jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Saad Bin Safiullah | Qiye Zheng
Source: Journal of Physics Condensed Matter (article, 2025-11-14)
Field: Materials Science — Materials Chemistry

Atomic-scale thermal metrology with electron beams: thermometry, phonon dynamics spectroscopy, and nanoscale heat sources—a review Understanding how heat flows and dissipates at the atomic scale is critical for improving the performance and reliability of modern nanoelectronics and energy materials. Electron beam techniques-enabled by the high spatial resolution of transmission and scanning electron microscopy (TEM and SEM)-offer powerful, non-contact methods to probe local temperature and atomic vibrations beyond the reach of traditional optical or scanning probe approaches. In this review, we focus on three major fronts of recent progress in electron beam-based thermal analysis. First, we highlight advances in mapping atomic vibrations using vibrational electron energy-loss spectroscopy (EELS) in scanning TEM, which allow researchers to study how heat-carrying quasiparticles such as phonons, magnons, and polaritons behave near defects, grain boundaries, and interfaces. Second, we provide a comprehensive overview of electron beam-based thermometry techniques, including both EELS-based methods-such as plasmon energy shifts, core-loss edge shifts, and phonon peak analysis-and non-EELS thermometric signals derived from electron scattering, diffraction, cathodoluminescence, and electron beam current-based responses. These approaches are compared in terms of temperature sensitivity, spatial resolution, and practical implementation across TEM and SEM platforms. Third, we examine the use of focused electron beams as localized, non-contact nanoscale heat sources, which enables direct measurement of thermal conductivity, interfacial resistance, and heat dissipation pathways in nanostructures. This capability also supports emerging applications in materials characterization and additive nanofabrication. We further discuss the underlying electron-matter interactions that give rise to these thermometric signals, and outline current limitations, including moderate sensitivity, demanding sample preparation, and the need for advanced calibration and modeling-particularly in heterogeneous or device-scale systems. We conclude by highlighting opportunities for advancing e-beam thermal metrology through improved instrumentation, robust theoretical modeling, and data-driven analysis, establishing these approaches as powerful and quantitative framework for studying heat flow and energy transport at the atomic scale. Scanning thermal microscopy ; Plasmon ; Phonon ; Nanoelectronics ; Scanning transmission electron microscopy ; Metrology ; Electron energy loss spectroscopy ; Electron ; Nanoscopic scale ; Scanning electron microscope Thermal properties of materials ; Advanced Electron Microscopy Techniques and Applications ; Silicon and Solar Cell Technologies
Authors: Jorell D. Pelingo
Source:  (article, 2025-12-02)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Hybrid Evaluation of Pure Argon Plasma Treatment for Enhanced Wire Bonding and Manufacturing Efficiency in Microelectronics Wire bonding in microelectronic packaging requires exceptionally clean surfaces, as even minor contamination can compromise bond integrity and device performance. This study evaluates pure Argon plasma cleaning as a cleaner, more controlled alternative to the conventional Argon-Nitrogen gas mixture. This study presents a first-of-its-kind hybrid evaluation approach that uniquely integrates established metrologies to assess pure argon plasma treatment. Rapid diagnostic tools-Plazmark™ plasma indicators and the Konica Minolta® FD-5 spectro-densitometer-were synergistically combined with advanced surface characterization techniques, including Time-of-Flight Secondary Ion Mass Spectrometry (ToF-SIMS) and Atomic Force Microscopy (AFM). This novel integration enables a comprehensive, multidimensional analysis of plasma effects, bridging rapid process feedback with indepth surface insights. A Design of Experiment (DOE) identified RF Power and Step Time as key parameters influencing cleaning performance. Results showed that pure Argon plasma significantly