jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Hafiz Saad Khaliq | Hak Rin Kim
Source: Laser & Photonics Review (article, 2026-03-09)
Field: Materials Science — Surfaces, Coatings and Films

Recent Advances in Photonic Bound States in the Continuum: From Fundamentals to Engineering and Implementation ABSTRACT Recent remarkable advancements in meta‐photonics are shaping the future of innovation, enhancing capabilities across various fields, and promoting sustainability through energy‐efficient solutions. In particular, bound states in the continuum (BICs) have attracted significant attention as a highly versatile and powerful platform for advancing photonic technologies in fundamental and practical applications. BICs are non‐radiative states that enable precise confinement and control of light properties, including polarization, amplitude, and phase. This review paper offers a comprehensive understanding of photonic BICs, from fundamental principles to engineering and implementation across various optical regimes for diverse applications. Based on the implementation and promising potential to revolutionize a wide range of optical applications, the BIC systems are categorized and discussed across different optical regimes, from ultraviolet to visible, infrared, and terahertz. Moreover, a section discussed the crucial role of asymmetry in generating and controlling BICs, leading to practically implementable quasi‐BICs with finite yet high Q‐factors. Furthermore, dynamic BIC devices, including phase change materials (PCMs) are discussed as advanced photonic concepts to achieve real‐time tunable and adaptable light confinement in modern optical technologies. Additionally, integrating artificial intelligence in BIC‐related technologies has also been discussed to expedite the development and optimization of advanced BIC designs with enhanced light‐matter interactions and high‐Q factors. In addition, implementing BIC devices is addressed within the context of passive and active photonic systems. Finally, the potential future developments of BIC‐based devices are highlighted, underscoring their critical role in next‐generation photonic technologies. Photonics ; Context (archaeology) ; Photonic crystal ; Photonic integrated circuit ; Optical engineering ; Silicon photonics Optical Coatings and Gratings ; Photonic and Optical Devices ; Plasmonic and Surface Plasmon Research
Authors: Bin Cao | Qian Zhang | Zhenjie Feng | Taolue Zhang | Jiaqiang Huang | Lu-Tao Weng | Tong-Yi Zhang
Source: arXiv (Cornell University) (article, 2026-02-18)
Field: Materials Science — Materials Chemistry

AI-Driven Structure Refinement of X-ray Diffraction Artificial intelligence can rapidly propose candidate phases and structures from X-ray diffraction (XRD), but these hypotheses often fail in downstream refinement because peak intensities cannot be stably assigned under severe overlap and diffraction consistency is enforced only weakly. Here we introduce the whole-pattern expectation--maximization (WPEM) algorithm, a physics-constrained whole-pattern decomposition and refinement workflow that turns Bragg's law into an explicit constraint within a batch expectation--maximization framework. WPEM models the full profile as a probabilistic mixture density and iteratively infers component-resolved intensities while keeping peak centres Bragg-consistent, producing a continuous, physically admissible intensity representation that remains stable in heavily overlapped regions and in the presence of mixed radiation or multiple phases. We benchmark WPEM on standard reference patterns (PbSO$_4$ and Tb$_2$BaCoO$_5$), where it yields lower $R_p/R_{wp}$ than widely used packages (FullProf and TOPAS) under matched refinement conditions. We further demonstrate generality across realistic experimental scenarios, including phase-resolved decomposition in multiphase materials, quantitative recovery of mixture compositions, separation of crystalline peaks from amorphous backgrounds in semicrystalline systems, high-throughput operando lattice tracking, automated refinement of compositionally disordered solid solutions, and quantitative phase-resolved analysis of complex archaeological samples from synchrotron powder XRD. By providing Bragg-consistent, uncertainty-aware intensity partitioning as a refinement-ready interface, WPEM closes the gap between AI-generated hypotheses and diffraction-admissible structure refinement on challenging XRD data. Diffraction ; Probabilistic logic ; Rietveld refinement ; Decomposition ; Amorphous solid ; Benchmark (surveying) ; Representation (politics) ; Workflow ; Powder diffraction Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advanced Condensed Matter Physics
Authors: Jer‐Chyi Wang | Han‐Hsiang Tai | Jun‐Kai Lai | Kuan‐Lin Chen | Wen‐Hao Chang | Vincent Tung | Chao‐Sung Lai
Source: Advanced Optical Materials (article, 2026-02-25)
Field: Materials Science — Materials Chemistry

