jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yoo Duk Choi | Yumi Kim | Chaewon Kim | Yul Roh
Source: Economic and Environmental Geology (article, 2025-04-28)
Field: Materials Science — Polymers and Plastics

Mineralogical Characteristics and Asbestos Analysis of Talc-Containing Cosmetics and Household Products Talc is the most widely imported material among the four minerals likely to contain asbestos (talc, vermiculite, sepiolite, and serpentine), accounting for 88.4% of domestic imports from 2017 to 2021.It is primarily used as a raw material in the production of cosmetics.Recently, there has been growing attention in the United States on the potential risks of asbestiform amphibole minerals, in addition to the six recognized types of asbestos minerals (length 5 m, aspect ratio 3:1).As a result, a newly proposed rule is being developed to revise the asbestos testing standards for talc-containing cosmetics.However, in South Korea, there are no established testing methods for detecting and identifying asbestos in talc-containing cosmetic products.This study aims to investigate the presence of asbestos, asbestiform amphibole minerals, and elongated mineral particles (EMPs) in talc-containing cosmetics and other products.X-ray diffraction (XRD), Polarized light microscopy (PLM), and Transmission electron microscopy-energy dispersive spectroscopy (TEM-EDS) analyses were performed on 20 products, including talc-containing cosmetics and household chemical products in powdered form, following sample preparation.The results showed that no asbestos was detected in any of the talc-containing products.However, TEM-EDS analysis revealed the presence of EMPs (below 5 m) in talc-containing cosmetics and household chemical products with potential for dispersion.These findings suggest that current domestic asbestos analysis methods (XRD and PLM) are insufficient for detecting asbestiform amphibole minerals or EMPs that fall below the established threshold for fibers longer than 5 m (with a length-to-width ratio of 3:1 or greater).TEM-EDS analysis can complement these methods, potentially improving the detection of both asbestiform amphibole minerals and EMPs with greater accuracy. Cosmetics ; Talc ; Asbestos ; Chemistry ; Metallurgy ; Materials science ; Organic chemistry Textile materials and evaluations
Authors: Eleonora Davide | Guido Domingo | Angela Di Iacovo | Milena Marsoni | Ping Yun | Chris Gehring | Elena Bossi | Sergey Shabala | Marcella Bracale | Candida Vannini
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2025-10-10)
Field: Chemistry — Organic Chemistry

Delineating plant responses to the 3’,5’- and 2’,3’-cAMP isomers Abstract Similar to animals, both the 3’,5- and the 2,’3’-cAMP isomers are present in plants. The former is the enzymatic product of adenylate cyclases (ACs), the latter is an RNA degradation product. While there is increasing evidence that both isomers can elicit or modulate a broad range of physiological responses, the question of isomer specificity of responses has remained largely unresolved. To delineate isomer-specific responses in Arabidopsis thaliana at the systems level, we have combined a comparative proteomics and electrophysiological approaches. Both isomers cause distinct systemic effects on the proteome, with the 2’,3’ isomer notably affecting systems-level functions like transcriptional regulation. None of the isomers affects net ion fluxes in the root under control conditions, but both were able to attenuate the magnitude of oxidative stress-induced K + net loss and Ca 2+ uptake by 2-fold. Isomer-specific responses of single molecular targets were assessed in the cyclic nucleotide-gated channels 2 and 18 (CNGC2 and CNGC18). Both channels are gated by the 3’,5’-cAMP isomer only, suggesting that the gating is isomer-specific and this implies that gating in vivo depends on catalytically active ACs. Highlights Distinct Arabidopsis responses to 3’,5’- and 2’,3’-cAMP uncover isomer-specific molecular targets and physiological effects in cyclic nucleotide signaling. Gating ; Ion channel ; Enzyme ; Arabidopsis ; RNA ; Electrophysiology ; Nucleotide ; Cis–trans isomerism Chemical synthesis and alkaloids
Authors: Elsa Sulay Mora-Muñoz | José Rafael Posso-Pasquel | Elvis Ramírez Encalada
Source: Data & Metadata (article, 2025-11-25)
Field: Materials Science — Polymers and Plastics

