jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Xuan Dung James Nguyen | Y. A. Liu | Christopher McDowell | Luke Dooley
Source: Bioprocess and Biosystems Engineering (article, 2025-06-26)
Field: Chemistry — Analytical Chemistry

Methodology for contamination detection and reduction in fermentation processes using machine learning This paper demonstrates an accurate and efficient methodology for fermentation contamination detection and reduction using two machine learning (ML) methods, including one-class support vector machine and autoencoders. We also optimize as many hyperparameters as possible prior to the training of the ML models to improve the model accuracy and efficiency, and choose a Python platform called Optuna, to enable the parallel execution of hyperparameter optimization (HPO). We recommend using Bayesian optimization with hyperband algorithm to carry out HPO. Results show that we can predict contaminated fermentation batches with recall up to 1.0 without sacrificing the precision and specificity of non-contaminated batches, which read up to 0.96 and 0.99, respectively. One-class support vector machine outperforms autoencoders in terms of precision and specificity even though they both achieve an outstanding recall of 1.0. These models demonstrate high accuracy in detecting contamination without requiring labeled contaminated data and are suitable for integration into real-time fermentation monitoring systems with minimal latency and retraining needs. In addition, we benchmark our ML methods against a traditional threshold-based contamination detection approach (mean <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mo>±</mml:mo></mml:math> 3 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>σ</mml:mi></mml:math> rule) to quantify the added value of using data-driven models. Finally, we identify important independent variables contributing to the contaminated batches and give recommendations on how to regulate them to reduce the likelihood of contamination. Hyperparameter ; Machine learning ; Computer science ; Artificial intelligence ; Support vector machine ; Precision and recall ; Contamination ; Python (programming language) ; Algorithm ; Data mining Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Anomaly Detection Techniques and Applications
Authors: Shuang Lyu | Yuanhang Xia | Wei Li | Te Zhu | Yue Chen | A.H.W. Ngan
Source: Acta Materialia (article, 2025-04-02)
Field: Materials Science — Materials Chemistry

Statistical mechanics, entropy and temperature analog of dislocations moving on fluctuating resistance landscapes High/medium-entropy alloys, also known as complex concentrated alloys (CCAs), are so called because the mixing entropy reaches a maximum when the constituent multi-elements adopt equiatomic ratios. However, the mixing entropy relates little to mechanical strength for which these alloys are most studied. By analyzing dislocations in VCoNi via electron microscopy and molecular-dynamics from a machine interatomic potential, their energies are found to obey a maximum-entropy distribution in the random alloy state, but not in the annealed state where local chemical order (LCO) exists. The maximum-entropy distribution is characterized by an athermal, mechanical analog of temperature which relates directly to the alloy strength and dominates over the real temperature over a wide range. The entropy of dislocations is a fingerprint of LCO, and statistical mechanics is an impeccable theoretical framework for understanding dislocations and strength in CCAs. Materials science ; Statistical mechanics ; Entropy (arrow of time) ; Statistical physics ; Thermodynamics ; Condensed matter physics ; Classical mechanics ; Physics Microstructure and mechanical properties ; Thermal properties of materials ; Force Microscopy Techniques and Applications
Authors: Yuxin Xiao | Yuanning Zhai | Lei Zhou | Yiming Yin | Hengnian Qi | Chu Zhang
Source: Foods (article, 2025-06-13)
Field: Chemistry — Analytical Chemistry

