jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Sherzod Korabayev | Xusanxon Bobojanov | Jakhongir Soloxiddinov | Zoxitjon Inomov | Sherzod Boltabayev
Source: The American Journal of Engineering And Technology (article, 2026-01-01)
Field: Materials Science — Polymers and Plastics

Hybrid Modeling And Digital Twin Integration For Predictive Quality Control And Resource Optimization In Smart Knitted Fabric This research presents a comprehensive hybrid-modeling framework that integrates physics-based simulations with machine learning algorithms to establish a predictive digital twin for smart knitted fabric manufacturing. The system addresses critical challenges in quality prediction, resource optimization, and process parameter control by creating a virtual replica of the entire production chain—from yarn input to finished fabric. The framework consists of three interconnected modules: a physics-based finite element model simulating yarn mechanics and loop formation dynamics during knitting; a data-driven deep learning module trained on historical production data to predict defect occurrence probability based on real-time sensor inputs; and an optimization engine using multi-objective genetic algorithms to balance competing production objectives including quality, speed, and resource consumption. The digital twin was implemented and validated over six months in a pilot production facility using four LONG XING SM-252 flat knitting machines producing technical knitted fabrics. Results demonstrate unprecedented predictive capabilities: the system achieved 94.7% accuracy in predicting defect occurrences 15 minutes before manifestation, enabling proactive intervention. Process parameter optimization reduced yarn waste by 23.8% while maintaining product quality standards with defect rates below 0.5%. Energy consumption decreased by 18.2% through optimized machine scheduling and parameter adjustments. The integration of IoT sensors including tension, vibration, thermal and visual sensors provided real-time data streams updating the digital twin at 1-second intervals. Comparative analysis against traditional statistical process control methods showed a 67.3% reduction in quality-related production stoppages and a 41.5% improvement in overall equipment effectiveness. This work establishes a practical roadmap for Industry 4.0 transformation in textile manufacturing, demonstrating how digital twin technology can bridge the gap between theoretical process understanding and practical production optimization, ultimately creating more sustainable, efficient, and quality-conscious manufacturing systems. Model predictive control ; Process (computing) ; Yarn ; Scheduling (production processes) ; Process control ; Statistical process control ; Energy consumption ; Production (economics) ; Resource (disambiguation) Textile materials and evaluations ; Mechanical Behavior of Composites ; Advanced Sensor and Energy Harvesting Materials
Authors: Rebecca L. Pinals | Han Li | Randhal Ramirez | Sibylle Pfammatter | Monika Zimmermann | D. F. Torres | Benjamin S. Flavel | Lela Vuković | Inge K. Herrmann | Robert Nißler
Source: ChemRxiv (article, 2026-01-26)
Field: Materials Science — Materials Chemistry

Protein Corona Selectivity on Carbon Nanotube Biosensors is Driven by Surface Coating and Chirality Semiconducting single-walled carbon nanotubes (SWCNTs) are versatile near-infrared fluorophores that are engineered to create sensors displaying modulated fluorescence upon interaction with target analytes. SWCNT properties depend on both surface functionalization and underlying lattice structure, defined by the chiral indices (n,m), enabling the development of wavelength-tunable nanosensors for biological research and diagnostic applications. While the principles of specific SWCNT surface modification with biomacromolecules have been widely investigated, the impact of surface chemistry and nanotube structure on protein interactions in complex biological fluids remains underexplored. Here, we investigated the role of SWCNT surface coating and chirality in protein corona formation in human blood plasma using mass spectrometry–based proteomics. We systematically compared protein corona formation across single-stranded (ssDNA)- and PEG-phospholipid-coated SWCNTs, as well as across nanotube chiralities ranging from (6,4) to (9,4). Our findings indicate that surface coating identity is the dominant factor governing differential protein adsorption. SWCNTs coated with tight-binding ssDNA sequences exhibited greater protein-corona selectivity, preferentially enriching apolipoproteins and more internally structured proteins, whereas the PEG-phospholipid coating globally reduced, though did not eliminate, protein adsorption. Larger-diameter SWCNTs adsorbed proteins with lower secondary-structure content and greater molecular mass. Protein corona formation was further shaped by protein–protein cooperative binding effects in a SWCNT coating-dependent manner. Finally, molecular dynamics simulations provide mechanistic support for the experimental finding that proteins with higher glycine content display enhanced binding to (GT)6- compared to (TAT)4-coated SWCNTs through distinct glycine-peptide binding modes. These insights provide a framework for the rational design of SWCNT-based sensors for protein-profile recognition in complex biological environments. Carbon nanotube ; Chirality (physics) ; Biosensor ; Nanosensor ; Nanobiotechnology ; Coating ; Nanotube ; Protein adsorption ; Surface modification Carbon Nanotubes in Composites ; Electrochemical sensors and biosensors ; Diatoms and Algae Research
Authors: Philipp Müller | Gary A. Eiceman | Anton Rauhameri | Anton Kontunen | Antti Roine | Niku Oksala | Antti Vehkaoja | Maiju Lepomäki
Source: ArXiv.org (article, 2026-01-01)
Field: Chemistry — Spectroscopy

