jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Rosa Perestrelo
Source: Beverages (article, 2025-02-20)
Field: Chemistry — Spectroscopy

Nanostrategy for Selective Ethyl Carbamate Removal from Fermented Alcoholic Beverages via Molecular Imprinting Technology Ethyl carbamate (EC), known as urethane, is a naturally occurring potentially carcinogenic metabolite that is widely found in alcoholic beverages and other food-related fermented products. The concern related to the presence of the EC and its toxicity in regularly consumed fermented alcoholic beverages raises global interest in assessing the possible risks to human health. EC mitigation approaches, such as molecular imprinting technology (MIT), have been proposed to target EC while preserving the sensory quality of fermented alcoholic beverages. This review explores the principles of MIT, the advantages and disadvantages of the most common polymerisation approach for molecularly imprinted polymer (MIP) synthesis, the analytical techniques used for MIP characterisation, and the strategies used to mitigate EC in fermented alcoholic beverages, with studies reporting removal efficiencies of up to 84%. Additionally, it highlights the novelty and potential of MIPs, offering practical insights into their integration within the production of fermented alcoholic beverages, highlighting their scalability and cost-effectiveness compared to traditional EC mitigation strategies. Ethyl carbamate ; Chemistry ; Food science ; Carbamate ; Molecular imprinting ; Imprinting (psychology) ; Fermentation ; Ethanol fermentation ; Chromatography ; Organic chemistry ; Biochemistry ; Selectivity ; Gene ; Wine ; Catalysis Analytical Chemistry and Chromatography ; Analytical chemistry methods development ; Advanced Chemical Sensor Technologies
Authors: Yan Yan | Yiling Elaine Chen | Litao Huang | Menghan Cai | Yin Xia | Yi Zhun Zhu | Li Ye
Source: Journal of Functional Biomaterials (review, 2025-10-10)
Field: Materials Science — Biomaterials

Bioengineered In Situ-Forming Hydrogels as Smart Drug Delivery Systems for Postoperative Breast Cancer Immunotherapy: From Material Innovation to Clinical Translation Local recurrence after breast cancer surgery presents a critical challenge, demanding novel local immunotherapies capable of eliminating residual disease while avoiding systemic toxicity. In situ-forming hydrogels, functionalized with bioactive cargoes, represent a promising platform for precise spatiotemporal drug delivery directly into the post-resection tumor microenvironment. This review comprehensively examines the core design principles governing these advanced materials, highlighting their biocompatibility, stimuli-responsive behavior, tunable mechanics for conforming to surgical cavity, and capacity for multifunctional integration. A key mechanism discussed is how this controlled release profile orchestrates a temporal progression from innate immune activation to robust adaptive immunity. Despite significant promise, translational success faces substantial hurdles, including efficacy validation, scalable manufacturing, regulatory pathway definition, and the lack of predictive biomarkers. Future research priorities include optimizing drug/antigen release kinetics, establishing standardized characterization methods for complex biohybrid systems, and designing adaptive clinical trials incorporating detailed immunomonitoring. By integrating functional biomaterials with immuno-oncology, in situ-forming hydrogels offer a paradigm-shifting approach for postoperative cancer treatment. This review provides a strategic roadmap to accelerate their translation from bench to bedside. Drug delivery ; Breast cancer ; Self-healing hydrogels ; Translation (biology) ; Mechanism (biology) ; Bench to bedside ; Clinical trial ; Targeted drug delivery ; Drug Nanoparticle-Based Drug Delivery ; Graphene and Nanomaterials Applications ; Advanced Drug Delivery Systems
Authors: Enfu Wang | Siqun Wang | Wenzhu Li | Wenbiao Zhang | Chunping Dai | Kuichuan Sheng | Jingda Huang
Source: Advanced Composites and Hybrid Materials (article, 2025-11-04)
Field: Materials Science — Surfaces, Coatings and Films

