jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jinqiao Jia | Qi Lei | Zhengjiang Liu | Xiumei Zhang | Xiaomin Guan | Zhicheng Bai | Shilei Zhu | Ya  Nan Ye | Jinke Chang | Di Huang
Source: Advanced Fiber Materials (article, 2026-01-03)
Field: Materials Science — Biomaterials

Advances in Melt-Electrowriting of Fibrous Bioscaffolds: A High-Resolution Manufacturing Strategy for Tissue Regeneration Abstract Melt-electrowriting (MEW) is a high-resolution additive manufacturing technique that has demonstrated significant progress in recent years. Owing to its precise control over fiber deposition, MEW is especially suitable for fabricating fine structures that mimic the natural extracellular matrix (ECM), thereby presenting considerable promise for applications in tissue engineering and regeneration. This review systematically examines the fundamental design principles and recent progress in MEW-based strategies for different tissue engineering and regeneration fields. Initially, the components of the MEW system, the underlying printing mechanisms, and the role of key process parameters are introduced, thereby providing a comprehensive framework for the rational design of scaffolds that replicate both the structural and functional characteristics of native ECM. Subsequently, the selection and performance of commonly employed biomaterials are discussed, with an emphasis on the versatility for diverse tissue engineering applications. The integration of MEW with bioactive materials is further highlighted as an effective approach to enhance the biological functionality of printed constructs and extend their therapeutic potential. Finally, current challenges and future perspectives are outlined, aiming to guide ongoing research and facilitate the clinical translation of MEW-based biofabrication technologies. Graphical abstract Biofabrication ; Tissue engineering ; Process (computing) ; Regeneration (biology) ; Flexibility (engineering) ; Manufacturing process Electrospun Nanofibers in Biomedical Applications ; Microbial Inactivation Methods ; Nanomaterials and Printing Technologies
Authors: T. Doca | R.M. Silva Filho
Source: Ibero-Latin American Congress on Computational Methods in Engineering (CILAMCE) (article, 2026-03-18)
Field: Materials Science — Polymers and Plastics

Using the Arruda-Boyce Elasto-Viscoplastic Constitutive Model to Predict the Monotonic Compressive Response of the PC/ABS 80:20 This study evaluates the capability of the Arruda–Boyce elasto-viscoplastic model to predict the monotonic compressive response of the PC/ABS 80:20 polymer blend. A finite element model of a standard compression specimen, following ASTM D695, was developed in the commercial package ABAQUS. The model incorporates geometric, contact, and material nonlinearities via a custom VUMAT routine. It effectively captured the material’s nonlinear response, including strain softening, and showed good agreement with experimental stress–strain data. The work emphasizes the superiority of force–displacement data for constitutive calibration and demonstrates the potential of population-based optimization methods using a one-dimensional viscoplastic model implementation. These findings establish a solid basis for further extension of the model to more complex loading scenarios. Monotonic function ; Viscoplasticity ; Finite element method ; Constitutive equation ; Compression (physics) ; Nonlinear system ; Calibration ; Experimental data Polymer crystallization and properties ; Elasticity and Material Modeling ; Mechanical Behavior of Composites
Authors: Stefano Pittalis | Filippo Troiani | Celestino Angeli | Irene D'Amico | Tim Gould
Source: ArXiv.org (article, 2026-04-02)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Quantum-Information Measure of Electron Localization Understanding electron localization in molecules and materials plays a central role in electronic structure theory, and will increase in importance with the rise of data driven approaches. The electron localization function (ELF) is widely used to visualize electron organization in molecules and materials, and it remains a central ingredient in modern density functional approximations. Yet its formulation retains highly empirical elements. Here we introduce a fully non empirical measure of electron localization derived from the concurrence of a correlated two spin mixed state. This construction yields a genuine two point localization indicator grounded in quantum information theory, removing the ad hoc steps underlying the ELF. We show that atomic shells, covalent and ionic bonds, lone pairs, molecular dissociation, and charge transfer processes are captured. The method is straightforward to evaluate numerically. Electron localization function ; Measure (data warehouse) ; Electron density ; Charge (physics) ; Electron ; Point (geometry) ; Electron transfer ; Electronic structure ; Function (biology) Magnetism in coordination complexes ; Machine Learning in Materials Science ; Advanced Chemical Physics Studies
Authors: Ismael Sánchez-Gomar | Mercedes Cáceres Medina | Cristina Cejudo-Bastante | Casimiro Mantell-Serrano | Lourdes Casas-Cardoso | Ma Carmen Durán-Ruiz
Source: Preprints.org (preprint, 2026-03-02)
Field: Materials Science — Polymers and Plastics

