jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Rafael G. Uceda | Sandra Míguez‐Lago | Carlos M. Cruz | Sara P. Morcillo | Luis Álvarez De Cienfuegos | Víctor Blanco | Araceli G. Campaña | María Ribagorda | D. Miguel | Juan M. Cuerva
Source: Advanced Optical Materials (article, 2026-02-27)
Field: Chemistry — Organic Chemistry

Do's and Don'ts When Visiting Circularly Polarized Luminescence ABSTRACT This perspective offers our overview on the principles, challenges, and opportunities of circularly polarized luminescence (CPL), drawn from our complementary experience over the past decade. Rather than a comprehensive review, we aim to share insights on the often demanding yet rewarding path of CPL research. We first highlight key questions for newcomers, then organize selected sections by molecular structure, with emphasis on our own contributions. Central to the discussion is the dissymmetry factor ( g lum ), governed by the interplay between electric and magnetic dipole transition moments, though other descriptors are also considered. Strategies in organic and inorganic molecular design leading to enhanced CPL efficiency are discussed, together with a critical evaluation of potential applications, particularly in sensing. In this context, NIR‐emitting CPL fluorophores emerge as highly promising. The integration of CPL‐active molecules in solid‐state devices, as well as links to other chiral‐related phenomena such as chiral‐induced spin selectivity, are also addressed. Looking ahead, theoretical models and artificial intelligence are considered valuable tools to complement advanced experimental approaches, helping to overcome current limitations and to unlock new opportunities in both fundamental studies and technological applications. We conclude with our own reflections, practical advice, and perspectives for future research. Luminescence ; Perspective (graphical) ; Magnetic dipole ; Key (lock) ; Dipole Synthesis and Properties of Aromatic Compounds ; Luminescence and Fluorescent Materials ; Magnetism in coordination complexes
Authors: Xin-De Wang | Zhi-Rui Chen | Peng-Jie Guo | Ze-Feng Gao | Cheng Mu | Zhong-Yi Lu
Source: Communications Materials (article, 2026-02-10)
Field: Materials Science — Materials Chemistry

Perovskite-R1: a domain-specialized large language model for intelligent discovery of precursor additives and experimental design Perovskite solar cells have emerged as leading photovoltaic technologies, owing to their exceptional efficiencies. However, challenges such as stability and scalable manufacturing hinder commercialization. Precursor additive engineering addresses these issues, yet the explosive growth of literature makes it difficult to efficiently utilize domain knowledge. Here, we introduce Perovskite-R1, a specialized large language model with advanced reasoning capabilities tailored for the discovery and design of perovskite precursor additives. By systematically mining and curating 1,232 high-quality scientific publications and integrating a comprehensive library of 33,269 candidate materials, we constructed a domain-specific instruction-tuning dataset. Experimental validation of several model-proposed strategies confirms their effectiveness in improving material stability and performance. Our work demonstrates the potential of domain-adapted large language models in accelerating materials discovery and provides an integrated workflow for intelligent, data-driven advancements in perovskite photovoltaic research. Perovskite solar cells promise high efficiency but face hurdles in stability and scalable production, complicating their commercialization. Here, the authors introduce Perovskite-R1, a large language model designed to enhance precursor additive discovery, which is validated by experimental confirmation of improved material stability and performance, providing an integrated workflow for intelligent, data-driven advancements in perovskite photovoltaics. Workflow ; Scalability ; Photovoltaic system ; Stability (learning theory) ; Domain (mathematical analysis) ; Perovskite (structure) ; Explosive material Machine Learning in Materials Science ; Perovskite Materials and Applications ; Advanced Photocatalysis Techniques
Authors: Ting-Ju Wei | Wen-Ning Wan | Chuin-Shan Chen
Source: Multiscale Science and Engineering (article, 2026-02-16)
Field: Materials Science — Materials Chemistry

Deep Material Network: Overview, Applications, and Current Directions Homogenization (climate) ; Representation (politics) ; Nonlinear system ; Scalability ; ENCODE ; Artificial neural network ; Multiscale modeling ; Deep neural networks Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Block Copolymer Self-Assembly
Authors: Yifan Sun | Tamihiro Gotoh | Jiayi Zhao | Mengfei Zhang | Shucheng Shi | Hui Zhang | Zhi Liu | Jiabin Shen | Richard Dronskowski | Zhitang Song | S. R. Elliott | Min Zhu
Source: Nature Materials (article, 2026-03-02)
Field: Materials Science — Materials Chemistry

