jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yunfei Xu | Xueqian Zhang | Xiaoxi Ding | Wenbin Huang | Fuh‐Gwo Yuan
Source: Advanced Functional Materials (article, 2025-07-02)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Conformal Signal Processing Metasurface‐Enabled Lamb Wave Synchronous Directional Multiple Access Edge Communication Abstract Constructing future intelligent industrial systems requires an indispensable edge real‐time information interaction capability in closed metal environments, which are of great importance to underwater sciences, defense, space exploration, hazardous materials storage, and unmanned operations. However, wired communication through perforations and wireless electromagnetic wave communication are impractical there. Inspired by the powerful signal processing capability of electromagnetic wave metasurfaces, a conformal signal processing metasurface (CSPM)‐enabled Lamb wave synchronous multiple access communication scheme is reported to achieve near‐zero‐algorithm and bit‐error‐free demodulation, which breaks through the limitation that Lamb wave communication cannot be real‐time due to the large computational complexity. The elaborate CSPM integrates filter and amplifier directly into the physical channel, combining easy‐to‐machine, excellent filtering and amplification performance, as well as space utilization. A CSPM‐enabled dual‐user synchronous communication system prototype was built and achieved the first edge real‐time multi‐user Lamb wave communication, demonstrating significant superiority in bit error rate, computational resource, power consumption, and demodulation rate. This work offers a reliable solution for the challenging information interaction in future intelligent industrial systems. Materials science ; Conformal map ; Signal processing ; Enhanced Data Rates for GSM Evolution ; SIGNAL (programming language) ; Acoustics ; Optoelectronics ; Optics ; Telecommunications ; Computer science ; Physics ; Radar Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Indoor and Outdoor Localization Technologies
Authors: Seyyed Mohsen Mortazavinejad | Mostafa Alakhdar | Ludwig Vinches | Stéphane Hallé
Source: International Communications in Heat and Mass Transfer (article, 2025-07-01)
Field: Materials Science — Polymers and Plastics

A novel methodology for calculating thermal conductivity of natural hollow fibers with validation in nonwoven fabric structures Natural fibers, especially hollow ones, are increasingly used in nonwoven insulation structures for their superior thermal insulation performance, renewability, and biodegradability, as hollow fibers offer lower thermal conductivity than solid fibers. However, accurately measuring the thermal conductivity of single hollow fibers, particularly given their extremely small diameter (10–50 μm) and thin wall thickness (around 1 μm), remains challenging, limiting the understanding of their role in composite materials. To address this, a novel approach combines experimentally measured bulk thermal conductivity with theoretical models for effective and radiative thermal conductivity in a numerical iterative process. Additionally, a theoretical framework was established to analyze composite thermal conductivity and was validated through experimental bench tests. After successfully predicting the thermal conductivity of a single hollow fiber, results indicated minimal anisotropy in the examined fibers that can be attributed to their thin wall thickness. Furthermore, the study demonstrated that fiber arrangement had little impact on thermal conductivity in highly porous structures, while decreasing fiber diameter significantly reduced radiative thermal conductivity due to increased scattering. These findings provide a comprehensive framework for evaluating the thermal behavior of hollow fibers and optimizing natural-fiber-based insulation materials, contributing to the development of more efficient and sustainable thermal insulation solutions. Materials science ; Nonwoven fabric ; Thermal conductivity ; Composite material ; Thermal ; Synthetic fiber ; Natural fiber ; Fiber ; Thermodynamics ; Physics Textile materials and evaluations ; Natural Fiber Reinforced Composites ; Additive Manufacturing and 3D Printing Technologies
Authors: Ya-Yung Huang | Jianguo Lai | Hsien-Huang P. Wu
Source: Preprints.org (preprint, 2025-04-16)
Field: Materials Science — Polymers and Plastics