Detection of Soluble Solid Content in Citrus Fruits Using Hyperspectral Imaging with Machine and Deep Learning: A Comparative Study of Two Citrus Cultivars Hyperspectral imaging (HSI) has broad applications for detecting the soluble solids content (SSC) of fruits. This study explores the integration of HSI with machine learning and deep learning to predict SSC in two mandarin varieties: Ponkan and Tianchao. Traditional machine learning models (support vector machines and partial least squares regression) and deep learning models (convolutional neural networks, long short-term memory, and Transformer architectures) were evaluated for SSC prediction performance. Combined models that integrated different deep learning architectures were also explored. Results revealed varietal differences in prediction performance. For Ponkan mandarins, the best SSC prediction model was achieved by partial least squares regression, outperforming deep learning models. In contrast, for Tianchao mandarins, the deep learning model based on convolutional neural network slightly surpassed the traditional model. SHapley Additive exPlanations (SHAP) analysis indicated that the influential wavelengths varied between varieties, suggesting differences in key spectral features for SSC prediction. These findings highlight the potential of combining HSI with advanced modeling for citrus SSC prediction, while emphasizing the need for variety-specific models. Future research should focus on developing more robust and generalized prediction models by incorporating a broader range of citrus varieties and exploring the impact of varietal characteristics on model performance. Hyperspectral imaging ; Artificial intelligence ; Deep learning ; Convolutional neural network ; Partial least squares regression ; Machine learning ; Support vector machine ; Computer science ; Predictive modelling ; Artificial neural network ; Mathematics ; Pattern recognition (psychology) Spectroscopy and Chemometric Analyses ; Horticultural and Viticultural Research ; Water Quality Monitoring and Analysis
Authors: Ran He | Pingjun Ying | Shuo Chen | Zhifeng Ren | Kornelius Nielsch
Source: MRS Bulletin (review, 2025-06-30)
Field: Materials Science — Materials Chemistry

Magnesium-based thermoelectric materials and modules for low-temperature applications (below 300°C) Potential application scenarios of Mg-based Te-free thermoelectric technology. Thermoelectric effect ; Materials science ; Thermoelectric materials ; Engineering physics ; Magnesium ; Nanotechnology ; Optoelectronics ; Process engineering ; Metallurgy ; Composite material ; Thermodynamics ; Thermal conductivity ; Engineering ; Physics Advanced Thermoelectric Materials and Devices ; Heusler alloys: electronic and magnetic properties ; Thermal Expansion and Ionic Conductivity
Authors: Giuseppe Altieri | Sabina Laveglia | Mahdi Rashvand | Francesco Genovese | Attilio Matera | Alba N. Mininni | M. Calabritto | Giovanni Carlo Di Renzo
Source: Applied Sciences (article, 2025-06-01)
Field: Chemistry — Analytical Chemistry

Portable NIR Spectroscopy Combined with Machine Learning for Kiwi Ripeness Classification: An Approach to Precision Farming This study aims to evaluate and classify the ripening stages of yellow-fleshed kiwifruit by integrating spectral and physicochemical data collected from the pre-harvest phase through 60 days of storage. A portable near-infrared (NIR) spectrometer (900–1700 nm) was used to develop predictive models for soluble solids content (SSC) and firmness (FF), testing multiple preprocessing methods within a Partial Least Squares Regression (PLSR) framework. SNV preprocessing achieved the best predictions for FF (R2P = 0.74, RMSEP = 12.342 ± 0.274 N), while the Raw-PLS model showed optimal performance for SSC (R2P = 0.93, RMSEP = 1.142 ± 0.022°Brix). SSC was more robustly predicted than FF, as reflected by RPD values of 2.6 and 1.7, respectively. For ripening stage classification, an Artificial Neural Network (ANN) outperformed other models, correctly classifying 97.8% of samples (R2 = 0.95, RMSE = 0.08, MAE = 0.03). These results demonstrate the potential of combining NIR spectroscopy with AI techniques for non-destructive quality assessment and accurate ripeness discrimination. The integration of regression and classification models further supports the development of intelligent decision-support systems to optimize harvest timing and postharvest handling. Ripeness ; Kiwi ; Computer science ; Agricultural engineering ; Environmental science ; Chemistry ; Food science ; Engineering ; Ripening Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Leaf Properties and Growth Measurement
Authors: Bárbara G. S. Guinati | Rhett C. Smith
Source: Sustainable Chemistry (article, 2025-09-08)
Field: Materials Science — Polymers and Plastics