Bibliometric Analysis of Sports Running Socks Made from Bamboo, Cotton, and Acrylic This bibliometric analysis examines sports socks for running made from bamboo, cotton, and acrylic, with a focus on comfort, durability, and sustainability in the sports field. This study analyzed the evolution of publications using the Scopus database, reviewing 153 documents since 1990. Using bibliometric tools such as Bibliometrix and VOSviewer, trends, patterns, and gaps in the scientific literature were identified, offering a comprehensive view of the development of knowledge in this area. The results show that the main focus of the research is on the relationship between the friction and thermoregulation properties of socks, aimed at preventing blisters and improving performance, especially in high-friction sports such as running. The driving themes identified include the impact on foot health, biomechanics, and athletic performance, while the basic themes analyze friction and the interaction between materials. Likewise, niche topics such as product design and antimicrobial properties emerge, reflecting a growing interest in the development of functional and sustainable materials. The citation trend highlights the relevance of early studies addressing the structure and properties of socks to prevent injuries and improve user comfort. The conclusions emphasize the need for more comprehensive research on the use of alternative materials and their impact on health and athletic performance, contributing to the advancement of sustainable, high-performance sports textiles. SOCKS ; Clothing ; Sustainability ; Relevance (law) ; Scopus ; Citation ; Product (mathematics) Natural Fiber Reinforced Composites ; Textile materials and evaluations ; Bamboo properties and applications
Authors: Indira Rodríguez | Eduardo García‐Meléndez | Montserrat Ferrer‐Julià | W.H. Bakker | Juncal A. Cruz | Antonio Espín de Gea
Source: Geogaceta (article, 2025-06-20)
Field: Chemistry — Analytical Chemistry

Hyperspectral proximal sensing for carbonate rocks characterization in the SWIR (Short-Wave Infrared) In this work proximal hyperspectral images are used to make a compositional map of one sample of ornamental carbonate rock (formed by variable content of dolomite and calcite) in terms of mineralogical composition. The hyperspectral dataset consists of a total number of 278 bands corresponding to the short-wave infrared (SWIR) wavelengths. After visual analysis and interpretation, 8 points or pixels were selected as reference spectra for image classification through the Spectral Angle Mapper (SAM) algorithm. The results show the spatial distribution of calcite and dolomite based on their characteristic and diagnostic absorption features (at 2335 and 2315 nm respectively), and areas with different proportions of calcite and dolomite mixture, and the presence of carbonates and clay mineral mixtures. The applied technique demonstrates the potential of hyperspectral proximal sensing procedures for mineral characterization of samples in the laboratory, expanding the application for the analysis and interpretation in field outcrops. Dolomite ; Hyperspectral imaging ; Calcite ; Carbonate ; Carbonate minerals ; Characterization (materials science) ; Pixel ; Mineral ; Carbonate rock Spectroscopy and Chemometric Analyses ; Geochemistry and Geologic Mapping ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Laura Martelo‐Vidal | Sara Vázquez‐Mera | Pablo Miguéns‐Suárez | Susana B. Bravo | Heidi Makrinioti | Vicente Domínguez-Arca | Javier de Miguel-Dı́ez | Alberto Gómez‐Carballa | Antonio Salas | Francisco Javier González‐Barcala | Francisco J. Salgado | Juan José Nieto‐Fontarigo
Source: Biomolecules (article, 2025-01-03)
Field: Chemistry — Spectroscopy

