jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Pompilia Mioara Lopes | Liviu Mare | Lucian Barbu-Tudoran | Alina Gadja | Violeta Popescu
Source: Gels (article, 2026-03-31)
Field: Materials Science — Biomaterials

Gelatin Hydrogel pH Sensors Incorporating Anthocyanins for Intelligent Food Packaging: Towards Enhanced Food Spoilage Detection Protein-based hydrogels composed of gelatin, whey and glycerol were functionalized with red cabbage extract (RCE) to develop natural colorimetric pH sensors for intelligent food packaging. Structural analysis by X-ray diffraction (XRD) and scanning electron microscopy (SEM) revealed amorphous, hierarchically organized networks where RCE molecules interact with protein chains. The resulting microstructure, consisting of compact surface domains and a porous internal network, may regulate the diffusion of volatile amines into the hydrogel matrix, enabling gradual and stable pH-dependent color transitions. The resulting biocomposite hydrogel exhibited a stable and time-resolved optical response to meat spoilage, correlating structural relaxation with colorimetric sensitivity. Color difference values (ΔE00) calculated based on recorded images indicated strong chromatic changes in the presence of spoilage-related volatiles. Under refrigeration, ΔE00 remained below five, suggesting negligible color shifts. At room temperature, ΔE00 exceeded 20 after 48 h, confirming significant anthocyanin transformation linked to increased alkalinity (pH 7.2–7.5). A positive correlation between ΔE00 and pH was observed, highlighting the hydrogel’s high sensitivity to environmental changes. These findings confirm the potential of RCE-loaded hydrogels as eco-friendly, visual freshness indicators suitable for intelligent packaging applications. The hydrogel films demonstrated a distinct color transition within the pH range of 5.75–7.5, corresponding to the freshness variation interval of chicken meat. Self-healing hydrogels ; Gelatin ; Structural coloration ; Anthocyanin ; Food spoilage ; pH indicator ; Red Color ; Scanning electron microscope Nanocomposite Films for Food Packaging ; Meat and Animal Product Quality ; Hydrogels: synthesis, properties, applications
Authors: Zheng Mingqi | Ma Xiaoli | Y. Songlin | Danchen Wang | Qi Zhang | Guo xiuzhi | Xu Jingrong | Kui Zhang | Ling Qiu | X. Liangyu
Source: Journal of Clinical Laboratory Analysis (article, 2026-02-03)
Field: Chemistry — Spectroscopy

Calcium Dobesilate Interference in Two Urinary Total Protein Detection Methods Calcium dobesilate did not significantly interfere with the pyrogallol red-molybdate method but did significantly interfere with the benzethonium chloride method. The direction and degree of interference were related to the urinary protein and drug concentrations. Discontinuing calcium dobesilate for 1-2 days before obtaining a urine sample for total protein detection is recommended. Pyrogallol ; Urine ; Calcium ; Interference (communication) ; Urinary system Advanced Proteomics Techniques and Applications ; Analytical chemistry methods development ; Advanced Biosensing Techniques and Applications
Authors: Wanqian Li | Jintao Peng | Zongfei Jing | Tianyu Zhang | Ze Long | Xianjie Qiao | Xiaoming Chen | Dongxu Yang | Kefeng Duan | June Yang
Source: ArXiv.org (article, 2026-04-02)
Field: Materials Science — Materials Chemistry

DWDP: Distributed Weight Data Parallelism for High-Performance LLM Inference on NVL72 Large language model (LLM) inference increasingly depends on multi-GPU execution, yet existing inference parallelization strategies require layer-wise inter-rank synchronization, making end-to-end performance sensitive to workload imbalance. We present DWDP (Distributed Weight Data Parallelism), an inference parallelization strategy that preserves data-parallel execution while offloading MoE weights across peer GPUs and fetching missing experts on demand. By removing collective inter-rank synchronization, DWDP allows each GPU to progress independently. We further address the practical overheads of this design with two optimizations for split-weight management and asynchronous remote-weight prefetch. Implemented in TensorRT-LLM and evaluated with DeepSeek-R1 on GB200 NVL72, DWDP improves end-to-end output TPS/GPU by 8.8% at comparable TPS/user in the 20-100 TPS/user serving range under 8K input sequence length and 1K output sequence length. Asynchronous communication ; Inference ; Workload ; Sequence (biology) ; Range (aeronautics) ; Parallelism (grammar) ; Multi-core processor Machine Learning in Materials Science ; Advanced Neural Network Applications ; Natural Language Processing Techniques
Authors: Huabing Liu | Zilin Chen | Lok Hin Law | Yang Liu | Ziyan Wang | Jiawen Wang | Yi Zhang | Dinggang Shen | Jianpan Huang | Kannie W. Y. Chan
Source: Communications Engineering (article, 2026-01-10)
Field: Materials Science — Materials Chemistry