reduced surface contaminants, improved surface roughness, and enhanced metallurgical bonding. Ball Shear Tests (BST) confirmed stronger bonds, and Intermetallic Compound (IMC) analysis revealed more uniform coverage. While the Wire Pull Test (WPT) and Stitch Pull Test (SPT) remained consistent, a 21% increase in Wire Bond Units Per Hour (UPH) was achieved due to faster bonding after performing wire bond parameter optimization. Reliability testing, including Temperature Cycling (TC) and High Temperature Storage Life (HTSL), confirmed the durability of pure Argon plasma-treated samples. Evaluations showed higher shear strength, thicker IMC layers, and stable failure modes. No electrical failures were observed after Unbiased Highly Accelerated Stress Test (uHAST), validating performance under thermal and humidity stress. These findings support pure Argon plasma as a superior, scalable, data-driven, and cost-effective solution for high-throughput semiconductor packaging. Wire bonding ; Argon ; Microelectronics ; Plasma cleaning ; Plasma ; Intermetallic ; Temperature cycling ; Durability Copper Interconnects and Reliability ; Plasma Diagnostics and Applications ; Electronic Packaging and Soldering Technologies
Authors: Ulviyya J. Yolchuyeva | Vagif М. Abbasov | Orhan R. Abbasov | Yusif Abdullayev | Rena A. Jafarova | Ayaz M. Mammadov | Ravan A. Rahimov | Gunay A. Hajiyeva | Jochen Autschbach
Source: Fuel (article, 2025-07-21)
Field: Chemistry — Analytical Chemistry

N-Octylaminopropan-2-ol surfactant for crude-oil asphaltene dispersion: Integrated experimental and modeling insights Asphaltene ; Pulmonary surfactant ; Crude oil ; Dispersion (optics) ; Petroleum engineering ; Chemistry ; Chromatography ; Chemical engineering ; Materials science ; Organic chemistry ; Geology ; Physics ; Engineering ; Optics Petroleum Processing and Analysis ; Enhanced Oil Recovery Techniques ; Hydrocarbon exploration and reservoir analysis
Authors: akeely, adam
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2025-10-27)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Critical Charge-Activated Magnetic Amplification in Ni-Doped SiC: Evidence of a Lattice-Bound Magnetic Soliton A fundamental breakthrough in condensed matter physics and materials science. This work reports the first-principles discovery of a novel, non-linear magnetic phenomenon in Nickel-doped Silicon Carbide (SiC) that challenges decades of conventional wisdom on impurity magnetism. We demonstrate that the magnetic response is not a simple, decaying polarization but a complex, collective excitation: a lattice-bound magnetic soliton. SCIENTIFIC BREAKTHROUGH: The central discovery is a "critical charge-activated magnetic switch." We prove that induced magnetism does not scale with proximity to the impurity. Instead, it is "switched on" only when surrounding host atoms surpass a critical charge transfer threshold of |ΔQ| ≈ 0.32e. This mechanism explains the observed, and previously unknown, spatial structure: A "Magnetic Silence Zone" near the impurity, where magnetism is actively suppressed—a direct violation of all standard models. A "Magnetic Amplification Shell" at a specific radial distance, where induced moments are dramatically enhanced. This work represents a paradigm shift from "diffusion and decay" models to a new understanding of "charge-activated, non-linear amplification." Crucially, this discovery was achieved through a purely data-driven scientific approach, where the physics was revealed directly from computational data without reliance on pre-existing analytical equations. KEY RESULTS: Direct evidence of non-linear amplification: A Ni impurity (+0.214 µB) induces moments up to 0.32 µB in the host lattice. Failure of conventional models: Standard power-law, exponential, and Gaussian decay models are shown to fail completely (R² ≈ 0 for the latter two), proving the non-trivial nature of the phenomenon. Identification of the switching mechanism: A clear threshold in charge transfer (|ΔQ|) is identified as the trigger for magnetic activation. Characterization of the magnetic soliton: The spatial and electronic properties of this new quasi-particle are described and visualized. Magnetism ; Polarization (electrochemistry) ; Physics beyond the Standard Model ; Magnetic moment ; Magnetic field ; Charge (physics) Magnetic and transport properties of perovskites and related materials ; Physics of Superconductivity and Magnetism ; Magnetism in coordination complexes
