jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Danielle Cristine Mota Ferreira | Camila Rodrigues Carneiro | José Roberto Miranda Júnior | Jane Sélia dos Reis Coimbra | Franciele Maria Pelissari | Eduardo Basílio de Oliveira
Source: Environmental Science and Pollution Research (article, 2026-03-06)
Field: Materials Science — Biomaterials

Environmental fate, biodegradation pathways, and phytotoxicological risks of biopolymer-based packaging: a critical review Biopolymer-based packaging has been widely promoted as a sustainable alternative to conventional plastics. However, biodegradability claims are often supported by incomplete assessments that prioritize disintegration or weight loss over actual mineralization, neglecting the ecotoxicological impacts of handling intermediates and waste. This review provides a critical and integrative analysis of current methodologies used to assess the biodegradation and environmental safety of biopolymer-based materials in different environments. The ecotoxicological discussion focuses on phytotoxicity as a sensitive indicator of environmental safety, while microbial processes are considered complementarily due to their central role in biodegradation. The review further discusses how environmental variations and polymer composition influence handling kinetics and toxicity results, highlighting the need for harmonized protocols that reflect real-world environmental conditions. Future perspectives highlight the integration of laboratory and field research, the application of advanced analytical and predictive tools (omics, sensors, artificial intelligence), and the proposal of a conceptual framework for "effective environmental degradability," coupling mineralization parameters with ecotoxicological metrics. Overall, this work presents a comprehensive framework that connects biodegradation tests, phytotoxicity assessment, and regulatory interpretation, contributing to a more realistic understanding of the environmental fate and sustainability of biopolymer-based packaging. Biodegradation ; Sustainability ; Environmental risk assessment ; Environmental impact assessment ; Work (physics) ; Environmental safety biodegradable polymer synthesis and properties ; Microplastics and Plastic Pollution ; Effects and risks of endocrine disrupting chemicals
Authors: Ali Hatamizadeh | Shrimai Prabhumoye | Igor Gitman | Ximing Lu | Seungju Han | Wei Ping | Yejin Choi | Jan Kautz
Source: ArXiv.org (article, 2026-02-09)
Field: Materials Science — Materials Chemistry

iGRPO: Self-Feedback-Driven LLM Reasoning Large Language Models (LLMs) have shown promise in solving complex mathematical problems, yet they still fall short of producing accurate and consistent solutions. Reinforcement Learning (RL) is a framework for aligning these models with task-specific rewards, improving overall quality and reliability. Group Relative Policy Optimization (GRPO) is an efficient, value-function-free alternative to Proximal Policy Optimization (PPO) that leverages group-relative reward normalization. We introduce Iterative Group Relative Policy Optimization (iGRPO), a two-stage extension of GRPO that adds dynamic self-conditioning through model-generated drafts. In Stage 1, iGRPO samples multiple exploratory drafts and selects the highest-reward draft using the same scalar reward signal used for optimization. In Stage 2, it appends this best draft to the original prompt and applies a GRPO-style update on draft-conditioned refinements, training the policy to improve beyond its strongest prior attempt. Under matched rollout budgets, iGRPO consistently outperforms GRPO across base models (e.g., Nemotron-H-8B-Base-8K and DeepSeek-R1 Distilled), validating its effectiveness on diverse reasoning benchmarks. Moreover, applying iGRPO to OpenReasoning-Nemotron-7B trained on AceReason-Math achieves new state-of-the-art results of 85.62\% and 79.64\% on AIME24 and AIME25, respectively. Ablations further show that the refinement wrapper generalizes beyond GRPO variants, benefits from a generative judge, and alters learning dynamics by delaying entropy collapse. These results underscore the potential of iterative, self-feedback-based RL for advancing verifiable mathematical reasoning. Reinforcement learning ; Verifiable secret sharing ; Generative grammar ; Entropy (arrow of time) ; Quality (philosophy) ; Markov decision process Machine Learning in Materials Science ; Multimodal Machine Learning Applications ; Topic Modeling
Authors: Lin Cong | Xubo Hou | Liang Guo | Guangfu Liu | Jike Si
Source:  (article, 2026-02-26)
Field: Chemistry — Spectroscopy

