jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Dongxin Xu | Maohui Yuan | Wei Wang | Qizhi Zhang | Fei Lin | Yi Qu | Zaijin Li | Hao Chen | Lina Zeng | Guojun Liu
Source:  (article, 2025-10-28)
Field: Chemistry — Spectroscopy

Development status and research trends of mid-infrared quantum cascade lasers The mid-infrared (MIR) spectral region (3−12&mu;m) plays a critical role in a wide range of applications, including gas sensing, free-space optical communications, biomedical diagnostics, and infrared countermeasures. Quantum cascade lasers (QCLs), as semiconductor light sources capable of delivering high-power, single-mode, and continuous-wave emission within this wavelength range, have emerged as a cornerstone of MIR photonic device research. Their unique intersubband transition mechanism, high design flexibility, and excellent tunability offer distinct advantages over conventional semiconductor lasers. This review provides a comprehensive overview of recent advances and key technologies in MIR QCLs, with a particular focus on the evolution of active region structures and their impact on electron transport, gain characteristics, and thermal stability. We also highlight significant progress in representative application areas, such as trace gas detection, medical diagnostics, ranging, free-space optical communications, and infrared countermeasures. Through selected case studies, the superior performance of QCLs in high-sensitivity, high-bandwidth, and high-resolution measurements is demonstrated. Furthermore, the integration of artificial intelligence (AI) algorithms into device design, performance prediction, and manufacturing process control is accelerating innovation in QCL technology. These developments are enhancing design efficiency and expanding functional capabilities, offering new pathways for performance optimization in complex operational environments. Finally, we outline future research directions in structure design, material engineering, on-chip integration, and intelligent control, underscoring the growing significance of QCLs in next-generation high-performance photonic systems. Photonics ; Cascade ; Laser ; Quantum cascade laser ; Semiconductor laser theory ; Process (computing) ; Semiconductor Spectroscopy and Laser Applications ; Semiconductor Lasers and Optical Devices ; Photonic Crystal and Fiber Optics
Authors: Wajid Ali Khan | Muhammad Balal Arain | Stig Pedersen‐Bjergaard | Hameed Ul Haq | Roberto Castro‐Muñoz | Grzegorz Boczkaj
Source: Talanta (review, 2025-02-13)
Field: Chemistry — Analytical Chemistry

The role of nanomaterials in tailoring electromembrane extraction performance: A review Chemistry ; Nanomaterials ; Nanotechnology ; Extraction (chemistry) ; Chromatography Analytical chemistry methods development ; Electrochemical sensors and biosensors ; Extraction and Separation Processes
Authors: Turyahabwe Alex | Selvam Dennison Milon | Samuel George Onep
Source: i-manager s Journal on Material Science (article, 2025-01-01)
Field: Materials Science — Polymers and Plastics

Hybrid natural fiber composites for automotive interior applications: A comprehensive review Hybrid natural fiber composites have emerged as promising sustainable alternatives to synthetic materials in the automotive industry, particularly for interior applications. By combining natural fibers such as flax, jute, hemp, or kenaf with synthetic fibers or polymer matrices like polypropylene (PP), these composites offer a balanced performance profile, including lightweight properties, improved acoustic insulation, a reduced environmental footprint, and cost efficiency. This review offers a comprehensive analysis of the materials, manufacturing techniques, properties, and industrial trends related to hybrid natural fiber composites in automotive interiors. Key fabrication methods such as compression molding, injection molding, and thermoforming are discussed, alongside surface treatments like alkali, silane, and maleic anhydride grafting, which enhance interfacial bonding and durability. Applications in door panels, dashboards, seatbacks, and trims are explored through case studies from leading OEMs, including Ford, BMW, and Toyota. Challenges, including moisture sensitivity, recyclability, quality variability, and compliance with safety standards, are critically examined. The study also highlights advancements in smart composites, nanofillers, and AI-assisted material optimization. Moreover, life cycle assessment (LCA) insights reveal the environmental advantages of these materials over conventional composites. The review concludes with a gap analysis and outlook emphasizing the need for standardized testing, durable bio-based matrices, scalable recycling solutions, and multifunctional integration. Automotive industry ; Thermoforming ; Natural fiber ; Kenaf ; Fiber ; Life-cycle assessment ; Polypropylene ; Synthetic fiber Natural Fiber Reinforced Composites ; Polymer Foaming and Composites ; Composite Material Mechanics
Authors: Mingxu Zhang | Dazhong Shen | Ying Sun
Source: ArXiv.org (preprint, 2025-11-28)
Field: Materials Science — Materials Chemistry