Authors: Zhichao Zhang | Jun Zhou | Xuerui You | Yuting Bai | Jianfang Liu | Jifan Hu
Source: Journal of Luminescence (article, 2025-11-04)
Field: Materials Science — Materials Chemistry

Multi-objective prediction of Eu3+ doped double perovskite oxide phosphors with explainability and photoluminescence properties of novel Sr2ScNbO6:Eu3+ phosphors Phosphor ; Perovskite (structure) ; Monoclinic crystal system ; Stability (learning theory) ; Thermal stability ; Photoluminescence ; Oxide ; Doping Luminescence Properties of Advanced Materials ; Microwave Dielectric Ceramics Synthesis ; Thermal Expansion and Ionic Conductivity
Authors: Suyang Wang | Qingqi Zhu | Jiangshan Hao | Fang Wang | Fucun Li | G. Zhao | Fang Dong
Source: Complex & Intelligent Systems (article, 2025-11-24)
Field: Materials Science — Materials Chemistry

Bayesian-Optimized Ensemble Models for Predicting Steel Mechanical Properties Abstract Steel mechanical properties such as yield strength (YS), tensile strength (TS), and elongation (EL) are critical indicators of product quality and safety in downstream applications. In industrial practice, these properties are commonly obtained through tensile testing, which is destructive, time-consuming, and costly, and thus provides limited support for rapid quality feedback in continuous production. This work explores a data-driven alternative that leverages historical production records to predict steel mechanical properties. Motivated by the strongly coupled chemical and physical evolution of steel during heating, rolling, cooling, and heat treatment, we propose an ensemble-learning framework with Bayesian optimization for robust multi-target prediction under real production conditions. Specifically, we construct predictive datasets covering all steel grades produced on the same production line and design a stacking ensemble that uses Random Forest (RF) and CatBoost (CATB) as base learners and Linear Regression (LR) as a meta-learner, with Bayesian optimization for automated hyperparameter tuning. Extensive experiments on independent test data and online production data demonstrate strong predictive performance and practical applicability. Under industrial tolerance bands, the proposed method achieves 92.62% hit rate for YS (±30 MPa), 97.41% for TS (±30 MPa), and 94.53% for EL (±4%), outperforming conventional single-model baselines. These results indicate that the proposed approach can deliver accurate, stable, and generalizable predictions across steel grades, reducing reliance on frequent destructive testing and enabling timely process monitoring and quality control in steel manufacturing. Hyperparameter ; Random forest ; Production (economics) ; Bayesian probability ; Ensemble forecasting ; Process (computing) ; Quality (philosophy) ; Ensemble learning ; Bayesian inference Machine Learning in Materials Science ; Microstructure and Mechanical Properties of Steels ; Metallurgy and Material Forming
Authors: Jorell D. Pelingo
Source:  (article, 2025-12-02)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Hybrid Evaluation of Pure Argon Plasma Treatment for Enhanced Wire Bonding and Manufacturing Efficiency in Microelectronics Wire bonding in microelectronic packaging requires exceptionally clean surfaces, as even minor contamination can compromise bond integrity and device performance. This study evaluates pure Argon plasma cleaning as a cleaner, more controlled alternative to the conventional Argon-Nitrogen gas mixture. This study presents a first-of-itskind hybrid evaluation approach that uniquely integrates established metrologies to assess pure argon plasma treatment. Rapid diagnostic tools-Plazmark™ plasma indicators and the Konica Minolta® FD-5 spectro-densitometer-were synergistically combined with advanced surface characterization techniques, including Time-of-Flight Secondary Ion Mass Spectrometry (ToF-SIMS) and Atomic Force Microscopy (AFM). This novel integration enables a comprehensive, multi-dimensional analysis of plasma effects, bridging rapid process feedback with in-depth