Extracting Patterns of Chemical Information from Differential Mobility Spectrometry Measurements under Varying Conditions of Humidity and Temperature Differential Mobility Spectrometry (DMS), also known as Field Asymmetric Ion Mobility Spectrometry, is a rapid and affordable technology for extracting information from gas phase samples containing complex volatile organic compounds, and can therefore be used for analyzing surgical smoke. One obstacle to its widespread application is the dependence of DMS measurements on humidity and, to a lesser degree, temperature, making comparison of data measured under different environmental conditions arbitrary. The commonly used solution is to regulate these environmental conditions to some predefined humidity and temperature levels. However, this approach is often unfeasible or even impossible. Therefore, in this paper we analyzed a dataset of 1,852 DMS measurements of surgical smoke evaporated from porcine adipose and muscle tissue to get an understanding of the impact of varying humidity and temperature on DMS measurements. Our analysis confirmed clear dependence of the measurements on these two factors. To overcome this challenge, we fitted regression models to raw and normalized DMS measurement data. Subsequently, these models were used for estimating DMS measurements for known tissue types based on recorded humidity and temperatures. Our test suggests that it is possible to estimate DMS measurements of surgical smoke from porcine adipose and muscle tissue under specific environmental conditions by standardizing DMS measurements separation voltage-wise and training multivariate regression models on the normalized data, which is the first step in removing the need for standardized measurement conditions. Humidity ; Ion-mobility spectrometry ; Relative humidity ; Temperature measurement ; Linear regression ; Mass spectrometry ; Accuracy and precision ; Adipose tissue Mass Spectrometry Techniques and Applications ; Advanced Chemical Sensor Technologies ; Metabolomics and Mass Spectrometry Studies
Authors: Junjie LIU | Thossaporn Wijakmatee | Hideyuki Matsumoto | Teruoki Tago | Masaru Ogura
Source: Journal of the Japan Petroleum Institute (article, 2026-02-28)
Field: Materials Science — Materials Chemistry

SVGD-HMC Hybrid Inference for Microkinetic Modeling of Novel NO&lt;sub&gt;&lt;i&gt;x&lt;/i&gt;&lt;/sub&gt; Adsorption Process System identification remains a bottleneck in digital modeling of emerging chemical processes and catalytic materials, where limited observability, high model complexity and incomplete mechanistic knowledge hinder predictive modeling. In this work, hybrid Bayesian inference framework combining Stein Variational Gradient Descent (SVGD) and Hamiltonian Monte Carlo (HMC) was presented for identifying NOx adsorption microkinetics model under high-dimensional parameter space. The method begins with SVGD to efficiently explore the posterior via particle-based, repulsion-driven dynamics. To address search stagnation caused by model stiffness, a particle reassignment strategy is introduced. Additionally, Root Mean Square Propagation (RMSProp)-based stepping is employed to improve convergence robustness. Consequentially, this hybrid approach leverages the global search efficiency of SVGD while incorporating HMC to enhance local posterior resolution in regions where searching stagnate. The presented approach was applied to model a novel NOx adsorption system with Pd/Beta-zeolite being adsorbent, as well as a published Pd/SSZ13-zeolite NOx capture model as benchmark. The proposed SVGD-HMC pipeline offers a scalable and interpretable inference strategy for data-constrained models, enabling richer uncertainty-aware predictions. These results highlight the need for integrated digital-experimental workflows where robust Bayesian methods complement limited experimental information, particularly for next-generation materials and non-steady-state reactor systems. Bayesian inference ; Bottleneck ; Inference ; Scalability ; Particle filter ; Pipeline (software) ; Markov chain Monte Carlo ; Identification (biology) ; Bayesian probability Catalytic Processes in Materials Science ; Industrial Gas Emission Control ; Adsorption and biosorption for pollutant removal
Authors: Yue Chen | Yao Zhu | Li Qin | Xiaoling Yang | Qizhi Long | Bo Zhou | Haiyan Zhong
Source: Current Research in Food Science (article, 2026-01-01)
Field: Chemistry — Organic Chemistry

