jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Lukas Seifert | Lisa Leuchtenberger-Engel | Christian Hopmann
Source: Polymers (article, 2025-01-07)
Field: Materials Science — Polymers and Plastics

Development of an Analytical Model for Predicting the Shear Viscosity of Polypropylene Compounds The need for an efficient adaptation of existing polypropylene (PP) formulations or the creation of new formulations has become increasingly important in various industries. Variations in viscosity resulting from changes in raw materials, fillers, and additives can have a significant impact on the processing and quality of PP products. This study presents the development of an analytical model designed to predict the shear viscosity of complex PP blends. By integrating established mixing rules with novel fitting parameters, the model provides a systematic and efficient method for managing variability in PP formulations. Experimental data from binary and multi-component blends were used to validate the model, demonstrating high prediction accuracy over a range of shear rates. The proposed model serves as a valuable tool for compounders and manufacturers to optimise PP formulations and develop new recipes with consistent processing and product quality. Future work will include industrial-scale trials and further evaluation against advanced machine learning approaches. Polypropylene ; Process engineering ; Viscosity ; Shear rate ; Materials science ; Biochemical engineering ; Computer science ; Mixing (physics) ; Shear (geology) ; Component (thermodynamics) ; Shear viscosity ; Scale (ratio) ; Thermodynamics ; Engineering ; Composite material Polymer crystallization and properties ; Rheology and Fluid Dynamics Studies ; Injection Molding Process and Properties
Authors: Megan Teng | Peggy Tsao | Yue Wei | Fan Xia | Peisheng He | Yande Peng | Liwei Lin
Source:  (article, 2025-01-19)
Field: Materials Science — Materials Chemistry

Dynamic PMUTs Packaging Using Shape Memory Alloy This work proposes a dynamic package scheme for piezoelectric micromachined ultrasonic transducers (PMUTs) by a Shape Memory Alloy (SMA) controlled platform to actively change the acoustic wave directivity patterns and frequency responses. Innovations include: (1) a shape-morphing package capable of switching between two states in seconds using SMA Nitinol wires; (2) a 278% increase in the received signal at 50 kHz between two states at an 45° angle; and (3) a design scheme by generative and surrogate machine learning models for optimal parameters according to desired outputs. The dynamic package enables designated acoustic fields across different frequencies for potential applications in ultrasonic communication, medical imaging, and environmental sensing. Materials science ; Shape-memory alloy ; Alloy ; Dynamic random-access memory ; Computer science ; Composite material ; Semiconductor memory ; Computer hardware Silicone and Siloxane Chemistry ; Advanced Computing and Algorithms ; Polymer composites and self-healing
Authors: Veronika Pilařová | Kateřina Plachká | Taťána Gazárková | František Švec | Jean‐Christophe Garrigues | Lucie Nováková
Source: Journal of Chromatography A (article, 2025-05-16)
Field: Chemistry — Spectroscopy

AI-powered insights into the UniSpray ionization in supercritical fluid chromatography-mass spectrometry Chemistry ; Chromatography ; Supercritical fluid chromatography ; Mass spectrometry ; Supercritical fluid ; Direct electron ionization liquid chromatography–mass spectrometry interface ; Chemical ionization ; Ionization ; Gas chromatography ; Organic chemistry ; Ion Analytical Chemistry and Chromatography ; Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Ahmed Hosney | Algimanta Kundrotaitė | Donata Drapanauskaitė | Marius Urbonavičius | Šarūnas Varnagiris | Sana Ullah | Karolina Barčauskaitė
Source: Polymers (article, 2025-11-06)
Field: Materials Science — Materials Chemistry