surface insights. A Design of Experiment (DOE) identified RF Power and Step Time as key parameters influencing cleaning performance. Results showed that pure Argon plasma significantly reduced surface contaminants, improved surface roughness, and enhanced metallurgical bonding. Ball Shear Tests (BST) confirmed stronger bonds, and Intermetallic Compound (IMC) analysis revealed more uniform coverage. While the Wire Pull Test (WPT) and Stitch Pull Test (SPT) remained consistent, a <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{2 1 \%}$</tex> increase in Wire Bond Units Per Hour (UPH) was achieved due to faster bonding after performing wire bond parameter optimization. Reliability testing, including Temperature Cycling (TC) and High Temperature Storage Life (HTSL), confirmed the durability of pure Argon plasma-treated samples. Evaluations showed higher shear strength, thicker IMC layers, and stable failure modes. No electrical failures were observed after Unbiased Highly Accelerated Stress Test (uHAST), validating performance under thermal and humidity stress. These findings support pure Argon plasma as a superior, scalable, data-driven, and cost-effective solution for highthroughput semiconductor packaging. Argon ; Wire bonding ; Microelectronics ; Plasma cleaning ; Plasma ; Intermetallic ; Temperature cycling ; Durability Copper Interconnects and Reliability ; Plasma Diagnostics and Applications ; Electronic Packaging and Soldering Technologies
Authors: Maria Osetrova | Arsenii Onuchin | Elena Stekolshchikova | Philipp Khaitovich | Kirill Polovnikov
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2025-09-19)
Field: Chemistry — Spectroscopy

Hyperspherical geometry positions the lipidome as a partly independent axis of human brain organization Abstract The brain’s transcriptome is well mapped, but the spatial organization of lipids—over half of brain dry mass—remains poorly defined. We profiled lipidomic (419 species) and transcriptomic (15,013 genes) signatures from 35 anatomically defined regions in four healthy adult donors, measured from the same tissue samples. Both modalities recapitulate major neuroanatomical divisions, yet the lipidome shows distinctive features: a pronounced white–gray asymmetry, a smooth neocortical rostrocaudal gradient, and limbic-specific lipid clusters absent in transcriptomic space. Using regression against gene-expression principal components and two null models (random and anatomy-aware), we define three data-driven classes of lipid–gene relationships: Synchronizers (10%) tightly coupled to gene programs, Anchors (67%) predictable at levels expected from gross anatomy and cell-type composition, and Drifters (23%) largely transcription-independent. A simple geometric framework unifies these patterns: after centering and normalization, molecular profiles lie on a hypersphere where two interpretable coordinates—polar latitude relative to transcriptome-defined white/gray poles and nearest-gene angular distance—jointly index coupling; an analytical null quantitatively explains the observed scaling. Key effects are robust in leave-one-donor-out analyses and position the lipidome as a partly independent organizational axis of the human brain. Broadly, our results provide an interpretable geometric framework for multi-omics integration, supported by an interactive platform. Lipidome ; Hypersphere ; Human brain ; Transcriptome ; Orientation (vector space) ; Geometric shape ; Position (finance) ; Ellipsoid Molecular spectroscopy and chirality
Authors: Peifeng Ling | Siguo Xiao Siguo Xiao
Source:  (article, 2025-12-26)
Field: Materials Science — Materials Chemistry

