jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Guilherme Dognani | Francisca Belen Fuenzalida | Carlos José Leopoldo Constantino | Santiago Sánchez‐Cortés
Source: ACS Omega (article, 2025-08-05)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Overcoming Detection Challenges of 2,4-D Herbicides via SERS through a Simple Modification of Citrate-Reduced Silver Nanoparticles The difficulty in detecting certain pesticides at low concentrations in aqueous media made it necessary to search for new strategies to facilitate the detection of these contaminants. In this context, surface-enhanced Raman scattering (SERS) is a promising technique capable of carrying out the detection of hard-to-detect molecules. A pesticide called 2,4-dichlorophenoxyacetic acid (2,4-D) is within the group of these molecules that is difficult to detect. Currently, 2,4-D is recognized as one of the main herbicides used around the world, which has attracted the attention of researchers. This study investigates the synthesis of silver nanoparticles using a modified method based on citrate reduction. Three different colloids, AgCit<sub>1.0</sub>, AgCit<sub>0.50</sub>, and AgCit<sub>0.25</sub>, were synthesized with varying concentrations of citrate reductant. UV-vis extinction spectroscopy confirmed the formation of silver nanoparticles, exhibiting plasmon peaks at 405, 414, and 417 nm for AgCit<sub>1.0</sub>, AgCit<sub>0.50</sub>, and AgCit<sub>0.25</sub>, respectively. The SERS effect demonstrated the impact of citrate concentration on signal intensity and revealed characteristic peaks associated with citrate and the pesticide 2,4-D. The results demonstrated that there is a cutoff range where lower citrate concentrations (AgCit<sub>0.50</sub> and AgCit<sub>0.25</sub>) presented higher limits of detection (LOD) values compared with the traditional silver-citrate nanoparticle (AgCit<sub>1.0</sub>). Therefore, the colloids AgCit<sub>0.50</sub> and AgCit<sub>0.25</sub> present a LOD by the signal/noise method of 1.85 × 10<sup>-7</sup> and 1.20 × 10<sup>-7</sup> mol/L, respectively, while AgCit<sub>1.0</sub> showed a LOD of 3.10 × 10<sup>-6</sup> mol/L. Linear regression confirms the LOD cutoff values. Thus, it is shown that the variation in citrate has an effect on the detection of the present pesticide. Silver nanoparticle ; Nanotechnology ; Simple (philosophy) ; Nanoparticle ; Surface modification ; Combinatorial chemistry ; Chemistry ; Materials science Gold and Silver Nanoparticles Synthesis and Applications ; Biosensors and Analytical Detection ; Water Quality Monitoring and Analysis
Authors: Marwah Ali Harb
Source: International Journal of Materials Science (article, 2025-01-01)
Field: Materials Science — General Materials Science

Effect of Al2O3 nanoparticle content on the mechanical properties of aluminum matrix composites The impact of Al2O3 nanoparticle content on aluminum matrix composites' mechanical characteristics, particularly their compressive strength and sliding wear behavior, has been studied. The findings show that adding Al2O3 nanoparticles considerably improves the composite's hardness, wear resistance, scratch resistance, and strength during compression. To achieve the best static yield strength and the least amount of material loss during wear, the ideal nanoparticle content was determined to be 5 weight percent. However, the mechanical performance of the composite suffered when this concentration was exceeded since it resulted in increased brittleness. Interestingly, when the nanoparticle content beyond the ideal threshold, the wear characteristics declined more dramatically than in composites with lower reinforcement percentages. These results demonstrate how important nanoparticle concentration is for maximizing the mechanical properties of aluminum matrix composites. Composite material ; Nanoparticle ; Materials science ; Aluminium ; Matrix (chemical analysis) ; Nanotechnology Material Properties and Applications ; Aluminum Alloys Composites Properties ; Advanced Theoretical and Applied Studies in Material Sciences and Geometry
Authors: Xinzhe Lu | Nan Wei | H. K. Fang | Feng Hu | Jianjun Cheng | Rui Sun | Yining Chen | Jianyu Zhang | Yanfang Chen | Xu-chuan ZHANG | Kokyo Oh | Tetsushi Yonekura | Xiaochen Chen | Jia Niu | Xiaozhi Wang
Source: Agronomy (article, 2025-10-07)
Field: Materials Science — Materials Chemistry