Urinary Proteome and Exosome Analysis Protocol for the Discovery of Respiratory Diseases Biomarkers This study aims to develop a protocol for respiratory disease-associated biomarker discovery by combining urine proteome studies with urinary exosome components analysis (i.e., miRNAs). To achieve this, urine was DTT treated to decrease uromodulin, then concentrated and ultracentrifuged. Proteomic analyses of exosome-free urine were performed using LC-MS/MS. Simultaneously, miRNA expression from urine exosomes was measured using either RTqPCR (pre-amplification) or nCounter Nanostring (non-amplication) analyses. We detected 548 different proteins in exosome-free urine samples (N = 5) with high confidence (FDR < 1%), many of them being expressed in different non-renal tissues. Specifically, lung-related proteins were overrepresented (Fold enrichment = 1.31; FDR = 0.0335) compared to whole human proteome, and 10-15% were already described as protein biomarkers for several pulmonary diseases. Urine proteins identified belong to several functional categories important in respiratory pathology. We could confirm the expression of miRNAs previously connected to respiratory diseases (i.e., miR-16-5p, miR-21-5p, miR-146a-5p, and miR-215-5p) in urine exosomes by RTqPCR. Finally, we detected 333 miRNAs using Nanostring, 15 of them up-regulated in T2<sup>high</sup> asthma (N = 4) compared to T2<sup>low</sup> asthma (N = 4) and healthy subjects (N = 4). Therefore, this protocol combining the urinary proteome (exosome free) with the study of urinary exosome components (i.e., miRNAs) holds great potential for molecular biomarker discovery of non-renal and particularly respiratory pathologies. Exosome ; Proteome ; Urinary system ; Biomarker discovery ; Respiratory system ; Computational biology ; Medicine ; Microvesicles ; Proteomics ; Bioinformatics ; Biology ; Internal medicine ; microRNA ; Biochemistry ; Gene Advanced Proteomics Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Influenza Virus Research Studies
Authors: Yifeng Huang | Xingxu Yan | Toshihiro Aoki | Xiaoqing Pan
Source: Microscopy and Microanalysis (article, 2025-07-01)
Field: Materials Science — Materials Chemistry

Observation of Strong Phonon Anisotropy Across Dislocation Region in Oxide Freestanding Film Materials science ; Condensed matter physics ; Dislocation ; Anisotropy ; Phonon ; Oxide ; Crystallography ; Composite material ; Optics ; Metallurgy ; Physics ; Chemistry Thermal properties of materials ; Thermal Expansion and Ionic Conductivity ; Ultrasonics and Acoustic Wave Propagation
Authors: Nodira Rustamova | Mamura Khakimova | Maftuna Islamova | Gulshad Yuldasheva | Shaymardon Norov | Bekzod Islamov
Source:  (article, 2025-12-18)
Field: Materials Science — Materials Chemistry

Autonomous AI pipelines for high-throughput optical material screening The integration of autonomous AI pipelines into the field of material screening represents a significant and innovative advance in the workflows associated with screening processes, potentially leading to expansive and transformative impacts on the discipline of material science as a whole. The development of such a sophisticated pipeline specifically aimed at optical materials screening effectively addresses many of the persistent challenges that are frequently encountered within high-throughput screening environments. These environments necessarily involve the handling of numerous samples and the need for rapid and efficient characterization of each individual sample. By implementing this cutting-edge approach, there is a strong promise to enhance the speed of throughput significantly, while simultaneously broadening the scope of investigation. This expanded capacity allows researchers to examine a wider variety of novel materials that have been previously unexplored within the confines of many laboratories, largely due to experimental constraints that have historically limited such explorations. Pipeline (software) ; Expansive ; Workflow ; Scope (computer science) ; Variety (cybernetics) ; Pipeline transport ; Transformative learning Machine Learning in Materials Science ; Surface Roughness and Optical Measurements ; Catalysis and Oxidation Reactions
Authors: Jiayu Peng | Chuanyu Liu | Yiwen Luo | Kritarth Dandapat
Source: ChemRxiv (article, 2025-11-05)
Field: Materials Science — Materials Chemistry

Accelerating Multimetallic Catalyst Discovery with Robotics and Agentic AI Bespoke ; Robotics ; Automation ; Workflow ; Applications of artificial intelligence ; Space (punctuation) ; Identification (biology) ; Masking (illustration) Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Nanoporous metals and alloys
Authors: Garbar, Iryna
Source: Zenodo (CERN European Organization for Nuclear Research) (preprint, 2025-12-07)
Field: Materials Science — Materials Chemistry