Design of ion migration spectrum system based on four vacuum ultraviolet lamps An ion migration spectrometry (IMS) system for the separation and detection of trace molecules was developed using four vacuum ultraviolet lamps as ionization sources. The IMS system of the four vacuum UV lamp has a larger ionization region, and the number of gas molecules captured by it is more and the number of ions generated is more under the same gas concentration, and the use of PCB splice method to build an ion migration tube, the electrode spacing can reach 2 mm, the smaller the spacing, the more uniform the internal electric field, thereby reducing the ion diffusion phenomenon. Both methods improve the sensitivity of ion migration spectra and reduce the minimum detection limit. The sensitivity of IMS system was analyzed experimentally by using a mixture of isobutene gas and pure air. The experimental results showed that the four-vacuum ultraviolet lamp system could detect isobutene gas with a concentration below 0.3 ppm, providing a reliable solution for trace molecular detection. Ultraviolet ; Ion ; Ionization ; Analytical Chemistry (journal) ; Mass spectrometry ; Electrode ; Ion-mobility spectrometry ; Trace gas ; Molecule Mass Spectrometry Techniques and Applications ; Photocathodes and Microchannel Plates ; Plasma Diagnostics and Applications
Authors: Xinhan Qiao | Wentian Zeng | Wenyu Ye | Xize Dai | Jing Zhang | Yue Ming
Source: Polymers (article, 2026-04-10)
Field: Materials Science — Materials Chemistry

Mechanisms of Key Performance Degradation in Silicone Rubber Polymer Insulation for High-Voltage Composite Bushings Under Coupled Temperature, Humidity, and Corona Aging To investigate the multi-factor aging mechanisms of silicone rubber used in the outer sheath of composite bushings, this study focused on HTV silicone rubber employed in the sheath layer of 1100 kV high-voltage bushings. The samples were subjected to temperature–humidity–corona coupled aging in a multi-factor aging platform. The aged samples were characterized by scanning electron microscopy, energy-dispersive spectroscopy, Fourier-transform infrared spectroscopy, hydrophobicity measurements, hardness tests, and dielectric constant measurements. The results indicate that different aging factors affect the material differently. Corona aging primarily affects the sample surface, leading to substantial methyl group detachment, surface oxidation, and a decrease in hydrophobicity, with the local static contact angle decreasing by up to 70%. In contrast, wet heat aging affects the bulk material; under high-temperature and high-humidity conditions, the internal small-molecule chains accelerate silicon-oxide crosslinking, leading to a marked increase in hardness and a relative dielectric constant that initially decreases and then increases. Considering the complex field environment, surface performance measurements are easily influenced by external factors. Therefore, hardness and relative dielectric constant are proposed as key indicators for evaluating the aging degree of silicone rubber sheaths in service. The findings provide a valuable reference for the service-life evaluation of composite bushings. Silicone rubber ; Composite number ; Natural rubber ; Dielectric ; Accelerated aging ; Polymer ; Degradation (telecommunications) High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Dielectric materials and actuators
Authors: Amani Abdallah Hepautwa | Askwar Hilonga | Register Mrosso | Tusekile Said Alfredy | Gabriel Mwalusambo | Fina Lesafi | Yusufu Abeid Chande Jande
Source: PLoS ONE (article, 2026-03-05)
Field: Materials Science — Polymers and Plastics