Passivation Remediation of Cd-Contaminated Farmland in Yongkang, China by CaAl-LDH: A Mechanism and Application Study The enrichment of cadmium (Cd) in farmland soil poses serious risks to agricultural safety and remains challenging to remediate. This study evaluated CaAl-layered double hydroxide (CaAl-LDH) as a highly efficient and stable passivator for Cd-contaminated soil. Laboratory adsorption tests demonstrated that Cd2+ adsorption on CaAl-LDH followed pseudo-second-order kinetics and the Langmuir model, indicating monolayer chemisorption, with a maximum capacity of 469.48 mg·g−1 at pH 6. The adsorption mechanisms include surface complexation, interlayer anion exchange, dissolution–precipitation, and isomorphic substitution. A three-year field trial in Yongkang City, China showed that CaAl-LDH promoted the transformation of Cd in rhizosphere soil from the ion exchange state (F2) to the residual state (F7) and Fe–Mn oxidized state (F5), reducing the exchangeable Cd content by 26.71%. Consequently, Cd content in rice grains decreased by 68.42% in the first year and remained over 37% lower in the second year, consistently below the national food safety limit. Future research should focus on the optimization of material’s stability and application protocol. The results demonstrate that CaAl-LDH provides a cost-effective and sustainable strategy for the in situ passivation remediation of Cd-contaminated farmland, contributing to food safety and sustainable agriculture. Environmental remediation ; Adsorption ; Passivation ; Cadmium ; Hydroxide ; Langmuir ; Ion exchange ; Langmuir adsorption model Layered Double Hydroxides Synthesis and Applications ; Magnesium Oxide Properties and Applications ; Recycling and utilization of industrial and municipal waste in materials production
Authors: T. Bertie Ansell | Louis Berrios | Kabir Peay | Peter D. Dahlberg
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2025-07-13)
Field: Materials Science — Surfaces, Coatings and Films

Cryogenic Electron Tomography by the Numbers: Charting Underexplored Lineages in Structural Cell Biology Abstract Imaging of cells and their interactions across the whole biosphere, with molecular-scale resolution, is key for understanding structure-function relations. Cryogenic electron tomography (cryo-ET) is a powerful method for obtaining this critical information. However, cryo-ET studies are challenging and often limited to a small number of cell types per study. Here, we bring together cryo-ET data from hundreds of cells and tissues across the biosphere to (i) identify emerging methodological trends, (ii) reduce imaging time and costs, (iii) quantitively compare methods for cell freezing and sectioning and (iv) census cryo-ET species coverage across all domains of life. Comparison of the fraction of cellular material within lamellae across all domains of life reveals an order of magnitude difference between bacteria (9%) and archaea (15%) compared to eukaryotes (1%). We calculate the fraction of cellular material which can be imaged using distinct sectioning methods on multicellular communities and tissues - identifying serial lift-out as a powerful new approach for obtaining more complete cellular depictions. Finally, we show that the biodiversity of current cryo-ET studies is 2-3 order of magnitude lower than in sequence libraries and 4-5 lower than the total predicted on Earth. Our analyses reveal major evolutionary lineages which remain critically understudied and where future cryo-ET research would be most impactful. Significance Statement Cryogenic electron tomography (cryo-ET) is a powerful method for imaging inside cells, but it is challenging and typically limited to a few cells per study. We consolidate and quantify data from hundreds of cellular cryo-ET studies across the biosphere. The methodological trends we identify will enhance cryo-ET throughput and heighten biological insight from underexplored lineages of life on Earth. Electron tomography ; Cryo-electron tomography ; Structural biology ; Cell ; Tomography ; Biology ; Cell biology ; Computational biology ; Physics ; Evolutionary biology ; Genetics ; Electron microscope ; Quantum mechanics ; Optics Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications ; Advanced Materials Characterization Techniques
Authors: Aleksandar Nedeljković | Aristide Maggiolino | Gabriele Rocchetti | Weizheng Sun | Volker Heinz | Ivana D. Tomašević | Vesna Djordjević | Igor Tomašević
Source: Foods (article, 2025-09-02)
Field: Chemistry — Analytical Chemistry

