jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yahia Halabi | Hu Xu | Zhixiang Yu | Wael Alhaddad | Cheng Yang | Changgen Wu
Source: Fatigue & Fracture of Engineering Materials & Structures (article, 2025-03-26)
Field: Materials Science — Polymers and Plastics

Predictive Modeling of Tensile Fracture in Synthetic Fiber Ropes Using Generalized Additive Models ABSTRACT This study investigates the tensile failure behavior of synthetic fiber ropes composed of nylon (6‐PA), polypropylene (i‐PP), and polyester (PET) fibers. A comprehensive experimental program was conducted using 202 rope specimens, with nominal diameters ranging from 4 to 20 mm, subjected to monotonic and cyclic loading conditions. The resulting load–strain data were systematically compiled for advanced statistical analysis. Generalized additive models (GAMs) were developed using R programming to predict failure forces and strains, incorporating a Weibull distribution framework for failure responses. The GAM approach demonstrated superior predictive capability, revealing a linear correlation between rope diameter and failure strain across all materials, as well as a nonlinear relationship between diameter and failure force. Notably, GAM models utilizing natural‐spline representations exhibited enhanced performance over conventional Weibull models. These findings contribute to a deeper understanding of the tensile properties of synthetic ropes, offering a data‐driven approach to optimize testing efforts and improve the reliability of these materials for engineering applications. Materials science ; Ultimate tensile strength ; Fracture (geology) ; Composite material ; Fiber ; Generalized additive model ; Structural engineering ; Engineering ; Computer science ; Machine learning Textile materials and evaluations ; Mechanical Behavior of Composites ; Structural Analysis of Composite Materials
Authors: Sukhadeve, Abhilash A | Tayade, Rajendra R
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2025-11-30)
Field: Materials Science — Materials Chemistry

Functionalized Nanoparticles for Detection of Pesticides and Toxic Metal Ions: A Comprehensive Review The emerging technology of nanotechnology has fundamentally changed the approach to environmental monitoring, especially in the process of pesticides and toxic metal ions detection in various environmental and biological samples. It is the functionalised nanoparticles (particularly gold and silver) which have emerged as a potent sensing surface, due to their unique physicochemical properties including large surface areas, surface plasmon resonance at the limit and tuneable optical results. This is a review of recent advances in the detection methodologies of substances in the nanoparticle-based detection of pesticides and toxic metal ions with emphasis on functionalisation strategies and sensing mechanisms along with practical applications. Conventional methods of analysis of pesticides and heavy metals, although accurate, are often time consuming, expensive and require use of advanced equipment and highly skilled employees. Compared to these, nanoparticle-based colourimetric sensors are fast, cost-efficient, and on-site applications with a visual output, which makes them highly applicable in the field and in environmentally limited conditions. The review critically analyzes a diversity of functionalised nanoparticles, such as carbon based, metal based, metal-oxide based and metal-organic framework based nanomaterials, with their synthesis protocols, characterisation mechanisms and detection mechanisms. Specific focus is made on the multimodal sensing strategies, a combination of colourimetric, fluorescent, electrochemical and surface enhanced Raman spectroscopies techniques, thus providing increased sensitivity and selectivity. The new developments in technology in nanozymes, aptamer-functionalised nanoparticles and portable sensors integrated into smartphones are mentioned. The current issues that have also been discussed in the review include matrix interference, reproducibility, standardisation and long-term stability of nanoparticle-based sensors. Future trends especially the incorporation of artificial intelligence, a microfluidic platform and point-of-care testing devices are addressed. This tedious examination is informative to the researcher involved in the manufacturing of next generation nanoparticles based sensors in environmental and food safety monitoring. Surface plasmon resonance ; Metal ions in aqueous solution ; Nanoparticle ; Complex matrix ; Optical sensing ; Pesticide ; Environmental analysis ; Detection limit Advanced Nanomaterials in Catalysis ; Gold and Silver Nanoparticles Synthesis and Applications ; Carbon and Quantum Dots Applications
Authors: Hiroki Kaifu | Sandra M. Troian
Source: Physical Review Applied (article, 2025-12-18)
Field: Materials Science — Materials Chemistry

