jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Fu-Sheng Yang | Yen-Hung Hung | Min-Ru Wu | Liang-Chia Chen
Source: Measurement Science and Technology (article, 2025-12-17)
Field: Materials Science — Surfaces, Coatings and Films

Optimized polarization in DUV scatterometry with global sensitivity analysis for accurate CD metrology of sub-micron microstructure structures Abstract The advancement of chip-on-wafer-on-substrate technology has positioned three-dimensional (3D) packaging as a key enabler for next-generation artificial intelligence chips, thereby sustaining the trajectory of Moore’s Law. However, the continued scaling of critical dimensions (CDs), such as through-silicon vias (TSVs) and redistribution layers (RDLs), presents major challenges for CD metrology, primarily due to the difficulty of characterizing submicron hidden structures and limited light penetration. This study introduces an optimization framework based on global sensitivity analysis (GSA) to quantify the impact of CDs on optical responses and assess interaction effects on metrology performance. By combining GSA with rigorous electromagnetic simulations, the proposed approach enhances the extraction of CD information in optical CD (OCD) metrology. In particular, this work systematically analyzes key structural parameters in both submicron silicon trench structures and copper RDLs (Cu RDLs), providing a unified evaluation across two representative classes of semiconductor architectures. Key structural parameters, including depth, top CD, sidewall angle (SWA), and trench spacing, are systematically analyzed in submicron silicon structures. Furthermore, polarization optimization guided by sensitivity analysis is applied to maximize optical response sensitivity. Experimental validation shows that the GSA-optimized scatterometry setup achieves high measurement accuracy, maintaining a bias below 2% relative to focused ion beam/scanning electron microscope benchmarks. The findings demonstrate that CDs traditionally difficult to measure, such as depth and SWA of hidden submicron microstructures, can be accurately determined. Overall, the proposed methodology significantly enhances the accuracy and robustness of OCD metrology, providing valuable insights for advancing measurement strategies in 3D semiconductor packaging. Metrology ; Robustness (evolution) ; Polarization (electrochemistry) ; Dimensional metrology ; Nanometrology ; Sensitivity (control systems) ; Semiconductor ; Optical proximity correction Optical Coatings and Gratings ; 3D IC and TSV technologies ; Copper Interconnects and Reliability
Authors: Revista, Zen | CHEMISTRY, 10
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2025-11-28)
Field: Materials Science — Materials Chemistry

Arrow-Pushing Redefined: Quantitative Electron Flow Analysis for Woodward-Hoffmann Selectivity in Pericyclic Reactions The traditional arrow-pushing formalism, while indispensable for qualitative understanding of organic reaction mechanisms, often falls short in providing a rigorous, quantitative basis for predicting subtle selectivities, particularly in complex pericyclic reactions. This paper introduces a novel approach to redefine arrow-pushing through Quantitative Electron Flow Analysis (QEFA), specifically applied to elucidate and predict Woodward-Hoffmann selectivity in pericyclic processes. By employing advanced quantum chemical methodologies, including electron density deformation analysis, Natural Bond Orbital (NBO) theory, and the Quantum Theory of Atoms in Molecules (QTAIM), we quantitatively map the dynamic reorganization of electron density along the reaction coordinate. Our methodology focuses on tracking the precise movement of electron packets and changes in bond orders from reactants through the transition state to products. We demonstrate how this quantitative tracking provides a clear, numerical fingerprint distinguishing between Woodward-Hoffmann allowed and forbidden pathways, and accurately rationalizes observed diastereo-, enantio-, and regioselectivity in various pericyclic systems, such as Diels-Alder reactions, electrocyclic ring openings/closings, and sigmatropic rearrangements. The results show that QEFA not only reproduces established Woodward-Hoffmann rules with quantitative rigor but also offers unprecedented insights into the electronic origins of selectivity, enabling a more precise predictive capability than traditional qualitative methods. This work represents a significant step towards a data-driven understanding of chemical reactivity, transforming arrow-pushing from an intuitive tool into a powerful, quantifiable analytical method for mechanistic elucidation and rational design in organic synthesis. Pericyclic reaction ; Density functional theory ; Regioselectivity ; Atomic orbital ; Molecule ; Quantum ; Ring (chemistry) ; Electron Machine Learning in Materials Science ; Organic Chemistry Cycloaddition Reactions ; N-Heterocyclic Carbenes in Organic and Inorganic Chemistry
Authors: Leonard Whye Kit Lim
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Biomaterials