Synergistic charge and proton transfer–assisted lignin-based conductive and superhydrophobic coatings for non-strain raindrop monitoring Developing effective raindrop monitors for complex outdoor environments remains a significant challenge. Herein, an innovative raindrop monitoring mechanism based on synergistic charge and proton transfer was proposed to overcome the limitations of traditional deformation-based raindrop monitors, whose accuracy is compromised by interference from wind and insect micro-vibrations. A span-new conductive and superhydrophobic coating was designed through integrating lignin nanospheres, in situ grown ZnO, in situ polymerized polypyrrole (PPY), and polydimethylsiloxane, transforming natural wood into a raindrop monitor via simple spraying. When raindrops contact the raindrop monitor, the charges and protons in the water vapor penetrate the coating to interact with the surface holes of PPY, thereby causing measurable changes in resistance. The coating exhibits excellent superhydrophobicity (water contact angle of 165.8°) and conductivity (0.52 S/m), precisely responding to raindrops with various volumes, heights, and pH values (response time of 282 ms). Furthermore, the coating demonstrates outstanding environmental stability, including remarkable abrasion resistance (maintaining superhydrophobicity after 30 abrasion cycles), outstanding ultraviolet (UV) resistance (retaining superhydrophobicity for 250 h and 13 h under 15 W/m2 and 1000 W/m2 UV irradiation respectively), and high antibacterial property (99.9% against both Escherichia coli and Staphylococcus aureus), ensuring its long-term stable operation in complex outdoor environments. Given the simple preparation and superior performance, this work provides a foreseeable prosperity for the long-term application of raindrop monitor in complex outdoor environments to promote ecological research. Coating ; Proton ; Contact angle ; Abrasion (mechanical) ; Electrical conductor ; Conductivity ; Superhydrophobic coating ; Natural rubber Surface Modification and Superhydrophobicity ; Polymer-Based Agricultural Enhancements ; High voltage insulation and dielectric phenomena
Authors: Xiaochun Lin | Shitao Yin | Xiang Li
Source: Preprints.org (preprint, 2025-04-30)
Field: Chemistry — Analytical Chemistry

A Trustworthy Artificial Intelligence Framework for Predicting Gasoline Octane Loss Using Sparse Autoencoder and Stacking Ensemble Learning in Petrochemical Processes In response to the increasing demand for reliable and robust artificial intelligence (AI) applications in petrochemical and process industries, this study proposes an intelligent prediction framework for estimating Research Octane Number (RON) loss during gasoline refining. The approach integrates a Sparse Autoencoder (SAE) for feature extraction and a Stacking Ensemble Learning (StackingEL) model for predictive regression, thereby enhancing performance in high-dimensional and noisy industrial datasets. Real-world process data obtained from a petrochemical enterprise were utilized for model training and evaluation. After comprehensive data preprocessing, the SAE effectively captured latent representations of complex process variables, which were then used to train twelve regression models including Lasso and advanced ensemble techniques. Experimental results indicate that the proposed SAE+StackingEL framework outperforms conventional methods in prediction accuracy, robustness, and generalization ability. This AI-assisted process modeling strategy contributes to optimizing gasoline production, reducing environmental emissions, and supporting cleaner and more sustainable industrial practices. The proposed method demonstrates significant potential for integration into Industry 4.0 systems and petrochemical process improvement. Autoencoder ; Stacking ; Gasoline ; Trustworthiness ; Octane rating ; Artificial intelligence ; Computer science ; Petrochemical ; Octane ; Machine learning ; Ensemble learning ; Pattern recognition (psychology) ; Deep learning ; Chemistry ; Computer security ; Organic chemistry Petroleum Processing and Analysis
Authors: Raja Vavekanand
Source: ICCK Transactions on Emerging Topics in Artificial Intelligence (article, 2025-02-26)
Field: Chemistry — Spectroscopy

NMRGen: A Generative Modeling Framework for Molecular Structure Prediction from NMR Spectra Interpreting NMR spectra to accurately predict molecular structures remains a significant challenge in chemistry due to the complexity of spectral data and the need for precise structural elucidation. This study introduces NMRGen, a generative modeling framework that predicts molecular structures from NMR spectra and molecular formulas. The framework combines a SMILES autoencoder (GRU-based encoder-decoder) and an NMR encoder (CNN and DNN layers) to map spectral data to molecular representations. The SMILES autoencoder compresses and reconstructs SMILES strings, while the NMR encoder processes NMR spectra to generate latent vectors aligned with those from the SMILES encoder. Experiments were conducted using NMR spectra and SMILES datasets. The model was trained in three stages: (1) training the SMILES autoencoder, (2) aligning latent vectors from the NMR encoder, and (3) simultaneous training of both components. Results revealed that while the SMILES autoencoder performed adequately, the NMR encoder struggled to map spectral data effectively. Most generated SMILES strings were invalid, with valid ones primarily consisting of carbon chains (e.g., CCC...C). The Tanimoto coefficient between generated and target molecules ranged from 0.1 to 0.2, indicating low similarity. Despite these limitations, NMRGen demonstrates the potential of generative models for molecular structure prediction. Future work will focus on improving performance through larger datasets, advanced loss functions, and enhanced architectures. Generative grammar ; NMR spectra database ; Spectral line ; Computer science ; Artificial intelligence ; Physics ; Quantum mechanics Molecular spectroscopy and chirality ; Metabolomics and Mass Spectrometry Studies ; Advanced Text Analysis Techniques
Authors: Han Zhang | Xiang Peng | Hao Song | Y. H. Tan | Jing Xu | Qunfang Li | Zhuangqiang Gao
Source: Biosensors (article, 2025-04-09)
Field: Materials Science — Materials Chemistry

