jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Derick Malavi | Katleen Raes | Sam Van Haute
Source: Current Research in Food Science (article, 2024-01-01)
Field: Chemistry — Analytical Chemistry

Integrating near-infrared hyperspectral imaging with machine learning and feature selection: Detecting adulteration of extra-virgin olive oil with lower-grade olive oils and hazelnut oil Detecting adulteration in extra virgin olive oil (EVOO) is particularly challenging with oils of similar chemical composition. This study applies near-infrared hyperspectral imaging (NIR-HSI) and machine learning (ML) to detect EVOO adulteration with hazelnut, refined olive, and olive pomace oils at various concentrations (1%, 5%, 10%, 20%, 40%, and 100% m/m). Savitzky-Golay filtering, first and second derivatives, multiplicative scatter correction (MSC), standard normal variate (SNV), and their combinations were used to preprocess the spectral data, with Principal Component Analysis (PCA) reducing dimensionality. Classification was performed using Partial Least Squares-Discriminant Analysis (PLS-DA) and ML algorithms, including k-Nearest Neighbors (k-NN), Naïve Bayes, Random Forest (RF), Support Vector Machine (SVM), and Artificial Neural Networks (ANN). PLS-DA, k-NN, RF, SVM, NB, and ANN models achieved accuracy rates of 97.0-99.0%, 96.2-100%, 96.5-100%, 98.6-99.5%, 93.9-99.7%, and 99.2-100%, respectively, in discriminating between pure EVOO, adulterants, and adulterated oils. PLS-DA, RF, SVM, and ANN significantly outperformed Naïve Bayes (p < 0.05) in binary classification, with Matthews correlation coefficient (MCC) values exceeding 0.90. All the binary classifiers except Naïve Bayes, when coupled with SNV/MSC, Savitzky-Golay smoothing and derivatives, consistently achieved perfect scores (1.0) for accuracy, sensitivity, specificity, F1 score, precision, and MCC in distinguishing pure EVOO from adulterated oils. No significant differences (p > 0.05) in model performance were found between those using full spectra and those based on key variable selection. However, PLS-DA and ANN significantly outperformed k-NN, RF, and SVM (p < 0.05), with MCC values ranging from 0.95 to 1.00, indicating superior classification performance. These findings demonstrate that combining NIR-HSI with machine learning, along with key variable selection, potentially offers an effective, non-destructive solution for detecting adulteration in EVOO and combating fraud in the olive oil industry. Hyperspectral imaging ; Olive oil ; Snack food ; Feature selection ; Selection (genetic algorithm) ; Artificial intelligence ; Food science ; Pattern recognition (psychology) ; Environmental science ; Mathematics ; Computer science ; Chemistry Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Edible Oils Quality and Analysis
Authors: R. Saravanan | Jothi Arunachalam Solairaju | T. Sathish | Jayant Giri | Muhammad Imam Ammarullah
Source: Engineering Reports (article, 2024-12-03)
Field: Materials Science — Polymers and Plastics

Influence of Silane‐Treated Jute/Kenaf Fibers on the Mechanical Properties of Polymer Composites for Biomedical Applications: Optimization Using <scp>RSM</scp> and <scp>ANN</scp> Approaches ABSTRACT This study investigates the effect of silane‐treated jute/kenaf fibers on the flexural and hardness properties of polymer composites for potential biomedical applications, such as prosthetics and bio‐implants. Using response surface methodology (RSM) and artificial neural network (ANN), the optimal combination of nanoparticle percentage, silane concentration, and silane dipping duration was identified to enhance the mechanical properties of the composites. RSM, coupled with analysis of variance (ANOVA), evaluated the influence of these variables on composite performance, revealing that silane treatment significantly improved flexural strength, while all fiber‐related variables impacted both flexural strength and hardness. The silane dipping duration emerged as the most influential factor, with nanoparticle addition enhancing fiber–matrix interactions and promoting better agglomeration. The ANN model accurately predicted the composite's properties, with results strongly correlating to experimental data. The optimized formulation, consisting of 5% nanoparticle content, 10% silane treatment, and a 20‐min silane dipping duration, demonstrated a 26.22% increase in flexural strength and a 33.15% improvement in hardness. This optimized composite formulation holds promise for use in biomedical applications requiring high mechanical strength and durability, such as in prosthetic materials and orthopedic implants, where enhanced flexural strength and hardness are critical for longevity and performance. Kenaf ; Silane ; Composite material ; Materials science ; Polymer ; Fiber Natural Fiber Reinforced Composites ; Mechanical Engineering and Vibrations Research ; Tribology and Wear Analysis
Authors: Ivan Žugec | Auguste Tetenoire | Alberto S. Muzas | Yaolong Zhang | Bin Jiang | M. Alducin | J. I. Juaristi
Source: JACS Au (article, 2024-05-10)
Field: Materials Science — Materials Chemistry