Accelerating CEST MRI through complementary undersampling and multi-offset transformer reconstruction Chemical exchange saturation transfer (CEST) is a promising magnetic resonance imaging (MRI) technique that provides molecular-level information in vivo. To obtain this unique contrast, repeated acquisition at multiple frequency offsets is needed, resulting a long scanning time. In this study, we propose a hybrid strategy at k-space and image domain to accelerate CEST MRI to facilitate its wider application. In k-space, we developed a complementary undersampling strategy which enforces adjacent frequency offsets by acquiring different subregions of k-space. Both Cartesian and spiral k-space trajectories were applied to validate its effectiveness. In the image domain, we developed a multi-offset transformer reconstruction network that uses complementary information from adjacent frequency offsets to improve reconstruction performance. Additionally, we introduced a data consistency layer to preserve undersampled k-space and a differentiable coil combination layer to leverage multi-coil information. The proposed method was evaluated on rodent brain and multi-coil human brain CEST images from both pre-clinical and clinical 3 T MRI scanners. Compared to fully-sampled images, our method outperforms a number of state-of-the-art CEST MRI reconstruction methods in both accuracy and image fidelity. CEST maps, including amide proton transfer (APT) and relayed nuclear Overhauser enhancement (rNOE), were calculated. The results also showed close agreement with fully-sampled ones. Undersampling ; Scanner ; Iterative reconstruction ; Real-time MRI ; Leverage (statistics) ; Magnetic resonance imaging ; Pattern recognition (psychology) ; Electromagnetic coil ; Magnetic resonance elastography Lanthanide and Transition Metal Complexes ; Advanced MRI Techniques and Applications ; Electron Spin Resonance Studies
Authors: Abbas Rammal | Rabih Assaf | Eric Perrin | Valériu Vrabié
Source: Scientific Reports (article, 2026-02-21)
Field: Chemistry — Analytical Chemistry

Enhancing strawberry maturity assessment using mid-infrared spectral analysis with advanced variable selection and supervised classification Accurate and non-destructive assessment of fruit maturity is critical for sustainable agricultural practices. This study proposes a novel framework for evaluating strawberry ripeness using Mid-Infrared (MIR) spectroscopy combined with metaheuristic feature selection and supervised classification. A dataset of 443 strawberries spanning eight maturity stages was analyzed using six metaheuristic algorithms—Binary Grey Wolf Optimizer, Binary Particle Swarm Optimizer, Bee Colony Optimizer, Genetic Algorithm, Ant Colony Optimizer, and Gravitational Search Optimizer—integrated with four classifiers: Naïve Bayes, Decision Tree, Linear Discriminant Analysis, and Support Vector Machine. A new fitness function was designed to optimize classifier performance, and results were validated through Self-Organizing Map Neural Networks, cross-validation, and statistical significance testing. The Genetic Algorithm–Linear Discriminant Analysis combination achieved the highest and most stable accuracy (94.6–99%), outperforming existing image-based, deep learning, and conventional spectroscopic approaches while retaining interpretability. These findings demonstrate that metaheuristic-driven MIR analysis provides a robust, explainable, and efficient method for precise strawberry maturity assessment, offering significant potential for advancing eco-friendly and intelligent agricultural practices. Feature selection ; Linear discriminant analysis ; Support vector machine ; Pattern recognition (psychology) ; Fitness function ; Selection (genetic algorithm) ; Classifier (UML) ; Ant colony ; Metaheuristic Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Postharvest Quality and Shelf Life Management
Authors: Denis Dertli | Thomas Speck
Source: Soft Matter (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Response to dynamic shape changes in suspensions of hard rectangles While the autonomous assembly of hard nanoparticles with different shapes has been studied extensively both in experiments and simulations, little is known about systems where particle shape can be dynamically altered. DNA origami nanostructures offer an alternative route to synthesize nanoparticles that can change their shape on demand. Motivated by recent experiments, here we study the structure and dynamics of suspensions of hard squares in response to an elongation into a rectangle. Performing dynamic hard-particle Monte Carlo simulations at constant volume and employing two protocols, we numerically analyze the collective diffusion and ordering during the shape change and the subsequent relaxation towards the new equilibrium state. We find that the cascading protocol, which mimics experimentally realized DNA origami, can become dynamically arrested due to the increase in effective packing fraction. Particle (ecology) ; Shape change ; Nanoparticle ; Shape analysis (program analysis) ; Aspect ratio (aeronautics) ; Particle system Pickering emulsions and particle stabilization ; Material Dynamics and Properties ; Block Copolymer Self-Assembly
Authors: Shachar Bressler | Dana Grunhaus | Mattan Hurevich | Assaf Friedler
Source: Biochemical Society Transactions (article, 2026-03-01)
Field: Chemistry — Spectroscopy