Upcycling Nutshells: Reinforced Polymers and Biocomposites This review highlights recent advances in the use of nutshell-derived materials, including peanut, walnut, and other lignocellulosic shell wastes, as reinforcers in polymer composites. The focus is placed on evaluating how the incorporation of nutshell fillers influences the mechanical and thermal properties of various polymer matrices. Key findings across multiple studies show that nutshell reinforcement can significantly enhance tensile strength, modulus, thermal stability, and biodegradability, depending on filler concentration, particle size, and surface treatment. The review also discusses the sustainability and economic benefits of using agricultural waste as a functional additive, offering insights into the design of low-cost, eco-friendly polymer composites for packaging, construction, and environmental applications. Filler (materials) ; Polymer ; Ultimate tensile strength ; Sustainability ; Shell (structure) Natural Fiber Reinforced Composites ; biodegradable polymer synthesis and properties ; Nanocomposite Films for Food Packaging
Authors: Wenliang Qi | Qingqing Jiang | Erihemu
Source: Beni-Suef University Journal of Basic and Applied Sciences (article, 2025-07-31)
Field: Chemistry — Analytical Chemistry

Research progress on the application of near-infrared spectroscopy in liquid food quality testing Abstract Near-infrared spectroscopy is a spectral analysis technique that involves electromagnetic radiation at wavelengths between the visible spectrum and the mid-infrared spectrum. The technique can realize non-destructive testing of samples and is particularly suitable for rapid analysis in the field and online quality monitoring. This technique utilizes the absorption of near-infrared light by hydrogen-containing groups within a sample, enabling the extraction of specific information regarding the characteristics of these hydrogen-containing groups within the analyzed organic molecules. The application of chemometrics in near-infrared spectral analysis reduces the data set size, excludes noise interference and improves the efficiency of multivariate correction. The finite variable model construction reduces the random errors and improves the accuracy of the prediction results. The present document synthesizes the application of near-infrared spectroscopy and chemometrics in the context of liquid food safety detection. It provides a comprehensive overview of the state-of-the-art research in this field, emphasizing the role of chemometrics analysis in evaluating the quality and safety of liquid foods. Additionally, it delves into the merits and limitations of this technological approach, offering insight into its potential for future advancement and expansion. Quality (philosophy) ; Spectroscopy ; Food quality ; Infrared spectroscopy ; Materials science ; Food science ; Chemistry ; Physics Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Zhihao Li | Fulong Liu | Peng Cao | Yuqing Sun | Panpan Peng | Yujie Zong | Ying Zhang | Jinze Zhai | Chunlei Wang | Hongchao Wang
Source: Advanced Functional Materials (article, 2025-05-26)
Field: Materials Science — Materials Chemistry

Chemical Pressure‐Driven Three‐ and Four‐Phonon Scattering in SnTe: Toward Suppressed Lattice Thermal Conductivity and Enhanced Thermoelectric Performance Abstract Thermoelectric efficiency of SnTe is constrained by high lattice thermal conductivity. This study demonstrates that chemical pressure, engineered via Pb substitution, suppresses phonon propagation by amplifying lattice anharmonicity. Raman and Brillouin spectroscopy reveals that chemical pressure enhances four‐phonon scattering and reduces optical and acoustic phonon lifetimes, while density functional theory (DFT) calculations correlate these effects with a more diffuse phonon dispersion, a broadened phonon density of states, and a distinct 3 THz spectral feature. These modifications elevate three‐ and four‐phonon scattering rates and expand the scattering phase space, reducing lattice thermal conductivity to 0.6 W m −1 K −1 . The resultant peak zT of 1.3 at 873 K underscores chemical pressure as a potent phonon‐engineering strategy. Notably, it is revealed that chemical pressure predominantly mimics the “pressure” effect on phonon dynamics through comparative analysis with physically pressurized SnTe, establishing a universal strategy for thermoelectric optimization by tailoring anharmonic lattice dynamics, with implications for advancing high‐performance energy conversion materials. Materials science ; Thermoelectric effect ; Phonon ; Thermal conductivity ; Condensed matter physics ; Thermoelectric materials ; Phonon scattering ; Scattering ; Lattice (music) ; Thermal ; Composite material ; Thermodynamics ; Optics Advanced Thermoelectric Materials and Devices ; Thermal properties of materials ; Chalcogenide Semiconductor Thin Films
Authors: Bhawana Dabas | Antoine Ruffini | Yann Le Bouar | Thomas Jourdan | A. Finel
Source: Acta Materialia (article, 2025-04-24)
Field: Materials Science — Materials Chemistry