Atomic Pt-Layer-Coated Au Peroxidase Nanozymes with Enhanced Activity for Ultrasensitive Colorimetric Immunoassay of Interleukin-12 Interleukin-12 (IL-12), a crucial biomarker for immune and inflammatory responses, plays a pivotal role in diagnosing and managing diverse pathological conditions. Although colorimetric enzyme-linked immunosorbent assays (CELISAs) have been extensively employed to detect IL-12 in biological samples, their sensitivity is inherently limited by the catalytic efficiency of enzyme labels, presenting substantial challenges in achieving ultrasensitive detection and enabling pre-symptomatic diagnosis of diseases. In this study, we address this limitation by developing a novel peroxidase nanozyme, featuring ultrathin Pt skins consisting of only ~4 atomic layers, coated on Au nanoparticles (denoted as Au@Pt<sub>4L</sub>NPs). These Au@Pt<sub>4L</sub>NPs exhibit remarkable catalytic performance, achieving a ~1063-fold enhancement in peroxidase-like activity compared to horseradish peroxidase (HRP), while minimizing Pt consumption, thereby improving Pt utilization efficiency and reducing costs. This advancement facilitates the construction of an ultrasensitive CELISA capable of detecting IL-12 at femtomolar concentrations. Using Au@Pt<sub>4L</sub>NPs as the signal labels, the developed CELISA demonstrates a quantitative detection range from 0.1 to 100 pg mL<sup>-1</sup>, with a limit of detection (LOD) as low as 0.084 pg mL<sup>-1</sup> (1.1 fM), offering ~10 times greater sensitivity than the HRP-based CELISA. This study highlights the potential of Au@Pt<sub>4L</sub>NP nanozymes as advanced signal labels, opening new avenues for next-generation ultrasensitive bioassays. Immunoassay ; Chemistry ; Peroxidase ; Chromatography ; Medicine ; Enzyme ; Biochemistry ; Immunology ; Antibody Advanced Nanomaterials in Catalysis ; Advanced biosensing and bioanalysis techniques ; Nanocluster Synthesis and Applications
Authors: Yilin Cao | Warabhorn Rodphon | Turki M. Alturaifi | Al Vicente Riano D. Lisboa | Zhouyang Ren | Job J. C. Struijs | Hui‐Qi Ni | Taras Savchuk | Richard P. Loach | Shouliang Yang | Indrawan McAlpine | Donna G. Blackmond | Pavel K. Mykhailiuk | Peng Liu | K. Barry Sharpless | Keary M. Engle
Source: Nature Synthesis (article, 2025-12-16)
Field: Chemistry — Organic Chemistry

Alkyl sulfonyl fluorides as ambiphiles in the stereoselective palladium(II)-catalysed cyclopropanation of unactivated alkenes Here we present the ambiphilic reactivity of alkyl sulfonyl fluorides in the stereoselective synthesis of diverse cyclopropanes from olefins, under palladium(II) catalysis. The sulfonyl fluoride functionality serves as both an acidifying group and an internal oxidant within the ambiphile, enabling successive carbopalladation and oxidative addition steps in the catalytic cycle, respectively. The transformation grants access to <i>cis</i>-substituted cyclopropanes and exhibits broad compatibility with various alkyl sulfonyl fluorides, including those bearing -CN, -CO<sub>2</sub>R, isoxazolyl, pyrazolyl and aryl groups. With internal alkene substrates, 1,2,3-trisubstituted cyclopropanes that are otherwise challenging to synthesize are formed in good-to-moderate yields and predictable diastereoselectivity. Detailed mechanistic insights from reaction progress kinetic analysis and density functional theory calculations reveal that the S<sub>N</sub>2-type C-SO<sub>2</sub>F oxidative addition is the turnover-limiting and diastereoselectivity-determining step. Cyclopropanation ; Sulfonyl ; Stereoselectivity ; Alkene ; Alkyl ; Reactivity (psychology) ; Aryl ; Catalysis Cyclopropane Reaction Mechanisms ; Catalytic C–H Functionalization Methods ; Sulfur-Based Synthesis Techniques
Authors: Guixiang Xie | Yaqi Lu | Xiaochun Lu | Zhu-Sen Zhang | Sen Lin
Source: Polymers (article, 2025-09-01)
Field: Materials Science — Materials Chemistry

