jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Danny Reidenbach | Filipp Nikitin | Olexandr Isayev | Saee Paliwal
Source: Digital Discovery (article, 2026-01-01)
Field: Chemistry — Physical and Theoretical Chemistry

Applications of modular co-design for <i>de novo</i> 3D molecule generation A scalable and accurate transformer model unifies diffusion and flow-matching approaches to generate realistic 3D molecules. Modular design ; Scalability ; Transformer ; Encapsulation (networking) ; Molecule Crystallography and molecular interactions ; Supramolecular Self-Assembly in Materials ; Chemical Synthesis and Analysis
Authors: Zhengling Lei | Xuanyu Wang | Fang Wang | Haibo Huo | Biao Wang
Source: Energies (article, 2026-01-23)
Field: Materials Science — Materials Chemistry

Surrogate Modeling of a SOFC/GT Hybrid System Based on Extended State Observer Feature Extraction Solid oxide fuel cell (SOFC) and gas turbine (GT) hybrid systems exhibit inherent system uncertainties and unmodeled dynamics during operation, which compromise the accuracy of predicting gas turbine power. This poses challenges for system operation analysis and energy management. To enhance the prediction accuracy and stability of gas turbine power in SOFC/GT hybrid systems, a power prediction method capable of incorporating total system disturbance information is investigated. This study constructs a high-fidelity simulation model of an SOFC/GT hybrid system to generate gas turbine power prediction datasets. With fuel utilization (FU) as the input and gas turbine power as the output, this system is assumed to be a first-order dynamic system. Building upon this foundation, an extended state observer (ESO) is employed to extract the total system disturbance (f) that affects the power output of the gas turbine, excluding fuel utilization. The total disturbance f and fuel utilization are used as inputs to a Backpropagation (BP) neural network to construct a disturbance-aware power prediction model. The predictive performance of the proposed method is evaluated by comparison with a BP neural network without disturbance estimation information and several benchmark models. Simulation results indicate that incorporating the disturbance term estimated by ESO enhances both the accuracy and stability of the BP neural network’s power prediction, particularly under operating conditions characterized by significant power fluctuations. Quantitatively, when comparing the predictive model with disturbance included to the model without disturbance, including the disturbance reduces the prediction error by approximately 89.33% (MSE) and 67.34% (RMSE), while the coefficient of determination R2 increases by 0.1132, demonstrating a substantial improvement in predictive performance under the same test conditions. The research findings indicate that incorporating disturbance information into data-driven prediction models represents a viable modeling approach, providing effective support for predicting gas turbine power in SOFC/GT hybrid systems. Control theory (sociology) ; Benchmark (surveying) ; Artificial neural network ; Electric power system ; Turbine ; Model predictive control ; Backpropagation ; Stability (learning theory) ; Power (physics) Advancements in Solid Oxide Fuel Cells ; Thermodynamic and Exergetic Analyses of Power and Cooling Systems ; Advanced Aircraft Design and Technologies
Authors: Lei Shi | Jun Sun | Bin Zhang | Xiaojiao Du | ZhiYan SHI | Xin Zhou | Xudong Wu
Source: Journal of Food Process Engineering (article, 2026-02-01)
Field: Chemistry — Analytical Chemistry