Real-Time 3D Vision-Based Robotic Path Planning for Automated Adhesive Spraying on Lasted Uppers in Footwear Manufacturing The automation of adhesive application in footwear manufacturing is challenging due to complex surface geometries and model variability. This study presents an integrated 3D vision-based robotic system for adhesive spraying on lasted uppers. A triangulation-based scanning setup reconstructs each upper into a high-resolution point cloud, enabling customized spraying path planning. A six-axis robotic arm executes the path using an adaptive transformation matrix that aligns with surface normals. UV fluorescent dye and inspection are used to verify adhesive coverage. Experimental results confirm high repeatability and precision, with most deviations within the industry-accepted &amp;plusmn;1 mm range. While localized glue-deficient areas were observed around high-curvature regions such as the toe cap, these remain limited and serve as a basis for further system enhancement. The system significantly reduces labor dependency and material waste while improving production efficiency. It has been successfully installed and validated on a production line in Hanoi, Vietnam, meeting real-world industrial requirements. This research contributes to advancing intelligent footwear manufacturing by integrating 3D vision, robotic motion control, and automation technologies. Computer vision ; Adhesive ; Computer science ; Artificial intelligence ; Computer graphics (images) ; Materials science ; Composite material Textile materials and evaluations
Authors: Chinika Dangi | Susmita Naskar
Source: Advanced Theory and Simulations (article, 2025-05-24)
Field: Materials Science — Materials Chemistry

Surface Elasticity Based Nonlocal Vibration Analysis of Bidirectional Functionally Graded Tapered Nanobeam Abstract The present paper proposes the impact of the mutual interplay of nonuniform geometry with surface and nonlocal stresses on the vibration characteristics of bi‐directional functionally graded tapered nanobeam with surface layers. The material composition of nanobeam is assumed to follow a power‐law distribution along the thickness and exponential along the length. The nonuniformity in the geometry of nanobeam arises due to the linear variation of thickness along its length. The considered nanobeam is modeled as a Timoshenko nanobeam with surface layers of negligible thickness. The nonlocal and surface effects are incorporated using Eringen's nonlocal theory with Gurtin‐Murdoch's surface elasticity theory. Hamilton's energy principle is employed to derive the nonlocal equations of motion with boundary conditions. The differential quadrature method is exploited to obtain the natural frequencies and the convergence of the method is demonstrated. A parametric study is introduced to investigate the influence of critical parameters such as taper parameter, surface parameter and nonlocal parameter on the vibration characteristics of bi‐directionally graded nanobeam. This work explains that the nonuniformity in the geometry of nanobeam significantly influences the frequency range of tapered nanobeam with surface layers. These results will serve as a benchmark for future work on nonuniform nanostructures. Elasticity (physics) ; Vibration ; Materials science ; Surface (topology) ; Structural engineering ; Composite material ; Mechanics ; Physics ; Mathematics ; Acoustics ; Geometry ; Engineering Nonlocal and gradient elasticity in micro/nano structures ; Composite Structure Analysis and Optimization ; Numerical methods in engineering
Authors: Mostafa Alakhdar | Ludwig Vinches | Stéphane Hallé
Source: Journal of Industrial Textiles (article, 2025-03-23)
Field: Materials Science — Polymers and Plastics

Assessing thermal resistance of a nonwoven textile under wind exposure: Challenging ISO 9920 with experimental insights Numerous workers across various industries, from construction and transportation to agriculture and emergency response, face harsh working environments characterized by cold temperatures and intense winds. These conditions present serious health and safety risks, which may result in hypothermia. Although established standards, such as ISO 11092, are crucial in measuring the thermal resistance of textile assemblies, an essential factor is often overlooked: the influence of wind speed and direction. In this context, this article aimed to address this gap in current knowledge by investigating the effects of wind on the thermal resistance of nonwoven textile assemblies, to develop a more effective protective clothing system for harsh environments. This study investigated the effect of horizontal and vertical wind speeds on the thermal resistance of a technical bio-based nonwoven assembly, composed of milkweed, kapok and polylactic acid, aiming to understand how forced convection influences heat transfer in real-world conditions. Three samples (A, B, and C) were tested under wind speeds ranging from 0 to 4 m·s −1 , and their thermal resistance was measured in both horizontal and vertical wind directions. Results showed that increasing wind speed consistently decreased thermal resistance for all samples. Vertical wind demonstrated a more pronounced effect, with reductions in thermal resistance reaching 81% for Sample C compared to 51% under horizontal wind. Comparison of experimental and theoretically predicted thermal resistance values using the models presented in the ISO 9920 standard, indicated significant discrepancies. Textile ; Materials science ; Thermal comfort ; Thermal ; Thermal resistance ; Resistance (ecology) ; Nonwoven fabric ; Composite material ; Mechanical engineering ; Engineering ; Ecology ; Meteorology ; Fiber Textile materials and evaluations ; Thermoregulation and physiological responses ; Additive Manufacturing and 3D Printing Technologies
Authors: Xiaoqin Wang | Jia Wang | C.R Duan | Xinjing Wang | Dongli Liang
Source: Sustainability (article, 2025-10-08)
Field: Materials Science — Materials Chemistry

