jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Giuseppina Raffaini | Michelina Catauro | F. Bolzoni | Marco Ormellese
Source: Coatings (article, 2025-01-02)
Field: Materials Science — Materials Chemistry

Glutamate- and Tartrate-Based Inhibitor Films to Prevent Chloride-Induced Corrosion in Reinforced Concrete: Efficiency of Dry or Hydrated Films via Molecular Dynamics Simulations Due to their barrier effect against chloride penetration, corrosion inhibitors are widely used to prevent chloride-induced corrosion in reinforced concrete structures. The mechanisms of interaction between the protective film on carbon steel and chloride ions represent a crucial aspect of the design of new inhibitors. Theoretical studies based on molecular mechanic (MM) and molecular dynamic (MD) methods have proven useful in research on the formation of passive films and their intermolecular interactions with chloride ions, both under dry and hydrated conditions, allowing for further comparison with experimental data. In this study, glutamate- and tartrate-based inhibitor films are investigated. After MM/MD simulations, chloride ions are found to be kept away from the dry protective films via electrostatic repulsion, hence remaining distant from the lepidocrocite γ-FeO(OH) surface. These coatings are able to efficiently prevent chloride adsorption and film penetration due to the presence of COO− groups, which electrostatically and dynamically repel chlorides over time. Even on hydrated coatings, when complete coverage of the solid surface occurs, these OCIs are good potential candidates for preventing chloride-induced corrosion; however, in the presence of water, the penetration of chloride ions across the protective films can occur, thus rendering them relatively less effective. Tartrate ; Corrosion ; Molecular dynamics ; Chloride ; Materials science ; Chemical engineering ; Corrosion inhibitor ; Composite material ; Chemistry ; Inorganic chemistry ; Metallurgy ; Computational chemistry ; Engineering Corrosion Behavior and Inhibition ; Concrete Corrosion and Durability ; Concrete and Cement Materials Research
Authors: Byung‐Hyun Kim | Daesung Park | Sooyeon Jeong | Hyobin Yoo
Source: Han-guk hyeonmigyeong hakoeji/Applied microscopy (article, 2025-11-11)
Field: Materials Science — Materials Chemistry

Electron diffraction and dark-field TEM for structural analysis of 2D van der Waals materials Two-dimensional (2D) van der Waals materials possess structural degrees of freedom that set them apart from conventional bulk crystals and strongly influence their physical properties. Such freedom, enabled by the weak interlayer bonding, permits stacking, twisting, and lateral sliding of layers, leading to structural variations such as out-of-plane corrugations, layer-number-dependent electronic and optical responses, and interlayer registry variations that produce stacking domains with distinct functionalities. Capturing and understanding these variations is essential for linking structure to function. Transmission electron microscopy (TEM) offers complementary approaches for this purpose: electron diffraction provides quantitative crystallographic fingerprints, while dark-field (DF) imaging translates selected diffraction information into spatial maps of local structure. When combined, these techniques can resolve complex structural modulations across multiple length scales and under diverse experimental conditions. Recent advances have extended diffraction and DF imaging into in-situ and operando regimes, enabling real-time observation of domain reconfiguration, phase transitions, and polarization switching under external stimuli. This review discusses how these methods are applied to 2D van der Waals materials to reveal structural degrees of freedom and illustrates their unique capability to directly connect structural evolution to functional behavior. van der Waals force ; Stacking ; Electron diffraction ; Diffraction ; Degrees of freedom (physics and chemistry) ; Transmission electron microscopy 2D Materials and Applications ; Graphene research and applications ; Transition Metal Oxide Nanomaterials
Authors: Umuhoza Aline | Dennis Semyalo | Muhammad Fahri Reza Pahlawan | Tanjima Akter | Mohammad Akbar Faqeerzada | Seo‐Young Kim | Dongho Oh | Byoung–Kwan Cho
Source: Agriculture (article, 2025-08-08)
Field: Chemistry — Analytical Chemistry