Lateral Inhibitory Modulation in Selenium‐Passivated Graphene/Al <i> <sub>x</sub> </i> O <i> <sub>y</sub> </i> /MoS <sub>2</sub> ‐Nanodisks Heterostructure Photodetectors for Visual Persistence and Glare Adaptation of the Human Visual System ABSTRACT Bioinspired optoelectronic devices capable of emulating neural functions in the human visual system hold great potential for next‐generation visual artificial intelligence and neuromorphic vision. Here, we propose a Se‐passivated graphene/Al x O y /MoS 2 ‐naondisks (MoS 2 ‐NDs) heterostructure photodetector to mimic visual persistence and glare adaptation through material modification and circuit design. Benefiting from the localized surface plasmon resonance induced by MoS 2 ‐NDs, the photodetector achieved a responsivity of 41.06 A/W, an external quantum efficiency of 11703%, and a peak detectivity of 1.03 × 10 9 Jones under illumination at a light wavelength of 435 nm. Additionally, the photodetector with Se passivation effectively passivated the defects at the perimeter of MoS 2 ‐NDs and enhanced the photoresponse in the red to near‐infrared region, extending the spectral adaptability. The differentiated optical synaptic characteristics under repetitive light stimuli of 5‐ms biological pulses at wavelengths of 435 and 740 nm were obtained, mimicking the visual processes of visual persistence and afterimage. By integrating the photodetector with a lateral inhibitory circuit, the system successfully demonstrated glare adaptation under high‐intensity light illumination, closely resembling the retina's inhibitory response by dynamically regulating the excessive photocurrent to realize an advanced human vision system with capabilities including broadband photoresponse, adaptive light regulation, and neuromorphic vision. Photodetector ; Responsivity ; Neuromorphic engineering ; Adaptation (eye) ; Photocurrent ; Heterojunction ; Plasmon ; Modulation (music) 2D Materials and Applications ; Graphene research and applications ; Advanced Sensor and Energy Harvesting Materials
Authors: Wenru Chen | Binbin Zheng | Lin Zhu | Anjan K. Tula
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Materials Chemistry

A Hybrid Physics–Data Digital Twin for Industrial Batch Melt Crystallization Crystallization ; Interpretability ; Process (computing) ; Heat transfer ; Kriging ; Gaussian process ; Gaussian ; Production (economics) Machine Learning in Materials Science ; Digital Transformation in Industry ; Model Reduction and Neural Networks
Authors: Ruinian Peng | Hongming Tang | Jian Wu
Source: Applied Thermal Engineering (article, 2026-02-06)
Field: Materials Science — Materials Chemistry

Multi-objective optimization of Magnetic Czochralski crystal growth process using surrogate modeling and evolutionary algorithm Multi-objective optimization ; Surrogate model ; Sorting ; Evolutionary algorithm ; sort ; Process (computing) ; Genetic algorithm ; Pareto principle ; Evolution strategy Solidification and crystal growth phenomena ; Silicon and Solar Cell Technologies ; Metallurgical Processes and Thermodynamics
Authors: Raşit Arsoy | Serkan Aslan
Source: Sustainability (article, 2026-02-06)
Field: Materials Science — Polymers and Plastics