High-performance eco-concrete beams with calcined montmorillonite, metakaolin pyrolyzed coffee grounds, and hybrid fibers for enhanced bending strength and microstructural refinement This study investigates the flexural performance and microstructural evolution of reinforced concrete beams incorporating calcined montmorillonite (CMMT), metakaolin (MK), pyrolyzed coffee grounds (PCG), and hybrid fibers (steel or banana) as sustainable partial replacements for natural sand and cementitious binders. The replacement levels-12.5% SCM (CMMT or MK) and 15% PCG-were selected based on preliminary optimization trials and evidence from prior studies that identify these ranges as the threshold at which pozzolanic reactivity, workability, and particle packing achieve maximum benefit without compromising matrix integrity. Thirteen mix groups and a control beam were cast and tested under four-point bending, with three replicates per group. Statistical analysis using one-way ANOVA (α = 0.05) confirmed significant differences in peak load, stiffness, ductility, and energy absorption across mixes (p < 0.05). Beams containing CMMT and 1.5% steel fibers achieved the highest flexural capacity, exhibiting a 92-105% increase in ultimate load relative to the control, while mixes with 1.0% banana fibers demonstrated the greatest ductility and post-cracking deformation capacity. Improvements in stiffness (up to 68%) and energy absorption (up to 120%) were closely linked to microstructural refinement observed through XRD and SEM. Quantitative indicators-including a 22-34% reduction in portlandite peak intensity, an increase in amorphous C-S-H content, and visibly compacted interfacial transition zones-corroborated the enhanced matrix densification induced by CMMT and PCG.The combined use of PCG, CMMT, and natural/steel fibers significantly reduced reliance on natural sand and cement while improving structural performance, demonstrating a viable pathway for developing high-performance eco-concretes for structural applications. Although direct durability tests were not conducted, the observed microstructural densification suggests potential improvements in long-term resistance to moisture and chloride ingress, warranting further research. Metakaolin ; Flexural strength ; Pozzolan ; Cementitious ; Portlandite ; Ductility (Earth science) ; Biocomposite ; Deformation (meteorology) Natural Fiber Reinforced Composites ; Concrete and Cement Materials Research ; Innovative concrete reinforcement materials
Authors: Wenlong LI | Dejia Meng | Hongda Wei | Qiang Fu | Zhiyu Zhang | Quanquan Mu | Deng Weijie
Source:  (article, 2026-01-19)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Incoherent Multi-Depth-of-Field Edge Imaging Based on Single Metalens Benefiting from high efficiency and low energy consumption, optical edge imaging has been employed as a preprocessing module for optical recognition systems. Compared with fixed depth-of-field (DOF) device, multi-DOF device can simultaneously capture objects at multiple depths. To address the key issues of non-uniform focal intensity and reduced signal-to-noise ratio (SNR) in multi-DOF edge imaging, this paper proposes an incoherent multi-DOF edge imaging approach. Polarization multiplexing of a single metalens is introduced to construct distinct phase profiles. The focal intensity uniformity of multi-DOF edge imaging is improved through phase holograms to control the focusing intensity for each polarization channel across the bifocal plane. In post-processing, this method achieves noise reduction through the integration of a window function within the frequency domain. Results demonstrate that focal intensity uniformity and system noise resistance are enhanced. This research verifies the feasibility of omnidirectional edge detection by second-order differentiation, providing significantly enhanced quality for incoherent multi-DOF edge imaging. Holography ; Polarization (electrochemistry) ; Image quality ; Enhanced Data Rates for GSM Evolution ; Multiplexing ; Edge enhancement ; Point spread function ; Edge detection ; Cardinal point Metamaterials and Metasurfaces Applications ; Near-Field Optical Microscopy ; Advanced Optical Imaging Technologies
Authors: Karolina Młynarczyk | Beata Podkościelna | Monika Osińska-Jaroszuk | Katarzyna Szałapata | Magdalena Jaszek
Source: Pure and Applied Chemistry (article, 2026-01-06)
Field: Chemistry — Organic Chemistry