Emission Spectrum Prediction of Hybrid Phosphors Based on NRBO-XGBoost Accurate prediction of emission spectra is critical for designing high-performance hybrid phosphors. This paper proposes a novel data-driven approach combining XGBoost with Newton-Raphson-based Optimization (NRBO) for emission spectrum prediction of mixed phosphors. The method integrates particle encoding and objective function optimization to enhance model performance. Experiments are conducted on the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{3 d}^{\mathbf{3}}$</tex> ions <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\left(\text{Mn}^{4+}, \text{Cr}^{3+}\right)$</tex> activated phosphors database, predicting both 2E <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\rightarrow$</tex> 4A2 and 4T2 → 4A2 emission maxima. Results demonstrate that the proposed NRBO-XGBoost model achieves superior performance with an <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{R}^{\mathbf{2}}$</tex> of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{0. 9 9 9 9 8 3}$</tex>, MSE of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{0. 0 0 0 2 6 1}$</tex>, and RMSE of 0.0153, significantly outperforming traditional methods including Ridge, Lasso, KNN, and Random Forest. Ablation studies confirm the effectiveness of the NRBO module, while sensitivity analysis validates model robustness across various hyperparameter configurations. This framework provides an efficient and reliable tool for spectral prediction in phosphor materials research. Emission spectrum ; Phosphor ; Noise (video) ; Spectrum (functional analysis) ; Black-body radiation Luminescence Properties of Advanced Materials ; Radiation Detection and Scintillator Technologies ; Ga2O3 and related materials
Authors: Di Wang | Dafeng Luo | Hongwei Li | Xinrui Han | Yanhong Wang | Chaoyang Wang | Dongwei Li | Yimin Hou
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Analysis of Dynamic Characteristic in a Solid Oxide Fuel Cell Multi-Physics Coupling Model Coupling (piping) ; Oxide ; Fuel cells ; Solid oxide fuel cell ; Physics ; Materials science ; Nuclear engineering ; Statistical physics ; Mechanical engineering ; Engineering ; Chemical engineering ; Quantum mechanics ; Metallurgy Advancements in Solid Oxide Fuel Cells
Authors: Yinghui Gu | Shiwei Yuan | Xiaodan Wang | Kai Song
Source: ChemSci advances. (article, 2025-12-01)
Field: Materials Science — Materials Chemistry

Interpreting the Triple Roles of Luminescent Nanomaterials in Plant Rhizosphere Microecology: From Fluorescent Probes and Stressors to Smart Tools The plant rhizosphere microbiome serves as a central regulatory hub for maintaining plant health and ecosystem functions; however, the complexity of this micro-ecological interface has long impeded the resolution of deep-seated mechanisms. Luminescent nanomaterials (LNMs), such as quantum dots and carbon dots, offer revolutionary technical variables for penetrating this "black box" by virtue of their superior optical properties and interfacial activities. Drawing on the Web of Science Core Collection database, this study employs bibliometric methods to construct a visual knowledge graph, aiming to quantitatively elucidate the knowledge architecture and evolutionary trajectory of this interdisciplinary field from a data-driven perspective. Keyword co-occurrence network analysis reveals that the field has differentiated into three core thematic clusters with intrinsic logical connections: (1) nanomaterials as "in situ optical probes" for the visualization of dynamic rhizosphere processes; (2) nanomaterials as novel "environmental stressors," with a focus on their perturbation mechanisms regarding microbial community structure and function; and (3) nanomaterials as frontier "smart regulatory tools," demonstrating potential in targeted delivery, stress resistance enhancement, and microbiome engineering. The mapping further highlights structural bottlenecks currently facing the field: the elucidation of molecular interaction mechanisms at the micro-scale remains superficial, there is a lack of an effective technical closed-loop between "probe" development and "tool" application, and a significant ecological validity gap exists between experimental systems and real-world field scenarios. Future research urgently requires interdisciplinary synergy involving multi-omics integration, the construction of smart nanoplatforms, and full life-cycle ecological safety assessments. This will drive a paradigm shift from "phenomenon observation" to "rational design," ultimately laying the scientific foundation for a safe and precise agricultural nanotechnology system. Rhizosphere ; Microbiome ; Visualization ; Interface (matter) ; Field (mathematics) ; Thematic map Carbon and Quantum Dots Applications ; Nanoparticles: synthesis and applications ; Plant-Microbe Interactions and Immunity