&lt;p class="MDPI12titleori1" style="mso-line-height-alt: 14.0pt;"&gt;&lt;span style="mso-bidi-font-size: 18.0pt; mso-ligatures: standardcontextual;"&gt;Functionalization of 3D Printed Polylactic Acid by Supercritical CO&lt;sub&gt;2&lt;/sub&gt; Impregnation with Mango Leaf Extract and Evaluation with Endothelial Colony Forming Cells and Mesenchymal Stromal Cells Poly(lactic acid) (PLA) devices can be functionalized with plant derived bioactives to introduce antioxidant activity while maintaining manufacturability and cytocompatibility. Here, a polyphenol rich mango leaf extract (MLE) was obtained by enhanced solvent extraction and incorporated into PLA using supercritical carbon dioxide assisted impregnation. Two manufacturing sequences were compared: impregnation after three dimensional (3D) printing of discs and impregnation of filaments prior to printing. Extract yield and radical scavenging capacity were quantified, and impregnation efficiency was assessed as a function of pressure and temperature. Biological performance was evaluated using adipose tissue derived endothelial colony forming cells (ECFCs) and adipose tissue derived mesenchymal stromal cells (MSCs), cultured separately and in co culture on functionalized substrates. Impregnation after printing provided higher and more reproducible loading while preserving disc geometry, whereas impregnation before printing promoted swelling and printing associated deformation that compromised structural fidelity. Cell based analyses supported improved adhesion, spatial distribution and proliferative status on discs produced by impregnation after printing under low temperature and high pressure conditions, without evidence of selective loss of either population in co culture by flow cytometry. These results support post print supercritical impregnation as a robust route to generate antioxidant, cell supportive PLA scaffolds from agricultural by products with potential relevance for vascular oriented biomedical applications. Mesenchymal stem cell ; Supercritical carbon dioxide ; Supercritical fluid ; Adipose tissue ; Population ; Defatting ; Yield (engineering) ; Stromal cell ; Tissue engineering Polymer Foaming and Composites ; biodegradable polymer synthesis and properties ; Additive Manufacturing and 3D Printing Technologies
Authors: Douglas Luiz da Cruz | Matheus José Cunha de Oliveira | Cristian Pohl Meinhardt
Source: Revista Principia - Divulgação Científica e Tecnológica do IFPB (article, 2026-01-22)
Field: Materials Science — Materials Chemistry

Analysis of the C260 brass after mechanical forming and annealing Copper alloys such as brass are highly used in the most varied branches of industry due to their characteristics such as corrosion resistance and formability. Among the processes used to manufacture parts made with such alloys, processes such as deep stamping or inlaying and stretching stand out. Such mechanical conformation processes require the use of thermal treatments such as annealing, in order to obtain specific properties in the resulting material. Therefore, this work aimed to evaluate the mechanical behavior of C260 brass when subjected to mechanical shaping (blanking and stretching) and annealing heat treatment. For this, a script clipping of an ammunition component was used, taking samples step by step including the raw material and dividing them into different regions, when in the shape of a cup, to understand the processes. Metallographic tests were carried out where, along with the microstructural evaluation, it was possible to determine grain size in addition to the Vickers microhardness. The grain size data were treated statistically, thus making it possible to judge the significance of each microstructure alteration. Thus, it was possible to observe that the mechanical forming steps actually hardened the material with an increase in hardness, unlike the annealing. However, a difference in behavior was noted in two different regions of the cups, where the wall region showed significant microstructural variations as well as its hardness, unlike the bottom region. The percentage of cold work parameters was identified as responsible for this difference both for recrystallizations to occur, as well as for the resulting microstructure and consequent hardness. The evaluation of the data referring to the raw material confirmed the fulfillment of its technical requirements. Brass ; Microstructure ; Annealing (glass) ; Grain size ; Nanoindentation ; Indentation hardness ; Vickers hardness test ; Formability Metallurgy and Material Science ; Metallurgy and Cultural Artifacts ; Metal Forming Simulation Techniques
Authors: Rukmini | Antoni Arif Priadi | Azwar Hayat
Source: Engineering Technology & Applied Science Research (article, 2026-02-09)
Field: Materials Science — Materials Chemistry