Charge-triggered switching mechanism in selenium selector enabling ultralow leakage current Photoexcitation ; Amorphous solid ; Selenium ; Current (fluid) ; Excitation ; Non-volatile memory ; Spectroscopy ; Density functional theory Phase-change materials and chalcogenides ; Advanced Memory and Neural Computing ; Transition Metal Oxide Nanomaterials
Authors: Zhucheng Yang | Yuhan Yi | Yue Wang | Yongying Ye | Ruixuan Zhang | Guanyu Ma | Tiankai Chen | Wen Wu Xu | Jianping Xie
Source: Journal of the American Chemical Society (article, 2026-01-13)
Field: Materials Science — Materials Chemistry

Assembly of Tetrahedral Units in the Atomic-Resolved Structural Evolution of Gold Nanostructures Materials achieve complexity from simple building blocks, yet directly capturing the atomic pathways of their dynamic assembly has remained a long-standing challenge. Here, we employ atomically precise gold nanoclusters as a model system to resolve a tetrahedral assembly toward [Au<sub>25</sub>(SR)<sub>18</sub>]<sup>-</sup> (SR = thiolate). In this pathway, tetrabutylammonium acts as a supramolecular director, selectively stabilizing metastable intermediates and steering Au(I)-SR reduction along a dominant trajectory. High-throughput robotic synthesis combined with tandem in situ spectroscopy uncovers a growth sequence: Au(I)-SR → [Au<sub>15</sub>(SR)<sub>13</sub>]<sup>0</sup> → [Au<sub>20</sub>(SR)<sub>16</sub>]<sup>0</sup> → [Au<sub>22</sub>(SR)<sub>16</sub>]<sup>0</sup> → [Au<sub>23</sub>(SR)<sub>16</sub>]<sup>-</sup> → [Au<sub>25</sub>(SR)<sub>18</sub>]<sup>-</sup>. The cluster core grows through the stepwise incorporation of Au<sub>4</sub> tetrahedral units from one to four corner-sharing tetrahedrons, culminating in a spontaneous rearrangement into a symmetric Au<sub>13</sub>. These results reveal atomic-level evidence that discrete Au<sub>4</sub> units direct cluster growth and fuse into higher-order building blocks, resolving a long-sought question in nanomaterial formation and establishing smart, data-driven synthesis as a powerful paradigm for decoding complex reaction networks. Nanoclusters ; Tetrahedron ; Metastability ; Cluster (spacecraft) ; Supramolecular chemistry ; Nanostructure ; Nanomaterials ; Tandem ; Spectroscopy Nanocluster Synthesis and Applications ; Gold and Silver Nanoparticles Synthesis and Applications ; Advanced Nanomaterials in Catalysis
Authors: Shiqing Ma | Xiaotong Liu | Baichuan Xiao | YiFan Xiao | Yilin Yang | Beibei Ma | Yucheng Shang | Hao Zhu | Yixiang Zhou | Yong‐Biao Zhang | Jiayin Deng
Source: Advanced Healthcare Materials (article, 2026-02-18)
Field: Materials Science — Biomaterials

An Artificial Intelligence‐Selected Multifunctional Oligopeptide Coated Biphasic Calcium Phosphate (BCP) Scaffold Promotes Bone Defect Repair Bone defects caused by inflammation, trauma, tumor resection, or tooth extraction present persistent clinical challenges, primarily because of the limitations of current bone graft materials and growth factor therapies. In this study, we developed a novel strategy for bone regeneration by integrating AI-driven peptide discovery with bone tissue engineering. Utilizing the Deepeptide platform, we screened and identified bifunctional oligopeptides from intrinsically disordered regions (IDRs) with both osteogenic and angiogenic activities. Among the nine candidates, the most potent peptide was fused with a hydroxyapatite-binding domain via peptide engineering to enhance scaffold coupling and achieve sustained release. The recombinant fusion peptide was then covalently anchored onto biphasic calcium phosphate (BCP) ceramic scaffolds. And evaluations demonstrated that the modified scaffolds significantly promoted osteogenic differentiation of bone marrow mesenchymal stem cells (BMSCs), angiogenic activity of human umbilical vein endothelial cells (HUVECs), and effective bone regeneration and vascularization in vivo. These findings suggest that AI-discovered, functionally integrated oligopeptide-modified BCP ceramics hold promise as next-generation bone graft for repairing critical-sized bone defects. Scaffold ; Oligopeptide ; Umbilical vein ; Peptide ; Mesenchymal stem cell ; Regeneration (biology) ; Bone marrow ; Calcium Supramolecular Self-Assembly in Materials ; Bone Tissue Engineering Materials ; Chemical Synthesis and Analysis
Authors: Vahid Ashrafi Lighvan | Nasser Arsalani | Mehmet Gülcan | Rahel Yıldırım Aytin
Source: Microchimica Acta (article, 2026-01-12)
Field: Materials Science — Materials Chemistry