Measurement Error Analysis and Thermal Degradation Kinetic Model Improvement for Thermogravimetric Analyzers Thermogravimetric analysis (TGA) has been extensively applied in polymeric characterization and quality inspection, facilitating in-depth investigations of the microstructural thermal response characteristics of polymers, including thermal stability, composition analysis, and thermal decomposition mechanisms. Here, the impacts of six factors on the TG thermal analysis curves obtained during operation are systematically examined while analyzing their causes and recommending solutions. Furthermore, the thermal degradation kinetics of an ionomer formed by neutralizing an ethylene-methacrylic acid copolymer with metal ions (SGP membrane) used in laminated tempered glass is analyzed using the Arrhenius equation, Ozawa-Flynn-Wall hypothesis and Kissinger method. Kinetic parameters at 5% degradation are fitted and used to predict the service lifetime of the SGP membrane. The results indicate that the SGP membrane sample exhibits activation energy <i>Ea</i> = 136.90 kJ/mol, reaction order <i>n</i> = 1.65 and pre-factor <i>A</i> = e<sup>25.93</sup>. It can be seen that the service lifetime of the SGP membrane sample is 16 years at 80 °C and 1.65 years at 100 °C. Thermogravimetric analysis ; Degradation (telecommunications) ; Kinetic energy ; Thermal analysis ; Materials science ; Thermal ; Thermodynamics ; Computer science ; Chemical engineering ; Engineering ; Physics ; Telecommunications Thermal and Kinetic Analysis ; Polymer Science and PVC ; Polymer crystallization and properties
Authors: Ioannis Adamopoulos | Antonios Valamontes | John T. Karantonis | Niki Syrou | Ioanna Damikouka | George Dounias
Source: Preprints.org (preprint, 2025-02-17)
Field: Chemistry — Analytical Chemistry

The Impact of PFAS on the Public Health and Safety of Future Food Supply in Europe: Challenges and AI Technologies Solutions of Environmental Sustainability Per- and polyfluoroalkyl substances (PFAS) are persistent organic pollutants used in everyday products. They pose a significant threat to global agricultural sustainability and food security, particularly in European farmlands. Contamination occurs through industrial discharges, biosolid applications, and contaminated irrigation water. PFAS contamination affects soil fertility, water quality, and food safety, and has broader implications for Europe's food security. This research explores the scale of PFAS contamination, its implications for food security, and innovative remediation solutions. A multi-faceted strategy integrating detection tools, advanced remediation technologies, and policy initiatives is proposed to mitigate PFAS contamination while ensuring sustainable agricultural practices. A significant threat to global agricultural, public health, and food security the AI technologies revolutionizing environmental sustainability by developing machine learning algorithms, predictive statistics, and data analysis tools. Technologies are enhancing ecosystem management, advancing environmental research, providing digital solutions to complex environmental problems, and generative and using AI in Sustainability Environmental Education. Sustainability ; Business ; Food safety ; Emerging technologies ; Environmental safety ; Food supply ; Environmental planning ; Environmental economics ; Natural resource economics ; Environmental science ; Environmental health ; Human health ; Economics ; Computer science ; Medicine ; Agricultural science ; Ecology Dye analysis and toxicity
Authors: Florian M. Dietrich | Matteo Salvalaglio
Source: The Journal of Chemical Physics (article, 2025-10-13)
Field: Materials Science — Materials Chemistry

On the reproducibility of free energy surfaces in machine-learned collective variable spaces As Machine-Learned Collective Variables (MLCVs) are becoming increasingly relevant in the molecular simulation literature, we discuss the necessary conditions to enable reproducibility in calculating and representing free energy surfaces. We note that the variability of the training process and the roughness of the hyperparameter space impose inherent limits on the reproducibility of results even when the mathematical structure of the model defining a collective variable is consistent. To this end, we propose the adoption of a geometric (gauge invariant) free energy representation to obtain consistent free energy differences across training instances and architectures. Furthermore, we introduce a normalization factor to model gradients for biased enhanced sampling. This factor effectively unifies free energy definitions and addresses practical issues preventing the widespread use and deployment of MLCVs. Normalization (sociology) ; Reproducibility ; Variable (mathematics) ; Energy (signal processing) ; Representation (politics) ; Hyperparameter ; Process (computing) Machine Learning in Materials Science ; Protein Structure and Dynamics ; Topic Modeling
Authors: A.Kh. Mammadov | Rana F. Khankishiyeva
Source: PPOR (article, 2025-01-01)
Field: Materials Science — Polymers and Plastics