AtomDisc: An Atom-level Tokenizer that Boosts Molecular LLMs and Reveals Structure--Property Associations Advances in large language models (LLMs) are accelerating discovery in molecular science. However, adapting molecular information to the serialized, token-based processing of LLMs remains a key challenge. Compared to other representations, molecular graphs explicitly encode atomic connectivity and local topological environments, which are key determinants of atomic behavior and molecular properties. Despite recent efforts to tokenize overall molecular topology, there still lacks effective fine-grained tokenization of local atomic environments, which are critical for determining sophisticated chemical properties and reactivity. To address these issues, we introduce AtomDisc, a novel framework that quantizes atom-level local environments into structure-aware tokens embedded directly in LLM's token space. Our experiments show that AtomDisc, in a data-driven way, can distinguish chemically meaningful structural features that reveal structure-property associations. Equipping LLMs with AtomDisc tokens injects an interpretable inductive bias that delivers state-of-the-art performance on property prediction and molecular generation. Our methodology and findings can pave the way for constructing more powerful molecular LLMs aimed at mechanistic insight and complex chemical reasoning. Key (lock) ; ENCODE ; Encoding (memory) ; Lexical analysis ; Security token ; Molecular evolution Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Guangxing Gu | Zhenchuang Xu | N T Yu | Yanchuan Zhao
Source: Coordination Chemistry Reviews (article, 2025-12-11)
Field: Chemistry — Spectroscopy

Harnessing dynamic recognition for enhanced NMR-based detection Analyte ; Hyperpolarization (physics) ; Modular design ; Multiplex ; Molecular recognition ; Leverage (statistics) ; Workflow ; Biocompatibility Advanced NMR Techniques and Applications ; Molecular spectroscopy and chirality ; Lanthanide and Transition Metal Complexes
Authors: Pengfei Liu | Jaewon Heo | Hyuk Jong Bong | J.-W. Choe | Huiyoung Lee | Won‐Kyu Lee | Myung‐Gil Kim | Donghee Son | Joohoon Kang | Taeyong Eom | Sungjin Park | In Soo Kim
Source: International Journal of Extreme Manufacturing (article, 2025-11-10)
Field: Materials Science — Materials Chemistry

Synthesis of functional chalcogenide materials for memory/sensing devices and their integration into artificial sensory systems Abstract With distinctive phase-change and switching properties, chalcogenide materials have emerged as critical components in various cutting-edge technologies. This review attempts to provide an overview of chalcogenide materials, from their fundamental properties to their diverse applications with focus on memory and sensing technologies, which are indispensable components in human-like electronic artificial sensory systems. After reviewing the synthesis and application of chalcogenide materials with respect to dimensionality, we focus on the key advances in (1) memory devices, including phase-change memory (PCM), ovonic threshold switching (OTS) selectors, and selector-only memory (SOM), and (2) sensing devices, including optical sensors, gas sensors, and neuromorphic sensors. Emphasis will be given on how chalcogenide materials can be integrated into next-generation systems, such as wearable platforms, artificial intelligence, and neuromorphic/quantum computing systems, to meet the growing demands for high-performance memory and multi-functional sensing. We also provide an overview of emerging research trends as well as a comprehensive perspective on the current status of research on chalcogenides. Finally, we attempt to provide insights into how chalcogenides can continue to drive technological breakthroughs in both memory and sensing applications while shaping the future landscape of intelligent systems, smart sensing platforms, and sustainable technology development. Chalcogenide ; Neuromorphic engineering ; Focus (optics) ; Perspective (graphical) ; Key (lock) ; Wearable computer ; Chalcogenide glass Phase-change materials and chalcogenides ; Transition Metal Oxide Nanomaterials ; 2D Materials and Applications
Authors: Weiwei Qin
Source: Scientific and Social Research (article, 2025-08-12)
Field: Materials Science — Ceramics and Composites