Spider Silk Applications: Dermatologic Theragnostic, Cosmetics, Aerospace, Textile and Electronics Textile ; Aerospace ; Spider ; CLARITY ; Spider silk ; Electronics Silk-based biomaterials and applications ; Electrospun Nanofibers in Biomedical Applications ; Dyeing and Modifying Textile Fibers
Authors: Manuel Palma Banos | Joel D. Kress | Rigoberto Hernandez | Galen T. Craven
Source: ArXiv.org (preprint, 2025-10-23)
Field: Materials Science — Materials Chemistry

Stoichiometrically-informed symbolic regression for extracting chemical reaction mechanisms from data A data-driven computational method is introduced to extract chemical reaction mechanisms from time series chemical concentration data. It is realized through the use of dynamic symbolic regression in which a sparse analytical form for a dynamical system is discoverable from the underlying data. We specifically develop the stoichiometrically-informed symbolic regression (SISR) method to address a standing challenge in complex chemical reaction networks: Given a time-series dataset of concentrations of several components, what is the mechanism and the associated rate constants? SISR finds the optimal mechanism, kinetic equations and rate constants by combining differential optimization with a genetic optimization approach that searches a symbolic space of possible reaction mechanisms. Use of SISR in several paradigmatic examples spanning linear and nonlinear reaction schemes results in excellent agreement between true and predicted mechanisms, including when the method is applied to noisy data. The advantages of a stoichiometrically-informed approach such as SISR to address reaction discovery is illustrated through comparison with the use of generic state-of-the-art data-driven approaches. Symbolic regression ; Nonlinear system ; Nonlinear regression ; Regression ; Differential evolution ; Series (stratigraphy) ; Linear regression ; Chemical reaction ; Genetic algorithm Machine Learning in Materials Science ; Microbial Metabolic Engineering and Bioproduction ; Gene Regulatory Network Analysis
Authors: Nirma Kumari | Jaywan Chung | Seunghyun Oh | Jeongin Jang | Jongho Park | Ji Hui Son | SuDong Park | Byungki Ryu
Source: arXiv (Cornell University) (preprint, 2025-07-08)
Field: Materials Science — Materials Chemistry

Reference compositions for bismuth telluride thermoelectric materials for low-temperature power generation Thermoelectric (TE) technology enables direct heat-to-electricity conversion and is gaining attention as a clean, fuel-saving, and carbon-neutral solution for industrial, automotive, and marine applications. Despite nearly a century of research, apart from successes in deep-space power sources and solid-state cooling modules, the industrialization and commercialization of TE power generation remain limited. Since the new millennium, nanostructured bulk materials have accelerated the discovery of new TE systems. However, due to limited access to high-temperature heat sources, energy harvesting still relies almost exclusively on BiTe-based alloys, which are the only system operating stably near room temperature. Although many BiTe-based compositions have been proposed, concerns over reproducibility, reliability, and lifetime continue to hinder industrial adoption. Here, we aim to develop reference BiTe-based thermoelectric materials through data-driven analysis of Starrydata2, the world's largest thermoelectric database. We identify Bi0.46Sb1.54Te3 and Bi2Te2.7Se0.3 as the most frequently studied ternary compositions. These were synthesized using hot pressing and spark-plasma sintering. Thermoelectric properties were evaluated with respect to the processing method and measurement direction. The results align closely with the median of reported data, confirming the representativeness of the selected compositions. We propose these as reference BiTe materials, accompanied by transparent data and validated benchmarks. Their use can support the standardization of TE legs and modules while accelerating performance evaluation and industrial integration. We further estimated the performance of a thermoelectric module made from the reference composition, which gives the power output of over 2.51 W and an efficiency of 3.58% at a temperature difference of 120 K. Thermoelectric generator ; Thermoelectric materials ; Thermoelectric effect ; Bismuth telluride ; Thermoelectric cooling ; Electricity generation ; Seebeck coefficient ; Standardization Advanced Thermoelectric Materials and Devices ; Topological Materials and Phenomena ; Heusler alloys: electronic and magnetic properties
Authors: Nirma Kumari | Jaywan Chung | Seunghyun Oh | Jeongin Jang | Jongho Park | Ji Hui Son | SuDong Park | Byungki Ryu
Source: Science and Technology of Advanced Materials Methods (article, 2025-11-10)
Field: Materials Science — Materials Chemistry

