jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Taichuan Li | Lei Li | Kewei Xu | Yanqing Wang | Chaoyang Zhang | Xiang Huang
Source: Advanced Composites and Hybrid Materials (article, 2026-03-09)
Field: Materials Science — Materials Chemistry

Leveraging explainable AI in the design of carbon composites with high thermal conductivity for thermal management Achieving high thermal conductivity in polymer composites with low filler loading is crucial for thermal management. Constructing continuous and directional carbon networks has emerged as a promising strategy to enhance thermal conductivity. However, challenges remain, including the absence of accurate predictive models and explainable optimization strategies. In this study, we propose a design methodology leveraging explainable AI to optimize geometric patterns formed by carbon fillers, with a focus on sub-10 wt% carbon content. High-throughput finite element simulations are employed to generate datasets for prediction models, which incorporated variables including filler content, interfacial conductivity, connectivity, and gap distance. Symbolic regression yields predictive equations for thermal conductivity with high accuracy (R2 > 0.99), and SHAP analysis identifies the filler connectivity and content as key determinants. As a proof of concept, graphene-based composites with continuous and directional networks achieve thermal conductivities exceeding 33 W m− 1 K− 1, demonstrating that sub-10 wt% carbon fillers can reach high thermal conductivities, achieving high directional conductivity without requiring high global loading in composites. This work establishes an explainable AI-driven strategy for enhancing thermal conductivity in low-content carbon composites, expanding their applications in thermal management. Thermal conductivity ; Thermal ; Filler (materials) ; Carbon fibers ; Finite element method ; Work (physics) ; Thermal grease Thermal properties of materials ; Smart Materials for Construction ; Carbon Nanotubes in Composites
Authors: Sarad Pawar Naik Bukke | Chandrashekar Thalluri | Jharna Medhi | Mallikarjun Vasam | Neha Gupta | Piyali Dey | Milon S Dennison | Muhamad Mustafa Mundu
Source: Journal of Materials Chemistry B (article, 2026-01-01)
Field: Materials Science — Biomaterials

Proteomics in tissue regeneration: insights into protein alterations and post-translational modifications during healing Tissue regeneration is a complex, multi-phase process orchestrated by dynamic changes in protein expression and post-translational modifications (PTMs). Proteomics has emerged as a transformative tool for decoding these molecular events, providing comprehensive insights into the mechanisms underlying wound healing, angiogenesis, extracellular matrix remodeling, and stem cell differentiation. High-throughput proteomic technologies, including mass spectrometry, quantitative labeling approaches, and spatial/single-cell analyses, enable the identification of phase-specific protein signatures and PTMs such as phosphorylation, glycosylation, ubiquitination, acetylation, and redox modifications. Mapping these proteome alterations across the inflammatory, proliferative, and remodeling phases reveals critical biomarkers, therapeutic targets, and regulatory networks driving tissue repair. Translational applications of proteomics include the development of predictive biomarkers for wound healing prognosis, PTM-targeted therapies, and stem cell or exosome-based regenerative interventions. Moreover, patient-specific proteomic signatures offer avenues for personalized medicine strategies in regenerative care. Despite technical challenges related to tissue-specific proteome complexity, dynamic PTMs, and large-scale data interpretation, integration with multi-omics approaches and artificial intelligence promises to accelerate the discovery of novel regenerative therapies. This review highlights current advances in proteomic profiling, the functional role of PTMs during tissue healing, and their clinical and translational relevance, emphasizing how these molecular insights can inform precision regenerative medicine and guide the development of next-generation interventions for improved healing outcomes. Proteomics ; Regeneration (biology) ; Protein expression ; Transformative learning ; Proteome ; Process (computing) Collagen: Extraction and Characterization ; Peptidase Inhibition and Analysis ; Silk-based biomaterials and applications
Authors: Jon Gabirondo-López | Mireia Sainz-Menchón | Irati Tolosa-Lecea | Iñigo González de Arrieta | Telmo Echániz | Irene Urcelay-Olabarria | Iñaki López-Ferreño | Raquel Fuente | Gabriel A. López | Iñigo Arredondo | Josu M. Igartua
Source: Scientific Data (article, 2026-03-24)
Field: Materials Science — Materials Chemistry