Integration of Hyperspectral Imaging and Chemometrics for Internal Quality Evaluation of Packaged and Non-Packaged Fresh Fruits Research on packaged fruits has seen a notable upturn primarily driven by consumers’ desire for fruit safety and quality across the distribution network. This study examined the effectiveness of hyperspectral imaging (HSI) combined with chemometrics to assess the internal quality of packaged and non-packaged fresh fruits. Visible–near-infrared (Vis-NIR; 400–1000 nm) and short-wave infrared (SWIR; 1000–2500 nm) hyperspectral images of apples and plums were captured using 200 samples for each fruit across three groups—plastic wrap (PW), polyethylene terephthalate (PET) box, and non-packaged (NP)—for the prediction of soluble solid content (SSC), moisture content (MC), and pH. A partial least square regression (PLSR) model demonstrated promising results on SSC and MC across all sample groups in both Vis-NIR and SWIR, with performance ranked NP &gt; PW &gt; PET. Calibration and prediction coefficients of determination (R2) exceeded 0.82, 0.80, and 0.79, with root mean square errors (RMSE) less than 0.57, 0.59, and 0.59 for NP, PW, and PET, respectively. This research outcome confirmed the suitability of HSI as a critical instrument for predicting the composition of fresh fruits inside plastic packaging, offering a quick and non-invasive approach for quality evaluation in supply chains. Hyperspectral imaging ; Chemometrics ; Quality (philosophy) ; Environmental science ; Remote sensing ; Computer science ; Artificial intelligence ; Geography ; Machine learning ; Physics Spectroscopy and Chemometric Analyses ; Meat and Animal Product Quality ; Advanced Chemical Sensor Technologies
Authors: Xiaoyun Wang | Jianxian Sun | Shabbir Ahmad | Diwen Yang | Fengling Li | U Hang Chan | Hongcheng Zeng | Conrad V. Simoben | Stuart R. Green | Madhushika Silva | Scott Houliston | Aiping Dong | Albina Bolotokova | Elisa Gibson | Maria Kutera | Pegah Ghiabi | Ivan S. Kondratov | Tetiana Matviyuk | Alexander Chuprina | Danai Mavridi | Christopher Lenz | Andreas C. Joerger | Benjamin P. Brown | R. L. Heath | Wyatt W. Yue | Lucy K Robbie | Tyler S. Beyett | Susanne Müller | Stefan Knapp | Dafydd R. Owen | Rachel Harding | Matthieu Schapira | Peter J. Brown | Vijayaratnam Santhakumar | Suzanne Ackloo | C.H. Arrowsmith | A.M. Edwards | Hui Peng | Levon Halabelian
Source: Nature Communications (article, 2025-12-17)
Field: Chemistry — Spectroscopy

Enantioselective protein affinity selection mass spectrometry (E-ASMS) We report an enantioselective protein affinity selection mass spectrometry screening approach (E-ASMS) that enables the detection of weak binders, informs on selectivity, and generates orthogonal confirmation of binding. After method development with control proteins, we screen 31 human proteins against a designed library of 8,217 chiral compounds. We identify 16 binders to 12 targets, including many proteins predicted to be "challenging to ligand", and confirm their interactions through orthogonal biophysical assays. Seven binders to six targets display enantioselective binding, with K<sub>D</sub> values ranging from 3 to 20 µM. Binders for four targets (DDB1, WDR91, WDR55, and HAT1) are selected for in-depth characterization using X-ray crystallography. In all four cases, the mechanisms underlying enantioselectivity are readily explained. These results demonstrate that E-ASMS enables the identification and characterization of selective and weakly binding ligands for novel protein targets with unprecedented throughput and sensitivity. Enantioselective synthesis ; Mass spectrometry ; High-throughput screening ; Characterization (materials science) ; Selection (genetic algorithm) ; Plasma protein binding ; Identification (biology) ; Target protein ; Protein–protein interaction Mass Spectrometry Techniques and Applications ; Advanced Proteomics Techniques and Applications ; Chemical Synthesis and Analysis
Authors: Kimiya NouraniNiaki | Murali Uddagiri | Daysianne Isidorio | Oleg Shchyglo | Ingo Steinbach
Source: Metals (article, 2025-01-29)
Field: Materials Science — Materials Chemistry

Phase Field Simulation of Al-Fe-Mn-Si Quaternary Eutectic Solidification This study investigates the eutectic equilibrium phases in a multicomponent system through 3-D multi-phase-field simulations. Emphasizing the directional solidification process, the work examines the growth dynamics of intermetallic phase Al13Fe4, a lamellar structure (FCC-A1), and a quaternary phase beta-AlMnSi from the liquid that is solidified at a specific temperature. The eutectic transformation, described by the four phase reaction L→Al13Fe4+FCC-A1+beta-AlMnSi, is analyzed to develop a microstructure selection map. This map correlates stable growth modes with initial system composition and lamellar spacing. The results provide detailed insights into the segregation behaviour of alloying elements and their influence on transformation kinetics, enhancing the understanding of eutectic microstructure evolution in complex alloy systems. Eutectic system ; Materials science ; Quaternary ; Phase (matter) ; Field (mathematics) ; Metallurgy ; Geology ; Microstructure ; Chemistry ; Mathematics Solidification and crystal growth phenomena ; Aluminum Alloy Microstructure Properties ; Metallurgy and Material Forming
Authors: Maria Cristina Molaro | Chiara Battisegola | Marica Erminia Schiano | Mariacristina Failla | Maria Grazia Rimoli | Loretta Lazzarato | Konstantin Chegaev | Federica Sodano
Source: Pharmaceutics (review, 2025-01-16)
Field: Chemistry — Organic Chemistry