Reference compositions for bismuth telluride thermoelectric materials for low-temperature power generation Thermoelectric (TE) technology enables direct heat-to-electricity conversion and is gaining attention as a clean, fuel-saving, and carbon-neutral solution for industrial, automotive, and marine applications. Despite nearly a century of research, apart from successes in deep-space power sources and solid-state cooling modules, the industrialization and commercialization of TE power generation remain limited. Since the new millennium, nanostructured bulk materials have accelerated the discovery of new TE systems. However, due to limited access to high-temperature heat sources, energy harvesting still relies almost exclusively on BiTe-based alloys, which are the only system operating stably near room temperature. Although many BiTe-based compositions have been proposed, concerns over reproducibility, reliability, and lifetime continue to hinder industrial adoption. Here, we aim to develop reference BiTe-based thermoelectric materials through data-driven analysis of Starrydata2, the world's largest thermoelectric database. We identify Bi0.46Sb1.54Te3 and Bi2Te2.7Se0.3 as the most frequently studied ternary compositions. These were synthesized using hot pressing and spark-plasma sintering. Thermoelectric properties were evaluated with respect to the processing method and measurement direction. The results align closely with the median of reported data, confirming the representativeness of the selected compositions. We propose these as reference BiTe materials, accompanied by transparent data and validated benchmarks. Their use can support the standardization of TE legs and modules while accelerating performance evaluation and industrial integration. We further estimated the performance of a thermoelectric module made from the reference composition, which gives the power output of over 2.51 W and an efficiency of 3.58% at a temperature difference of 120 K. Thermoelectric generator ; Thermoelectric materials ; Thermoelectric effect ; Bismuth telluride ; Thermoelectric cooling ; Electricity generation ; Seebeck coefficient ; Standardization Advanced Thermoelectric Materials and Devices ; Topological Materials and Phenomena ; Heusler alloys: electronic and magnetic properties
Authors: X. Hu | Huan Ye | Beitong Zhu | Ruilong Sheng | Hong Chen
Source: Microchemical Journal (article, 2025-10-18)
Field: Chemistry — Spectroscopy

Advanced optical organohydrogel for rapid, sensitive and visual detection of explosive ethylenediamine Ethylenediamine ; Detection limit ; Fluorescence ; Explosive material ; Naked eye Molecular Sensors and Ion Detection ; Luminescence and Fluorescent Materials ; Advanced Chemical Sensor Technologies
Authors: Yunyan Peng | Jianhua Yang | Fan Bai | S S Feng | Kong De | Zhijuan Zhao
Source: Journal of Physics Conference Series (article, 2025-08-01)
Field: Materials Science — Metals and Alloys

Intergranular Corrosion of Stainless Steel and Its Research Methods Abstract Stainless steel, known for its excellent corrosion resistance, is widely used in various fields such as energy, chemical processing, and marine engineering. However, under certain environmental conditions—especially high temperature and high chloride environments—intergranular corrosion (IGC) remains one of the primary causes of stainless steel failure, posing serious safety risks in critical applications. This paper reviews recent advances in the study of IGC in stainless steels. It first introduces classical mechanisms such as the chromium depletion effect, and then discusses newly proposed mechanisms including sigma (σ) phase precipitation and impurity element segregation. Subsequently, the article summarizes detection methods for IGC, with a focus on techniques such as electrochemical potentiokinetic reactivation (EPR) and localized electrochemical methods (SVET/LEIS), which offer improved precision and broader applicability. Finally, strategies for enhancing IGC resistance are discussed, including alloy composition optimization, heat treatment processes, and data-driven service life prediction models. Future research directions include the development of innovative testing technologies and advanced corrosion-resistant stainless steel design to address challenges under complex service conditions. Intergranular corrosion ; Metallurgy ; Corrosion ; Materials science Hydrogen embrittlement and corrosion behaviors in metals ; Corrosion Behavior and Inhibition ; Concrete Corrosion and Durability
Authors: Erdal Camci | Fehim Findik
Source: SHILAP Revista de lepidopterología (article, 2025-11-01)
Field: Materials Science — Materials Chemistry