Enhancing Ship Generator Reliability by Modifying Bearings to Prevent Electrical Arcing Damage Stray electrical currents in marine generators cause catastrophic bearing fluting, leading to frequent failures and operational downtime. The harsh maritime environment exacerbates this issue, demanding a more robust solution than conventional methods. This study introduces an innovative porcelain insulation sleeve applied to the bearing housing of a 250 kVA ship generator. Its effectiveness was evaluated through comparative in-situ testing, measuring vibration, temperature, and electrical parameters under a 22 kW load, with insulation thicknesses varying from 2.0 mm to 2.75 mm. The porcelain armor performed exceptionally. A 2.20 mm thickness was identified as optimal, achieving a 94% reduction in vibration and a 3.7 °C drop in operating temperature. Critically, the insulation's ultra-high insulation resistance (1000 GΩ) completely blocked the path of parasitic currents, eliminating the root cause of electrical arcing and fluting damage. This work demonstrates that applying porcelain insulation provides a definitive, maintenance-free solution to bearing fluting. The research validates a highly effective and practical engineering intervention that significantly enhances the reliability, safety, and service life of marine generators. Service life ; Reliability (semiconductor) ; Bearing (navigation) ; Electric arc ; Vibration ; Armour ; Generator (circuit theory) High voltage insulation and dielectric phenomena ; Electrical Fault Detection and Protection ; Electromagnetic Compatibility and Noise Suppression
Authors: Licai Chen | Zheng Zou | Shulin Yin | Jiang Luo | Xinhai Wu | Huaichun Xiao | Jing Xu
Source: Sensors (article, 2026-04-10)
Field: Chemistry — Analytical Chemistry

Study on Quality Detection Methods for Table Grapes Based on Spectral and Imaging Information The increasing demand for high-quality grapes necessitates rapid and objective quality assessment methods to overcome the limitations of traditional subjective and destructive techniques. This study investigated the feasibility of using hyperspectral imaging combined with machine learning for non-destructive quality evaluation of fresh grapes. Hyperspectral data were acquired from four table grape varieties (“Rose”, “Yongyou”, “Xiahei”, and “Jumbo”), and their Soluble Solids Content (SSC) was measured, which varied significantly among varieties. We extracted texture features using the Gray-Level Co-occurrence Matrix (GLCM) from images at key wavelengths, which were a combination of those selected by the Successive Projections Algorithm (SPA) and sensitive wavelengths. Comparative models for variety classification (qualitative) and SSC prediction (quantitative) were built using Extreme Learning Machine (ELM), Convolutional Neural Network (CNN), and Partial Least Squares (PLS) with full-range spectra and texture features as inputs. The results showed that the ELM model using full-range spectra was superior for both tasks, achieving a classification accuracy of 97.56% and, for SSC prediction, an Rp2 of 0.75 and an RMSEP of 0.81. Notably, CNN models also showed considerable robustness. Our findings confirm that combining hyperspectral imaging with machine learning is a viable strategy for fresh grape quality assessment. Hyperspectral imaging ; Convolutional neural network ; Partial least squares regression ; Pattern recognition (psychology) ; Extreme learning machine ; Table (database) ; Artificial neural network Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Remote Sensing in Agriculture
Authors: Xuan Yu | Ke Zhao | LEI SUN | Jiansheng Yuan
Source: Energies (article, 2026-03-31)
Field: Materials Science — Materials Chemistry