Green Synthesis of Chitosan Silver Nanoparticle Composite Materials: A Comparative Study of Microwave and One-Pot Reduction Methods Green synthesis methods of silver nanoparticles have gained great attention because they offer sustainable, eco-friendly, and less-toxic alternatives to traditional methods. This study sheds light on the green synthesis of chitosan silver nanoparticle composites, providing a comparative evaluation of microwave-assisted (M1) and a one-pot (M2) reduction methods. The morphological, crystallinity, and structural uniformity characteristics were evaluated by UV-Visible, Raman spectroscopy, X-ray diffraction (XRD) and scanning electron microscopy (SEM) with employing image processing pipeline based on deep learning model for segmentation and particles size estimation. The UV-visible spectrum exhibited independent SPR peaks ranging from 400 to 450 nm for all samples; however, microwave assisted-synthesis possessed narrower and more intense peaks indicative of better crystallinity and mono-dispersity. SEM depicted smaller, more uniformly dispersed particles for microwave-assisted (M1), while deep learning segmentation showed lower particle size variability (σ ≈ 24-43 nm), compared to polydisperse (σ ≈ 16-59 nm) in M2 samples. XRD showed crystalline face-centered cubic (FCC) silver with dominant peaks in M1 samples, whereas M2 had broader, less intense peaks with amorphous features. Raman vibrations revealed more structural order and homogenous capping in M1 than M2. Therefore, microwave-assisted (M1) showed better control on nucleation, particle size, crystallinity, and homogeneity due to a faster and uniform energy distribution. The future research would focus on the antimicrobial evaluation of such nanoparticles in agronomy. Crystallinity ; Silver nanoparticle ; Raman spectroscopy ; Scanning electron microscope ; Nanoparticle ; Composite number ; Particle size ; Amorphous solid Nanoparticles: synthesis and applications ; Gold and Silver Nanoparticles Synthesis and Applications ; Nanomaterials for catalytic reactions
Authors: Žarko Kulić | Valentin J.N. Steiner | Andreas Butterer
Source: Planta Medica (article, 2025-10-29)
Field: Chemistry — Spectroscopy

NMR Chemical Shifts of Common Flavonoids We present <sup>1</sup>H and <sup>13</sup>C nuclear magnetic resonance (NMR) chemical shifts for the most common flavonoids, a class of natural products widely present in the plant kingdom. Our data are acquired in three common NMR solvents: methanol d<sub>4</sub>, dimethyl sulfoxide d<sub>6</sub>, and pyridine d<sub>5</sub> and may be used as a reference for isolated compounds, for detecting flavonoids within mixtures like plant extracts as shown in an example, as reference data for natural product databases, for refined training of prediction software, and for machine learning in resonance assignment and NMR-based structure elucidation. Solvent- and substitution-induced resonance shifts are discussed. Chemical shift ; Natural product ; Methanol ; Nuclear magnetic resonance spectroscopy ; Pyridine ; Carbon-13 NMR ; Dimethyl sulfoxide ; Two-dimensional nuclear magnetic resonance spectroscopy Molecular spectroscopy and chirality ; Metabolomics and Mass Spectrometry Studies ; Ziziphus Jujuba Studies and Applications
Authors: Alireza Zargaran | Timothy Alexander Listyawan | Shailendra Kumar Verma | Ji Hoon Kim | Jeremy Dudo | Changning Niu | Abhinav Saboo | Jiadong Gong | Hua Yang | Kyoungdoc Kim
Source: Materials & Design (article, 2025-02-13)
Field: Materials Science — Materials Chemistry

Integrated Computational Materials Engineering of Fire-Resistant Steels • We investigate over 5,000 compositional space of low-carbon fire-resistant steels using the high-throughput CALPHAD modeling. • The Cr and Mo are the most influential elements that enhance solid-solution and precipitate strengthening. • The new alloys represent yield strength of 520–770 MPa at 600 °C, more than twice of the strength of the commercial steel. • The improvement of the tensile strength is due to the formation of nano-sized vanadium carbide at high temperature of 600 °C. We explore a wide compositional space of low-carbon steel, containing 11 alloying elements, assess its feasibility for fire-resistant applications via modeling yield strength at elevated temperatures. We employ the high-throughput CALPHAD-based modeling to calculate the contributions of solid solution, precipitation, and dislocation strengthening for specific compositions. Over 30,000 yield strength predictions are made at two elevated temperatures (600 °C and 700 °C) across about 5,000 unique compositions, each with three different heat treatment conditions. We analyze the big data base and optimize using the machine-learning techniques to understand the significance of different parameters on strength. Experimental validation include thermomechanical treatments, high-temperature tensile tests, and microstructural characterizations. The newly developed alloys demonstrate a yield strength of 520–770 MPa at 600 °C, more than twice the strength of the commercial S355 steel. This approach facilitates the rapid discovery of novel fire-resistant steel compositions and has a high potential for other alloy systems. Materials science ; Fire resistance ; Fire safety ; Forensic engineering ; Metallurgy ; Mechanical engineering ; Systems engineering ; Construction engineering ; Composite material ; Civil engineering ; Engineering High-Velocity Impact and Material Behavior ; Microstructure and Mechanical Properties of Steels ; Material Properties and Failure Mechanisms
Authors: Sumani Mukherjee | Santanu Ghorai | Hemanta Naskar | S Manna | A.H.M. Toufique Ahmed | Don Biswas | Samhita Dasgupta | Ipshita Bhattacharya | Bipan Tudu | Arpitam Chatterjee | Rajib Bandyopadhyay
Source: Journal of Food Composition and Analysis (article, 2025-06-16)
Field: Chemistry — Analytical Chemistry