Systematic Review on the Life Cycle Assessment of Manure-Based Anaerobic Digestion System Manure-based anaerobic digestion (AD) systems serve multiple functions, including waste treatment, energy recovery, and nutrient cycling. However, they also entail additional energy consumption and pollutant emissions. Life cycle assessment (LCA) methodology is typically used to holistically quantify the actual environmental impacts of these systems. Nevertheless, comprehensive reviews synthesizing LCA studies in this field remain limited. Following PRISMA guidelines, this study conducted a systematic literature review of LCA studies on manure-based AD systems, focusing on advancements, inconsistencies, and limitations in LCA methodologies and environmental impact results. The findings indicate considerable variability in functional units, allocation methods, system boundaries, and inventory analysis methods across the literature. These methodological discrepancies and the lack of standardized protocols result in remarkable variability in environmental impact potentials. Additionally, there is lack of consensus on the environmental benefits of AD systems compared to traditional manure management, and co-digestion with energy crops or food waste compared to mono-digestion of manure. Consequently, the environmental impacts of manure-based AD systems remain inconclusive due to methodological heterogeneity and data inconsistencies. Future research should develop scientific and standardized approaches and focus on the completeness of system boundaries, selection of key environmental impact categories, environmental load allocation, inventory data quality, and the transparency of the analysis. Life-cycle assessment ; Environmental impact assessment ; Anaerobic digestion ; Life cycle inventory ; Energy consumption ; Food waste ; Impact assessment Metallurgy and Material Science ; Anaerobic Digestion and Biogas Production
Authors: Yi Mou | Long Zhou | Weizhen Chen | Jianguo Liu | Teng Li
Source: Algorithms (article, 2025-07-09)
Field: Chemistry — Analytical Chemistry

Filter Learning-Based Partial Least Squares Regression and Its Application in Infrared Spectral Analysis Partial Least Squares (PLS) regression has been widely used to model the relationship between predictors and responses. However, PLS may be limited in its capacity to handle complex spectral data contaminated with significant noise and interferences. In this paper, we propose a novel filter learning-based PLS (FPLS) model that integrates an adaptive filter into the PLS framework. The FPLS model is designed to maximize the covariance between the filtered spectral data and the response. This modification enables FPLS to dynamically adapt to the characteristics of the data, thereby enhancing its feature extraction and noise suppression capabilities. We have developed an efficient algorithm to solve the FPLS optimization problem and provided theoretical analyses regarding the convergence of the model, the prediction variance, and the relationships among the objective functions of FPLS, PLS, and the filter length. Furthermore, we have derived bounds for the Root Mean Squared Error of Prediction (RMSEP) and the Cosine Similarity (CS) to evaluate model performance. Experimental results using spectral datasets from Corn, Octane, Mango, and Soil Nitrogen show that the FPLS model outperforms PLS, OSCPLS, VCPLS, PoPLS, LoPLS, DOSC, OPLS, MSC, SNV, SGFilter, and Lasso in terms of prediction accuracy. The theoretical analyses align with the experimental results, emphasizing the effectiveness and robustness of the FPLS model in managing complex spectral data. Partial least squares regression ; Regression ; Regression analysis ; Filter (signal processing) ; Infrared ; Computer science ; Mathematics ; Statistics ; Artificial intelligence ; Pattern recognition (psychology) ; Physics ; Computer vision ; Optics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Statistical Methods and Models
Authors: Stefan P. Schmid | Ella Miray Rajaonson | Cher Tian Ser | Mohammad Haddadnia | Shi Xuan Leong | Alán Aspuru‐Guzik | Agustinus Kristiadi | Kjell Jorner | Felix Strieth‐Kalthoff
Source: ArXiv.org (preprint, 2025-02-26)
Field: Materials Science — Materials Chemistry