Catechinic compounds suppress free radicals in camellia oil by donating hydrogen atoms: quantum chemistry calculation combined with EPR detection Phenolic compounds have been serving as primary antioxidants in retarding lipid oxidation, however, the conventional method for screening phenolic candidates is time-consuming and costly. In this study, phenolics were selected in accordance with the bond dissociation energy (BDE) of O-H and the Gibbs free energy change (ΔG) of the scavenging reaction via quantum chemistry calculation, the efficiency of selected phenolics was subsequently assessed by free energy barrier (ΔG‡), eventually, the suppression efficiency of the candidates was verified by using electron paramagnetic resonance (EPR) determination. Among six catechinic compounds, (-)-gallocatechin gallate (GCG) and (-)-epigallocatechin gallate (EGCG) were identified as the most promising candidates, both of which showed potential efficiency for scavenging alkyl peroxyl (•OOR), alkoxyl (•OR), oleic acid (•OA-C11) and linoleic acid (•LA-C11) radicals. In the thermally-induced camellia oil model (180 °C, 20 min), the inhibition rates of EGCG against alkyl (•R), alkoxyl (•OR) and DMPO oxidative free radicals (•X) were 60.06 ± 0.38 %, 59.74 ± 2.57 % and 92.05 ± 1.70 %, respectively, analogously, the efficiencies of GCG scavenging these radicals ranged from 68.40 ± 0.82 % to 94.27 ± 0.33 %. This indicates that the proposed methodology can effectively screen lipid antioxidants, furthermore, both GCG and EGCG are capable of retarding oxidation in camellia oil. Radical ; Propyl gallate ; Gallate ; Electron paramagnetic resonance ; Oleic acid ; Bond-dissociation energy ; Scavenging ; Linoleic acid Edible Oils Quality and Analysis ; Free Radicals and Antioxidants ; Phytochemicals and Antioxidant Activities
Authors: Mudasir Majeed | Shafkat Hussain | Ruhit Ashraf | Imtiyaz Hussain | Dakush Mahajan
Source: Pan-African Journal of Health and Psychological Sciences (article, 2026-01-10)
Field: Materials Science — Biomaterials

Targeted Drug Delivery in Oncology: Recent Advances in Nanoparticle-BasedChemotherapeutics Cancer is one such health concern that has been ravaging and taking away many peopleworldwide in the last four decades despite the four decades of therapeutic progress. Eventhough traditional chemotherapy is an ancient treatment option, its use is restricted because ofits extreme systemic toxicity, nonspecific ablation of normal tissues, and emergence ofmultidrug resistance. Such a weakness results in making the treatment non-sustainable in thelong term and restricting the survival of patients. In order to overcome such barriers,nanoparticle-based drug delivery systems (NDDS) have emerged as a significant and possiblydisruptive technology in cancer therapy. The advantages of NDDS are improved aqueoussolubility of the poorly water-soluble compounds, prolongation of circulation half-life,improved pharmacokinetics, and passive and active targeting of tumor tissue. Besides thisNDDS can be regulated drug delivery, stimulus responsive and therapeutic specificity in off-target effect to the cost. They might also serve as versatile vectors to co-releasechemotherapeutics and biomolecules such as nucleic acids, proteins/immune modulators andoffer synergistic and personalized therapy. To enhance compatibility and uptake of NDDS bythe device, the engineering of flexibility of the structure and surfaces to enable the device tooperate in the heterogeneous tumor microenvironment is also enhanced. Moreover,theranostics has been created to have diagnostic and therapeutic capabilities that allow realtime therapy monitoring. This review gives a summary on the design strategies and deliverysystem, targeting, preclinical and clinical outcomes, and approved nanomedicines andemerging nanocarriers technologies. Nanocarriers ; Drug delivery ; Targeted drug delivery ; Cancer therapy ; Drug ; Nanomedicine ; Preclinical testing ; Cancer treatment Nanoparticle-Based Drug Delivery ; Nanoplatforms for cancer theranostics ; Cancer Research and Treatment
Authors: Zheng Tang | Shuxia Qiu | Zhili Cai | Chung-Lim Law | Xin Mao | Peng Xu
Source: ADVANCES IN GEO-ENERGY RESEARCH (article, 2026-01-26)
Field: Materials Science — Materials Chemistry