EKHI: an open database of optical and thermal radiative properties of solid materials We present a curated and openly accessible dataset of optical and thermal radiative properties of solid materials, digitized from the TPRC Data Series edited by Y. S. Touloukian et al. The dataset comprises 1251 curves describing emittance, reflectance, transmittance, and absorptance of solid materials, covering broad temperature and spectral ranges. Each data record includes detailed metadata and is fully traceable to the original publication cited by the TPRC volumes. The digitization workflow, data structure, and quality controls were designed to support the FAIR principles, enabling reproducible reuse of radiative property data in thermophysics and materials research. The dataset has been used to feed an open database of optical and thermal radiative properties: EKHI. Its website (thermomat.ehu.eus/ekhi) provides interactive querying and visualization of the dataset. This dataset expands the availability of machine-readable radiative property data for solids and offers a foundational resource for modeling, data-driven analysis, and materials informatics. Radiative transfer ; Metadata ; Digitization ; Reuse ; Property (philosophy) ; Thermal ; Visualization Machine Learning in Materials Science ; Atmospheric aerosols and clouds ; Radiative Heat Transfer Studies
Authors: H.G.V. Nauchali | S.M. Keerthisinghe | T.P. Gamagedara
Source: Journal of Water Process Engineering (article, 2026-01-08)
Field: Materials Science — Materials Chemistry

Sustainable approaches to pharmaceutical detection and removal in aquatic systems: A review of green technologies Human health ; Sewage treatment ; Wastewater ; Aquatic ecosystem ; Scalability ; Aquatic environment Carbon and Quantum Dots Applications ; Pharmaceutical and Antibiotic Environmental Impacts ; Advanced Photocatalysis Techniques
Authors: PRABIN KUMAR
Source: Open MIND (article, 2026-02-16)
Field: Materials Science — Materials Chemistry

This work presents ANCM-X, an advanced multi-layer adaptive nuclear-conductive matrix engineered for stable electrical conductivity and structural integrity across extreme thermal ranges. The system integrates ultra-high-temperature ceramics, cryogenic conductive structures, graded neutral buffer layers, and embedded AI-driven sensing architecture. Theoretical modeling, thermal cycle simulations (1000 day-night cycles), regression-based predictive stability modeling, and structural compounding methods are presented. The framework demonstrates feasibility for aerospace, energy, defense, cryogenic infrastructure, and high-temperature industrial applications. Keywords: ANCM-X, Ultra High Temperature Ceramics, Cryogenic Conductivity, AI-Integrated Materials, Smart Materials, Multi-Layer Thermal Systems This work presents ANCM-X, an advanced multi-layer adaptive nuclear-conductive matrixengineered for stable electrical conductivity and structural integrity across extreme thermal ranges.The system integrates ultra-high-temperature ceramics, cryogenic conductive structures, gradedneutral buffer layers, and embedded AI-driven sensing architecture. Theoretical modeling, thermalcycle simulations (1000 day-night cycles), regression-based predictive stability modeling, andstructural compounding methods are presented. The framework demonstrates feasibility foraerospace, energy, defense, cryogenic infrastructure, and high-temperature industrial applications. Electrical conductor ; Thermal conductivity ; Work (physics) ; Structural integrity ; Compounding ; Electrical resistivity and conductivity ; Thermal stability ; Thermal Machine Learning in Materials Science ; Advanced ceramic materials synthesis ; MXene and MAX Phase Materials
Authors: PRABIN KUMAR
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2026-02-16)
Field: Materials Science — Materials Chemistry

This work presents ANCM-X, an advanced multi-layer adaptive nuclear-conductive matrix engineered for stable electrical conductivity and structural integrity across extreme thermal ranges. The system integrates ultra-high-temperature ceramics, cryogenic conductive structures, graded neutral buffer layers, and embedded AI-driven sensing architecture. Theoretical modeling, thermal cycle simulations (1000 day-night cycles), regression-based predictive stability modeling, and structural compounding methods are presented. The framework demonstrates feasibility for aerospace, energy, defense, cryogenic infrastructure, and high-temperature industrial applications. Keywords: ANCM-X, Ultra High Temperature Ceramics, Cryogenic Conductivity, AI-Integrated Materials, Smart Materials, Multi-Layer Thermal Systems This work presents ANCM-X, an advanced multi-layer adaptive nuclear-conductive matrixengineered for stable electrical conductivity and structural integrity across extreme thermal ranges.The system integrates ultra-high-temperature ceramics, cryogenic conductive structures, gradedneutral buffer layers, and embedded AI-driven sensing architecture. Theoretical modeling, thermalcycle simulations (1000 day-night cycles), regression-based predictive stability modeling, andstructural compounding methods are presented. The framework demonstrates feasibility foraerospace, energy, defense, cryogenic infrastructure, and high-temperature industrial applications. Electrical conductor ; Thermal conductivity ; Work (physics) ; Structural integrity ; Compounding ; Electrical resistivity and conductivity ; Thermal stability ; Thermal Machine Learning in Materials Science ; Advanced ceramic materials synthesis ; MXene and MAX Phase Materials
Authors: Muhammad Usamah Shahid | Muhammad Noman Shahid | Zulfiqar Ahmad | Hafiz Muhammad Abdullah | Muhammad Muzammil Azad
Source: Environmental Research (article, 2026-03-17)
Field: Materials Science — Polymers and Plastics

