers, phase transitions, and material stability. This work demonstrates that foundation model potentials can reach chemical accuracy when fine-tuned using transfer learning with partially frozen weights and biases. For two challenging datasets on reactive chemistry at surfaces and stability and elastic properties of tertiary alloys, we show that frozen transfer learning with 10-20% of the data (hundreds of datapoints) achieves similar accuracies to models trained from scratch (on thousands of datapoints). Moreover, we show that an equally accurate, but significantly more efficient surrogate model can be built using the transfer learned potential as the ground truth. In combination, we present a simulation workflow for machine learning potentials that improves data efficiency and computational efficiency. Computer science ; Stability (learning theory) ; Foundation (evidence) ; Workflow ; Scalability ; Transfer of learning ; Scope (computer science) ; Artificial intelligence ; Experimental data ; Range (aeronautics) ; Model building ; Machine learning ; Materials science ; Physics Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Fuel Cells and Related Materials
Authors: Antoine Loew | D. Y. Sun | Hai‐Chen Wang | Silvana Botti | Miguel A. L. Marques
Source: npj Computational Materials (article, 2025-06-12)
Field: Materials Science — Materials Chemistry

Universal machine learning interatomic potentials are ready for phonons Abstract There has been an ongoing race for the past several years to develop the best universal machine learning interatomic potential. This progress has led to increasingly accurate models for predicting energy, forces, and stresses, combining innovative architectures with big data. Here, we benchmark these models on their ability to predict harmonic phonon properties, which are critical for understanding the vibrational and thermal behavior of materials. Using around 10 000 ab initio phonon calculations, we evaluate model performance across various phonon-related parameters to test the universal applicability of these models. The results reveal that some models achieve high accuracy in predicting harmonic phonon properties. However, others still exhibit substantial inaccuracies, even if they excel in the prediction of the energy and the forces for materials close to dynamical equilibrium. These findings highlight the importance of considering phonon-related properties in the development of universal machine learning interatomic potentials. Phonon ; Interatomic potential ; Computer science ; Physics ; Statistical physics ; Condensed matter physics ; Quantum mechanics ; Molecular dynamics Machine Learning in Materials Science ; Nuclear Physics and Applications ; Advanced Materials Characterization Techniques
Authors: Zhiming Feng | Yue Luo | Da Li | Jianxin Pan | Rui Tan | Yi Chen
Source: CHAIN (article, 2025-03-01)
Field: Materials Science — Materials Chemistry

Integrating Digital Twins and Machine Learning for Advanced Control in Green Hydrogen Production The successful reduction of carbon emissions in major sectors such as heavy industry and long-distance transport depends crucially on the ability to produce green hydrogen on a large scale. This involves generating hydrogen via water electrolysis, utilizing power sourced from renewable energies. However, persistent challenges, such as dynamic inefficiencies, material degradation, and renewable intermittency, demand a paradigm shift from static control strategies to adaptive, self-optimizing systems. This perspective argues that the synergistic integration of digital twins (DTs) and machine learning (ML) offers a transformative framework for real-time optimization, predictive maintenance, and resilient grid integration. By synthesizing physics-based modeling with data-driven intelligence, DT-ML systems enable closed-loop control architectures that dynamically adapt to operational uncertainties. We analyze the technical foundations of this integration, address critical barriers, and propose actionable pathways for stakeholders to accelerate the hydrogen economy's transition from promise to practice. Production (economics) ; Hydrogen production ; Control (management) ; Computer science ; Artificial intelligence ; Hydrogen ; Chemistry ; Economics Machine Learning in Materials Science ; Iron and Steelmaking Processes ; Catalysis and Oxidation Reactions
Authors: Z. Lü | Wei Sun | Pingwei Cai | Linfeng Fan | Kai Chen | Jiyuan Gao | Hao Zhang | Junxiang Chen | Zhenhai Wen
Source: Energy & Environmental Science (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