Recent developments in ballistics Ballistics, the science of projectile motion, encompasses the study of objects such as bullets, missiles, and rockets from launch to impact, divided into internal, external, terminal, and forensic ballistics. This paper explores recent advancements in ballistic materials, methods, and sustainability challenges. Traditional and self-healing materials, including microcapsule-based, bio-inspired, and metallic self-repairing systems, are examined for their applications in armor, projectiles, and thermal protection. Experimental techniques like light gas guns and high-speed photography, alongside numerical simulations such as finite element analysis (FEA) and smoothed particle hydrodynamics (SPH), are compared for their efficacy in ballistic research. High-velocity projectiles exceeding Mach 5, including hypersonic and kinetic energy penetrators, are analyzed for their aerodynamic and material challenges, with future directions pointing toward AI-guided systems and 3D-printed materials. The study also highlights green ammunition innovations, such as lead-free bullets and biodegradable cartridges, to address environmental concerns like toxic propellants and heavy metal contamination. Sustainability efforts focus on resource efficiency, including recycled composites and additive manufacturing, while military and civilian applications explore hypersonic swarms and non-lethal munitions. The paper concludes with future perspectives, emphasizing digital twins in forensics, space ballistics, and closed-loop ammunition recycling. By integrating experimental and computational approaches, this research aims to advance ballistic technologies while addressing ecological and ethical challenges in the field. Ballistics ; Ammunition ; Propellant ; Hypersonic speed ; Projectile ; Sustainability ; Ballistic missile ; Scramjet High-Velocity Impact and Material Behavior ; Electromagnetic Launch and Propulsion Technology ; Energetic Materials and Combustion
Authors: Zesheng Shen | Kaifeng Wang | Y. Y. Chen | Xingchao Mao | Shichen Xie | Yingxia Liu
Source: Materials & Design (article, 2025-12-13)
Field: Materials Science — Materials Chemistry

h-BN-based lamellar films with ultralow dielectric constant and high thermal conductivity The rapid development of artificial intelligence, the Internet of Things, and cloud computing has driven demand for high-performance chips. However, RC delay and limitations in heat dissipation have become major obstacles for the future design of high-end chips. Therefore, materials with ultralow dielectric constant and high thermal conductivity are highly desired. The major challenge in preparing such materials is that factors that promote a low dielectric constant often hinder improvements in thermal conductivity. In this work, we synergistically achieved an ultralow dielectric constant of 1.37 at 10 MHz and a high thermal conductivity of 19.65 W m<sup>−1</sup> K<sup>−1</sup> in a hexagonal boron nitride based lamellar composite film using boron nitride nanoflakes and one-dimensional nanofiber cellulose. These results are mainly attributed not only to the synergistic effects of low-polarizability functional groups and air gaps in lowering the dielectric constant, but also to the high crystallinity of the composite film, with parallelly stacked highly (0 0 2) oriented boron nitride nanoflakes, which promoted heat transport. Our research demonstrates the possibility of manufacturing a highly thermal conductive material without compromising its dielectric properties. The prepared composite films also exhibited excellent electrical insulation and mechanical properties. © 2025 The Author(s). Dielectric ; Boron nitride ; Thermal conductivity ; Lamellar structure ; Composite number ; High-κ dielectric ; Thermal conduction ; Crystallinity Thermal properties of materials ; Boron and Carbon Nanomaterials Research ; Graphene research and applications
Authors: Shuaishuai Zhou | Peiwen Sun | Min Zhong | Shaohua Li | Pengfei Zhang | Meihong Liao | Peng Ding | Jingjie Dai
Source: Composites Science and Technology (article, 2025-11-15)
Field: Materials Science — Materials Chemistry

High-performance epoxy composites based on 3D interconnected hybrid filler network interface engineering: Synergistic enhancement of thermal and mechanical properties Epoxy ; Thermal conductivity ; Boron nitride ; Composite number ; Thermal grease ; Thermal ; Thermal management of electronic devices and systems ; Filler (materials) Thermal properties of materials ; Aerogels and thermal insulation ; Epoxy Resin Curing Processes
Authors: Haonan Ren (10778000) | Jingzhe Pang (22495693) | Na Jia (190895) | Ran Chen (182828) | Shixin Gao (10816200) | Tun Cao (2821162)
Source: Figshare (article, 2025-10-25)
Field: Materials Science — Materials Chemistry