Definition of Features for GIS Partial Discharge Signals and Recognition of Discharge Types Based on Histograms Different types of partial discharge (PD) cause varying degrees of insulation damage in gas-insulated switchgear (GIS), making accurate recognition of discharge types crucial for the safe and stable operation of GIS. The PD signal analysis method, including feature definition and extraction, is the foundation for recognizing discharge types. This paper first presents some measurement results of four PD types obtained on a GIS experimental platform. By analyzing the measurement results and corresponding physical mechanisms, we proposed two histogram-based features, which are discharge count versus phase histogram and discharge count versus amplitude histogram. To leverage the complementary advantages of these two features, distance-level fusion is achieved based on histogram distance. Following feature fusion, a distance-based k-nearest neighbors (KNN) classifier is used to recognize the four PD types. Compared with traditional feature fusion methods using feature concatenation, distance-level fusion improves recognition accuracy. The proposed PD type recognition method is also compared with two existing methods: one based on statistical features and another based on phase-resolved partial discharge (PRPD) image features. The results show that the proposed method achieves better recognition performance, with an accuracy of 95.8%. Partial discharge ; Pattern recognition (psychology) ; Histogram ; Switchgear ; Fusion ; Classifier (UML) ; Feature (linguistics) ; Feature extraction High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Elevator Systems and Control
Authors: Tin Hadži Veljković | Erik J. Bekkers | Michael Tiemann | Jan-Willem van de Meent
Source: ArXiv.org (article, 2026-01-29)
Field: Materials Science — Materials Chemistry

CORDS: Continuous Representations of Discrete Structures Many learning problems require predicting sets of objects when the number of objects is not known beforehand. Examples include object detection, molecular modeling, and scientific inference tasks such as astrophysical source detection. Existing methods often rely on padded representations or must explicitly infer the set size, which often poses challenges. We present a novel strategy for addressing this challenge by casting prediction of variable-sized sets as a continuous inference problem. Our approach, CORDS (Continuous Representations of Discrete Structures), provides an invertible mapping that transforms a set of spatial objects into continuous fields: a density field that encodes object locations and count, and a feature field that carries their attributes over the same support. Because the mapping is invertible, models operate entirely in field space while remaining exactly decodable to discrete sets. We evaluate CORDS across molecular generation and regression, object detection, simulation-based inference, and a mathematical task involving recovery of local maxima, demonstrating robust handling of unknown set sizes with competitive accuracy. Inference ; Object (grammar) ; Set (abstract data type) ; Field (mathematics) ; Invertible matrix ; Feature (linguistics) ; Task (project management) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Machine Learning and Algorithms
Authors: Ruyue Han | Dayu Jia | Bo Li | Shun Feng | G. B. Zhang | Yi Sun | Zheng Han | Chi Liu | Hui-Ming Cheng | Dong-Ming Sun
Source: Light Science & Applications (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Bioinspired phototransistor with tunable sensitivity for low-contrast target detection Accurate recognition of low-contrast targets in complex visual environments is essential for advanced intelligent machine vision systems. Conventional photodetectors often suffer from a weak photoresponse and a linear dependence of photocurrent on light intensity, which restricts their ability to capture low-contrast features and makes them susceptible to noise. Inspired by the adaptive mechanisms of the human visual system, we present a molybdenum disulfide (MoS<sub>2</sub>) phototransistor with tunable sensitivity, in which the gate stack incorporates a heterostructure diode-composed of O-plasma-treated MoS<sub>2</sub> and pristine MoS<sub>2</sub>-that serves as the photosensitive layer. This configuration enables light-intensity-dependent modulation of the diode's conductance, which dynamically in turn alters the voltage distribution across the gate dielectric and transistor channel, leading to a significant photoresponse. By modulating the gate voltage, the light response range can be finely tuned, maintaining high sensitivity to low-contrast targets while suppressing noise interference. Compared to conventional photodetectors, the proposed device achieves a 1000-fold improvement in sensitivity for low-contrast signal detection and exhibits significantly enhanced noise immunity. The intelligent machine vision system built on this device demonstrates exceptional performance in detecting low-contrast targets, underscoring its promise for next-generation machine vision applications. Photodetector ; Sensitivity (control systems) ; Photodiode ; Photocurrent ; Transistor ; Noise (video) ; SIGNAL (programming language) ; Dark current ; Image sensor ; Detector 2D Materials and Applications ; Advanced Sensor and Energy Harvesting Materials ; Ferroelectric and Negative Capacitance Devices
Authors: Dongil Shin | Ricardo A. Lebensohn | Remi Dingreville
Source: Computational Mechanics (article, 2026-01-22)
Field: Materials Science — Materials Chemistry