Methods for studying the effects of phosphorylation patterns in proteins Protein phosphorylation is one of the most common and versatile regulatory mechanisms in cells. Most human proteins are phosphorylated at multiple sites, giving rise to large numbers of possible phosphorylation patterns. Each phosphorylation pattern can lead to a different functional or pathological outcome. Yet, linking defined phosphorylation patterns to specific biological functions remains a major experimental challenge. In this review we describe the main strategies to study phosphorylation patterns at the protein and domain levels and highlight how they complement each other. We first discuss cellular approaches, including phosphomimetics, kinase-based assays, and genetic code expansion, which allow working in a native environment but have their significant drawbacks. We then describe in vitro methods, such as enzymatic phosphorylation and semi-synthetic phosphoproteins generated by ligation, which afford mechanistic insights but result in low yields and are difficult to scale for producing libraries. We focus on synthetic phosphopeptide libraries as tools that offer precise control over the number and position of phosphosites and are uniquely suited for systematic mapping of phosphorylation patterns. This comes at a price of not working at the protein level, but rather at the domain level. Peptide libraries are often used for preliminary identification of key phosphorylations, later studied in detail at the protein level. We conclude that ideally more than one method should be used and that these methods should not be viewed as competing but rather as complementary. A combined use of several of these approaches provides a practical toolbox for dissecting how phosphorylation patterns regulate protein behavior. Phosphorylation ; Phosphopeptide ; Protein phosphorylation ; Domain (mathematical analysis) ; Phosphorylation cascade ; Protein domain ; Complement (music) Advanced Proteomics Techniques and Applications ; Chemical Synthesis and Analysis ; Protein Structure and Dynamics
Authors: Zhi Shen | Qigui Deng | Zhongyi Wu | Kang Jin | Yuliang Zhong | Yiqing Shi | Yunhan Ling | Shan Hu
Source: Scientific Reports (article, 2026-03-26)
Field: Materials Science — Materials Chemistry