Hyperspectral Imaging for Identification of Early Freeze Damage in Oranges ABSTRACT The orange affected by freeze damage will experience the loss of partial nutrients, decline in taste quality, and acceleration of deterioration. According to the early signs of freeze damage, it becomes challenging for the naked eye to differentiate between oranges with freeze damage and those that are intact. This study aimed to investigate the feasibility of using hyperspectral imaging for identification of oranges with different degrees of early freeze damage. Firstly, the fundamental mechanism of hyperspectral imaging for identifying early freeze damage of orange was investigated by weight change before and after freeze damage, soluble solids content, and fluorescence images of the oranges with different degrees of early freeze damage. Subsequently, a variance inflation factor modified competitive adaptive reweighted sampling (VIF‐CARS) algorithm and a reptile search algorithm optimized support vector machine (RSA‐SVM) were proposed to extract effective wavelengths associated with freeze damage information of oranges and identify oranges with different degrees of early freeze damage. The results demonstrated that VIF‐CARS exhibited promising outcomes in dimensionality reduction, effective information extraction, and wavelength interpretability compared with traditional methods. Moreover, when combined with the RSA‐SVM model, this combination showcased reliable discrimination capabilities for identifying oranges with different degrees of early freeze damage, achieving the optimal classification accuracy of 94.44% and the F1‐score of 0.9484. Therefore, hyperspectral imaging offers a feasible approach for identification of oranges with different degrees of early freeze damage, which is a crucial step toward improving the detection system for the fruit supply chains. Hyperspectral imaging ; Interpretability ; Pattern recognition (psychology) ; Orange (colour) ; Citrus × sinensis ; Orange juice Spectroscopy and Chemometric Analyses ; Postharvest Quality and Shelf Life Management ; Smart Agriculture and AI
Authors: Alfianita Nuril Hidayaty | Saidun Fiddaroini | Ahmad Luthfi Fahmi | Qonitah Fardiyah | Akhmad Sabarudin
Source: Science & Technology Indonesia (article, 2026-01-02)
Field: Materials Science — Biomaterials

Optimization and Stability Assessment of Chitosan/PVA Smart Sensor Films Incorporated with Roselle Anthocyanins for Real-Time Visual Monitoring of Chicken and Shrimp Freshness under Different Storage Conditions The development of intelligent packaging systems has become a promising approach to ensure food safety and quality by enabling real-time freshness monitoring. In this study, chitosan/polyvinyl alcohol (Cs/PVA)-based smart sensor films incorporated with roselle (Hibiscus sabdariffa L.) anthocyanins were fabricated and optimized for the visual detection of chicken and shrimp spoilage under variations storage. Anthocyanins were extracted from fresh roselle petals through different maceration periods (R1–R5) to investigate their influence on film properties. Physicochemical characterization revealed that extended maceration enhanced anthocyanin loading, leading to increased film thickness, higher color saturation, and improved optical responsiveness. FESEM micrographs demonstrated homogeneous polymer matrices at lower anthocyanin concentrations, while higher loadings induced micro-aggregates that enhanced volatile adsorption and sensing sensitivity. The fabricated films exhibited clear and progressive color transitions aligned with the spoilage process: red–purple at fresh conditions (pH 5–6), brown–green at intermediate spoilage (pH 7–9), and yellow–brown at advanced spoilage (pH ≥10), consistent with anthocyanin structural transformations. Storage trials with chicken and shrimp confirmed that the Cs/PVA–R5 film displayed the intense coloration and strong response to volatile amines such as ammonia, trimethylamine, and dimethylamine, enabling reliable freshness monitoring. Importantly, the films provided a visual indication when the pH exceeded the edibility threshold of 7.0–7.5, beyond which the samples were deemed unsuitable for consumption. Overall, this work demonstrates the potential of Cs/PVA–roselle anthocyanin films as eco-friendly, low-cost, and effective smart indicators for meat and seafood freshness, offering a practical platform for intelligent food packaging applications. Food spoilage ; Anthocyanin ; Active packaging ; Shrimp ; Food storage ; Food packaging Nanocomposite Films for Food Packaging ; Postharvest Quality and Shelf Life Management ; Advanced Chemical Sensor Technologies
Authors: Xuan Liu | Jianping Quan | Ying Li | Xiao‐Fan Wang | Jiangchao Zhao
Source: Journal of Animal Science and Biotechnology/Journal of animal science and biotechnology (article, 2026-01-20)
Field: Materials Science — Biomaterials