High-entropy alloy catalysts for advanced hydrogen-production zinc-based batteries The high-entropy alloy facilitates the spatial-temporal separation of H 2 and O 2 , enabling the zinc-based battery to produce H 2 at industrial-level current density (500 mA cm −2 ) and deliver energy with a record-breaking power density (537 mW cm −2 ). Alloy ; Hydrogen production ; Zinc ; Catalysis ; Production (economics) ; Hydrogen ; Materials science ; Metallurgy ; Environmental science ; Chemistry ; Economics Catalytic Processes in Materials Science ; Electrocatalysts for Energy Conversion ; Catalysis and Hydrodesulfurization Studies
Authors: R. Kiran | Nagaraj Kamath | M.I. Sayyed | Aljawhara H. Almuqrin | Sudha D. Kamath
Source: RSC Advances (review, 2025-01-01)
Field: Materials Science — Materials Chemistry

A review of recent developments in rare earth-doped nanophosphors for emerging technological applications This article explores the synthesis of rare-earth-doped nanophosphors (RENPs), focusing on selecting optimal host materials like tungstates, vanadates, and aluminates to enhance luminescent properties. The incorporation of rare-earth elements, such as Eu<sup>3+</sup>, Tb<sup>3+</sup>, Dy<sup>3+</sup>, Sm<sup>3+</sup>, <i>etc.</i>, significantly improves emission spectra and efficiency by utilizing their unique electronic configurations and interactions with the host lattice. Nanophosphors were synthesized through sol-gel, combustion, and microwave-assisted methods, resulting in materials with tunable photoluminescence, high luminous efficiency, and appropriate chromaticity for wide-bandgap lighting applications. The study further investigates upconversion nanophosphors (UCNPs), demonstrating exceptional attributes like long luminescence lifetimes, high sensitivity, low energy loss, and tunable emission spectra. These characteristics make UCNPs ideal for advanced sensing applications. Additionally, RENPs exhibit great potential in thermoluminescence dosimetry, providing accurate radiation dose measurements with minimal fading and energy-dependent responses, thus advancing both medical and environmental dosimetry. Rare earth ; Doping ; Nanotechnology ; Materials science ; Engineering physics ; Optoelectronics ; Physics ; Metallurgy Luminescence Properties of Advanced Materials ; Solid-state spectroscopy and crystallography ; Crystal Structures and Properties
Authors: Zhihua Cheng | Kira M. Fahy | Gregory W. Peterson | Kent O. Kirlikovali | Omar K. Farha
Source: Advanced Materials (review, 2025-04-10)
Field: Chemistry — Inorganic Chemistry

Advancing Metal–Organic Framework‐Based Composites for Effective Chemical Warfare Agent Detoxification under Real‐World Conditions Threats from toxic chemical warfare agents (CWAs) persist due to war and terrorist attacks, endangering both human beings and the environment. Metal-organic frameworks (MOFs), which feature ordered pore structures and excellent tunability at both metal/metal cluster nodes and organic linkers, are regarded as the best candidates to directly remove CWAs and their simulants via both physical adsorption and chemically catalyzed hydrolysis or oxidization. MOFs have attracted significant attention in the last two decades that has resulted from the rapid development of MOF-based materials in both fundamental research and real-world applications. In this review, the authors focus on the recent advancements in designing and constructing functional MOF-based materials toward CWAs detoxification and discuss how to bridge the gap between fundamental science and real-world applications. With detailed summaries from different points of view, this review provides insights into design rules for developing next-generation MOF-based materials for protection from both organophosphorus and organosulfur CWAs to mitigate potential threats from CWAs used in wars and terrorism attacks. Chemical Warfare Agents ; Metal-organic framework ; Nanotechnology ; Materials science ; Chemical warfare ; Detoxification (alternative medicine) ; Adsorption ; Biochemical engineering ; Engineering ; Organic chemistry ; Chemistry ; Political science Metal-Organic Frameworks: Synthesis and Applications ; Advanced Nanomaterials in Catalysis ; Sulfur Compounds in Biology