The Feasibility of Artificial Intelligence and Raman Spectroscopy for Determining the Authenticity of Minced Meat Food fraud in meat products presents serious economic and public health challenges, underscoring the need for rapid and reliable detection methods. This study investigates the potential of Raman spectroscopy combined with machine learning to accurately discriminate between pure and mixed minced meat preparations. We evaluated three classification algorithms: Support Vector Machines (SVMs), Artificial Neural Networks (ANNs), and Random Forests (RFs). Raman spectra were collected from 19 distinct samples consisting of different ratios of pork, beef, and lamb minced meat. Our findings suggest that homogenization markedly enhances spectral consistency and classification accuracy. In the pure meat samples case, all three models (SVM, ANN, and RF) achieved notable increases in classification accuracies (from 0.50-0.70 to above 0.85), a dramatic improvement over unhomogenized samples. In more complex homogenized mixtures, SVM delivered the highest performance, achieving an accuracy of up to 0.88 for 50:50 mixtures and 0.86 for multi-ratio samples, often outperforming both ANN and RF. While the underlying interpretation of the classification models remains complex, the findings consistently underscore the critical role of homogenization on model performance. This work demonstrates the robust potential of the Raman spectroscopy-coupled machine learning approach for the rapid and accurate identification of minced meat species. Raman spectroscopy ; Food science ; Artificial intelligence ; Biochemical engineering ; Chemistry ; Nanotechnology ; Materials science ; Computer science ; Engineering ; Physics ; Optics Spectroscopy and Chemometric Analyses ; Identification and Quantification in Food ; Advanced Chemical Sensor Technologies
Authors: Abdellah Mourak | Aziz Ait-Karra | Mourad Ouhammou | Abdoussadeq Ouamnina | Abderrahim Boutasknit | Mohamed El Hassan Bouchari | N. Elhadiri | Abdelhakim Alagui
Source: Polysaccharides (article, 2025-07-28)
Field: Materials Science — Biomaterials

Optimization of TEMPO-Mediated Oxidation of Chitosan to Enhance Its Antibacterial and Antioxidant Activities This study systematically investigated the oxidation of chitosan using the TEMPO/NaClO/NaBr catalytic system under varying experimental conditions, namely temperature, reaction time, and pH, in order to optimize the oxidation process. Response surface methodology (RSM) was employed to determine the optimal parameters for maximizing the efficiency of the reaction. The structural modifications to the chitosan following oxidation were confirmed using Fourier-transform infrared spectroscopy (FTIR), alongside additional analytical techniques, which validated the successful introduction of carbonyl and carboxyl functional groups. Solvent-cast films were prepared from both native and oxidized chitosan in order to evaluate their functional performance. The antibacterial activity of these films was assessed against Gram-negative (Salmonella) and Gram-positive (Streptococcus faecalis) bacterial strains. The oxidized chitosan films exhibited significantly enhanced antibacterial effects, particularly at shorter incubation periods. In addition, antioxidant activity was evaluated using DPPH radical scavenging and ferrous ion chelation assays, which both revealed a marked improvement in radical scavenging ability and metal ion binding capacity in oxidized chitosan. These findings confirm that TEMPO-mediated oxidation effectively enhances the physicochemical and bioactive properties of chitosan, highlighting its potential for biomedical and environmental applications. Chitosan ; Ferrous ; Chemistry ; Antioxidant ; Fourier transform infrared spectroscopy ; Antibacterial activity ; Response surface methodology ; Nuclear chemistry ; Solvent ; DPPH ; Catalysis ; Chelation ; Organic chemistry ; Chemical engineering ; Bacteria ; Chromatography Nanocomposite Films for Food Packaging ; Nanoparticles: synthesis and applications ; Medicinal Plants and Neuroprotection
Authors: Roman Yu. Peshkov | Jorn D. Steen | Stefano Crespi
Source: ChemPhotoChem (article, 2025-07-21)
Field: Materials Science — Materials Chemistry

Semi‐Automated Pipeline for Transition State Search of Molecular Photoswitches In this work, a semi‐automated pipeline for the transition state search of molecular switches (PiTS 3 ) is reported, which facilitates the search, optimization, and conformational analysis of transition states of user‐generated molecular structures. This tool is oriented towards the casual user interested in simulating the thermal properties of photochemical switches. Hence, the pipeline can easily read the commonly used .cdxml files as input, and leverages different established packages such as ORCA, xTB, CREST, and pysisyphus. Additional scripts are provided to combine different inputs to create libraries of compounds from chemical drawings, to explore large chemical spaces of photoswitches based on the stilbene, imine, aza‐diarylethene, and norbornadiene structures. Pipeline (software) ; State (computer science) ; Nanotechnology ; Transition (genetics) ; Computer science ; Chemistry ; Materials science ; Algorithm Photochromic and Fluorescence Chemistry ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Wenjing Sun | Wenlu Yang | Zian Yang | Sheng Cao | Quan Xu | Fajun Zhao | Tianjiao Guo | Tianyi Sun
Source: Processes (article, 2025-08-30)
Field: Materials Science — Materials Chemistry