Power laws for the thermal slip length of a liquid/solid interface from the structure and frequency response of the contact zone The newest and most powerful electronic chips for applications such as artificial intelligence generate so much heat that liquid-based cooling has become indispensable to prevent breakdown from thermal runaway effects. While cooling schemes such as microfluidic networks or liquid immersion are proving effective for now, further progress requires tackling an age-old problem—namely, the intrinsic thermal impedance of the liquid/solid (L/S) interface, quantified either by the thermal boundary resistance or by the thermal slip length. While there are well-known models for estimating bounds on the thermal impedance of a superfluid/metal interface, no analytic models or experimental data are available for normal liquid/solid interfaces. Researchers therefore rely on nonequilibrium molecular dynamics simulations to gain insight into phonon transfer at the L/S interface. Here we explore correlated order and motion within the L/S contact zone in an effort to extract general scaling relations for the thermal slip length in Lennard-Jones (LJ) systems. We focus on the in-plane structure factor and dominant vibrational frequency of the first solid and liquid layer for 180 systems. When scaled by the temperature of the liquid contact layer and characteristic LJ interaction distance, the data collapse onto two power-law equations, one quantifying the reduction in thermal impedance from enhanced in-plane translational order and the other from enhanced frequency matching in the contact zone. More generally, these power-law relations highlight the critical role of surface acoustic phonons, an area of focus that may also prove useful to the development of analytic models as well as instrumentation for testing the relations proposed. Slip (aerodynamics) ; Thermal ; Thermal contact conductance ; Thermal resistance ; Electrical impedance ; Phonon ; Thermal runaway ; Contact area ; Heat transfer ; Thermal contact Thermal properties of materials ; Quantum, superfluid, helium dynamics ; Advanced Physical and Chemical Molecular Interactions
Authors: Suhib Fahmawi | Abed Elrahman Abu Dalu
Source:  (article, 2025-07-03)
Field: Materials Science — Biomaterials

Development of Nano-Robot Technology for Targeting Cancer Cells without Harming Healthy Cells in Patients with Advanced Cancer This comprehensive paper delves into the innovative field of nano-robotic technology and its transformative potential in the realm of cancer treatment, with a particular emphasis on advanced-stage malignancies where conventional therapeutic options often fall short (World Health Organization [WHO], 2023). The study investigates the conceptual framework, design intricacies, and biomedical engineering principles behind nano-robots—microscale machines capable of navigating the human circulatory system to selectively identify and neutralize malignant cells while preserving surrounding healthy tissues (Wang et al., 2021). By conducting a systematic and integrative review of recent scientific literature, preclinical trial data, and emerging laboratory findings from the past five years, this article offers a critical appraisal of the progress made in this field. It also presents visual models and statistical evidence to illustrate the performance, accuracy, and anticipated clinical outcomes of nano-robot-assisted cancer therapy (Chen et al., 2020; Li et al., 2022). Moreover, the paper examines a range of scientific and ethical considerations, including the biocompatibility of nanomaterials, immunogenic risks, cost-effectiveness, and regulatory hurdles related to mass clinical deployment (Singh &amp; Nair, 2021). It underscores the interdisciplinary nature of the technology, which sits at the nexus of nanotechnology, oncology, molecular biology, and artificial intelligence. By outlining current limitations and projecting future trajectories, the study aims to provide a forward-looking perspective on how nano-robots may redefine oncological practice, ushering in an era of precision medicine where cancer treatment is not only more effective but also significantly safer and more personalized (Chen et al., 2020). Cancer ; Cancer cell ; Medicine ; Nano- ; Cancer research ; Internal medicine ; Engineering Nanoparticle-Based Drug Delivery ; Graphene and Nanomaterials Applications ; Nanoplatforms for cancer theranostics
Authors: Elyar Tourani | Brian J. Edwards | Bamin Khomami
Source: ArXiv.org (preprint, 2025-12-18)
Field: Materials Science — Materials Chemistry