Advancing cellulose degradation through synthetic biology: engineered pathways and microbial systems for sustainable biomass conversion Fiber, the most abundant organic polymer in nature, is widely recognized as a foundational sustainable material with diverse applications across industrial, medical, and consumer domains. Owing to its renewability and widespread availability, it also serves as a critical alternative energy source in agriculture, enabling more sustainable livestock production through the efficient conversion of fibrous feedstuffs, thereby supporting the principles of a circular bioeconomy. Cellulose, which constitutes up to 80% of plant fiber, contains tightly packed crystalline regions that confer strong resistance to microbial degradation. Other key obstacles to efficient cellulose digestion in the gut include the absence of critical cellulolytic genes, low enzymatic activity, a lack of natural activators, and the presence of cellulase inhibitors. Synthetic biology provides innovative molecular-level strategies to overcome key technical barriers in cellulose degradation. These approaches employ targeted modifications at nucleic acid and protein levels, including the introduction of engineered genes, synthetic regulators, and optimized enzymes, to develop high-performance microbial systems with enhanced cellulose-degrading capabilities. Furthermore, genetic modifications like the knockout of inhibitory genes and knock-in of activator genes, combined with rational redesign of multi-enzyme complexes, can significantly improve the secretion and catalytic efficiency of cellulases. When integrated with artificial intelligence, synthetic biology enables predictive screening and precision engineering of microbial strains for highly efficient cellulose degradation. This review comprehensively summarizes recent advances in synthetic biology approaches for improving cellulose degradation and highlights how these tools can optimize fiber utilization in sustainable agricultural and industrial applications. Synthetic biology ; Cellulose ; Cellulase ; Bacterial cellulose ; Metabolic engineering ; Bioproducts ; Industrial biotechnology ; Cellulosic ethanol Advanced Cellulose Research Studies ; Biofuel production and bioconversion ; Enzyme-mediated dye degradation
Authors: Samar A. Salim | Abdullah M. M. Elbadry | Esraa B. Abdelazim | Tasneem Abed | Marwa M. Shakweer | Noura G. Eissa | Elbadawy A. Kamoun | Mahmoud Elsabahy
Source: Scientific Reports (article, 2026-02-20)
Field: Materials Science — Biomaterials

Enhancing wound healing with synergistic dual-drug electrospun roflumilast and L-arginine loaded PLA/PVA nanofibers through fabrication, optimization, and in vivo assessment Current trends in improved wound management emphasize the development of dual-drug-loaded electrospun nanofibrous scaffolds. Herein, electrospun dual-drug loaded PLA/PVA nanofibrous scaffolds, incorporating roflumilast, a selective phosphodiesterase-4 inhibitor with anti-inflammatory activity, and L-arginine, a precursor to nitric oxide with stimulating activity towards wound healing and tissue regeneration, were developed. A dual-spinneret electrospinning process enabled the co-loading of drugs in PLA and PVA phases. Scaffolds were characterized by SEM, FTIR, and XRD, showing consistent fiber morphology, and amorphous drug formation. FTIR analysis was performed to confirm drug-polymer compatibility and successful incorporation of roflumilast and L-arginine within the PLA/PVA nanofibrous matrix. Swelling ratio analysis indicated controlled hydration behavior regarding polymer-drug composition, and dual-drug-loaded fibers exhibited maximum initial swelling (610%) and stabilization at ~ 240%. In vivo rat excision wound healing model showed that the dual-drug loaded nanofibers demonstrated enhanced wound healing with ~ 99.8% healing on day 14, which was significantly better, compared to nanofibers incorporating a single drug and control groups. Moreover, histological studies revealed the absence of residual granulation tissue and enhanced re-epithelialization in the dual-drug treated group. These results indicate that roflumilast and L-arginine co-loaded PLA/PVA nanofibers exhibit wound-healing and regenerative properties, offering a promising therapeutic platform for enhanced wound repair. Wound healing ; Electrospinning ; Nanofiber ; Granulation tissue ; In vivo ; Swelling ; Nitric oxide Electrospun Nanofibers in Biomedical Applications ; Wound Healing and Treatments ; Graphene and Nanomaterials Applications
Authors: David Aebisher | Izabela Rudy | Kacper Rogóż | Dorota Bartusik-Aebisher
Source: Current Issues in Molecular Biology (article, 2026-01-02)
Field: Chemistry — Spectroscopy