Microwave-assisted synthesis of dual-emissive N, Fe-Co-doped carbon dot nanocomposites for latent fingerprint visualization via AI-enhanced analytical engineering Visualization ; Fingerprint (computing) ; Fluorescence ; Process (computing) ; Carbon fibers ; Similarity (geometry) ; Pattern recognition (psychology) Carbon and Quantum Dots Applications ; Forensic Fingerprint Detection Methods ; Analytical chemistry methods development
Authors: Bing He | Zhou Jiang | Kaixuan Wang | Qisen Wang | Zhicong Lai | Yueyu Zhang | Maxim Avdeev | Siqi Shi
Source: Journal of Materiomics (article, 2026-01-23)
Field: Materials Science — Materials Chemistry

Multirole collaborative and co-constructive materials design ecosystem enabled by using control and data flows decoupled workflows Data-driven approaches are attracting wide attention in the field of materials science due to their capacity to unravel complex structure-activity relationships deriving from nonlinear interplay of materials properties across multiple scales. However, unlocking their potential in materials discovery and design requires addressing two main challenges: multi-disciplinary knowledge barriers across the entire materials data lifecycle (acquisition, processing, and analysis), and the absence of an infrastructure that can accommodate the continuous proliferation of data volume, algorithms, and models. Here, we propose a multirole collaborative and co-constructive materials design ecosystem that restructures both the productive forces and the relations of production in materials design. By establishing a structured division of labor and a customized materials design infrastructure with a workflow system that decouples control and data flows, our framework reduces inter-module dependencies and enables the flexible, scalable integration of heterogeneous resources. A case study on electrochemical storage materials design demonstrates that this approach can improve streamlined collaborative efficiency by at least 50%, highlighting its potential to accelerate materials design. This work establishes a new paradigm for building intelligent materials design platforms, characterized by dynamic composability instead of static integration, thereby fostering an open and sustainable ecosystem for future materials discovery. • Proposing a loosely coupled multirole collaborative and co-constructive materials design ecosystem. • Designing a workflow system that decouples data and control flows. • Providing a co-constructive infrastructure prototype for materials design. • Supporting coordinated service and independent scaling of heterogeneous research resources. Workflow ; Composability ; Scalability ; Scope (computer science) ; Automation ; Control (management) ; Field (mathematics) Machine Learning in Materials Science ; Scientific Computing and Data Management ; CO2 Reduction Techniques and Catalysts
Authors: Zelan Li | Carolina Scagliarini | Alberto Mazzoleni | Sara Gariglio | Emilio Catelli | Cristina Malegori | Silvia Prati | Eugenio Alladio | Giorgia Sciutto | Paolo Oliveri
Source: TrAC Trends in Analytical Chemistry (article, 2026-01-24)
Field: Chemistry — Analytical Chemistry