Circular valorization of agricultural, industrial, and post-consumer waste into fiber-reinforced composites for environmental sustainability Sustainability ; Environmental impact assessment ; Municipal solid waste ; Circular economy Natural Fiber Reinforced Composites ; Fiber-reinforced polymer composites ; Flame retardant materials and properties
Authors: Lina G. Shimpi1, Roshni D. Patil*2, Yash S. Gaikwad3, Shailesh S. Chalikwar4
Source: Open MIND (article, 2026-02-01)
Field: Chemistry — Spectroscopy

HPLC FOR IMPURITY PROFILING AND QUANTIFICATION IN PHARMACEUTICALS: REGULATORY DEMANDS AND ADVANCED ANALYTICAL STRATEGIES High-performance liquid chromatography (HPLC), which makes precise impurity detection, profiling, and quantification possible, is a crucial component in guaranteeing the quality of pharmaceutical products. These pollutants, which range from genotoxic chemicals to degradation products and process-related contaminants, represent serious safety risks and are therefore closely regulated. This thorough analysis critically assesses the changing field of pharmaceutical impurity control, exploring the classification of impurities, the specific regulatory requirements under important ICH guidelines (Q3A, Q3B, M7, Q3C, and Q3D), and the significant influence of nitrosamine contamination on new analytical paradigms. We discuss robust lifecycle control strategies combined with Quality by Design (QbD) principles, modern and sophisticated approaches for trace-level detection, the critical role of hyphenated techniques like LC-MS/MS and LC-QTOF-MS in the identification of unknowns, and state-of-the-art sample preparation technologies. The goal of this article is to give researchers and quality control experts who are working toward strong impurity management with HPLC a critical and forward-looking guide. It also lays the groundwork for future developments such as Ultra-High Performance Liquid Chromatography (UHPLC), sophisticated automation, and the incorporation of AI-driven analysis. Profiling (computer programming) ; Quality by Design ; High-performance liquid chromatography ; Quality (philosophy) ; Identification (biology) ; Impurity ; Quality control Analytical Chemistry and Chromatography ; Pesticide Residue Analysis and Safety ; Analytical Methods in Pharmaceuticals
Authors: K. Theyagarajan | Sairaman Saikrithika | Young-Joon Kim
Source: Micromachines (article, 2026-02-20)
Field: Materials Science — Materials Chemistry

MXene- and MOF-Based Hydrogels: Emerging Platforms for Electrochemical Biosensing and Health Monitoring Smart healthcare is rapidly emerging as a transformative paradigm, enabling simultaneous health monitoring, therapeutic intervention, and early prediction of disease onset. In this context, electrochemical monitoring systems have attracted growing interest due to their cost-effectiveness, ease of operation, miniaturization and compatibility with wearable platforms. Accordingly, conductive hydrogel-based electrochemical (bio)sensors have gained significant attention for health monitoring owing to their soft mechanical properties, high water content, excellent biocompatibility, and ability to form intimate, conformal interfaces with biological tissues. Their three-dimensional polymeric networks facilitate efficient ion transport and mechanical flexibility, making them particularly suitable for wearable and noninvasive sensing and monitoring applications. However, the intrinsically limited conductivity and catalytic activity of pristine hydrogels often constrain their electrochemical performance. To overcome these limitations, functional nanomaterials such as metal-organic frameworks (MOFs) and MXene (MX) nanosheets have been increasingly integrated into hydrogel matrices to enhance conductivity and electrochemical activity. This review provides a comprehensive and critical comparison of recent advances in MOF- and MX-integrated conductive hydrogels for electrochemical health monitoring. In addition to material design strategies and sensing performance, emerging trends in data-driven sensing aimed at improving signal interpretation and multi-analyte discrimination are systematically discussed. Key challenges related to long-term stability, biocompatibility, scalability, and intelligent system integration are critically assessed, and the future potential of these platforms within closed-loop architectures is highlighted, paving the way for next-generation conductive hydrogel-based electrochemical sensors in smart healthcare applications. Wearable computer ; Self-healing hydrogels ; Wearable technology ; Structural health monitoring ; Nanomaterials ; Biosensor ; Healthcare system MXene and MAX Phase Materials ; Advanced Sensor and Energy Harvesting Materials ; Electrochemical sensors and biosensors
Authors: Denis V. Zhuravlev | S.A. Vasilkov | Ilia Elagin | V. A. Chirkov | Oleg V. Levin
Source: The Journal of Physical Chemistry C (article, 2026-03-10)
Field: Materials Science — Polymers and Plastics