Zinc oxide-modified composites as a new antimicrobial coatings for application in biomedical industry – synthesis, thermal stability and antimicrobial properties Abstract In recent years, the incidence of hospital-acquired infections has been increasing, which may be caused by the emergence of antibiotic-resistant bacteria. A solution to this problem may be the synthesis of composites containing the addition of compounds with antimicrobial properties, which can form a covering layer. They can limit the spread of bacteria and also reduce the formation of biofilm on the surface of equipment. The optimization of the polymer composition of methacrylate-based composites modified with zinc oxide was identified as the main objective of the research presented in this article. With the use of ATR/FT-IR technique, the structure and qualitative evaluation of the obtained composite materials were performed. The modified composites’ thermal resistance and decomposition process were also determined. The antimicrobial potential of the polymers against Gram-negative bacteria ( Pseudomonas aeruginosa ATCC 27853 and Escherichia coli ATCC 25922) and Gram-positive bacteria ( Staphylococcus aureus ATCC 25923) was determined with a modified disk diffusion method, the serial dilution method and the method with TTC. It was observed that incorporating zinc oxide into the structure of the described polymers significantly increased the antimicrobial potential of the composites and reduced bacterial biofilm formation on their surface. In the case of the effect of composite materials on P. aeruginosa , the largest zone of growth inhibition was determined for BPA.DM + AEH + 10 %ZnO, for E. coli , the largest zone of growth inhibition was 0.6 cm for BPA.DM + NVP + 10 %ZnO, and for S. aureus it was also 0.6 cm for BPA.DM + NVP + 10 %ZnO. Notably, all the materials exhibited antibacterial activity upon contact with bacterial cells. Assessing the percentage of bacterial growth inhibition revealed that had the greatest effect on P. aeruginosa was observed with BPA.DM + MMA + 10 % ZnO (49.7 % ± 0.5 % after 12 h and 49.9 % ± 2.1 % after 24 h) and BPA.DM + NVP + 10 % ZnO (62.4 % ± 2.4 % after 12 h). The other materials with the greatest effect on E. coli were BPA.DM + AEH (46.0 ± 3.1 % after 12 h and 48.0 ± 0.9 % after 24 h), BPA.DM + AEH + 10 % ZnO (46.7 ± 2.3 % after 12 h) and BPA.DM + NVP (53.5 ± 2.2 % after 24 h). Against S. aureus , the highest percentage of growth inhibition was determined for BPA.DM + MMA (56.3 % after 24 h), BPA.DM + HEMA (48.6 % after 24 h), and BPA.DM + NVP (47.9 % after 24 h). Antimicrobial ; Zinc ; Biofilm ; Thermal stability ; Polymer ; Composite number ; Bacteria ; Oxide Antimicrobial agents and applications ; Polymer Synthesis and Characterization ; Polymer Science and PVC
Authors: Yan-Hong Chen | Xiao-Nan Li | Wen-Xu Zhang | Tong Qin | Qi-Kun Cheng | Bin Wu
Source: Nanomaterials (article, 2026-02-14)
Field: Materials Science — Materials Chemistry

The Effects of K-BNNS Nanoparticles on PD Characteristics of Composite Aramid Paper Aramid paper serves as an insulating material in high-frequency power electronic transformers, and the incorporation of composite K-BNNS particles has been shown to enhance the insulating properties of aramid paper. Partial discharge (PD) is a predominant phenomenon that can lead to insulation failure in high-frequency transformers. Therefore, this paper primarily investigates the PD performances of different nanoparticle doping concentrations on aramid paper. Firstly, composite aramid papers containing K-BNNSs at different concentrations are prepared, namely 5%, 8%, 10% and 13%, respectively. Then, the experimental platforms of PD for composite aramid paper are established, and the PD performances, surface potentials, and hydrogen bonds under different high-frequency applied voltages are discussed. The experiment results show that the composite aramid paper with 10% K-BNNSs nanoparticle content has the optimal insulation performance. In the needle-plate and column-plate models, the PD amplitude decreases by 65.35% and 27.33%, respectively, when compared with non-doped aramid paper. Moreover, the breakdown voltage improves by 32.2% and 38.5%, respectively. After that, the influence mechanisms of residual charges and hydrogen bonds on the PD characteristics of composite aramid paper are analyzed. The results obtained in this paper can provide important reference for the design and selection of insulation materials for high-frequency transformers. Aramid ; Composite number ; Nanoparticle ; Doping ; Voltage ; Partial discharge High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Advanced DC-DC Converters
Authors: Yaniv Yosha | Michal Rivlin | Gil Navon | Or Perlman
Source: Research Square (preprint, 2026-03-23)
Field: Materials Science — Materials Chemistry

Metabolic CEST MRI of glucosamine uptake in brain tumor-bearing mice Magnetization transfer ; Magnetic resonance imaging ; Glioma ; Glucosamine ; Anaerobic glycolysis ; Cancer ; Brain tissue ; Glycolysis Lanthanide and Transition Metal Complexes ; Advanced MRI Techniques and Applications ; MRI in cancer diagnosis
Authors: Markus Schumann | Jonas Moske | Antonia Wüst | Felix Divo | Daria Gelbich | Philipp Niemietz | Kristian Kersting | Thomas Bergs | Peter Groche
Source: Research Square (preprint, 2026-01-13)
Field: Materials Science — Materials Chemistry