ClusTEK: A grid clustering algorithm augmented with diffusion imputation and origin-constrained connected-component analysis: Application to polymer crystallization Grid clustering algorithms are valued for their efficiency in large-scale data analysis but face persistent limitations: parameter sensitivity, loss of structural detail at coarse resolutions, and misclassifications of edge or bridge cells at fine resolutions. Previous studies have addressed these challenges through adaptive grids, parameter tuning, or hybrid integration with other clustering methods, each of which offers limited robustness. This paper introduces a grid clustering framework that integrates Laplacian-kernel diffusion imputation and origin-constrained connected-component analysis (OC-CCA) on a uniform grid to reconstruct the cluster topology with high accuracy and computational efficiency. During grid construction, an automated preprocessing stage provides data-driven estimates of cell size and density thresholds. The diffusion step then mitigates sparsity and reconstructs missing edge cells without over-smoothing physical gradients, while OC-CCA constrains component growth to physically consistent origins, reducing false merges across narrow gaps. Operating on a fixed-resolution grid with spatial indexing ensures the scaling of O(nlog n). Experiments on synthetic benchmarks and polymer simulation datasets demonstrate that the method correctly manages edges, preserves cluster topology, and avoids spurious connections. Benchmarking on polymer systems across scales (9k, 180k, and 989k atoms) shows that optimal preprocessing, combined with diffusion-based clustering, reproduces atomic-level accuracy and captures physically meaningful morphologies while delivering accelerated computation. Cluster analysis ; Spurious relationship ; Grid ; Preprocessor ; Granularity ; Scaling ; Benchmarking ; Mixture model ; Fuzzy clustering Block Copolymer Self-Assembly ; Polymer crystallization and properties ; Machine Learning in Materials Science
Authors: Maziyar Sabet
Source: Applied Physics A (article, 2025-10-23)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Defect-engineered reduced graphene oxide (rGO) for high-performance supercapacitors: bridging defect chemistry, electrochemical analysis, and scalable energy storage applications Graphene ; Supercapacitor ; Energy storage ; Raman spectroscopy ; Oxide ; X-ray photoelectron spectroscopy ; Exfoliation joint ; Electrochemistry Supercapacitor Materials and Fabrication ; Advancements in Battery Materials ; Graphene research and applications
Authors: Sadık Alper Yıldızel | Abdurrahim Toktaş | Gökhan Kaplan
Source: Defence Technology (article, 2025-11-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Artificial intelligence-optimized gypsum-based composites for stealth technology: Integrating high strength performance and radar absorption The demand for lightweight, high-performance materials with electromagnetic shielding capability is increasing, particularly in aerospace, defense, and infrastructure applications. This study introduces a novel gypsum-based composite system enhanced with carbon black (CB), magnetite (Fe 3 O 4 ), boron nitride (BN), and hybrid glass-basalt fibers to simultaneously improve mechanical strength and electromagnetic wave absorption. A limited experimental dataset of 13 formulations was extended to 260 using physics-consistent data augmentation. Multi-objective optimization was then performed using the Artificial Bee Colony (ABC) algorithm to maximize compressive, flexural, and splitting tensile strengths while minimizing return loss in the 8–12 GHz (X-band) range. The optimized formulation (85% gypsum, 15% resin, 6.8% CB, 18.5% Fe 3 O 4 , 2% BN, 1% glass fiber, 0.5% basalt fiber) achieved 34.41 MPa compressive strength (+6.1%), 7.71 MPa flexural strength (+5.3%), and 4.45 MPa splitting tensile strength (+5.7%). Simultaneously, the minimum return loss improved from −22.5 to −24.8 dB, resulting in an enhancement of electromagnetic absorption by 10.2% at 9.72 GHz. Validation tests confirmed the model's accuracy within a 1.35% margin of error. This hybrid methodology, which combines experimental science, AI-driven data expansion, and bio-inspired optimization, offers a scalable and cost-effective route to multifunctional composite design. The findings are directly relevant for radar-absorbing structures, EMI shielding in electronics, and smart civil infrastructure. • Radar absorption was increased by 10.22%, while compressive strength was increased by 6.05% simultaneously. • The best mix achieved a return loss of −24.8 dB and a compressive strength of 34.4 MPa. • Experimental validation confirmed predictions with 1.35% accuracy. Compressive strength ; Ultimate tensile strength ; Flexural strength ; Composite number ; Reflection loss ; Electromagnetic shielding ; Radar ; Absorption (acoustics) Electromagnetic wave absorption materials ; Aluminum Alloys Composites Properties ; Natural Fiber Reinforced Composites
Authors: Shihao Feng | Haifeng Chen | Jian Zhang | Meng An | Gang Zhang
Source: Chinese Physics Letters (article, 2025-12-29)
Field: Materials Science — Materials Chemistry