Limitations of Cyclic Voltammetry on Interdigitated Electrodes for Evaluating Polymer Conductivity The reliability of extracting the electrical conductivity of electroactive polymer films from cyclic voltammetry (CV) on interdigitated electrodes (IDEs) is assessed using a data-driven, physics-based modeling framework. Pseudo-two-dimensional simulations replicate the IDE geometry and timing of the CV protocols. A porous-layer model that couples Butler–Volmer interfacial kinetics, radial diffusion of dopant anions within polymer globules, and lateral current flow in the IDE reproduces the salient voltammetric features. However, at typical scan rates, conductivity inferred from IDE current differences departs from the equilibrium value: pronounced hysteresis emerges, twin peaks develop, and occasional unphysical negative values appear. Analysis attributes the discrepancies to overpotential, spatially nonuniform doping across the film thickness, and uncompensated transient faradaic contributions to the current difference. Two accuracy-oriented protocols are evaluated: slowing the scan toward a quasi-equilibrium regime and employing staircase voltammetry with step holds until current stabilization. Both approaches recover conductivities consistent with the equilibrium relation and substantially reduce hysteresis. The workflow provides actionable guidance for obtaining reliable conductivity parameters for modeling and designing potentioresistive protective layers in lithium-ion cells and is transferable to other electrochemical systems that require an accurate description of conducting polymers. Cyclic voltammetry ; Conductivity ; Hysteresis ; Diffusion ; Electrode ; Current (fluid) ; Polymer ; Conductive polymer ; Dopant Conducting polymers and applications ; Electrochemical Analysis and Applications ; Advanced Battery Materials and Technologies
Authors: Chao Gao | Jiahao Lu | Bo Zhao | Manman Li | Shengying Cai | Chenghua Cui | Ying Zhang | Yelong Tong | Tongliang Wang | Jie P. Li | Kai Pang | Peng Li | Zhen Xu | Zhijian Wang | Wenjun Li | Yingjun Liu
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Materials Chemistry

Scalable Graphene Fiber Arrays for Superior Thermal Interface Materials with High Reliability Thermal conductivity ; Thermal grease ; Thermal resistance ; Graphene ; Electrical conductor ; Thermal contact conductance ; Thermal ; Carbon nanotube ; FOIL method ; Contact resistance Thermal properties of materials ; Thermal Radiation and Cooling Technologies ; Advanced Thermoelectric Materials and Devices
Authors: Zebang Xia
Source: Journal of Environmental Science and Health Part B (article, 2026-02-27)
Field: Materials Science — Biomaterials

Valorization of durian ( <i>Durio zibethinus</i> ) shell waste: a bibliometric analysis and critical review of emerging applications The massive global production of durian (<i>Durio zibethinus</i>) generates millions of tonnes of durian shell waste, posing a severe disposal challenge while representing a vastly underutilized lignocellulosic resource. Although research into its valorization is expanding, a systematic, quantitative mapping of the intellectual landscape combined with a critical assessment of technological pathways is currently lacking. To address this gap, this review integrates a comprehensive bibliometric analysis with an in-depth critical evaluation of emerging functional applications. The data-driven bibliometric study maps the spatiotemporal distribution and thematic evolution of the field, revealing a definitive paradigm shift from low-value waste disposal toward the advanced design of functional materials. Subsequently, this paper consolidates the state-of-the-art across three primary valorization pathways: (1) thermochemical carbonization to engineer hierarchical porous architectures for environmental remediation and advanced energy storage systems; (2) biochemical and chemical extraction of nanocellulose, pectin, and bioactive fractions for sustainable biocomposites, smart packaging, and biomedical devices; and (3) microbial bioconversion routes tackling lignocellulosic recalcitrance to yield renewable biofuels and upgraded agricultural feed. Finally, persistent challenges concerning feedstock heterogeneity, conversion scalability, and economic viability are critically assessed, outlining the future research trajectory required to fully integrate durian shell waste into a cascaded, sustainable biorefinery framework. Biorefinery ; Life-cycle assessment ; Biomass (ecology) ; Renewable energy ; Commercialization ; Lignocellulosic biomass ; Raw material ; Bioconversion ; Emergy Calcium Carbonate Crystallization and Inhibition ; Lignin and Wood Chemistry ; Materials Engineering and Processing
Authors: Lina G. Shimpi1, Roshni D. Patil*2, Yash S. Gaikwad3, Shailesh S. Chalikwar4
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2026-02-01)
Field: Chemistry — Spectroscopy