Software-Based Optimization of Bias Tape Production for Sustainable Apparel Manufacturing In the ready-to-wear industry, accurate estimation of fabric consumption is essential for cost efficiency, particularly for bias tapes, which constitute a notable share of overall material usage. Although bias tape is widely used across the apparel industry, no formal mathematical model exists for calculating its consumption, and current practices rely heavily on technician experience and sample garments. Because bias tape consumption in multi-size garment manufacturing involves geometric adjustments and decision steps that are not captured by existing methods, this study develops the first mathematically structured model for bias tape estimation and implements it in a software tool, providing a more reliable and standardized alternative to manual calculations. The model was validated using data collected in a real production environment from 20 garment models (13,122 units), and statistical analyses confirmed that the software produced significantly more accurate estimates than conventional methods, resulting in fabric savings of up to 39.5%. These findings demonstrate that the system enhances material utilization and supports data-driven planning in apparel manufacturing. Clothing ; Production (economics) ; Software ; Technician ; Sample (material) ; Consumption (sociology) ; Estimation Textile materials and evaluations ; Sustainable Supply Chain Management ; Manufacturing Process and Optimization
Authors: Jonas Schaible | Asena Karolin Özdemir | Charlotte Debus | Sven Burger | Achim Streit | Christiane Becker | Klaus Jäger | Markus Götz
Source: ArXiv.org (article, 2026-04-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Inverse Design of Optical Multilayer Thin Films using Robust Masked Diffusion Models Inverse design of optical multilayer stacks seeks to infer layer materials, thicknesses, and ordering from a desired target spectrum. It is a long-standing challenge due to the large design space and non-unique solutions. We introduce \texttt{OptoLlama}, a masked diffusion language model for inverse thin-film design from optical spectra. Representing multilayer stacks as sequences of material-thickness tokens, \texttt{OptoLlama} conditions generation on reflectance, absorptance, and transmittance spectra and learns a probabilistic mapping from optical response to structure. Evaluated on a representative test set of 3,000 targets, \texttt{OptoLlama} reduces the mean absolute spectral error by 2.9-fold relative to a nearest-neighbor template baseline and by 3.45-fold relative to the state-of-the-art data-driven baseline, called \texttt{OptoGPT}. Case studies on designed and expert-defined targets show that the model reproduces characteristic spectral features and recovers physically meaningful stack motifs, including distributed Bragg reflectors. These results establish diffusion-based sequence modeling as a powerful framework for inverse photonic design. Inverse ; Stack (abstract data type) ; Probabilistic logic ; Transmittance ; Diffusion ; Inverse problem ; Photon diffusion ; Set (abstract data type) Metamaterials and Metasurfaces Applications ; Photonic Crystals and Applications ; Optical Coatings and Gratings
Authors: Xiaomeng Dong | Pengyu Xu | Bing Bai | Lixia Jia | Xinfu He | Wen Yang
Source: Materials Today Communications (article, 2026-03-01)
Field: Materials Science — Materials Chemistry

Advances in computer vision for recognizing microstructure images of metallic materials Computer vision (CV) techniques have been increasingly adopted to the quantitative analysis of metallic microstructures, offering new opportunities for automated feature extraction and microstructural characterization from microscopy images. This review presents a focused and application-oriented overview of CV-based approaches for metallic microstructure analysis, with particular emphasis on the roles and complementarities of semantic segmentation and object detection tasks. The workflow from image preprocessing and microstructural feature identification to quantitative descriptor extraction and microstructure-property correlation is systematically discussed. Representative applications in defect quantification, mechanical property evaluation, and performance-oriented materials design are summarized, together with current limitations related to data availability, generalization, and physical interpretability. This review aims to provide practical guidance and a structured reference for researchers seeking to adopt CV techniques in materials microstructure analysis. • Reviews evolution and trade-offs of CV methods for metallic microstructures • Discusses how physical knowledge improves interpretability of data-driven models • Outlines challenges and prospects of CV-driven microstructure analysis and design Microstructure ; Metal ; Machine vision ; Image processing Machine Learning in Materials Science ; Surface Treatment and Coatings ; Material Properties and Failure Mechanisms
Authors: Jonas Schaible | Asena Karolin Özdemir | Charlotte Debus | Sven Burger | Achim Streit | Christiane Becker | Klaus Jäger | Markus Götz
Source: arXiv (Cornell University) (preprint, 2026-04-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Inverse Design of Optical Multilayer Thin Films using Robust Masked Diffusion Models Inverse design of optical multilayer stacks seeks to infer layer materials, thicknesses, and ordering from a desired target spectrum. It is a long-standing challenge due to the large design space and non-unique solutions. We introduce \texttt{OptoLlama}, a masked diffusion language model for inverse thin-film design from optical spectra. Representing multilayer stacks as sequences of material-thickness tokens, \texttt{OptoLlama} conditions generation on reflectance, absorptance, and transmittance spectra and learns a probabilistic mapping from optical response to structure. Evaluated on a representative test set of 3,000 targets, \texttt{OptoLlama} reduces the mean absolute spectral error by 2.9-fold relative to a nearest-neighbor template baseline and by 3.45-fold relative to the state-of-the-art data-driven baseline, called \texttt{OptoGPT}. Case studies on designed and expert-defined targets show that the model reproduces characteristic spectral features and recovers physically meaningful stack motifs, including distributed Bragg reflectors. These results establish diffusion-based sequence modeling as a powerful framework for inverse photonic design. Inverse ; Stack (abstract data type) ; Probabilistic logic ; Transmittance ; Diffusion ; Inverse problem ; Photon diffusion ; Set (abstract data type) Metamaterials and Metasurfaces Applications ; Photonic Crystals and Applications ; Optical Coatings and Gratings
Authors: Fugui Zhao | Xiaolin Wei | Jue Cheng | Jiahao Ma | Feng Gao | Zhenghao Wu | Dengyu Chen | junying zhang
Source: Chemical Engineering Journal (article, 2026-01-18)
Field: Materials Science — Polymers and Plastics