Phase-field modeling of cavity growth and dislocation climb Climb ; Materials science ; Dislocation ; Field (mathematics) ; Phase (matter) ; Condensed matter physics ; Composite material ; Thermodynamics ; Physics Solidification and crystal growth phenomena ; Aluminum Alloy Microstructure Properties ; High Temperature Alloys and Creep
Authors: Yingtong Luo | Loai K. E. A. Abdelmohsen | Jingxin Shao | Jan C. M. van Hest
Source: Small Structures (article, 2025-08-26)
Field: Materials Science — Materials Chemistry

Beyond Aggregation: Engineering AIEgens from Molecular Design to Supramolecular Assembly for Advanced Photophysical Function Since the introduction of the concept of aggregation‐induced emission (AIE) in 2001, AIE molecules have garnered significant attention as versatile building blocks with much application potential. To optimally exploit their features, much effort has been devoted to exploring the fundamental photophysical mechanisms underlying AIE luminogens (AIEgens). Typically, AIEgens must transition from the monomeric to the aggregated state to exhibit their characteristic emission, a feature critical for practical applications. As a result, precise control over the photophysical properties of AIEgens in their aggregated state has become a key focus of AIE research. Drawing on both molecular and supramolecular theories, researchers have proposed and elucidated a range of models to better understand the relationship between aggregation and photophysical properties. Building on these insights, the rational design of AIEgenic nanostructures with tunable photophysical properties holds significant promise for enhancing their performance in biomedical applications, including fluorescence imaging, photoacoustic imaging, photodynamic therapy, and photothermal therapy. This review aims to provide a comprehensive overview of strategies for the rational design of AIEgens, modulation of their aggregation behavior, and development of assembly approaches to tailor the photophysical properties of the resulting nanostructures, guiding the construction of therapeutic agents for biomedical use. Supramolecular chemistry ; Aggregation-induced emission ; Molecular engineering ; Function (biology) ; Supramolecular assembly ; Nanotechnology ; Chemistry ; Materials science ; Cell biology ; Molecule ; Biology ; Fluorescence ; Physics ; Organic chemistry Luminescence and Fluorescent Materials ; Perovskite Materials and Applications ; Nanoplatforms for cancer theranostics
Authors: Farnaz Mortezapour | Nasrin Shadjou | Mehdi Mahmoudian
Source: BMC Chemistry (article, 2025-04-07)
Field: Chemistry — Organic Chemistry

Effective removal of anionic dyes from aqueous solution using polyethersulfone based membrane reinforced by montmorillonite The study highlights the development and characterization of a novel polymeric membrane composed of montmorillonite (MMT) and polyethersulfone (PES) using the phase inversion process. The membrane incorporates polyethylene glycol (PEG) as a pore-forming agent and N-methyl pyrrolidone (NMP) as a solvent. The addition of MMT significantly enhances the membrane's properties, including hydrophilicity, porosity, antifouling capacity, hydraulic resistance, water uptake, and dye removal efficiency. Characterization techniques such as FT-IR spectroscopy, FE-SEM, EDX spectroscopy, water flux measurements, water uptake analysis, contact angle studies, and fouling assessments confirm the improved performance of the PES/MMT composite membrane. The presence of MMT increases the negative surface charge of the membrane, making it particularly effective in removing anionic dyes like Congo red (CR), Quinoline yellow (QY), and Methyl orange (MO). The study demonstrates that membranes with up to 5 wt% MMT exhibit high porosity (68.2%) and enhanced water flux (35 L/m<sup>2</sup>·h), achieving dye rejection rates of 99% for CR, 92% for MO, and 81% for QY. The integration of MMT into PES membranes presents a significant advancement in sustainable water purification technologies. These modified membranes demonstrate enhanced mechanical strength, improved structural stability, and an increased surface area, making them highly effective for dye adsorption. Compared to traditional materials, PES/MMT membranes exhibit superior performance in wastewater treatment and dye removal, offering a promising and eco-friendly alternative for addressing environmental challenges. Montmorillonite ; Aqueous solution ; Membrane ; Chemistry ; Chemical engineering ; Organic chemistry Nanomaterials for catalytic reactions ; Synthesis of Tetrazole Derivatives ; Adsorption and biosorption for pollutant removal
Authors: Tim Van Den Bossche | Jean Armengaud | Dirk Benndorf | J. Blakeley-Ruiz | Madita Brauer | Kai Cheng | Marybeth Creskey | Daniel Figeys | Lucia Grenga | Timothy J. Griffin | Céline Henry | Robert L. Hettich | Tanja Holstein | Pratik Jagtap | Nico Jehmlich | Manuel Kleiner | Benoît J. Kunath | Xuxa Malliet | Lennart Martens | Subina Mehta | Bart Mesuere | Zhibin Ning | Alessandro Tanca | Sergio Uzzau | Pieter Verschaffelt | Jing Wang | Paul Wilmes | Xin Zhang | Leyuan Li
Source: ChemRxiv (preprint, 2025-01-08)
Field: Chemistry — Spectroscopy