A novel TMS@G-MIP electrochemical sensor for selective detection of p-coumaric acid in fruits using voltammetry signal and CNN Chemistry ; Voltammetry ; Cyclic voltammetry ; p-Coumaric acid ; SIGNAL (programming language) ; Electrochemistry ; Chromatography ; Computer science ; Electrode ; Ferulic acid Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Electrochemical sensors and biosensors
Authors: Suelem Kaczala | Vanderlei Aparecido de Lima | Maria Lurdes Felsner
Source: Food Science and Technology (article, 2025-03-05)
Field: Chemistry — Analytical Chemistry

A novel chemometric approach for classifying whole fruit juices, reconstituted juices, and nectars Fruit juices are widely enjoyed beverages, valued for their organoleptic qualities, chemical composition, and nutritional benefits. They are commonly sold as whole juices (WJs), reconstituted juices, or nectars, with WJs particularly favored by consumers seeking healthier, more balanced dietary options, often commanding a higher market value. However, these desirable characteristics make them vulnerable to fraud and adulteration. To address this issue, a novel strategy has been proposed to classify juices into two categories: WJs and nectar/reconstituted juices (NERJs). This approach combines key chemical markers––such as reducing sugars (RS), sodium (Na), and potassium (K)––with advanced chemometric techniques, including unsupervised methods like principal component analysis (PCA) and supervised methods, such as machine learning algorithms (random forest [RF] and extreme gradient boosting (XGBoost)). The models developed demonstrated remarkable accuracy, achieving 100% classification accuracy for WJs and good performance for NERJs, with accuracy rates &gt; 91.7%. This fast, accurate, and easily implementable strategy is vital for the quality control and authentication of fruit juices by regulatory bodies. It provides essential tools for detecting fraud and adulteration in the beverage industry, ultimately helping ensure the quality and consistency of products available to consumers. Fruit juice ; Food science ; Chemistry Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Metabolomics and Mass Spectrometry Studies
Authors: Xueming Hua | Yan Cao | Baoyu Liu | Xiaohui Yang | Hailong Xu | Lifen Li | Jing Wu
Source: Polymers (article, 2025-07-02)
Field: Materials Science — Polymers and Plastics