A lightweight metallographic defect detection framework via structured priors and energy-aware attention under strong grain boundary interference Accurate identification of preparation defects in 20 steel (equivalent to AISI 1020) metallographic specimens is the key to ensuring the reliability of microstructural rating. However, under high-magnification microscopic fields of view, weak defect features often suffer from severe aliasing with high-frequency, strongly structured grain boundary texture backgrounds. This causes conventional detectors to face serious issues of feature dilution and over-smoothing during feature extraction. To address this challenge, this paper proposes a lightweight recognition framework fusing structured priors and energy-aware attention, named RSS-YOLOv10. This method focuses on feature decoupling within local high-resolution fields of view. First, a RepHMA module is designed to construct a heterogeneous multi-scale receptive field, capturing defect morphologies with extreme scale variations through a structural re-parameterization mechanism. Second, a parameter-free SimAM attention mechanism based on energy functions is introduced. Leveraging the principle of information entropy, it adaptively suppresses high-frequency grain boundary noise in the feature space, significantly enhancing the signal-to-noise ratio (SNR) of weak defects against strong texture backgrounds. Finally, the feature aggregation path is reconstructed using SlimNeck to achieve a lightweight architecture. Experimental results demonstrate that on datasets retaining original micro-texture details, RSS-YOLOv10 exhibits superior identification capabilities, achieving mAP scores of 80.1% and 78.9% on a self-constructed dataset and the NEU-DET dataset, respectively. Compared with baseline models, this method effectively overcomes the bottleneck of missed detections under complex grain boundary backgrounds while reducing computational complexity (GFLOPs) and parameter redundancy, providing an efficient solution for the fine screening of challenging defect samples in metallographic analysis. Prior probability ; Pattern recognition (psychology) ; Feature (linguistics) ; Bottleneck ; Grain boundary ; Reliability (semiconductor) ; Preprocessor ; Boundary (topology) ; Noise (video) ; Estimator Machine Learning in Materials Science ; Advanced Neural Network Applications ; Generative Adversarial Networks and Image Synthesis
Authors: Yurong Wang | Xiaofei Jiao | Shilong Chen | W. D. Li | Hui Xiao | Baolong Liu | Shuwei Song | W. Q. Zhang | Ying Jin
Source: Corrosion Reviews (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Corrosion behavior of 60Si2MnA spring steel in mixed salt environments Abstract The atmospheric corrosion behavior of 60Si2MnA spring steel was investigated in environments containing chloride (Cl − ) and sulfate (SO 4 2− ). Bare steel specimens were exposed to controlled laboratory conditions that replicated key parameters of the target atmosphere. The deliquescence and weathering characteristics of the deposited salts were examined using electrochemical impedance spectroscopy, while a custom-built salt deposition system was employed to conduct corrosion tests at constant temperature and humidity (20 °C/75 % RH and 40 °C/75 % RH) with a surface salt load of 10 g/m 2 over varying exposure durations. Pronounced corrosion was observed under both temperature conditions. X-ray diffraction and Raman spectroscopy revealed that the corrosion products were primarily composed of iron oxyhydroxides and iron oxides. Scanning electron microscopy combined with electrochemical analyses showed that, at early exposure stages, the rust layer exhibited abundant cracks and pores, allowing for the ingress of corrosive species. The initial corrosion products acted as strong oxidants, enhancing cathodic reactions and thus accelerating metal degradation. With increasing exposure time, the thickening of the rust layer provided partial protection, slightly improving the corrosion resistance. Elevated temperature was found to significantly accelerate both the corrosion rate and the evolution of corrosion products. Corrosion ; Rust (programming language) ; Cathodic protection ; Dielectric spectroscopy ; Weathering steel ; Chloride ; Scanning electron microscope ; Metal ; Relative humidity Corrosion Behavior and Inhibition ; Hydrogen embrittlement and corrosion behaviors in metals ; Metal and Thin Film Mechanics
Authors: Muhammed Zahid Sahin
Source: Immuno (article, 2026-03-27)
Field: Materials Science — Materials Chemistry

Neurotoxic Effects of Metal and Metal Oxide Nanoparticles and the Protective Role of Natural Bioactive Compounds Nanomaterials (NMs) are increasingly utilized in drug delivery, diagnostic imaging, and therapeutic applications. However, their widespread use raises concerns regarding potential neurotoxicity, particularly for metal and metal oxide nanoparticles. Accumulating evidence indicates that these nanoparticles induce neurotoxicity through interconnected mechanisms, including excessive reactive oxygen species generation, activation of neuroinflammatory pathways, mitochondrial dysfunction, and disruption of blood–brain barrier integrity. These molecular events collectively lead to synaptic impairment, neuronal apoptosis, and progressive cognitive and behavioral deficits, with toxicity severity influenced by dose, exposure duration, and age. Given that in vitro models often fail to capture complex systemic interactions such as nanoparticle biodistribution, blood–brain barrier dynamics, and neuroimmune responses, this review places particular emphasis on in vivo studies to provide a more physiologically relevant understanding of nanoparticle-induced neurotoxicity. Importantly, a growing body of in vivo evidence demonstrates that natural bioactive compounds can mitigate these effects by targeting key pathogenic pathways, including oxidative stress, inflammation, and mitochondrial dysfunction, while preserving neuronal integrity. These findings highlight the therapeutic potential of natural bioactives as protective agents against nanoparticle-induced neurotoxicity and as candidates for broader neuroprotective strategies. This review summarizes the mechanistic basis of metal and metal oxide nanoparticle neurotoxicity and critically evaluates the protective role of natural bioactive compounds, with a focus on evidence derived from animal models. Neurotoxicity ; Neuroprotection ; Reactive oxygen species ; In vivo ; Nanotoxicology ; Oxidative stress ; Toxicity Nanoparticles: synthesis and applications ; Ginkgo biloba and Cashew Applications ; Curcumin's Biomedical Applications
Authors: Nurul Afira Syakirah binti Mohd Amin | Norazrina binti Mat Jali | Nor Azwin binti Shukri | Pairu bin Ibrahim | Ku Zarina Ku Ahmad | A’liya binti Abdul Laziz | Mohd Taufiq bin Ishak
Source: Journal of Radioanalytical and Nuclear Chemistry (article, 2026-01-20)
Field: Materials Science — Materials Chemistry