One Set to Rule Them All: How to Obtain General Chemical Conditions via Bayesian Optimization over Curried Functions General parameters are highly desirable in the natural sciences - e.g., chemical reaction conditions that enable high yields across a range of related transformations. This has a significant practical impact since those general parameters can be transferred to related tasks without the need for laborious and time-intensive re-optimization. While Bayesian optimization (BO) is widely applied to find optimal parameter sets for specific tasks, it has remained underused in experiment planning towards such general optima. In this work, we consider the real-world problem of condition optimization for chemical reactions to study how performing generality-oriented BO can accelerate the identification of general optima, and whether these optima also translate to unseen examples. This is achieved through a careful formulation of the problem as an optimization over curried functions, as well as systematic evaluations of generality-oriented strategies for optimization tasks on real-world experimental data. We find that for generality-oriented optimization, simple myopic optimization strategies that decouple parameter and task selection perform comparably to more complex ones, and that effective optimization is merely determined by an effective exploration of both parameter and task space. Bayesian optimization ; Set (abstract data type) ; Task (project management) ; Identification (biology) ; Optimization problem ; Range (aeronautics) ; Selection (genetic algorithm) ; Bayesian probability Machine Learning in Materials Science ; Advanced Multi-Objective Optimization Algorithms ; Advanced Bandit Algorithms Research
Authors: Danial Khatamsaz | Vahid Attari | Raymundo Arróyave
Source: Acta Materialia (article, 2025-10-01)
Field: Materials Science — Materials Chemistry

Microstructure-aware Bayesian materials design Bayesian optimization ; Curse of dimensionality ; Materials informatics ; Probabilistic logic ; Convergence (economics) ; Uncertainty quantification ; Material Design ; Bayesian probability ; Gaussian process ; Characterization (materials science) Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Anodic Oxide Films and Nanostructures
Authors: Danial Khatamsaz | Vahid Attari | Raymundo Arróyave
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Microstructure-Aware Bayesian Materials Design Microstructure ; Bayesian probability ; Materials science ; Computer science ; Artificial intelligence ; Metallurgy Machine Learning in Materials Science ; Thermal properties of materials ; Electronic Packaging and Soldering Technologies
Authors: Ivan A. Bespalov | Nikolai V. Krivoshchapov | Alexey A. Lisov | Vasiliy A. Chaliy | Michael G. Medvedev
Source: Journal of Chemical Information and Modeling (article, 2025-06-05)
Field: Materials Science — Materials Chemistry

Physics-Informed Bayesian Optimization for Conformational Ensemble Augmentation Conformational search is a key part of reaction modeling, molecular docking, and other fields of computational chemistry, where it is important to take molecules' flexibility into account. However, modern conformational search approaches provide no guarantee that they did not miss any important conformation. Thus, identifying missing conformations from an existing ensemble is of broad importance for computational chemistry. In this paper, we introduce a Bayesian optimization algorithm for conformational ensemble augmentation, that is, locating missing conformers in an existing ensemble, which employs Bayesian optimization with physics-informed torsion-potential-based kernel function and a novel acquisition function that prioritizes potential energy surface exploration for increased conformer diversity. The devised method demonstrates high efficiency on a test set of biologically relevant molecules. Bayesian probability ; Bayesian optimization ; Statistical physics ; Physics ; Computer science ; Artificial intelligence Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation ; Functional Brain Connectivity Studies
Authors: Hannes Holey | Peter Gumbsch | Lars Pastewka
Source: Science Advances (article, 2025-09-12)
Field: Materials Science — Materials Chemistry