3D Printing of Silicon Nitride Ceramics and Exploration of Their Properties Silicon nitride (Si3N4) offers significant advantages such as high strength, high hardness, thermal shock resistance, good stability, wear resistance, and chemical corrosion resistance. It is lighter than many other materials, has a low thermal expansion coefficient, excellent thermal shock resistance, and high fracture toughness. Leveraging these advantages, it has found promising applications in aerospace, the military industry, mechanical engineering, metallurgy, and other fields. In the future, it is expected to unleash greater value through the innovative development of big data and artificial intelligence technologies. The author believes that traditional silicon nitride forming processes face insurmountable challenges, making it necessary to use 3D printing technology—with its own unique advantages—to mitigate risks and enhance performance and utility. This paper discusses the 3D printing of silicon nitride ceramics and their properties, providing a detailed analysis to offer references for relevant practitioners. Silicon nitride ; Ceramic ; 3D printing ; Nitride ; Thermal shock ; Thermal ; Shock (circulatory) Advanced ceramic materials synthesis ; Additive Manufacturing and 3D Printing Technologies ; Innovations in Concrete and Construction Materials
Authors: Jessica Kong (5341595) | Gihan Panapitiya (2607712) | Emily Saldanha (8616534)
Source: Figshare (article, 2025-05-13)
Field: Materials Science — Materials Chemistry

Extracting Material Property Measurements from ScientificLiterature with Limited Annotations Extracting material property data from scientific text is pivotal for advancing data-driven research in chemistry and materials science; however, the extensive annotation effort required to produce training data for named entity recognition (NER) models for this task often makes it a barrier to extracting specialized data sets. In this work, we present a comparative study of the conventional, supervised NER methodology to alternative few-shot learning architectures and large language model (LLM)-based approaches that mitigate the need to label large training data sets. We find that the best-performing LLM (GPT-4o) not only excels in directly extracting relevant material properties based on limited examples but also enhances supervised learning through data augmentation. We supplement our findings with error and data quality assessments to provide a nuanced understanding of factors that impact property measurement extraction. Property (philosophy) ; Task (project management) ; Annotation ; Training set ; Quality (philosophy) ; Named-entity recognition ; Supervised learning ; Data modeling Machine Learning in Materials Science ; Topic Modeling ; Advanced Graph Neural Networks
Authors: Lixin Guo (370835) | Qiuying Wang (6383963) | Yaowen Xing (12162392) | Xiaojiao Zhao (20812979) | Rongheng Ma (20666846) | Danping Liu (3536417) | Peng Gao (35465) | Yang Li (7082)
Source: Figshare (article, 2025-10-02)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Self-AssembledPlasmonic Magnifier: A New Platformfor Ultra-Sensitive Detection of Respiratory Viruses Using Surface-EnhancedRaman Spectroscopy Accurate and sensitive pathogen detection is critical for the prevention and control of respiratory viral infections, which pose significant threats to global health, particularly for vulnerable populations such as children, the elderly, and immunocompromised individuals. Here, we present a novel detection platform, Surface-Enhanced Raman Scattering combined with Artificial Intelligence (SERS-AI). At the core of this platform lies the independently developed self-assembled plasmonic magnifier (SPM), an enhanced substrate. Unlike traditional SERS substrates that rely on random aggregation or prefabricated nanostructures, this platform employs a virus-triggered self-assembly mechanism. Through the electrostatic attraction of C12 DNA molecules and the aggregation regulation of calcium ions, the self-assembled plasmonic magnifier (SPM) can significantly increase the probability of forming highly localized plasmonic “hotspots” near viral particles. This virus-associated hotspot formation strategy, which enhances the correlation between hotspot distribution and viral particles, significantly improves the specificity, intensity, and reproducibility of signals. Integrating surface-enhanced Raman spectroscopy (SERS) with artificial intelligence (AI) technology, the platform enables rapid, accurate, and label-free identification and quantitative analysis of respiratory viruses. The platform demonstrated exceptional sensitivity and reproducibility in detecting respiratory syncytial virus, human adenovirus type 5, influenza B virus, and H1N1 virus, with unique SERS fingerprints showing strong linear correlations with viral concentrations. The AI-driven spectral analysis allowed accurate differentiation of these viruses in serum and saliva samples, achieving detection within 2 min. Detection limits reached as low as 5 × 10<sup>–5</sup> copies/mL demonstrating robustness and reliability even in complex biological matrices. This SERS-AI-SPM platform represents a significant breakthrough in SERS technology by integrating advanced nanomaterial engineering with AI-powered data analysis. Its rapid, sensitive, and reliable performance underscores its transformative potential in clinical diagnostics, large-scale epidemic prevention, and personalized medicine. This innovation provides a powerful tool for real-time infectious disease monitoring and public health management. Plasmon ; Raman spectroscopy ; Raman scattering ; Reproducibility ; Spectroscopy ; Surface-enhanced Raman spectroscopy Gold and Silver Nanoparticles Synthesis and Applications ; SARS-CoV-2 detection and testing ; Biosensors and Analytical Detection
Authors: J. B. Wallace | Laura Riccardi | Fabrizio Mancin | Marco De Vivo
Source: Journal of Chemical Theory and Computation (article, 2025-10-24)
Field: Materials Science — Materials Chemistry