Data-Driven Prediction of Thermal Conductivity from Short MD Trajectories: A GCN-LSTM Approach Abstract We propose a data-driven framework for rapid prediction of thermal conductivity in solids based on short-time molecular dynamics (MD) simulations. By converting atomic configurations into graph representations, a graph convolutional network (GCN) is used to extract spatial features, which are then processed by a long short-term memory (LSTM) network to capture the temporal evolution of physical properties. The framework is validated using equilibrium MD simulations of germanium at 1000 K across various system sizes. With size-specific normalization and optimized hyperparameters, the model accurately predicts the converged thermal conductivity, achieving results consistent with experimental data. Notably, the proposed method significantly reduces computational time by up to 800-fold at large system sizes, which demonstrates its potential to accelerate thermal transport simulations in solid-state systems. Thermal conductivity ; Normalization (sociology) ; Molecular dynamics ; Thermal ; Graph ; Convolutional neural network ; Conductivity Machine Learning in Materials Science ; Thermal properties of materials ; Model Reduction and Neural Networks
Authors: Xuanyu Zhu (3774931) | Francesco Paesani (1272069)
Source: Figshare (article, 2025-07-05)
Field: Materials Science — Materials Chemistry

PermutationallyInvariant Fourier Series for Accurateand Robust Data-Driven Many-Body Potentials We present a robust solution to the long-standing challenge of eliminating unphysical energy predictions, or “holes,” in machine-learned many-body potentials, which can destabilize simulations when encountering configurations beyond the training set. By leveraging permutationally invariant Fourier series (PIFSs) within the MB-nrg data-driven many-body formalism, we introduce a new approach that significantly enhances the numerical stability of MB-nrg potential energy functions (PEFs) while preserving accuracy and transferability. Unlike conventional strategies that attempt to “plug holes” by expanding training data sets, PIFSs provide a more fundamental and efficient means of ensuring physically meaningful extrapolation across diverse molecular configurations. Using water as a benchmark system, we demonstrate that the MB-pol(PIFS) PEF retains the high accuracy of MB-pol across gas and condensed phases while extending the PEF’s stability to a much broader range of thermodynamic conditions. Our results suggest that the PIFS-based MB-nrg many-body formalism provides a general framework for constructing accurate and robust physics-based/machine-learned potentials applicable to a broad range of molecular systems. Extrapolation ; Formalism (music) ; Invariant (physics) ; Fourier series ; Stability (learning theory) ; Series (stratigraphy) ; Fourier transform ; Range (aeronautics) Machine Learning in Materials Science ; Quantum many-body systems ; Quantum, superfluid, helium dynamics
Authors: Magdalena Szumera | Monika Maruszak | Agnieszka Kijo–Kleczkowska | Paweł Rutkowski | Anna Berezicka | Jarosław Krzywański | Iliya Iliev
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Ceramics and Composites

Innovative insights into heat conduction mechanisms of phosphate-silicate glasses via experimental and SHAP-driven feature importance Thermal conductivity ; Differential scanning calorimetry ; Thermal conduction ; Feature (linguistics) ; Phonon ; Conductivity ; Crystallization ; Thermal stability ; Thermal Glass properties and applications ; Building materials and conservation
Authors: Asahel Marinelarena-Diaz | Aaron Rodriguez | Abdiel Cruz | Stephanie Vargas Esquivel | Yanwen Xu | Anabel Renteria
Source:  (article, 2025-08-17)
Field: Materials Science — Materials Chemistry