Effect of gamma irradiation on the mechanical and dielectric properties of XLPE/TiO₂ nanocomposites Polyethylene (PE) is widely used in power cables, but its performance limits its use in high-voltage applications. This study investigates the effect of gamma irradiation (20–100 kGy) on low-density polyethylene (LDPE) nanocomposites containing titanium dioxide (TiO₂) at 0, 0.5, and 2 phr and ethylene–vinyl acetate (EVA) at 0, 1, and 5 phr. Based on the results, X5 (LDPE/TiO₂-0.5) achieved the highest crosslinking efficiency at 100 kGy, while X16 (LDPE/TiO₂-2/EVA-5) exhibited superior tensile strength at 80–100 kGy. Dielectric permittivity decreased with increasing irradiation dose, particularly at 100 kGy, and breakdown strength peaked at 60–80 kGy. These findings provide insight into optimizing irradiation conditions for XLPE-based high-voltage insulation materials. Nanocomposite ; Ultimate tensile strength ; Irradiation ; Polyethylene ; Dielectric ; Gamma irradiation ; Dielectric strength ; Titanium dioxide High voltage insulation and dielectric phenomena ; Polymer Nanocomposite Synthesis and Irradiation ; Dielectric materials and actuators
Authors: Alex Finkelstein | Ron Moneta | Or Zohar | Michal Rivlin | Zaiss, Moritz | Dinora Friedmann Morvinski | Or Perlman
Source: arXiv (Cornell University) (article, 2026-02-03)
Field: Materials Science — Materials Chemistry

Multiparameter Uncertainty Mapping in Quantitative Molecular MRI using a Physics-Structured Variational Autoencoder (PS-VAE) Quantitative imaging methods, such as magnetic resonance fingerprinting (MRF), aim to extract interpretable pathology biomarkers by estimating biophysical tissue parameters from signal evolutions. However, the pattern-matching algorithms or neural networks used in such inverse problems often lack principled uncertainty quantification, which limits the trustworthiness and transparency, required for clinical acceptance. Here, we describe a physics-structured variational autoencoder (PS-VAE) designed for rapid extraction of voxelwise multi-parameter posterior distributions. Our approach integrates a differentiable spin physics simulator with self-supervised learning, and provides a full covariance that captures the inter-parameter correlations of the latent biophysical space. The method was validated in a multi-proton pool chemical exchange saturation transfer (CEST) and semisolid magnetization transfer (MT) molecular MRF study, across in-vitro phantoms, tumor-bearing mice, healthy human volunteers, and a subject with glioblastoma. The resulting multi-parametric posteriors are in good agreement with those calculated using a brute-force Bayesian analysis, while providing an orders-of-magnitude acceleration in whole brain quantification. In addition, we demonstrate how monitoring the multi-parameter posterior dynamics across progressively acquired signals provides practical insights for protocol optimization and may facilitate real-time adaptive acquisition. Autoencoder ; Pattern recognition (psychology) ; Bayesian probability ; Covariance ; Inverse problem ; Magnetic resonance imaging ; Artificial neural network ; Uncertainty quantification Lanthanide and Transition Metal Complexes ; Advanced MRI Techniques and Applications ; MRI in cancer diagnosis