A Scoring Function for Monolayer-Protected Gold Nanoparticles Capable of Recognizing Small Organic Molecules in Solution Ligand-coated gold nanoparticles (AuNPs) can act as self-organized nanoreceptors capable of selectively recognizing small organic molecules (analytes) in solution. This ability can be applied in several fields, with NMR chemosensing being a notable example. To advance the rational design of such AuNP-based nanosensors, we present a data-driven scoring function to rapidly estimate AuNP-analyte binding affinities, thus allowing fast <i>in silico</i> prescreening of ligand-coated AuNP sensors. This scoring function implements chemical similarity, hydrophobicity, and charge complementarity as key molecular descriptors, demonstrating excellent predictive accuracy when validated against experimental data (<i>R</i><sup>2</sup> = 0.85, MAE = 0.45 kcal/mol). Enhanced sampling molecular dynamics on representative systems revealed that ligand flexibility, monolayer packing, and hydrogen bonding critically shape binding interactions, particularly for weak binding systems. Together, these data-driven and atomistic insights offer a robust framework for the rational design and optimization of AuNP-based nanosensors. Rational design ; Colloidal gold ; Organic molecules ; Molecule ; Molecular recognition ; Complementarity (molecular biology) ; Function (biology) ; Nanoparticle Nanocluster Synthesis and Applications ; Gold and Silver Nanoparticles Synthesis and Applications ; Advanced biosensing and bioanalysis techniques
Authors: Haoyang Wu (108097) | Anna C. Doner (6613109) | Hao-Wei Pang (8416053) | William H. Green (1318902)
Source: Figshare (article, 2025-04-20)
Field: Chemistry — Organic Chemistry