Synthesis of Arginase Inhibitors: An Overview Arginase (ARG) is a binuclear manganese-containing metalloenzyme that can convert L-arginine to L-ornithine and urea and plays a key role in the urea cycle. It also mediates different cellular functions and processes such as proliferation, senescence, apoptosis, autophagy, and inflammatory responses in various cell types. In mammals, there are two isoenzymes, ARG-1 and ARG-2; they are functionally similar, but their coding genes, tissue distribution, subcellular localization, and molecular regulation are distinct. In recent decades, the abnormal expression of ARG-1 or ARG-2 has been reported to be increasingly linked to a variety of diseases, including cardiovascular disease, inflammatory bowel disease, Alzheimer's disease, and cancer. Therefore, considering the current relevance of this topic and the need to address the growing demand for new and more potent ARG inhibitors in the context of various diseases, this review was conceived. We will provide an overview of all classes of ARG inhibitors developed so far including compounds of synthetic, natural, and semisynthetic origin. For the first time, the synthesis protocol and optimized reaction conditions of each molecule, including those reported in patent applications, will be described. For each molecule, its inhibitory activity in terms of IC<sub>50</sub> towards ARG-1 and ARG-2 will be reported specifying the type of assay conducted. Arginase ; Chemistry ; Computational biology ; Biochemistry ; Biology ; Arginine ; Amino acid Synthesis and Catalytic Reactions ; Click Chemistry and Applications ; Synthesis and Biological Evaluation
Authors: Fruzsina Bencs | Nóra Taricska | Zsolt Dürvanger | Dániel Horváth | Zsolt Fazekas | Vince Grolmusz | Viktor Farkas | András Perczel
Source: Chemistry - A European Journal (article, 2025-04-08)
Field: Materials Science — Biomaterials

Chemical Evolution of Early Macromolecules: From Prebiotic Oligopeptides to Self‐Organizing Biosystems via Amyloid Formation Short amyloidogenic oligopeptides (APRs) are proposed as early macromolecules capable of forming solvent-separated nanosystems under prebiotic conditions. This study provides experimental evidence that APRs, such as the aggregation-prone oligopeptide A (APR-A), can undergo mutational transitions to form distinct variants and convert to APR-B, either amyloid-like or water-soluble and non-aggregating. These transitions occur along a spectrum from strongly amyloidogenic (pro-amyloid) to weakly amyloidogenic (anti-amyloid), with the mutation sequence order playing a key role in determining their physicochemical properties. The pro-amyloid pathway facilitates heterogeneous phase separation, leading to amyloid-crystal formation with multiple polymorphs, including the first class 3 amyloid topology. By mapping these transitions, we demonstrate the potential co-evolution of water-soluble miniproteins and insoluble amyloids, both of which could have been pivotal in early bio-nanosystem formation. These insights into amyloid modulation provide a crucial step toward understanding amyloid control mechanisms. Amyloid (mycology) ; Macromolecule ; Oligopeptide ; Chemistry ; Amyloid fibril ; Molecular dynamics ; Biophysics ; Peptide ; Biochemistry ; Amyloid β ; Biology ; Computational chemistry Supramolecular Self-Assembly in Materials ; Alzheimer's disease research and treatments ; Protein Structure and Dynamics
Authors: Randy Ngelale | Joanna McFarlane | Daniel Orea | Katherine R. Johnson
Source: Reaction Chemistry & Engineering (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Reassessment of caustic scrubbing for radioiodine capture during UNF processing This graphic conveys the complexity of radioiodine capture in the framework of traditional (PUREX-based) UNF recycling and technologies that are described within this perspective. Data scrubbing ; Caustic (mathematics) ; Environmental science ; Radiochemistry ; Chemistry ; Waste management ; Mathematics ; Engineering Nuclear Materials and Properties ; Radioactive element chemistry and processing ; Nuclear Physics and Applications
Authors: Jianping Shi | Mingdong Wang | Ting Feng | Xianglong Li | Yanbin Feng | Song Xue
Source: Catalysts (article, 2025-08-18)
Field: Chemistry — Organic Chemistry