Effect of bentonite clay reinforcement on the mechanical, thermal and aging properties of peroxide-vulcanized hydrogenated nitrile butadiene rubber composites Hydrogenated nitrile butadiene rubber (HNBR) composites reinforced with bentonite clay were developed via peroxide vulcanization to investigate their mechanical, thermal, and aging properties.Bentonite clay was added to the HNBR matrix at 0, 3, 9, and 15 wt.% loadings, and the composites were analyzed using rheometric tests, mechanical testing, FTIR spectroscopy, thermogravimetric analysis (TGA), differential thermogravimetry (DTG), and cross-linking density measurements.The optimal performance was observed at 9 wt.% clay, where tensile strength improved to 10.4 MPa (from 9.2 MPa for pure HNBR), and the maximum decomposition temperature increased from 457 to 467C, indicating enhanced thermal stability.Shore A hardness also increased from 70 to 75, reflecting higher rigidity.Aging tests at 125C for 1000 h showed a 1.5% increase in tensile strength at 9 wt.% clay, although elongation at break decreased due to filler-induced matrix stiffening.FTIR analysis revealed oxidation and cross-linking of nitrile groups, forming carboxylic acid functionalities.Cross-linking density initially decreased due to chain scission, then recovered via further cross-linking.TGA/DTG data confirmed bentonite clay's barrier effect above 410C, reducing degradation by inhibiting volatile release.Homogeneous clay dispersion improved crack resistance and lowered gas permeability, enhancing durability.These findings demonstrate that 9 wt.% bentonite clay provides an effective balance of mechanical strength, thermal resistance, and aging stability, making HNBR/BC composites promising for automotive, high-temperature, and oil exploration applications Vulcanization ; Composite material ; Materials science ; Bentonite ; Natural rubber ; Reinforcement ; Nitrile ; Nitrile rubber ; Thermal ; Geotechnical engineering ; Chemistry Polymer Nanocomposites and Properties ; Polymer Science and Applications ; Polymer Science and PVC
Authors: Sami M. Ibn Shamsah
Source: Crystals (article, 2025-02-19)
Field: Materials Science — Materials Chemistry

Thermal Conductivity Modeling for Liquid-Phase-Sintered Silicon Carbide Ceramics Using Machine Learning Computational Methods Silicon carbide is a covalently bonded engineering material and structural ceramic with excellent mechanical properties, high resistance to oxidation, corrosion, and wear, and tunable thermal conductivity. The exceptional thermal conductivity of silicon carbide ceramic promotes its candidature in many industrial applications, such as nuclear fuel capsule materials, substrate materials employed in semiconductor devices, heater plates, and heaters for processing semiconductor and gas seal rings employed in compressor pumps, among others. The synthesis of polycrystalline silicon carbide through the liquid-phase sintering approach results in lower thermal conductivity due to the presence of structural defects associated with grains, lattice impurities, grains’ random orientations, and the presence of secondary phases in polycrystalline silicon carbide ceramic. The conventional experimental method of enhancing thermal conductivity is laborious and expensive. This present work modeled the thermal conductivity of liquid-phase silicon carbide ceramic via intelligent approaches involving genetic algorithm-optimized support vector regression (SVR-GA), an extreme learning machine with a sine activation function (ELMS), and random forest regression (RFR). The descriptors for the models included the nature of sintering additives as well as their weights, sintering conditions, applied pressure, sintering temperature, and time. Using the mean absolute error (MAE) and root mean square error (RMSE) for performance assessment, it was observed that the ELMS outperformed the RFR and SVR-GA models with improvements of 40.50% and 25.76%, respectively, using the MAE metric and improvements of 16.57% and 24.43%, respectively, using the RMSE metric. The developed models were further used to investigate the effect of the weight of sintering additives and sintering time on the thermal conductivity of silicon carbide ceramic. The precision of the developed models facilitated a comprehensive investigation of the effect of sintering factors on thermal conductivity while hidden connections that exist between the factors are uncovered for enhancing application domains for silicon carbide ceramics. Silicon carbide ; Ceramic ; Materials science ; Thermal conductivity ; Liquid phase ; Phase (matter) ; Composite material ; Carbide ; Thermodynamics ; Chemistry ; Physics Thermal properties of materials ; Advanced ceramic materials synthesis ; Machine Learning in Materials Science