Exploiting the penetration depth of XRF and NIR radiation: from 2D to 3D spectral imaging The present review examines the fundamental mechanisms governing the penetration of X-ray and near-infrared (NIR) radiation under sample surface – a feature that is often disregarded in analytical applications, especially in the spectral imaging implementations, which are usually considered as surface analytical techniques. The impact of material composition and geometry, scattering effects, as well as instrumental factors are thoroughly described and critically discussed. A particular focus is placed on data processing techniques, from first-principle equations to data-driven multivariate models, implemented to estimate/assess the extent of penetration. Applications in several areas, including food, forensic, material and cultural heritage sciences, are comprehensively reviewed. The potential for exploiting penetration of electromagnetic radiation is highlighted, paving the way for the development of 3D-resolved X-ray fluorescence (XRF) and NIR imaging approaches able to characterize multilayer samples in a non-invasive way. • XRF and NIR spectroscopies are usually considered as surface analytical techniques • Actually, X-ray and NIR radiations penetrate under sample surface • Material composition, scattering effects, and instrumental factors have an effect • The potential for exploiting penetration for 3D spectroscopy and imaging is reviewed • The key role of multivariate signal processing is underlined Scattering ; Penetration depth ; Penetration (warfare) ; Imaging spectroscopy ; Radiation ; Spectral imaging ; Spectroscopy ; Signal processing ; Image processing Spectroscopy and Chemometric Analyses ; X-ray Spectroscopy and Fluorescence Analysis ; Cultural Heritage Materials Analysis
Authors: Cunming Hu | Ming Guan | Fang Mi | Shan Zhang | Pengfei Geng
Source: ACS Sensors (article, 2026-02-16)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Multimodal SERS Biosensing Platforms: Emerging Opportunities for Ultrasensitive Biomarker Detection and Intelligent Diagnostics Surface-enhanced Raman scattering (SERS) has emerged as a powerful analytical technique for biosensing owing to its ultrahigh sensitivity and molecular fingerprinting capability. However, the practical deployment of single-modality SERS is often hindered by signal fluctuations, matrix interference, and limited quantitative robustness in complex biological environments. To address these intrinsic limitations, multimodal SERS strategies have gained increasing attention by integrating SERS with complementary transduction modalities, enabling enhanced analytical reliability, internal cross-validation, and multidimensional biochemical profiling. In this review, we present a framework-driven and application-oriented overview of multimodal SERS biosensing, systematically covering fundamental design principles, nanomaterial engineering, and representative multimodal coupling strategies, including combinations with colorimetric (CM), fluorescence (FL), electrochemical (EC), and so forth. Rather than treating multimodality as a simple signal addition, we critically analyze how distinct modalities contribute complementary or synergistic information across different sensing scenarios. Furthermore, recent advances in integrated devices, microfluidic platforms, and data-driven analysis are discussed as key enablers for translating complex multimodal outputs into actionable diagnostic information. Importantly, we reorganize reported multimodal SERS systems according to major classes of disease-related biomarkers, highlighting how the choice of modality combinations should be guided by biomarker properties and clinical task requirements rather than by technological complexity alone. Finally, current challenges and future perspectives are outlined from the viewpoints of material standardization, device integration, data interoperability, and clinical translation, providing practical guidance for the rational design and deployment of next-generation multimodal SERS biosensing platforms. Biosensor ; Robustness (evolution) ; Multimodality ; Modalities ; Biomarker ; Clinical diagnosis Gold and Silver Nanoparticles Synthesis and Applications ; Biosensors and Analytical Detection ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Dan Chen | N. P. Colwell | Zhibo Yang
Source: TrAC Trends in Analytical Chemistry (article, 2026-02-23)
Field: Chemistry — Spectroscopy

Ambient high-spatial-resolution mass spectrometry imaging: recent advances in technologies and metabolomics studies Metabolomics ; Mass spectrometry imaging ; Sample (material) ; Image resolution ; Mass spectrometry ; Ambient pressure Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Advanced Proteomics Techniques and Applications
Authors: Qinglin Wang | Jian Jiang | Jing Dong | Wei Chen | Jihong Ye
Source: Structures (article, 2026-01-28)
Field: Materials Science — Metals and Alloys

Fatigue behavior and life prediction of stainless steels after hydrogen charging Hydrogen ; Durability ; Brittleness ; Austenitic stainless steel ; Austenite ; Fracture (geology) ; Stress (linguistics) ; Structural material ; Service life Hydrogen embrittlement and corrosion behaviors in metals ; Hydrogen Storage and Materials ; Hybrid Renewable Energy Systems
Authors: Halima Aliyu | Mohammed Zaharaddeen Musa | Sadiya Nuhu | Sanni Lawal Enesi | Nasar Mansir | Hafeez Yusuf Hafeez | Mustapha Balarabe Idris
Source: Discover Electrochemistry. (article, 2026-01-06)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Enhanced supercapacitive performance of nickel-based metal–organic framework/reduced graphene oxide composite Metal–organic frameworks (MOFs) are crystalline porous materials composed of central metal ions coordinated to organic ligands, forming tunable frameworks. Due to their excellent textural properties and superior mechanical and thermal stability, pristine MOFs have been utilised as electrode materials for electrochemical capacitors. Nevertheless, the limited electrical conductivity negatively affects their rate performance. Herein, we report the enhanced capacitance properties of Ni-based MOF/rGO composite prepared by a facile in situ method with a low loading level of rGO. The microstructural studies revealed that the presence of the GO in the reaction bath not only influences the morphology of MOF but also facilitates the transformation of the nickel salt precursor into the final product. The optimised Ni-MOF/rGO2.0 electrode exhibited a specific capacity of 527 C g⁻1 at 6 A g⁻1 as compared to pristine Ni-MOF (402 C g⁻1). Furthermore, Ni-MOF/rGO2.0 electrode demonstrated a superior rate capability by retaining 38.11% of its initial specific capacity when the current density is increased to 20 A g−1. The improved capacitance performance of Ni-MOF/rGO2.0 is attributed to the synergistic interaction between the conductive rGO network and the redox-active Ni-MOF, which facilitates faster charge transport and optimum redox activity of Ni-MOF. Composite number ; Electrode ; Capacitance ; Graphene ; Supercapacitor ; Electrochemistry ; Current density ; Oxide Supercapacitor Materials and Fabrication ; Metal-Organic Frameworks: Synthesis and Applications ; MXene and MAX Phase Materials
Authors: Zarfishan Zulfiqar | Zain Fatima | Nimrim Azeeqa | Fozia Iram | Irfan Ahmad | Muhammad Wajid | Aamna Majeed | Muhammad Naeem Qaiser | Gulzar Muhammad
Source: Environmental Quality Management (article, 2026-02-04)
Field: Materials Science — Materials Chemistry