Thermal Challengesand Performance Optimization ofITO Microheaters for Photonic Integrated Circuits Phase-change material (PCM) based optical switches enable low-power, large-scale integrated photonic networks through precise, nonvolatile splitting ratio control. The key technology lies in the material system and structural design of microsecond-scale, high-temperature-resistant microheaters. Indium tin oxide (ITO) microheaters offer advantages of low fabrication complexity and compatibility with passive photonic platforms. However, their thermal stability issues during switching operations lead to poor cycling reliability in PCM-based phase shifters. Here, we overcome this challenge through geometric optimization of ITO microheaters, suppressing current crowding to achieve transient thermal stability at 1400 K during 5-μs pulsed operationsignificantly exceeding the temperature requirements for PCM quenching. We characterize the switching speed by constructing a Mach–Zehnder interferometer (MZI) optical switch, demonstrating a fast response time of 2 μs while maintaining a low insertion loss of only 0.5 dB. These results establish ITO as a transformative platform for energy-efficient, reconfigurable photonic routers in AI-driven computing architectures. Photonics ; Microheater ; Optical switch ; Reliability (semiconductor) ; Photonic integrated circuit ; Fabrication ; Thermal management of electronic devices and systems ; Interferometry ; Thermal Phase-change materials and chalcogenides ; Neural Networks and Reservoir Computing ; Photonic and Optical Devices
Authors: Pingjun Luo | Yisimayili Tuersun | Yixin Chen | Zexi Chen | Qi Huang | Xuechen Chen | Zuxin Chen | Sheng Chu
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Extremely Low Thermal Resistance in Solid State Thermal Pad from Graphite Cracking for Artificial Intelligence Age Cracking ; Materials science ; Graphite ; Thermal ; Composite material ; Solid-state ; Engineering ; Engineering physics ; Thermodynamics ; Physics Graphite, nuclear technology, radiation studies ; Hydrogen embrittlement and corrosion behaviors in metals ; High Temperature Alloys and Creep
Authors: Kai Wu | P. Hu | Xue-Qing Gong
Source: Chinese Physics B (article, 2025-12-26)
Field: Materials Science — Materials Chemistry

Machine Learning-Based Prediction for Stability of Hydrogen Species on Metal-Doped CeO <sub>2</sub> (111) Surfaces Abstract Tuning the stability of hydrogen species on metal-doped CeO 2 surfaces is a crucial aspect in the rational design of more efficient CeO 2 based catalysts. However, traditional theoretical and experimental approaches are limited in their capacity to explore the complex chemical space arising from doping and defect introduction. To address this limitation, we establish a comprehensive dataset using high-precision density functional theory (DFT) calculations for the CeO 2 (111) surfaces doped with 43 different metals, encompassing three types of surface structures and five representative hydrogen adsorption configurations. A multi-level feature engineering strategy is then employed, combining elemental properties and multi-center Smooth Overlap of Atomic Positions (SOAP) feature. The ensemble ExtraTrees regression model can achieve reliable prediction of adsorption energies (R 2 = 0.85, RMSE = 0.59 eV), and its robustness is assessed through repeated cross-validation. Shapley Additive Explanations (SHAP) analysis shows that model predictions rely mainly on the principal component of local geometric features. Analysis across principal component intervals indicates that the SOAP-derived features differentiate coordination symmetry, anisotropy, and cooperative effects, and reveal their influences on adsorption energies. The results of Principal Component Analysis (PCA) and cross-validation suggest that representing samples within a physicochemical feature space provide a more nuanced understanding of microenvironmental effects on hydrogen stability compared with the traditional configuration-based approaches. This study provides a data-driven framework for high-throughput screening and mechanistic exploration of structure–property relationships in complex multi-component oxide catalysts. Principal component analysis ; Adsorption ; Robustness (evolution) ; Stability (learning theory) ; Oxide ; Feature (linguistics) ; Hydrogen ; Component (thermodynamics) ; Chemical stability Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Electrocatalysts for Energy Conversion