Engineering Malic Enzyme CO2 Fixation Activity via a Structure–Sequence–SCANNER (3S) Co-Evolution Strategy Enzymatic CO2 fixation offers great potential for the sustainable synthesis of value-added compounds. Malic enzyme (ME) catalyzes the reverse carboxylation of pyruvate to malate, enabling direct CO2 conversion into C4 compounds with broad biosynthetic applications. However, the reverse carboxylation activity of wild-type ME is insufficient, and conventional enzyme engineering strategies remain limited by the complexity of identifying distal functional sites. Here, we present a Structure–Sequence–SCANNER (3S) co-evolution strategy that integrates protein structural analysis, sequence conservation profiling, and co-evolutionary network analysis to enable systematic identification of functionally relevant hotspot residues. Using this approach, we engineered Escherichia coli ME (EcME) variants with enhanced CO2 fixation activities. In total, 106 single-point variants were constructed and screened. Among these, variants A464S and D97E exhibited significantly improved reverse carboxylation activities, with 1.7-fold and 1.6-fold increases in catalytic activity and 1.5-fold and 1.8-fold improvements in catalytic efficiency (kcat/Km), respectively, compared to wild-type EcME. Their catalytic efficiencies (kcat/Km) improved by 1.5-fold and 1.8-fold, increasing from 80 mM−1·min−1 for the wild-type enzyme to 120 and 130 mM−1·min−1, respectively. Mechanistic analyses revealed that A464S introduces a stabilizing hydrogen bond with N462, enhancing NADPH binding, while D97E forms a new salt bridge network with K513, resulting in contraction of the substrate pocket entrance and increased pyruvate affinity. These findings demonstrate the effectiveness of the 3S strategy in reprogramming enzyme functions and highlight its potential for constructing efficient artificial CO2 fixation systems. Sequence (biology) ; Scanner ; Computer science ; Computational biology ; Biochemistry ; Biology ; Artificial intelligence Click Chemistry and Applications ; Enzyme Catalysis and Immobilization ; Cyclopropane Reaction Mechanisms
Authors: Yichi Zhang | Kewei Huan | Xiaoxi Liu | Yuqing Fan | Xianwen Cao | Xueyan Han
Source: Foods (article, 2025-11-03)
Field: Chemistry — Analytical Chemistry

Multi-Scale Feature Attention Network for Rapid and Non-Destructive Quantification of Aflatoxin B1 in Maize Using Hyperspectral Imaging Maize, a globally important crop, is highly susceptible to aflatoxin contamination, posing a serious threat. Therefore, accurate detection of aflatoxin levels in maize is of critical importance. In this study, the Multi-Scale Feature Network with Efficient Channel Attention (MSFNet-ECA) model, based on near-infrared hyperspectral imaging combined with deep learning techniques was developed to analyze the content of aflatoxin B<sub>1</sub> (AFB<sub>1</sub>) in maize. Three data augmentation methods-multiplicative random scaling, bootstrap resampling, and Wasserstein generative adversarial networks (WGAN)-were compared with various preprocessing strategies to assess their impact on model performance. Multiplicative random scaling combined with second derivative (D2) preprocessing yielded the best predictive performance for the MSFNet-ECA model. Using this augmentation, the MSFNet-ECA model outperformed four conventional models (partial least squares regression (PLSR), support vector regression (SVR), extreme learning machine (ELM), and one-dimensional convolutional neural network (1D-CNN)), achieving a root mean square error of prediction (RMSEP) of 2.3 μg·kg<sup>-1</sup>, coefficient of determination for prediction (Rp2) of 0.99, and the residual predictive deviation (RPD) of 9, with accuracy improvements of 86.4%, 79.1%, 71.3%, and 42.5%, respectively. This finding demonstrates that applying data augmentation methods substantially improves the predictive performance of hyperspectral chemometric models driven by deep learning. Moreover, when combined with data augmentation techniques, the proposed MSFNet-ECA model can accurately predict AFB<sub>1</sub> content in maize, offering an efficient and reliable tool for hyperspectral applications in food quality and safety monitoring. Hyperspectral imaging ; Residual ; Pattern recognition (psychology) ; Feature (linguistics) ; Preprocessor ; Convolutional neural network ; Regression ; Artificial neural network ; Aflatoxin ; Partial least squares regression Spectroscopy and Chemometric Analyses ; Mycotoxins in Agriculture and Food ; Smart Agriculture and AI
Authors: J. Lange | Argimiro R. Secchi | Márcio Nele
Source: Langmuir (article, 2025-09-02)
Field: Chemistry — Analytical Chemistry