Flaxseed‐Derived Rhamnogalacturonan as a Biogenic Stabilizer for CuO Nanoparticles With Photocatalytic, Antimicrobial, and Pollutant‐Sensing Applications ABSTRACT The study presents the green synthesis of copper oxide nanoparticles (CuO NPs) using the rhamnogalacturonan‐rich mucilage from flaxseed ( Linum usitatissimum ) as a natural stabilizing agent. The CuO NPs were characterized using spectroscopic techniques, including UV‐Visible spectroscopy, FTIR, SEM, and XRD. The UV analysis revealed a sharp band at 294 nm with an energy band gap of 4.27 eV; hence, the prepared CuO NPs are semiconductor materials. FTIR spectrum revealed an intense peak at 590 cm −1 , attributed to Cu‐O bonds. SEM analysis discovered that the CuO NPs were non‐spherical, polydispersed, and agglomerated. The XRD analysis showed a monoclinic crystal structure with crystallite sizes of 11–31 nm. The photocatalytic activity of CuO NPs was responsible for the decomposition of 87.2% methylene blue (MB) in sunlight following pseudo‐first‐order (k = 0.01708 min −1 ) kinetics. The CuO NPs depicted significant antimicrobial activity and complete biofilm inhibition at 100 µ g/mL against Bacillus licheniformis, Escherichia coli , and Pseudomonas aeruginosa . In addition, CuO NPs acted as sensors for heavy metal ions such as Pb 2+ and Fe 2+ . The study suggests the potential of rhamnogalacturonan‐capped CuO NPs for successful use in antibacterial applications, environmental remediation, and pollutant detection. Monoclinic crystal system ; Crystallite ; Nanoparticle ; Crystal violet ; Photocatalysis ; Fourier transform infrared spectroscopy ; Copper ; Copper oxide ; Methylene blue ; Band gap Copper-based nanomaterials and applications ; Nanoparticles: synthesis and applications ; Phytochemical compounds biological activities
Authors: Madiha Rehman Farooqui | Anjal Abid | Rahila Khan | Furqan Choudhary | Pradakshina Sharma | A. Safdar | Humaira Farooqi
Source: Discover Sensors (article, 2026-02-15)
Field: Materials Science — Biomaterials

Advances in the synthesis, properties, and applications of iron oxide nanoparticles Iron oxide nanoparticles (IONPs) are one of the most highly promising nanomaterial families with many applications in numerous fields. The production techniques for IONPs are outlined in this paper, with particular emphasis placed on several strategies that enable fine control over their size, shape, and surface characteristics. Methods for physical, chemical, and biological synthesis are explored, along with the benefits and drawbacks of each. Additionally, altering surface features is essential for modifying the functionality and behaviour of IONPs. Their stability, biocompatibility, and targeting potential are considerably impacted by surface changes such as functionalization with organic ligands or inorganic coatings. It is thoroughly investigated how crucial surface engineering is to improving interactions with biological systems and increasing their effectiveness in biomedical applications. This article also explores the numerous uses of IONPs across several industries. The uses of nanoparticles in industries include MRI, medication delivery systems, environmental purification, and a cure for cancer using hyperthermia treatment. Additionally, particular features of IONPs, such as superparamagnetism and enormous surface area, have allowed them to be used as sensors for metabolites and biomolecules, advancing various diagnostic and sensing technologies. Surface modification ; Nanomaterials ; Nanoparticle ; Iron oxide nanoparticles ; Surface engineering ; Iron oxide Nanoparticle-Based Drug Delivery ; Iron oxide chemistry and applications ; Nanoparticles: synthesis and applications