Prediction of Thermomechanical Behavior of Wood–Plastic Composites Using Machine Learning Models: Emphasis on Extreme Learning Machine The dynamic thermomechanical properties of wood-plastic composites (WPCs) are influenced by various factors, such as the selection of raw materials and processing parameters. To investigate the effects of different wood fiber content ratios and temperature on the loss modulus of WPCs, seven different proportions of Masson pine (<i>Pinus massoniana</i> Lamb.) and Chinese fir [<i>Cunninghamia lanceolata</i> (Lamb.) Hook.] mixed-fiber-reinforced HDPE composites were prepared using the extrusion molding method. Their dynamic thermomechanical properties were tested and analyzed. The storage modulus of WPCs showed a decreasing trend with increasing temperature. A reduction in the mass ratio of Masson pine wood fibers to Chinese fir wood fibers resulted in an increase in the storage modulus of WPCs. The highest storage modulus was achieved when the mass ratio of Masson pine wood fibers to Chinese fir wood fibers was 1:5. In addition, the loss modulus of the composites increased as the content of Masson pine fiber decreased, with the lowest loss modulus observed in HDPE composites reinforced with Masson pine wood fibers. The loss tangent for all seven types of WPCs increased with rising temperatures, with the maximum loss tangent observed in WPCs reinforced with Masson pine wood fibers and HDPE. A prediction method based on the Extreme Learning Machine (ELM) model was introduced to predict the dynamic thermomechanical properties of WPCs. The prediction accuracy of the ELM model was compared comprehensively with that of other models, including Support Vector Machines (SVMs), Random Forest (RF), Back Propagation (BP) neural networks, and Particle Swarm Optimization-BP (PSO-BP) neural network models. Among these, the ELM model showed superior data fitting and prediction accuracy, with an <i>R</i><sup>2</sup> value of 0.992, Mean Absolute Error (MAE) of 1.363, and Root Mean Square Error (RMSE) of 3.311. Compared to the other models, the ELM model demonstrated the best performance. This study provides a solid basis and reference for future research on the dynamic thermomechanical properties of WPCs. Composite material ; Materials science ; Emphasis (telecommunications) ; Extreme learning machine ; Computer science ; Machine learning ; Artificial neural network Natural Fiber Reinforced Composites ; Additive Manufacturing and 3D Printing Technologies ; Injection Molding Process and Properties
Authors: Nama Yaa Akyea Prempeh | Xorlali Nunekpeku | Felix Y.H. Kutsanedzie | Arul Murugesan | Huanhuan Li
Source: Chemosensors (review, 2025-11-10)
Field: Chemistry — Analytical Chemistry

A Comprehensive Review of Non-Destructive Monitoring of Food Freshness and Safety Using NIR Spectroscopy and Biosensors: Challenges and Opportunities The demand for safe, high-quality, and minimally processed food has intensified interest in non-destructive analytical techniques capable of assessing freshness and safety in real time. Among these, near-infrared (NIR) spectroscopy and biosensors have emerged as leading technologies due to their rapid, reagent-free, and sample-preserving nature. NIR spectroscopy offers a holistic assessment of internal compositional changes, while biosensors provide specific and sensitive detection of biological and chemical contaminants. Recent advances in miniaturization, chemometrics, and deep learning have further enhanced their potential for inline and point-of-need applications across diverse food matrices, including meat, seafood, eggs, fruits, and vegetables. This review critically evaluates the operational principles, instrumentation, and current applications of NIR spectroscopy and biosensors in food freshness and safety monitoring. It also explores their integration, highlights practical challenges such as calibration transfer and regulatory hurdles, and outlines emerging innovations including hybrid sensing, Artificial Intelligence (AI) integration, and smart packaging. The scope of this review is to provide a comprehensive understanding of these technologies, and its objective is to inform future research and industrial deployment strategies that support sustainable, real-time food quality control. These techniques enable near real-time monitoring under laboratory and pilot-scale conditions, showing strong potential for industrial adaptation. The nature of these targets often determines the choice of transduction method. Scope (computer science) ; Food safety ; Software deployment ; Emerging technologies ; Quality (philosophy) ; Food industry Spectroscopy and Chemometric Analyses ; Melamine detection and toxicity ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: An Wang | Gabriele C. Sosso
Source: Physical review. E (article, 2025-06-04)
Field: Materials Science — Materials Chemistry

Graph-based descriptors for condensed matter Computational scientists have long been developing a diverse portfolio of methodologies to characterize condensed matter systems. Most of the descriptors resulting from these efforts are ultimately based on the spatial configurations of particles, atoms, or molecules within these systems. Noteworthy examples include symmetry functions and the smooth overlap of atomic positions (SOAP) descriptors, which have significantly advanced the performance of predictive machine learning models for both condensed matter and small molecules. However, while graph-based descriptors are frequently employed in machine learning models to predict the functional properties of small molecules, their application in the context of condensed matter has been limited. In this paper, we put forward a number of graph-based descriptors (such as node centrality and local clustering coefficients) traditionally utilized in network science as alternative representations for condensed matter systems. We apply this graph-based formalism to investigate the dynamical properties and phase transitions of the prototypical Lennard-Jones system. We find that our graph-based formalism outperforms symmetry function descriptors in predicting the dynamical properties and phase transitions of this system. These results demonstrate the broad applicability of graph-based features in representing condensed matter systems, paving the way for exciting advancements in the field of condensed matter through the integration of network science concepts. Formalism (music) ; Centrality ; Computer science ; Cluster analysis ; Graph ; Theoretical computer science ; Phase transition ; Statistical physics ; Artificial intelligence ; Physics ; Mathematics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Complex Network Analysis Techniques
Authors: Daniel T. Speckhard | Christian Carbogno | Luca M. Ghiringhelli | S. Lübeck | Matthias Scheffler | Claudia Draxl
Source: Physical Review Materials (article, 2025-01-13)
Field: Materials Science — Materials Chemistry