Multi-gate mixture-of-different-experts model for the prediction of multiple properties in multi-phase rock The prediction of petrophysical properties in multi-phase rock is critical for various geoenergy applications. To this end, deep learning-based methods have recently emerged as a prominent research focus in rock physics. However, due to the inherent scarcity of structural data in multi-phase rocks, coupled with the limitations of convolutional neural network in capturing multi-scale dynamic phase interface and mitigating parameter interference in multi-task learning, predictive performance has not yet reached a satisfactory level. To address this shortcoming, a new multi-task learning framework based on a multi-gate mixture-of-different-experts model is proposed to predict multiple properties of multi-phase rocks. A semi-supervised fractal-informed generative adversarial network is employed to reconstruct multi-phase rocks images, while the finite element method is used to compute their transport properties. The gating network allocates bespoke expert subsets to each task, and an automatic weighted loss function dynamically balances the task-specific loss contributions, enhancing performance and generalization. The results show that the statistical average of the predicted relative permeability aligns with the Brooks-Corey equation, demonstrating the reliability of the proposed model in preserving fundamental physical principles. On the validation dataset, the model achieves high predictive accuracy across all target properties, including fractal dimension, porosity, saturation, permeability of gas phase, permeability of water phase, and effective permeability. Comparative evaluation on the test dataset demonstrates that the proposed model significantly outperforms other multi-task models. These findings confirm that the proposed framework can simultaneously and accurately predict multi-phase rock properties under limited data conditions, holding a promise for guiding assessments in hydrocarbon and geothermal exploration, CO₂ sequestration, nuclear waste disposal, and geological hazard mitigation, among others. Document Type: Original article Cited as: Tang, Z., Qiu, S., Cai, Z., Law, C.-L., Mao, X., Xu, P. Multi-gate mixture-of-different-experts model for the prediction of multiple properties in multi-phase rocks. Advances in Geo-Energy Research, 2026, 19(2): 182-196. https://doi.org/10.46690/ager.2026.02.06 Convolutional neural network ; Petrophysics ; Permeability (electromagnetism) ; Artificial neural network ; Reliability (semiconductor) ; Relative permeability ; Deep learning ; Test data Machine Learning in Materials Science ; Rock Mechanics and Modeling ; Hydrocarbon exploration and reservoir analysis
Authors: Alireza Rezaei | Azam Bastanfard | Sasan Harifi
Source: Research Square (preprint, 2026-03-12)
Field: Materials Science — Materials Chemistry

Quantum-enhanced phonological rhyme detection in poetry: A parameter-efficient hybrid CRNN-VQC model Rhyme ; Robustness (evolution) ; Parameterized complexity ; Quantum ; Quantum circuit ; Artificial neural network Machine Learning in Materials Science ; Text Readability and Simplification ; Sentiment Analysis and Opinion Mining
Authors: Yan Teng | Shengzhu Fu | Yanan Lu | Hao Chen | Chun Li | Lei Wen | Zhengwei Huang | Ling Jiang
Source: Journal of Food Process Engineering (article, 2026-02-01)
Field: Chemistry — Analytical Chemistry

Intelligent Detection of Mold Infection Stage in Cherries Based on Hyperspectral Imaging ( <scp>HSI</scp> ) ABSTRACT This study addresses the critical challenge of early detection for Alternaria alternata infection in postharvest cherries by developing a hyperspectral imaging (HSI) system integrated with machine learning (ML) algorithms. After acquiring HSI data from cherries at different infection stages, principal component analysis (PCA) was first employed to demonstrate the superior discriminative capability of HSI‐based multivariate analysis over conventional color imaging. We then systematically evaluated the effects of preprocessing methods (raw data [RAW] vs. first‐derivative [FD] transformation) and wavelength selection algorithms (Successive Projections Algorithm [SPA] and Competitive Adaptive Reweighted Sampling [CARS]) on model performance. Through comprehensive comparison of multiple ML classifiers—including Support Vector Machine (SVM), Random Forest (RF), k‐Nearest Neighbors (KNN), and Partial Least Squares Discriminant Analysis (PLS‐DA)—combined with soft voting ensemble learning, we developed an optimized FD‐CARS‐Voting model that achieved exceptional classification accuracy (99.06% ACC, 98.59% Kappa). This approach provides an efficient, non‐destructive solution for real‐time quality monitoring in commercial supply chains, effectively balancing detection accuracy, processing speed, and cost‐effectiveness. The proposed framework demonstrates strong potential for extension to other perishable agricultural products requiring rapid quality assessment. Hyperspectral imaging ; Discriminative model ; Preprocessor ; Pattern recognition (psychology) ; Random forest ; Linear discriminant analysis ; Support vector machine ; Partial least squares regression Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Advanced Chemical Sensor Technologies
Authors: Wei Wang | Qing Wei | Igor M. Sokolov | Ralf Metzler | Aleksei V. Chechkin
Source: ArXiv.org (article, 2026-03-24)
Field: Materials Science — Materials Chemistry