Spiral Calcium Matrix Technology (SCMT) Spiral Calcium Matrix Technology (SCMT) introduces an interdisciplinary material framework that integrates calcium-based minerals (calcite, aragonite, dolomite, magnesite) with functional mineral analogues (zeolite, apatite, perlite, baryte, zircon, tungsten ores, quartz, tourmaline, sapphire, graphite, pyrophyllite) arranged in harmonic spiral geometries. The goal of SCMT is to create bio-compatible, radiation-modulating, thermally stable and electromagnetically resilient materials for advanced technology systems. Spiral layering significantly enhances: radiation attenuation and dispersion electromagnetic noise suppression thermal flow distribution mechanical damping biological compatibility and antibacterial properties resonance stability in AI, sensors and communication systems SCMT bridges geology, materials science, computational stability, robotics, medical technology and aerospace engineering.It provides a new class of materials suitable for: AI hardware shielding spacecraft electronics medical sensor housings communication devices biohybrid interfaces robotics in extreme environments Its spiral architecture uses natural mineral information order combined with engineered harmonic geometry, resulting in self-stabilizing, protective and energy-efficient material systems. Spiral (railway) ; Electronics ; Electromagnetic shielding ; Electromagnetic compatibility ; Attenuation ; Aerospace ; Acoustic attenuation Magnesium Oxide Properties and Applications ; Advanced ceramic materials synthesis ; Calcium Carbonate Crystallization and Inhibition
Authors: Garbar, Iryna
Source: Zenodo (CERN European Organization for Nuclear Research) (preprint, 2025-12-07)
Field: Materials Science — Electronic, Optical and Magnetic Materials

A Multi-Material Spiral Shielding Architecture for Radiation Attenuation, Electromagnetic Suppression and Thermal Stabilization in Aerospace, AI Hardware and Medical Technology The Spiral Triple-Layer Shield (STLS) introduces a multi-material shielding architecture that arranges high-density radiation absorbers, electromagnetic dampening minerals and resonant stabilization materials in a harmonic spiral geometry.Unlike conventional linear or homogeneous shielding designs, STLS uses three interacting spiral layers that distribute, absorb, reflect and harmonize incoming radiation and electromagnetic disturbances across multiple energy domains.The architecture aims to enhance robustness in spacecraft electronics, AI processors, medical sensors, autonomous robotics and high-frequency communication modules. Electromagnetic shielding ; Spiral (railway) ; Robustness (evolution) ; Electromagnetic radiation ; Robotics ; Electromagnetic interference ; Electromagnetic compatibility ; Radiation Electromagnetic wave absorption materials ; Radiation Shielding Materials Analysis ; Electromagnetic Compatibility and Measurements
Authors: Hui Li | Andy Huang | Xiaoping Jiang
Source: ECS Meeting Abstracts (article, 2025-11-24)
Field: Materials Science — Materials Chemistry

Flexible Autonomous Platforms for Accelerating Material Discovery in Battery R&amp;D The automation of experimental workflows with high flexibility is a critical part to construct a self-driven laboratories (SDL) for accelerating material discovery. The autonomous experiment platforms generate highly repeatable and reliable data pipeline and realize the close-loop workflow by paring with AI models. Multiple representative modules, such as precise solid &amp; liquid dispensing, uniform mixing &amp; pellet pressing &amp; sintering, as well as electrolyte filling and cell assembling, can be combined with intelligent robots to realize autonomous liquid &amp; solid-state electrolyte preparation and cell performance evaluation, which accelerates the battery material screening processes, obtains the optimized compositions and structures, and improves the battery performance and cycling stability. Battery (electricity) ; Pipeline (software) ; Workflow ; Flexibility (engineering) ; Automation ; Robot ; Construct (python library) Machine Learning in Materials Science ; Extraction and Separation Processes ; Advanced Battery Technologies Research
Authors: Biqing Chen | Jiayin Gao | Haizhu Sun | Yinghan Zhao | Yan Liu | Xiaohong Qiu
Source: Methods (article, 2025-11-03)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Carboxyl-PEG modified Fe3O4 nanoparticles as an ultrasensitive SERS substrate for multiplex detection of exogenous hormones related to endometrial cancer Detection limit ; Substrate (aquarium) ; Hormone ; Multiplex ; Analyte ; Nanoparticle ; Matrix (chemical analysis) Gold and Silver Nanoparticles Synthesis and Applications ; Spectroscopy Techniques in Biomedical and Chemical Research ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Hao Chen | Wei Zhang
Source: Frontiers in Applied Physics and Mathematics (article, 2025-12-30)
Field: Materials Science — Materials Chemistry