HPLC FOR IMPURITY PROFILING AND QUANTIFICATION IN PHARMACEUTICALS: REGULATORY DEMANDS AND ADVANCED ANALYTICAL STRATEGIES High-performance liquid chromatography (HPLC), which makes precise impurity detection, profiling, and quantification possible, is a crucial component in guaranteeing the quality of pharmaceutical products. These pollutants, which range from genotoxic chemicals to degradation products and process-related contaminants, represent serious safety risks and are therefore closely regulated. This thorough analysis critically assesses the changing field of pharmaceutical impurity control, exploring the classification of impurities, the specific regulatory requirements under important ICH guidelines (Q3A, Q3B, M7, Q3C, and Q3D), and the significant influence of nitrosamine contamination on new analytical paradigms. We discuss robust lifecycle control strategies combined with Quality by Design (QbD) principles, modern and sophisticated approaches for trace-level detection, the critical role of hyphenated techniques like LC-MS/MS and LC-QTOF-MS in the identification of unknowns, and state-of-the-art sample preparation technologies. The goal of this article is to give researchers and quality control experts who are working toward strong impurity management with HPLC a critical and forward-looking guide. It also lays the groundwork for future developments such as Ultra-High Performance Liquid Chromatography (UHPLC), sophisticated automation, and the incorporation of AI-driven analysis. Profiling (computer programming) ; Quality by Design ; High-performance liquid chromatography ; Quality (philosophy) ; Identification (biology) ; Impurity ; Quality control Analytical Chemistry and Chromatography ; Pesticide Residue Analysis and Safety ; Analytical Methods in Pharmaceuticals
Authors: S. M. Jeong | Hyun Woo Ko | Suyeon Jo | Joicy Selvaraj | Jung‐Min Kim | Namji Lee | Jae‐Hyeon Ahn | Hyeonchae Lee | Seungmin Jeong | Heedae Kim | Paul Hongsuck Seo | Jae Won Shim | Y. K Kim | Hyun‐Soo Ra | Min chul Park | Jong‐Soo Lee
Source: Advanced Materials (article, 2026-03-20)
Field: Materials Science — Materials Chemistry

High‐Gain Ag <sub>2</sub> Te/MoS <sub>2</sub> Hybrid Photodetectors for Short‐Wave Infrared Imaging Physical artificial intelligence has emerged as a pivotal component in next-generation humanoid technologies, including advanced optical sensors, as it enables autonomous acquisition of sensory information. This study reports a high-performance 0D/2D hybrid photodetector using a high-gain Ag<sub>2</sub>Te/MoS<sub>2</sub> hybrid structure for visible to short-wave infrared (SWIR) photodetection, achieved by the absorption of Ag<sub>2</sub>Te quantum dots in the infrared region (∼1450 nm). The Ag<sub>2</sub>Te/MoS<sub>2</sub> photodetector exhibits a high photoresponsivity of around 7.5 × 10<sup>5</sup> AW<sup>-1</sup> and a specific detectivity of over 9.9 × 10<sup>8</sup> Jones at 1 µW/cm<sup>2</sup> illumination power with a 0.2 V drain bias voltage. Furthermore, depending on the gain of the photodetector, a fast response speed can also be achieved, with rise and decay times as short as 13 and 23 ms. The 0D/2D hybrid devices were successfully implemented in a 32 × 32 array format for infrared imaging, with the results demonstrating spatially resolved pattern reconstruction and real-time photoresponse acquisition. By hybridizing quantum dots and 2D materials, the developed photodetector has broad potential applications, including use in highly integrated SWIR image sensors. Photodetector ; Infrared ; Quantum dot ; Absorption (acoustics) ; Image sensor ; Detector 2D Materials and Applications ; Transition Metal Oxide Nanomaterials ; Ga2O3 and related materials