Stability and Foam Performance Optimization of CO2-Soluble Foaming Agents: Influencing Factors and Mechanistic Analysis This study systematically analyzes the influencing factors and optimization strategies of foam stability and performance for CO2-soluble foaming agents in high-temperature and high-pressure (HTHP) complex reservoir environments. By constructing a HTHP experimental system and utilizing dynamic foam testing, interfacial tension analysis, and microscopic observation of liquid films, the effects of chemical factors (e.g., pH, foaming agent concentration, stabilizer synergy) and physical factors (e.g., temperature, pressure) on foam behavior are investigated. The results show that the nonionic surfactant E-1312 exhibits optimal foam performance in neutral to mildly alkaline environments. The foam performance tends to saturate at around 0.5% concentration. High pressure enhances the foam stability, whereas elevated temperature significantly reduces the foam lifetime. Moreover, the addition of nano-sized foam stabilizers such as silica (SiO2) can significantly delay liquid film drainage and strengthen interfacial mechanical properties, thereby improving foam durability. This study further reveals the key mechanisms of CO2-soluble foaming agents in terms of interfacial behavior, liquid film evolution, and foam formation in porous media, providing theoretical guidance and optimization pathways for the molecular design and field application of CO2 foam flooding technology. Foaming agent ; Stability (learning theory) ; Materials science ; Biochemical engineering ; Process engineering ; Computer science ; Composite material ; Engineering ; Porosity Pickering emulsions and particle stabilization ; Polymer Foaming and Composites ; Enhanced Oil Recovery Techniques
Authors: Shilpa Gaikwad | Sonali Kothari
Source: International Journal on Smart Sensing and Intelligent Systems (article, 2025-01-01)
Field: Chemistry — Analytical Chemistry

Deep learning framework for precision grading and non-invasive Apple sweetness evaluation Abstract This research aims to find an economical, non-invasive solution for grading apples by sweetness based on multispectral imaging. This paper examines the relationship between sugar levels and multispectral images of apple fruit taken inside a wooden multispectral imaging enclosure specially designed for taking apple pictures. In addition, using a hand-held refractometer, the sugar content of different apple samples and their corresponding multispectral images are collected. The methodology includes designing and building a prototype camera chamber, gathering pictures of various apples, and using advanced image analysis software to process the data. Prediction of outcomes: Building a practical grading system based on non-invasive multispectral imaging and finding a significant spectral feature of Apple fruit's sweetness levels in the project's range of concern. The proposed system implemented AppleNet, a convolutional neural network (CNN) within MATLAB, to process the multispectral images, and the accuracy achieved for grading the sweetness of apple fruit is 65%. Sweetness ; Grading (engineering) ; Computer science ; Grading scale ; Artificial intelligence ; Food science ; Engineering ; Chemistry ; Medicine ; Taste ; Surgery ; Civil engineering Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Leaf Properties and Growth Measurement
Authors: Joan Mariñoso Guiu | Joost M. Bakker | Thorsten M. Bernhardt | J. M. C. Plane | Stefan T. Bromley | Sandra M. Lang
Source: npj Climate and Atmospheric Science (article, 2025-04-23)
Field: Materials Science — Materials Chemistry

Oxygen-rich anionic metal silicate clusters as nucleation seeds for noctilucent clouds Silicate-based meteoric smoke particles (MSPs) resulting from extraterrestrial dust impacting on the upper atmosphere are likely involved in seeding the formation of mesospheric ice-based noctilucent clouds (NLCs). However, the detailed physicochemical properties of MSP seeds remain elusive. Considering that ice formation is sensitive to MSP charging efficiency, here we focus on species which can efficiently capture charge. As in situ MSP characterisation is highly challenging, we mimic atmospheric MSP formation using laser ablation and collisional cooling to produce small anionic magnesium silicate clusters. Our detailed experimental and theoretical characterisation shows that these clusters have unusual oxygen-rich structures. Moreover, the corresponding neutral clusters have extraordinarily high adiabatic electron affinities and 3-18% charging efficiencies, which is remarkably consistent with estimates required by models to simulate atmospheric measurements. Overall, our highly oxidised anionic silicate clusters exhibit all necessary chemical, electronic and optical properties to be highly credible MSP-based seed candidates for NLC nucleation. Silicate ; Nucleation ; Oxygen ; Metal ; Materials science ; Chemical engineering ; Chemistry ; Metallurgy Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Luminescence Properties of Advanced Materials
Authors: Alla P. Toropova | Andrey A. Toropov | Alessandra Roncaglioni | Emilio Benfenati
Source: C – Journal of Carbon Research (article, 2025-01-14)
Field: Materials Science — Materials Chemistry