The MALDI Method to Analyze the Lipid Profile, Including Cholesterol, Triglycerides and Other Lipids Lipid profiling is a key element of modern biology and medicine, providing information on the structure, function, and dynamics of lipid metabolism in health and disease. This review presents the latest state of the art in the application of matrix-assisted laser ionization mass spectrometry (MALDI-MS) in lipidomics, with a particular focus on the analysis of cholesterol, triglycerides (TAGs), and the main classes of phospholipids and sphingolipids. The theoretical basis of the technique is discussed, including ionization mechanisms, matrix selection and mass analyzer configurations, and the influence of experimental parameters on spectral quality. The specificity of sterol and TAG ionization, challenges associated with ion suppression, and strategies for improving detection sensitivity and selectivity are discussed. Particular attention is paid to molecular imaging (MALDI-MSI), which enables spatial mapping of lipids in tissues and is of increasing importance in neurobiological, oncological, and metabolic research. The review highlights the advantages of MALDI for rapid lipid profiling and tissue analysis, while also pointing out technical limitations (e.g., difficulties in detecting sterols, matrix interference, limited quantification) and the need for method standardization. MALDI-MS appears to be a complementary technique to LC-MS/ESI-MS and DESI-MS, with great translational potential, particularly in the context of diagnostics, biomarker studies, and in situ lipid imaging. Lipid metabolism ; Lipidomics ; Mass spectrometry ; Context (archaeology) ; Analyte ; Mass spectrometry imaging ; Profiling (computer programming) ; Metabolomics Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Advanced Proteomics Techniques and Applications
Authors: Koichi Matsuo | Satoshi Hashimoto | Ryota Imaura | Mohamed I. A. Ibrahim
Source: Chemistry - An Asian Journal (article, 2026-01-01)
Field: Chemistry — Spectroscopy

Advances in Synchrotron Radiation‐Based Vacuum‐Ultraviolet Circular Dichroism for Biomolecular Structural Analysis Vacuum-ultraviolet circular dichroism (VUVCD) spectroscopy using synchrotron radiation is a powerful tool for characterizing the structures of biomolecules in aqueous solutions. When combined with bioinformatics, VUVCD enables detailed determination of protein secondary-structure contents, segment numbers, and sequence distributions, while coupling with linear dichroism and shear flow techniques provides insights into the orientations of secondary structures. These analytical approaches have been applied to the structural characterization of membrane-bound α-synuclein (αS), a protein which transforms into amyloid fibrils associated with Parkinson's disease, successfully revealing the molecular mechanism of αS fibril-formation on membranes. Furthermore, time-resolved measurements using microfluidic device allow direct observation of protein structural dynamics during membrane interactions. The combination of VUVCD spectroscopy with molecular dynamics simulations in aqueous environments has also been applied to determine the absolute configurations of saccharides containing higher-energy chromophores, thereby elucidating hydration structures involving intra- and intermolecular hydrogen bonds. Recent studies have further expanded these applications to monitor the conformational changes of extracellular polysaccharides and polyhydroxyalkanoates as functions of temperature and membrane interactions. These advancements highlight that integrating VUVCD spectroscopy with computational and other experimental approaches can significantly enhance the structural characterization of diverse biomolecules at molecular level. Biomolecule ; Circular dichroism ; Molecular dynamics ; Spectroscopy ; Force spectroscopy ; Linear dichroism ; Synchrotron ; Characterization (materials science) Molecular spectroscopy and chirality ; Alzheimer's disease research and treatments ; Parkinson's Disease Mechanisms and Treatments
Authors: Ting Yu Yeh | Deniz Turan
Source: npj Science of Food (article, 2026-02-09)
Field: Materials Science — Biomaterials