Materials Property Prediction with Graph Transformers and Uncertainty-Calibrated Active Learning The acceleration of materials discovery relies heavily on the ability to predict physicochemical properties of crystal structures with high accuracy and computational efficiency. While Density Functional Theory (DFT) provides precise ground-truth data, its cubic scaling with electron count renders it prohibitive for high-throughput screening of vast chemical spaces. Consequently, machine learning surrogates have emerged as a critical alternative. However, conventional Message Passing Neural Networks (MPNNs) often struggle to capture long-range atomic interactions and suffer from over-smoothing in deep architectures. Furthermore, standard active learning frameworks frequently rely on uncalibrated uncertainty estimates, leading to suboptimal sampling strategies and wasted computational resources. This paper introduces a novel framework: the Graph Transformer with Uncertainty-Calibrated Active Learning (GT-UCAL). We propose a geometric graph transformer architecture that integrates structural positional encodings to capture global topology, coupled with an evidential deep learning mechanism to quantify both aleatoric and epistemic uncertainties. Through rigorous experimentation on the Materials Project and JARVIS datasets, we demonstrate that GT-UCAL achieves state-of-the-art predictive performance while reducing the requisite labeled data by approximately 40% compared to random sampling. Transformer ; Graph ; Deep learning ; Scaling ; Artificial neural network ; Property (philosophy) ; Training set ; Uncertainty quantification Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Computational Drug Discovery Methods
Authors: Duncan R. Sutherland | Rachel Ford | Yun Liu | Tyler B. Martin | Peter A. Beaucage
Source: APL Machine Learning (article, 2025-08-12)
Field: Materials Science — Materials Chemistry

AutoSAS: A new human-aside-the-loop paradigm for automated SAS fitting for high throughput and autonomous experimentation The advancement of artificial-intelligence driven autonomous experiments demands physics-based modeling and decision-making processes, not only to improve the accuracy of the experimental trajectory but also to increase trust by allowing transparent human-machine collaboration. High-quality structural characterization techniques (e.g., x ray, neutron, or static light scattering) are a particularly relevant example of this need: they provide invaluable information but are challenging to analyze without expert oversight. Here, we introduce AutoSAS, a novel framework for human-aside-the-loop automated data classification. AutoSAS leverages human-defined candidate models, high-throughput combinatorial fitting, and information-theoretic model selection to generate both classification results and quantitative structural descriptors. We implement AutoSAS in an open-source package designed for use with the Autonomous Formulation Laboratory for x-ray and neutron scattering-based optimization of multi-component liquid formulations. In a first application, we leveraged a set of expert defined candidate models to classify, refine the structure, and track transformations in a model injectable drug carrier system. We evaluated four model selection methods and benchmarked them against an optimized machine learning classifier, and the best approach was one that balanced quality of the fit and complexity of the model. AutoSAS not only corroborated the critical micelle concentration boundary identified in previous experiments but also discovered a second structural transition boundary not identified by the previous methods. These results demonstrate the potential of AutoSAS to enhance autonomous experimental workflows by providing robust, interpretable model selection, paving the way for more reliable and insightful structural characterization in complex formulations. Computer science ; Classifier (UML) ; Artificial intelligence ; Workflow ; Machine learning ; Human-in-the-loop ; Decision boundary ; Model selection ; Data-driven ; Set (abstract data type) ; Data mining Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Paula C.R. Corsato | Christian O. Silva | Iris R.S. Ribeiro | Renato S. Lima
Source: Analytica Chimica Acta (article, 2025-11-20)
Field: Chemistry — Electrochemistry

Review on electrochemical sensors for anticancer drug susceptibility testing Drug ; Anticancer drug ; In vivo ; Drug discovery ; Viability assay ; Pharmacokinetics ; High-throughput screening ; Electrochemical cell Electrochemical Analysis and Applications ; Advanced biosensing and bioanalysis techniques ; Electrochemical sensors and biosensors