Toward AccurateQuantum Mechanical Thermochemistry:(2) Optimal Methods for Enthalpy Calculations from Comprehensive Benchmarksof 284 Model Chemistries Accurate and efficient computations of the standard enthalpies of formation (Δ<i>H</i><sub>f</sub><sup>°</sup>) for small organic molecules are crucial for diverse chemical engineering and scientific applications. Building on part 1 of this work [<i>J. Phys. Chem. A</i> <b>2024</b>, 128, 21, 4335–4352], we systematically benchmark 284 model chemistries for Δ<i>H</i><sub>f</sub><sup>°</sup> computations. These methods span semiempirical approaches, density functional theory (DFT), wave function theory, and composite schemes. We derive Petersson- and Melius-type bond-additivity corrections (BACs) for each method using a curated database of 421 reference species. We further validate the top-performing methods using an independent test set of 500 species, including ions, radicals, and other challenging cases. Across nearly all methods and species, BACs significantly improve accuracy, especially for neutral singlet species. Composite schemes coupling moderate-level DFT geometries with local coupled-cluster single-point energies strike an excellent balance between cost and accuracy, often approaching chemical accuracy (≤1 kcal/mol). Notably, DLPNO-CCSD(T)-F12d/cc-pVTZ-F12//ωB97X-D/def2-TZVPD with Petersson BAC attains the benchmark-best mean absolute error (MAE) of 0.57 kcal/mol. Switching to DLPNO-CCSD(T)-F12d/cc-pVDZ-F12//GFN2-xTB reduces the computational cost by an order of magnitude, with only a modest increase in MAE (0.96 kcal/mol). Although carefully tuned model chemistries can also benefit charged and open-shell species, the scarcity of robust reference data in these areas highlights the need for broader, high-accuracy thermochemistry datasets. Overall, this benchmark provides practical guidance on selecting optimal model chemistries to efficiently compute accurate Δ<i>H</i><sub>f</sub><sup>°</sup> under varied computational constraints and molecular complexities, laying a foundation for large-scale, high-throughput thermochemical calculations that will support data-driven discovery and industrial applications. Thermochemistry ; Benchmark (surveying) ; Computation ; Set (abstract data type) ; Experimental data ; Work (physics) ; Density functional theory ; Function (biology) ; Reference data Chemical Thermodynamics and Molecular Structure ; Advanced Chemical Physics Studies ; Machine Learning in Materials Science
Authors: Joseph Wallace (8611623) | Laura Riccardi (2095021) | Fabrizio Mancin (1534513) | Marco De Vivo (7358288)
Source: Figshare (article, 2025-10-24)
Field: Materials Science — Electronic, Optical and Magnetic Materials

A Scoring Functionfor Monolayer-Protected Gold NanoparticlesCapable of Recognizing Small Organic Molecules in Solution Ligand-coated gold nanoparticles (AuNPs) can act as self-organized nanoreceptors capable of selectively recognizing small organic molecules (analytes) in solution. This ability can be applied in several fields, with NMR chemosensing being a notable example. To advance the rational design of such AuNP-based nanosensors, we present a data-driven scoring function to rapidly estimate AuNP–analyte binding affinities, thus allowing fast <i>in silico</i> prescreening of ligand-coated AuNP sensors. This scoring function implements chemical similarity, hydrophobicity, and charge complementarity as key molecular descriptors, demonstrating excellent predictive accuracy when validated against experimental data (<i>R</i><sup>2</sup> = 0.85, MAE = 0.45 kcal/mol). Enhanced sampling molecular dynamics on representative systems revealed that ligand flexibility, monolayer packing, and hydrogen bonding critically shape binding interactions, particularly for weak binding systems. Together, these data-driven and atomistic insights offer a robust framework for the rational design and optimization of AuNP-based nanosensors. Rational design ; Colloidal gold ; Molecule ; Organic molecules ; Molecular recognition ; Nanoparticle ; Small molecule Gold and Silver Nanoparticles Synthesis and Applications ; Machine Learning in Materials Science ; Nanocluster Synthesis and Applications
Authors: C EN editorial staff
Source: C&EN Global Enterprise (article, 2025-02-10)
Field: Materials Science — Materials Chemistry

C&amp;EN is using AI—very carefully “What are you all doing about artificial intelligence?” It’s a question C&EN staffers get a lot. Journalism as a profession was already reeling with the collapse of business models based on robust demand for print. And then along came a generative AI with tools that can crank out grammatical, plausible text on any subject on demand. Generative AI’s capacity to change the practice of chemistry is an opportunity and a concern. This week’s cover story by C&EN reporter Alex Scott takes a wider look at the implications. In both journalism and research, a central debate is whether AI will enhance our professional output or make our jobs redundant. As a start to answering that question—for ourselves and for our readers—C&EN published a statement about how we intend to approach AI , which you can view at cenm.ag/cenaiethics . The AI craze was kicked off by services such as ChatGPT and Chemistry Machine Learning in Materials Science ; Artificial Intelligence in Healthcare and Education ; Genetics, Bioinformatics, and Biomedical Research