Silane-hybridized polyphenylene oxide for electronic packaging and extreme-environment-resistant multi-functional coatings Thermosetting polymer ; Polydimethylsiloxane ; Thermoplastic ; Dielectric ; Electronic packaging ; Thermoplastic polyurethane ; Oxide ; Compounding ; Side chain ; Silicone Synthesis and properties of polymers ; Polymer composites and self-healing ; Epoxy Resin Curing Processes
Authors: Edgard Ngaboyamahina | Thaïs B. M. Matha | Samantha Feinstein | J B Smith | Allison Lim | Riley J. Donahue | Isha K. Maun
Source:  (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

AI-Augmented Insights for Re-X The complex landscape of Design for Reuse, Remanufacturing, Recovery, and Recycling (Re-X) requires a deep understanding of materials, reverse logistics, regulations, and secondary markets. While AI is often associated with operational tasks like sorting and automation, the most significant and lowest-risk gains for Re-X initiatives come from AI-enabled insights. These insights, derived from AI-driven analysis of vast data sources, can accelerate crucial foresight activities like technology and partner scouting, patent and intellectual property landscaping, and market forecasting. This paper presents a practical AI Playbook for integrating generative AI into the early stages of product development and lifecycle planning. By leveraging AI to extract actionable intelligence from diverse data sources, including patents, academic literature, startup ecosystems, and trade show activity, engineers, designers, R&amp;D, and policy leaders can prioritize Re-X investments, de-risk design decisions, and fast-track remanufacturing and recovery strategies. The playbook provides a comprehensive guide for using generative AI tools to gain knowledge more quickly, automate repetitive tasks, and explore broader solution spaces. The playbook's structure mirrors key project phases, offering strategic guidance and practical resources, such as example prompts. It serves as a flexible resource that can be referenced at any stage to enhance creativity and innovation. By strategically applying the playbook's methods, teams can mitigate common risks associated with generative AI and effectively leverage it to turn data into action, ultimately driving more successful Re-X outcomes. Futures studies ; Leverage (statistics) ; Generative grammar ; Bespoke ; New product development ; Remanufacturing ; Product (mathematics) ; Key (lock) ; Resource (disambiguation) Machine Learning in Materials Science ; Digital Transformation in Industry ; Innovation, Sustainability, Human-Machine Systems
Authors: Xuejun Dong | Ziyun Tang | Lichuan Chen | Chao Fang | Ruiyun Huang | Zhao Xiao | Jia Shi | Junyang Liu | Yujian Zhu | X. B. Li | Jie Bai | Wenjing Hong
Source: ACS Sensors (article, 2026-03-11)
Field: Chemistry — Spectroscopy