Structured Representation of Simulation and Annotation Data for Machine Learning in Forming Technologies Leverage (statistics) ; Raw data ; Scalability ; Schema (genetic algorithms) ; Annotation ; Domain knowledge ; Domain (mathematical analysis) ; Expert system Machine Learning in Materials Science ; Manufacturing Process and Optimization ; Flexible and Reconfigurable Manufacturing Systems
Authors: Yuri E.M. van der Burgt | C. Cobbaert
Source: Clinical Chemistry and Laboratory Medicine (CCLM) (article, 2026-04-02)
Field: Chemistry — Spectroscopy

Next-generation proteomics in medical laboratories: metrologically sound quantitative protein tests vs. innovative and personalized proteome patterns In precision medicine protein phenotypes are foreseen that combined with pattern recognition tools exhibit great potential in guaranteeing safe and accurate test results for patient management and intervention. For discovery purposes novel high-throughput technologies have emerged and this has led to a resurging interest in plasma proteomics. These next-generation proteomics technologies pursue enhanced speed and robustness of proteome readouts and have therefore been designed in combination with machine learning tools for pattern recognition to allow large-scale population studies. In the proximity extension assay two antibodies with single-stranded DNA tags are applied that hybridize upon binding to their target proteins. The second affinity-based assay relies on DNA-based aptamers with specific binding shape complementarity to their target proteins. In both assays the resulting protein-DNA barcodes are quantified through next-generation sequencing. On the one hand the ease of data analysis and integration with genomic and transcriptomic data is attractive compared to complex MS-based proteomics datasets. On the other hand MS allows an unequivocal characterization of the protein of interest and its proteoforms. Laboratory professionals now face the following protein diagnostics dilemma: either hold firm to metrologically sound protein or proteoform quantification by mass spectrometry or pursue individual protein profiles combined with machine learning algorithms without detailing various proteoforms. The first strategy has proven slow and may not be sustainable for all biomarkers, whereas the latter approach clearly requires concerted efforts from all stakeholders involved in medical test development before these can be adapted into medical laboratories. Proteome ; Proteomics ; Robustness (evolution) ; Precision medicine ; Protein microarray ; Personalized medicine ; Complementarity (molecular biology) ; Population Advanced Proteomics Techniques and Applications ; Advanced Biosensing Techniques and Applications ; vaccines and immunoinformatics approaches
Authors: Georg Auge | Matthis Clausen | Konstantin Ketterer | Jacob Schaefer | Nils Schmitt | Tom Altenburg | Yannick Hartmaring | Hendrik Raetz | Christoph N. Schlaffner | Bernhard Y. Renard
Source: bioRxiv (Cold Spring Harbor Laboratory) (article, 2026-03-18)
Field: Chemistry — Spectroscopy

usiGrabber: Automating the curation of proteomics spectra data at scale, making large datasets ready for use in machine learning systems Abstract Motivation An unprecedented amount of mass spectrometry-based proteomics data is publicly available through repositories such as the PRoteomics IDEntifications Database (PRIDE), and the field is increasingly leveraging machine learning approaches. However, the available data is not ready to be reused in a scalable way beyond the original acquisition purpose. Existing machine learning models commonly rely on a few manually curated datasets that require deep domain expertise and tedious technical work to construct. Importantly, these datasets have not been updated in recent years, so that newly published data remains inaccessible. We present usiGrabber, a scalable framework for assembling large proteomic datasets. usiGrabber is designed around portability and extensibility. It extracts spectra identification data from mzIdentML files, stores additional project-level metadata retrieved through the PRIDE API, indexes raw spectra using Universal Spectrum Identifiers (USIs), and offers download utilities to retrieve spectra data at scale. Results Within 49 hours, we parsed over 800 million peptide spectrum matches and corresponding USIs from over 1,200 projects. As a proof of concept, we used usiGrabber to construct a phosphorylation-specific training dataset of nearly 11 million spectra in under two days and used it to retrain a binary phosphorylation classifier based on the AHLF model architecture. With a balanced accuracy of 0.78, our model achieves comparable performance to the original model on an independent test set, showing that automated data extraction is an alternative to manual curation of static datasets. Availability All code is available at https://github.com/usiGrabber/usiGrabber ; the data is available at https://zenodo.org/records/18853258 . Scalability ; Metadata ; Identifier ; Classifier (UML) ; Software portability ; Field (mathematics) ; Identification (biology) ; Raw data Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Cell Image Analysis Techniques