Extrapolation to the complete basis-set limit in density-functional theory using statistical learning The numerical precision of density-functional-theory (DFT) calculations depends on a variety of computational parameters, one of the most critical being the basis-set size. The ultimate precision is reached in the limit of a complete basis set (CBS). Our aim in this work is to find a machine-learning model that extrapolates finite basis-size calculations to the CBS limit for periodic crystal structures. We start with a data set of 63 binary solids investigated with two all-electron DFT codes, and FHI-aims, which employ very different types of basis sets. A quantile-random-forest model and a symbolic regression approach using the SISSO model are used to estimate the total-energy correction with respect to a fully converged calculation as a function of the basis-set size. The random-forest model achieves a symmetric mean absolute percentage error of lower than 25% for both codes and outperforms previous approaches in the literature. SISSO outperforms the random forest model for the code. Our approach also provides prediction intervals, which quantify the uncertainty of the models' predictions. Extrapolation ; Basis (linear algebra) ; Limit (mathematics) ; Statistical physics ; Mathematics ; Basis set ; Applied mathematics ; Set (abstract data type) ; Algorithm ; Computer science ; Density functional theory ; Statistics ; Mathematical analysis ; Physics ; Quantum mechanics ; Geometry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Surface Chemistry and Catalysis
Authors: Mengchen Zhou | Xuyang Liu | XueGuang SHAO | Christophe Chipot | Wensheng Cai | Haohao Fu
Source: Accounts of Chemical Research (article, 2025-12-22)
Field: Materials Science — Materials Chemistry

One for All, All for One: A Unified Framework for Free-Energy Calculations ConspectusEnhanced-sampling techniques employed in free-energy calculations overcome the limitations of brute-force molecular dynamics (MD) and are widely used to interrogate complex biological and chemical systems at atomic resolution. Depending on the nature of the problem at hand, different strategies are utilized to estimate the underlying free-energy change. In geometrical transformations, sampling is accelerated along a defined set of collective variables (CVs) to reconstruct the associated free-energy landscape. Conversely, in alchemical transformations, the free-energy difference between the two end states is determined by tracing a nonphysical pathway. Generalized-ensemble techniques accelerate sampling through rapid exchanges between low and high temperatures, and the resulting trajectories are then reweighted to recover the free energy. This methodological diversity─paired with distinct schools of thought promoting incompatible or competing procedures─can often breed confusion and jeopardize the reproducibility of results. To alleviate this problem, we have recently expanded the theoretical foundation of the adaptive biasing force (ABF) framework─originally classified as an importance-sampling method─and have extended its application to geometrical, alchemical, and generalized-ensemble free-energy calculations. In this Account, we review these developments and introduce a unified strategy: Well-tempered metadynamics-<i>x</i>ABF (WTM-<i>x</i>ABF). WTM-<i>x</i>ABF accommodates geometrical, alchemical, generalized-ensemble, and hybrid schemes with minimal parameter tuning, making it a robust and accessible platform for a wide range of applications. Its geometrical and alchemical variants are demonstrably more efficient than, or at least competitive with, leading state-of-the-art algorithms. To illustrate its versatility, we demonstrate the use of WTM-<i>x</i>ABF in (1) disentangling coupled motions in complex biochemical systems by combining human-designed and machine-learning CVs, (2) performing extensive protein-ligand binding free-energy calculations for substrates of greater size and flexibility than traditional drug-like molecules, and (3) conducting fully blind folding simulations of fast-folding proteins. With its sound theoretical foundation, computational efficiency, and broad applicability, WTM-<i>x</i>ABF is poised to become a powerful method for MD across physical chemistry, biophysics, and drug discovery. Set (abstract data type) ; Range (aeronautics) ; Sampling (signal processing) ; Confusion ; Tracing ; Complex system Machine Learning in Materials Science ; Protein Structure and Dynamics ; Crystallography and molecular interactions