The Microbiologist’s Guide to Metaproteomics Metaproteomics is an emerging approach for studying microbiomes, offering the ability to characterize proteins that underpin microbial functionality within diverse ecosystems. As the primary catalytic and structural components of microbiomes, proteins provide unique insights into the active processes and ecological roles of microbial communities. By integrating metaproteomics with other omics disciplines, researchers can gain a comprehensive understanding of microbial ecology, interactions, and functional dynamics. This review, developed by the Metaproteomics Initiative (www.metaproteomics.org), serves as a practical guide for both microbiome and proteomics researchers, presenting key principles, state-of-the-art methodologies, and analytical workflows essential to metaproteomics. Topics covered include, among others, experimental design, sample preparation, mass spectrometry techniques, data analysis strategies, and statistical approaches. Metaproteomics ; Microbiome ; Workflow ; Metagenomics ; Proteomics ; Computational biology ; Data science ; Computer science ; Ecology ; Biology ; Bioinformatics Advanced Proteomics Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Microbial Community Ecology and Physiology
Authors: Graham Roberts | Mu‐Ping Nieh | W. K. Anson | Qian Yang
Source: Digital Discovery (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Automated structural analysis of small angle scattering data from common nanoparticles <i>via</i> machine learning A data-efficient and trustworthy machine learning approach is developed for automated inverse analysis of small angle scattering data. Nanoparticle ; Small-angle scattering ; Scattering ; Materials science ; Computer science ; Artificial intelligence ; Nanotechnology ; Optics ; Physics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Enhanced Oil Recovery Techniques
Authors: Julia Nguyen | Maksymilian Zabijak | Marcin Poręba
Source: Chemical Science (review, 2025-01-01)
Field: Chemistry — Organic Chemistry

Chemical probes for enzyme imaging: challenges in design, synthesis and biomedical applications Enzymes play central roles in cellular regulation, disease progression, and therapeutic response, making them ideal targets for diagnostic and therapeutic chemical probes. Among them, proteases and kinases are particularly valuable due to their dysregulation in cancer, immune disorders, and infectious diseases. In this Perspective, we highlight recent advances in chemical probe development aimed at these enzyme families, with a focus on imaging modalities and translational potential. Kinase-targeted probes are frequently derived from clinically approved inhibitors, facilitating their repurposing for diagnostic applications. In contrast, protease-responsive probes rely on detailed substrate specificity profiling, where the incorporation of unnatural amino acids serves as a key strategy for achieving selectivity and stability in biological systems. We explore the broad landscape of probe modalities, including activatable fluorescent substrates, PET tracers, and mass cytometry-compatible metal-tagged reagents, that collectively enable sensitive, real-time, and multiplexed analysis of enzyme function in cells and tissues. Notably, enzyme-activated theranostic systems are emerging that couple imaging with targeted drug release, expanding the functional scope of chemical probes beyond detection. We also discuss how artificial intelligence is beginning to support the design process, from structure prediction and binding affinity modeling to the generation of novel chemical scaffolds with favorable pharmacological properties. With several enzyme-targeted probes now in clinical trials or approved for human use, this field exemplifies the success of translational chemical biology and the potential of chemical tools to directly impact patient care. Chemical biology ; Repurposing ; Drug discovery ; Proteases ; Drug development ; Human disease ; Drug repositioning ; Protein engineering ; Amino acid residue Click Chemistry and Applications ; Nanoplatforms for cancer theranostics ; Photochromic and Fluorescence Chemistry