Genuine and spurious (non-)ergodicity in single particle tracking In single-particle tracking experiments measuring anomalous diffusion dynamics, understanding ergodicity is crucial, as it ensures that the time average of an observable matches the ensemble average, and can thus be fitted with known ensemble-averaged observables. A commonly used criterion for assessing the ergodicity of a stochastic process is based on the comparison of the mean-squared displacement (MSD) with the time-averaged MSD (TAMSD). This approach has been widely applied and proves effective in cases of weak ergodicity breaking across various systems in both theoretical and experimental studies. However, there is relatively little discussion regarding the theoretical justification and limitations of this definition. Here, we demonstrate that this widely accepted criterion to some extent contradicts the classical definition of ergodicity as well as physical intuition, leading to spurious (non-)ergodicity results when applied to several well-known stochastic models. To address this limitation, we propose using the mean-squared increment (MSI) instead of the MSD for comparison of ensemble- and time-averaged observables. Several well-established examples demonstrate that our MSI-TAMSD criterion not only effectively reveals weak ergodicity breaking, equivalent to the MSD-TAMSD approach, but also provides a more accurate characterization of the genuine (non-)ergodicity of systems where the MSD-TAMSD method fails. Additionally, for systems exhibiting "ultraweak" ergodicity breaking, the MSI can reveal the asymptotic stationarity and ergodic nature of the process' increments. Our findings emphasize the important role of the MSI observable for SPT experiments and anomalous diffusion studies. Ergodicity ; Spurious relationship ; Observable ; Ergodic theory ; Stationary ergodic process ; Diffusion ; Stochastic process ; TRACE (psycholinguistics) Material Dynamics and Properties ; stochastic dynamics and bifurcation ; Quantum many-body systems
Authors: Syahriati . | Imran Muhtar | Rahmawati Saleh | Justus Elisa Loppies | Rosniati . | Sitti Ramlah
Source: Asian Journal of Dairy and Food Research (article, 2026-01-19)
Field: Materials Science — Biomaterials

Enhancing the Physicochemical Properties of Tuna Skin Gelatin Through Ultrasound-assisted Green Extraction: A Comparative Study Background: Gelatin is widely used in food industry and other industries as an emulsifying and emulsifying agent. Ultrasound-assisted extraction (UAE) was used to produce gelatin from tuna skin to enhance its physicochemical properties and extraction efficiency. This study aimed to determine precise method produces gelatin with a better yield, gel strength, viscosity, color and other physicochemical properties. Methods: Two extraction sequences were compared: ultrasound extraction followed by acid-base extraction (method A) and vice versa (method B). Using both methods, gelatin was extracted from tuna skin, with yields measured, color analyzed by lightness and ΔE and gel strength tested. Viscosity, moisture, protein contents and pH were assessed to understand gelatin quality. Result: The result showed method B produced higher gelatin yield (13.34%) than method A (4.92%). Method B gelatin had greater gel strength (220 g vs. 200 g), brighter color (lower ÄE) and slightly higher viscosity (10 cP vs. 9 cP). SEM analysis revealed denser microstructure, supporting stronger gels. Significant differences occurred in yield, color and gel strength. Ultrasound-assisted acid-base extraction effectively produced high-quality tuna skin gelatin. This method offers potential applications in food, pharmaceutical and cosmetic industries, providing a sustainable alternative to mammalian gelatin. Gelatin ; Tuna ; Extraction (chemistry) ; Lightness Collagen: Extraction and Characterization ; Nanocomposite Films for Food Packaging ; Proteins in Food Systems