Quantitative Sensing of Monosaccharide Isomers with High Resolution and Attomolar Limit of Detection The quantitative detection of monosaccharide isomers holds significant promise for both the diagnosis of metabolic diseases and the development of carbohydrate-based pharmaceuticals. However, highly selective discrimination and trace detection of these isomers remain challenging. Herein, we reported a novel single-molecule tunneling sensor for the quantitative detection of glucose isomers with high performance. By directly characterizing the intrinsic conductance of single-molecule junctions formed by dynamic boronic ester bonding, we achieved a dramatic ∼60-fold difference in conductance signals for discrimination of glucose isomers. Furthermore, by integrating artificial-intelligence-based unsupervised data clustering, we realized quantitative sensing of glucose isomers with an ultrawide linear range from ∼0.1 fM to 0.1 mM and an ultralow limit of detection of 59 aM. Theoretical calculations revealed that the high selectivity originates from the stability energy difference of a single-molecule junction formed by different monosaccharide isomers. Our work establishes a paradigm for single-molecule stereochemical analysis, offering potential for ultrasensitive bioanalysis and drug discovery. Monosaccharide ; Detection limit ; Conductance ; Bioanalysis ; Selectivity ; Resolution (logic) ; Limit (mathematics) ; Analyte ; Analytical Chemistry (journal) Molecular Sensors and Ion Detection ; Molecular Junctions and Nanostructures ; Supramolecular Self-Assembly in Materials
Authors: Md Minhaj Ali | Nitish Ghosh | Puja Dey
Source: Journal of Materials Science Materials in Electronics (article, 2026-02-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Magneto-optical tunability of electrical irreversibility and broadband photodetection in hybrid inorganic–organic p-Si (100)/La0.7Sr0.3MnO3/P3HT/CuPc/Au heterostructure Photodetection ; Heterojunction ; Capacitive sensing ; Resistive touchscreen ; Broadband ; Modulation (music) ; Magnetic field ; Hysteresis Magnetic and transport properties of perovskites and related materials ; Advanced Memory and Neural Computing ; Multiferroics and related materials
Authors: Ping An | Tingting Li | Haonan Yu | Xinyi Liu | Haiyan Ma | Ning Ju | Zhongxiong Zhang | Songlei Wang
Source: International Dairy Journal (article, 2026-03-11)
Field: Chemistry — Analytical Chemistry

Recent advances in spectroscopic techniques for detecting foodborne pathogens in milk: A review Food microbiology ; Microbiological Techniques ; Foodborne pathogen ; Listeria monocytogenes Spectroscopy and Chemometric Analyses ; Biosensors and Analytical Detection ; Listeria monocytogenes in Food Safety
Authors: Kawen Li | Hangjin Cho | Richard Norte | Dongil Shin
Source: arXiv (Cornell University) (article, 2026-04-08)
Field: Materials Science — Materials Chemistry

Quasicrystal Architected Nanomechanical Resonators via Data-Driven Design From butterfly wings to remnants of nuclear detonation, aperiodic order repeatedly emerges in nature, often exhibiting reduced sensitivity to boundaries and symmetry constraints. Inspired by this principle, a paradigm shift is introduced in nanomechanical resonator design from periodic to aperiodic structures, focusing on a special class: quasicrystals (QCs). Although soft clamping enabled by phononic stopbands has become a central strategy for achieving high-$Q_m$ nanomechanical resonators, its practical realization has been largely confined to periodic phononic crystals, where band structure engineering is well established. The potential of aperiodic architectures, however, has remained largely unexplored, owing to their intrinsic complexity and the lack of systematic approaches to identifying and exploiting stopband behavior. Here we demonstrate that soft clamping can be realized in quasicrystal architectures and that high-$Q_m$ nanomechanical resonators can be systematically achieved through a data-driven design framework. As a representative demonstration, the 12-fold QC-based resonator exhibits a quality factor $Q_m \sim 10^7$ and an effective mass of sub-nanograms at MHz frequencies, corresponding to an exceptional force sensitivity of $26.4$~aN/$\sqrt{\text{Hz}}$ compared to previous 2D phononic crystals. These results establish QCs as a robust platform for next-generation nanomechanical resonators and open a new design regime beyond periodic order. Aperiodic graph ; Resonator ; Quasicrystal ; Clamping ; Stopband ; Sensitivity (control systems) ; Optomechanics ; String (physics) ; Realization (probability) Quasicrystal Structures and Properties ; Acoustic Wave Phenomena Research ; Cellular and Composite Structures