U-net architected deep material network training with microstructure local field information The Deep Material Network (DMN) has recently emerged as a powerful reduced-order modeling framework for simulating the mechanical response of heterogeneous materials such as composites. Unlike most data-driven approaches that directly learn a material’s response under prescribed loading, the DMN acts as a homogenization operator, learning the kinematic constraints and mechanical interactions of the underlying microstructure. However, traditional DMN training relies exclusively on homogenized effective properties derived from Direct Numerical Simulations (DNS), discarding the rich local field data that govern microstructural interactions. In this work, we extend the DMN framework to incorporate such local field information into the offline training process. Utilizing a U-Net architecture, we augment the DMN training objective to include the first and second statistical moments of the local stress fields obtained from linear DNS. This ensures that the learned network topology not only fits the effective stiffness but also accurately reflects the internal local stress and strain partitioning of the microstructure. The results confirm that supervising the localization process during training yields a superior surrogate model, reducing local prediction errors by an order of magnitude and significantly improving generalization to unseen nonlinear constitutive behaviors compared to traditional DMNs. Nonlinear system ; Artificial neural network ; Homogenization (climate) ; Local field ; Kinematics ; Field (mathematics) ; Process (computing) ; Stiffness Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Composite Material Mechanics
Authors: Franziska Elsaesser | Natalie de Souza | Paola Picotti
Source: Molecular & Cellular Proteomics (article, 2026-03-07)
Field: Chemistry — Spectroscopy

3D Proteomics: Structural, Functional, Chemical and Biomarker Discovery Proteomics With LiP-MS Protein structural dynamics drive changes in protein function, making the capture of such dynamics essential for interrogating biological systems. Here we review limited proteolysis coupled to mass spectrometry (LiP-MS), a structural and chemical proteomics method that uses changes in susceptibility to protease cleavage to profile proteome-wide protein structural changes within complex biological samples. In the decade since its development, LiP-MS has become a broadly used structural proteomics method, with peptide-level resolution. It has identified drug targets, delineated altered cellular pathways in response to complex perturbations, revealed structural information on otherwise challenging protein targets, and demonstrated the new concept of structural biomarkers of disease. Because LiP-MS simultaneously probes numerous types of molecular events, such as molecular binding, changes in enzyme activity, chemical modifications, allosteric conformational changes, aggregation, and unfolding, it supports a new proteomics workflow which we term 3D proteomics. This workflow enables the detection of specific functional sites within proteins that are altered upon perturbation, thereby guiding the generation of molecular hypotheses. Further, by globally profiling structural in addition to protein abundance changes, LiP-MS has proven able to greatly increase the information content of functional proteomics screens. In sum, LiP-MS has supported the development of a novel conceptual framework for generating, visualizing, and interpreting structural proteomics data with peptide level resolution, thereby comprehensively probing biological systems. Here we survey the applications of LiP-MS, discuss methodological variants developed by us and others, and describe the use of this new type of omics readout for structural, functional, chemical, and biomarker discovery proteomics. Biomarker discovery ; Proteomics ; Biomarker ; Drug discovery ; Proteome Advanced Proteomics Techniques and Applications ; Advanced Biosensing Techniques and Applications ; Mass Spectrometry Techniques and Applications