Molecular Dynamics Study of the Coalescence of Water Droplets with Anionic Asphaltene Molecules under a DC Electric Field During the extraction of crude oil, water-in-oil (W/O) emulsions are mostly formed at a high pH, where water droplets can be stabilized by anionic asphaltene molecules on the surface. The study of driving forces in the electro-coalescence of these emulsions is fundamental to the efficient design of the oil dehydration process. We studied by molecular dynamics the electro-coalescence of two asphaltene-laden droplets suspended in <i>n</i>-hexane as a model oil. The findings indicate that a low number of anionic asphaltenes per water droplet and a moderate electric field strength (<i>E</i>) allow optimal droplet-droplet coalescence conditions to be reached, which is favored by high electrical polarization of water droplets. Under these conditions, it has been found that the diffusion and polarity of water molecules are enhanced, favoring the formation of the liquid bridge between colliding droplets and reducing the droplet-droplet coalescence time. On the contrary, with a high number of asphaltenes per droplet and <i>E</i>, the droplet-droplet coalescence is hindered and/or retarded due to the steric effect of asphaltene aggregation at the interface between water droplets. Here, the high ionic conductivity (σ) of water droplets and low interfacial tension (γ) before the formation of the liquid bridge led to the formation of a water chain (WC) between electrodes, an undesirable phenomenon impairing the dehydration efficiency in the coalescer. This study demonstrates that W/O emulsions with anionic asphaltenes under conditions of relatively low σ and somewhat high γ at moderate <i>E</i> (around the critical <i>E</i>) promote complete droplet-droplet coalescence and prevent WC formation. Asphaltene ; Coalescence (physics) ; Chemistry ; Surface tension ; Emulsion ; Electric field ; Chemical physics ; Oil droplet ; Chemical engineering ; Molecule ; Dissociation (chemistry) ; Molecular dynamics ; Organic chemistry ; Thermodynamics ; Computational chemistry Petroleum Processing and Analysis ; Electrohydrodynamics and Fluid Dynamics ; Enhanced Oil Recovery Techniques
Authors: Ryota Akiyama | Yvan Llave | Takashi Matsumoto
Source: AgriEngineering (article, 2025-11-05)
Field: Chemistry — Analytical Chemistry

Japanese Rice Variety Identification by Fluorescence Fingerprinting, Near-Infrared Spectroscopy, and Machine Learning This study developed identification models for five domestic rice varieties—Akitakomachi (Akita 31), Hitomebore (Tohoku 143), Hinohikari (Nankai 102), Koshihikari (Etsunan 17) and Nanatsuboshi (Soriku 163)—using fluorescence spectroscopy, near-infrared (NIR) spectroscopy, and machine learning. Two-dimensional fluorescence images were generated from excitation emission matrix (EEM) spectra in the 250–550 nm and 900–1700 nm ranges. Four machine learning hybrid models combining a convolutional neural network (CNN) with k-nearest neighbor algorithm (KNN), random forest (RF), logistic regression (LR), and support vector machine (SVM), were constructed using Python (ver. 3.13.2) by integrating feature extraction from CNN with traditional algorithms. The performances of KNN, RF, LR, and SVM were compared with NIR spectra. The NIR+KNN model achieved 0.9367 accuracy, while the fluorescence fingerprint+CNN model reached 0.9717. The CNN+KNN model obtained the highest mean accuracy (0.9817). All hybrid models outperformed individual algorithms in discrimination accuracy. Fluorescence images revealed at 280 nm excitation/340 nm emission linked to tryptophan, and weaker peaks at 340 nm excitation/440 nm emission, likely due to advanced glycation end products. Hence, combining fluorescent fingerprinting with deep learning enables accurate, reproducible rice variety identification and could prove useful for assessing food authenticity in other agricultural products. Support vector machine ; Random forest ; Convolutional neural network ; Pattern recognition (psychology) ; Fluorescence ; Identification (biology) ; Artificial neural network Spectroscopy and Chemometric Analyses ; GABA and Rice Research ; Smart Agriculture and AI