Mapping gas permeability of sustainable packaging materials to link food barrier needs by clustering algorithms Food packaging is critical for ensuring food safety, quality, and shelf life. However, growing environmental concerns with conventional plastics drive the search for sustainable alternatives. A major challenge is that many biobased and biodegradable materials show poor barrier properties, limiting their use for food. This study provides a proof-of-concept for classifying sustainable packaging materials by clustering oxygen transmission rate (OTR) and water vapor transmission rate (WVTR) data. A dataset from 49 studies (2000 to 2016) was analyzed using K-Means, Gaussian Mixture Model (GMM), and Density-Based Spatial Clustering of Applications with Noise (DBSCAN). DBSCAN emerged as best performing algorithm, achieving the highest Silhouette Score (0.910) and lowest Davies-Bouldin Index (0.374). Results validated that while many sustainable films exhibit high permeability, nanocomposites achieved improved barrier performance. This data-driven framework demonstrates clustering as a tool for systematic grouping of packaging materials, with future work requiring broader datasets, industrial benchmarks, and standardized reporting for practical application. Cluster analysis ; Food packaging ; DBSCAN ; Silhouette ; Sustainability ; Work (physics) ; Transmission (telecommunications) Nanocomposite Films for Food Packaging ; Effects and risks of endocrine disrupting chemicals ; Microplastics and Plastic Pollution
Authors: Ali Öter | Zeynep Berktaş | Betül Ersöz | Şeref Sağıroğlu | Elif Orhan
Source: Scientific Reports (article, 2026-01-13)
Field: Materials Science — Materials Chemistry

Stacking ensemble machine learning for predicting photodetector performance under varying illumination intensities Photodetectors are essential components in modern optoelectronic technologies, yet experimental characterization of nanomaterial-based devices is often time-consuming and resource-intensive. To address this challenge, this study presents a stacking ensemble learning approach to predict the performance of bismuth-doped graphene quantum dots-based photodetectors under illumination levels ranging from 22 to 110 mW/mm². Four boosting algorithms—Adaptive Boosting, Gradient Boosting, Extreme Gradient Boosting, and Categorical Boosting—were trained on datasets obtained under dark, 22, 66, and 110 mW/mm², while 44 and 88 mW/mm² data were reserved for testing. A stacking ensemble learning model further enhanced prediction accuracy. The final model achieved a coefficient of determination of 0.9874 and a mean absolute error of 0.1840 at 88 mW/mm², effectively predicting the logarithmic current–voltage characteristics. The model also estimated key photodetector metrics, including sensitivity (1589.27), responsivity (2.389 mA/W), and specific detectivity (1.16 × 10¹⁰ Jones). This study explores the use of a stacking ensemble of four boosting algorithms to model the performance of Bi-GQD/p-Si photodetectors across different illumination levels, offering a data-driven alternative to traditional characterization. Photodetector ; Stacking ; Boosting (machine learning) ; Responsivity ; Gradient boosting ; Ensemble learning ; Logarithm Graphene research and applications ; Silicon Nanostructures and Photoluminescence ; Nanowire Synthesis and Applications
Authors: Namwook Hur | S. P. Kim | Yu Bin Park | Changhwan Kim | Sohui Yoon | Youngseok Cho | Tae Hoon Lee | Joonki Suh
Source: Nature Communications (article, 2026-01-13)
Field: Materials Science — Materials Chemistry

On-device cryogenic quenching enables robust amorphous tellurium for threshold switching Amorphous chalcogenide alloys exhibiting crystallization-free Ovonic threshold switching behaviour have gained immense attention as selector materials. While the switching characteristics depend on the chalcogen species, understanding device-level elemental behaviour, particularly for tellurium (Te), remains challenging due to its low crystallization temperature and poor glass-forming ability. Here, we realize an electrothermally induced amorphous Te (a-Te) phase via on-device cryogenic quenching, which rapidly suppresses crystallization in the supercooled liquid at low ambient temperature. The order-to-disorder transition yields a ~ 0.81 V increase in threshold voltage and a ~ 10³ reduction in subthreshold current, attributed to enhanced deep-level trap formation. The a-Te phase exhibits reliable self-regulated oscillations, driven by deep traps, distinguishing it from conventional capacitance-driven effects. These findings support that the threshold switching in Te originates from defect-mediated transitions occurring before melting, rather than solely from thermal phase-change effects. Our results provide insights into chalcogenide switching mechanisms and pave the way for stoichiometry-tuned selector devices, nano-oscillators, and selector-only memory applications. Chalcogenide ; Tellurium ; Chalcogen ; Amorphous solid ; Supercooling ; Crystallization ; Quenching (fluorescence) ; Phase-change memory Phase-change materials and chalcogenides ; Advanced Memory and Neural Computing ; Transition Metal Oxide Nanomaterials