Design, Simulation, and Machine Learning Optimization of Additively Manufacturable 316L Stainless Steel TPMS Lattices for Enhanced Energy Absorption Abstract Triply Periodic Minimal Surface (TPMS) gyroid lattices are mathematically defined surfaces with zero-mean curvature, making them ideal for structures that require high mechanical performance with minimal material use. In this study, TPMS gyroid lattices were analyzed to improve energy absorption by combining density gradients, computational design, simulations, and surrogate modeling using the Gaussian process technique. A total of 29 different lattice configurations were examined, including uniform, linear increase, and linear decrease density gradients, with a range of relative densities ranging from 15% to 80%. The lattices geometries were designed using nTop and tested under quasi-static compressive loading simulations in ANSYS with the properties of Stainless Steel 316L, a common additive manufacturing material. The strain energy at 35% was then extracted to calculate the specific energy absorption (SEA) in J/g. The surrogate model demonstrated a high prediction accuracy of 95.45%, allowing for optimization of density distribution to enhanced energy absorption. The results highlight the benefits of gradient-based TPMS designs over uniform lattices, showing improved performance absorbing total energy. This research supports the development of advanced lattice structures for metal paste deposition additive manufacturing, offering new possibilities for lightweight, smart, engineered and impact-resistant materials. The combination of density gradients and data-driven optimization can be applied to various engineering applications, including aerospace, automotive, and protective gear industries. Gyroid ; Lattice (music) ; Gaussian ; Absorption (acoustics) ; Energy (signal processing) ; Response surface methodology ; Minimal surface ; Relative density ; Genetic algorithm ; Ranging Catalytic Processes in Materials Science ; Additive Manufacturing Materials and Processes ; Additive Manufacturing and 3D Printing Technologies
Authors: Н.В. Лимаренко | Dmitry Savenkov | Dmitry N. Gladkih | Alexey A. Shcherbakov
Source: Engineering Technologies and Systems (article, 2025-09-25)
Field: Materials Science — General Materials Science

Distribution Law of Corrosion of Agricultural Machinery Components under the Influence of Salt Spray Introduction. The development of the agro-industrial complex until 2030 requires increasing the efficiency of the machine and tractor fleet with account for corrosion processes accelerating equipment wear. Existing assessment methods are based on empirical data and do not provide accurate forecasting. The introduction of AI and digital solutions will automate testing, reduce costs and improve the accuracy of assessing degradation caused by corrosion processes. Aim of the Study. The aim is to determine the compliance of experimental data on the effect of salt spray on the corrosion of agricultural machinery components with the distribution law through the example of reversing light switches. Materials and Methods. The object of study was reversing light switches widely used in agricultural machinery. The studies were carried out in a salt spray chamber with parame­ters corresponding to GOST R 52230 and GOST 9.302 for 200 hours with intermediate inspections every 48 hours. During the tests, the degree of corrosion was determined, calculated by the area of damaged sections. Using mathematical methods there was calculated the mean deviation, median, variance, standard deviation, skewness, kurtosis, and range of the sample. Results. As a result of the tests, the operability of the samples was confirmed and the obtained data compliance with the Weibull distribution law was assessed. It was experimentally established that the active phase of surface degradation of reversing light switches increases starting from 96 hours of exposure in the salt spray chamber that is caused by the destruction of protective coatings and progress of pitting corrosion. The use of a quadratic regression model made it possible to describe the dependence of corrosion damage on exposure time. Discussion and Conclusions. The statistical analysis of the sample confirmed Weibull distribution fit that makes it possible to predict further corrosion progress and improve performance assessment of products in salt spray conditions. Obtained parameters allow predicting the resource of components. The results of the study provide a basis for crea­ting digital twins and adaptive maintenance systems using artificial intelligence, which minimize machinery downtime. Reversing ; Corrosion ; Weibull distribution ; Agricultural machinery ; Welding ; Operability ; Pitting corrosion Material Properties and Applications ; Aerospace, Electronics, Mathematical Modeling ; Environmental and Industrial Safety