Active learning for nonparametric multiscale modeling of boundary lubrication Lubricated friction is a multiscale problem where molecular processes dictate the macroscopic response of the system. Traditional lubrication models rely on semiempirical constitutive relations, which become unreliable under extreme conditions. Here, we present a simulation framework that seamlessly couples molecular and continuum models for boundary lubrication without fixed-form constitutive laws. We train Gaussian process regression models as surrogates for predicting interfacial shear and normal stress in molecular dynamics simulations. An active learning algorithm ensures that our model adapts in scenarios where common constitutive laws fail, such as at layering transitions. We demonstrate our approach for nanoscale fluid flow over rough and heterogeneous surfaces, paving the way for accurate boundary lubrication simulations at experimental length and timescales. Multiscale modeling ; Lubrication ; Gaussian process ; Boundary (topology) ; Constitutive equation ; Kriging ; Process (computing) ; Boundary value problem ; Boundary lubrication Machine Learning in Materials Science ; Force Microscopy Techniques and Applications ; Protein Structure and Dynamics
Authors: Gandhi Piyushkumar Arvindbhai | N T Satish | P. V. R. Ravindra Reddy | P. Prabhakar Reddy
Source: International Journal on Interactive Design and Manufacturing (IJIDeM) (article, 2025-09-26)
Field: Materials Science — Polymers and Plastics

Self-healing shape nanocomposites for structural longevity in aerospace applications based on interpretable artificial intelligence Nanocomposite ; Durability ; Aerospace ; Ultimate tensile strength ; Epoxy ; Damage tolerance ; Surrogate model ; Piezoelectricity ; Graphene Polymer composites and self-healing ; Innovations in Concrete and Construction Materials ; Innovative concrete reinforcement materials
Authors: Niko Krebs | Martin Demleitner | Rodrigo Q. Albuquerque | Bernhard Schartel | Holger Ruckdäschel
Source: Computational Materials Science (article, 2025-09-16)
Field: Materials Science — Polymers and Plastics

Bayesian Optimization of flame-retardant performance in a high-Tg epoxy resin system Polymeric materials are widely used due to their mechanical properties and cost-effectiveness, but their inherent flammability requires effective flame-retardant additives to meet safety standards. Optimizing multi-component flame-retardant formulations is challenging due to the vast experimental space. This study applies Bayesian Optimization (BO) to optimize flame-retardant formulations in high glass transition temperature (Tg) epoxy resins. Aluminum diethyl phosphinate (AlPi) was systematically combined with three synergists: zinc stannate (ZnSt), a silicone-based additive (DowSil), and low-melting glass frits (Ceepree). BO-guided experimental design expanded from 16 initial formulations to a total of 28, minimizing the Maximum Average Rate of Heat Emission (MARHE) under the constraint of Total Smoke Production (TSP) < 17 m 2 using the epsilon-constraint method. BO revealed non-linear synergistic interactions: ZnSt significantly reduced smoke production while AlPi effectively lowered heat release. The optimized formulation (BO7) achieved the lowest MARHE (122 kW/m 2 ) while maintaining acceptable smoke levels, establishing a new Pareto front. The results demonstrate the effectiveness of BO in accelerating the development of synergistic, halogen-free flame-retardant polymer systems, offering a scalable and sustainable approach to polymer formulation design. • Workflow of Bayesian Optimization applied to flame-retardant formulation design in high-Tg epoxy resins. • Starting from initial experimental data, BO iteratively suggests new formulations,which are processed, characterized, and analyzed for heat release (MARHE) and smoke production (TSP). • The optimization cycle refines the dataset, revealing synergistic effects and guiding the discovery of optimal halogen-free formulations. Bayesian optimization ; Epoxy ; Smoke ; Ignition system ; Polymer ; Multi-objective optimization ; Aluminium ; Optimization problem Flame retardant materials and properties ; Fire dynamics and safety research ; Synthesis and properties of polymers