Monte Carlo Simulation of Aromatic Molecule Adsorption on Multi-Walled Carbon Nanotube Surfaces Using Coefficient of Conformism of a Correlative Prediction (CCCP) Using the Monte Carlo technique via CORAL-2024 software, models of aromatic substance adsorption on multi-walled nanotubes were constructed. Possible mechanistic interpretations of such models and the corresponding applicability domains were investigated. In constructing the models, criteria of the predictive potential such as the iIndex of Ideality of Correlation (IIC), the Correlation Intensity Index (CII), and the Coefficient of Conformism of a Correlative Prediction (CCCP) were used. It was assumed that the CCCP could serve as a tool for increasing the predictive potential of adsorption models of organic substances on the surface of nanotubes. The developed models provided good predictive potential. The perspectives on the improvement of the nano-QSPR/QSAR were discussed. Carbon nanotube ; Monte Carlo method ; Adsorption ; Correlative ; Molecule ; Materials science ; Nanotube ; Chemical physics ; Nanotechnology ; Computational chemistry ; Chemistry ; Physical chemistry ; Organic chemistry ; Mathematics ; Statistics Carbon Nanotubes in Composites ; Zeolite Catalysis and Synthesis ; Machine Learning in Materials Science
Authors: Jiahao Liu | Zhifang Yang | Xiang Yu
Source: Coatings (article, 2025-11-07)
Field: Materials Science — Materials Chemistry

Toughness Characterization Methods for Diamond-like Carbon Films Diamond-like carbon (DLC) films exhibit superior tribological properties; however, their widespread adoption in precision manufacturing is hampered by inherent brittleness and a lack of reliable toughness characterization methods at the micrometer scale. This review critically examines existing techniques for evaluating DLC film toughness, highlighting limitations due to film thickness constraints and subjective failure definitions. We focus on two prominent micro-scale methods: impact testing and scratch testing. Impact toughness is assessed through energy absorption analysis based on impact crater morphology, including crack patterns and delamination areas. Scratch toughness is evaluated using critical loads (Lc1, Lc2) and the derived Crack Propagation Resistance (CPRS) parameter, complemented by microscopic failure analysis. We argue that neither method alone suffices for comprehensive toughness assessment. Instead, we propose a synergistic strategy integrating both techniques to provide a practical and comprehensive evaluation encompassing energy- and stress-based failure mechanisms under varying loading conditions. This approach offers a practical framework for developing tougher DLC coatings. Toughness ; Brittleness ; Scratch ; Characterization (materials science) ; Damage tolerance ; Fracture toughness ; Delamination (geology) Diamond and Carbon-based Materials Research ; Metal and Thin Film Mechanics ; Force Microscopy Techniques and Applications
Authors: Lincoln Harris | William Stafford Noble
Source: Molecular & Cellular Proteomics (article, 2025-11-20)
Field: Chemistry — Spectroscopy

Proteomics Data Imputation With a Deep Model That Learns From Many Datasets Missing values are a major challenge in the analysis of mass spectrometry proteomics data. Missing values hinder reproducibility, decrease statistical power for identifying differentially abundant proteins, and make it challenging to analyze low-abundance proteins. We present Lupine, a deep learning-based method for imputing, or estimating, missing values in quantitative proteomics data. Lupine is, to our knowledge, the first imputation method that is designed to learn jointly from many datasets, and we provide evidence that this approach leads to more accurate predictions. We validated Lupine by applying it to tandem mass tag data from >1000 cancer patient samples spanning 10 cancer types from the Clinical Proteomics Tumor Atlas Consortium. Lupine outperforms the state of the art for proteomics imputation, uniquely identifies differentially abundant proteins and Gene Ontology terms, and learns a meaningful representation of proteins and patient samples. Lupine is implemented as an open-source Python package. Proteomics ; Missing data ; Imputation (statistics) ; Quantitative proteomics ; Python (programming language) ; Gene ontology ; Annotation ; Proteogenomics Advanced Proteomics Techniques and Applications ; Bioinformatics and Genomic Networks ; Machine Learning in Bioinformatics