Understanding the Photoinduced Desorption and Oxidation of CO on Ru(0001) Using a Neural Network Potential Energy Surface The study of ultrafast photoinduced dynamics of adsorbates on metal surfaces requires thorough investigation of laser-excited electrons and, in many cases, the highly excited surface lattice. While ab initio molecular dynamics with electronic friction and thermostats (<i>T</i><sub>e</sub>, <i>T</i><sub>l</sub>)-AIMDEF addresses such complex modeling, it imposes severe computational costs, hindering quantitative comparison with experimental desorption probabilities. In order to bypass this limitation, we utilize the embedded atom neural network method to construct a potential energy surface (PES) for the coadsorption of CO and O on Ru(0001). Our results demonstrate that this PES not only reproduces the short-time ab initio dynamics but is also able to yield statistically significant data for long lasting trajectories that correlate well with experimental findings. Furthermore, the analysis of the laser-induced dynamics reveals the existence of a dynamic trapping state that acts as a precursor for CO desorption, and it is not observed under thermal conditions. Altogether, our results validate the underlying theoretical framework, providing robust support for the description of not only the photoinduced desorption but also the oxidation of CO in terms of nonequilibrated but thermal hot electrons and phonons. Desorption ; Excited state ; Phonon ; Ab initio ; Chemical physics ; Potential energy surface ; Thermal desorption ; Materials science ; Electron ; Thermal desorption spectroscopy ; Ab initio quantum chemistry methods ; Molecular dynamics ; Laser ; Atomic physics ; Chemistry ; Physical chemistry ; Molecule ; Computational chemistry ; Physics ; Optics ; Condensed matter physics ; Adsorption Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; nanoparticles nucleation surface interactions
Authors: Cong Wei | Quan Zhou | Kai Deng | Yongquan Lin | Linge Wang | Yue Luo | Yuling Zhang | Heng Zhou
Source: Journal of Building Engineering (article, 2024-02-18)
Field: Materials Science — Biomaterials

Alkali resistance prediction and degradation mechanism of basalt fiber: Integrated with artificial neural network machine learning model Artificial neural network ; Mechanism (biology) ; Degradation (telecommunications) ; Fiber ; Basalt fiber ; Alkali metal ; Artificial intelligence ; Computer science ; Materials science ; Composite material ; Chemistry ; Physics ; Telecommunications Advanced Cellulose Research Studies ; Fiber-reinforced polymer composites ; Innovative concrete reinforcement materials
Authors: Antti Pihlajamäki | María Francisca Matus | Sami Malola | Hannu Häkkinen
Source: Advanced Materials (article, 2024-09-24)
Field: Materials Science — Materials Chemistry

GraphBNC: Machine Learning‐Aided Prediction of Interactions Between Metal Nanoclusters and Blood Proteins Hybrid nanostructures between biomolecules and inorganic nanomaterials constitute a largely unexplored field of research, with the potential for novel applications in bioimaging, biosensing, and nanomedicine. Developing such applications relies critically on understanding the dynamical properties of the nano-bio interface. This work introduces and validates a strategy to predict atom-scale interactions between water-soluble gold nanoclusters (AuNCs) and a set of blood proteins (albumin, apolipoprotein, immunoglobulin, and fibrinogen). Graph theory and neural networks are utilized to predict the strengths of interactions in AuNC-protein complexes on a coarse-grained level, which are then optimized in Monte Carlo-based structure search and refined to atomic-scale structures. The training data is based on extensive molecular dynamics (MD) simulations of AuNC-protein complexes, and the validating MD simulations show the robustness of the predictions. This strategy can be generalized to any complexes of inorganic nanostructures and biomolecules provided that one generates enough data about the interactions, and the bioactive parts of the nanostructure can be coarse-grained rationally. Nanoclusters ; Biomolecule ; Nanomedicine ; Materials science ; Nanostructure ; Nanotechnology ; Nanomaterials ; Biosensor ; Robustness (evolution) ; Molecular dynamics ; Nanoparticle ; Chemistry ; Computational chemistry Nanocluster Synthesis and Applications ; Protein Interaction Studies and Fluorescence Analysis ; Gold and Silver Nanoparticles Synthesis and Applications
Authors: Guotai Li | Jialin Tang | Jiongzhi Zheng | Qi Wang | Zheng Cui | Ke Xu | Jianbin Xu | Te‐Huan Liu | Guimei Zhu | Ruiqiang Guo | Baowen Li
Source: Physical review. B./Physical review. B (article, 2024-01-17)
Field: Materials Science — Materials Chemistry

Convergent thermal conductivity in strained monolayer graphene The strain dependence of thermal conductivity (\ensuremath{\kappa}) in monolayer graphene, with reports of enhancement, suppression, or even divergence, has been highly controversial. To address this open question, we have systematically investigated the effects of tensile strain on the \ensuremath{\kappa} of graphene using the exact solution of the Peierls-Boltzmann transport equation based on the first-principles interatomic force constants combined with machine learning assisted molecular dynamics simulations. In contrast to previous studies, we find that the \ensuremath{\kappa} in the strained graphene is convergent after considering four-phonon scattering, which is dominant for the long-wavelength flexural phonons because of its much weaker frequency dependence (${\ensuremath{\tau}}_{4}^{\ensuremath{-}1}\ensuremath{\propto}{\ensuremath{\omega}}^{\ensuremath{\beta}}$ with \ensuremath{\beta} 2) compared to the three-phonon scattering case (${\ensuremath{\tau}}_{3}^{\ensuremath{-}1}\ensuremath{\propto}{\ensuremath{\omega}}^{\ensuremath{\beta}}$ with \ensuremath{\beta} &gt; 2). Furthermore, \ensuremath{\kappa} exhibits nonmonotonic variations with increasing strain up to 8% due to the competition between phonon lifetime, group velocity, and heat capacity of acoustic phonons. Our results deepen the fundamental understanding of thermal transport in strained graphene and offer insights for tuning the thermal properties of two-dimensional materials through strain engineering. Phonon ; Condensed matter physics ; Graphene ; Thermal conductivity ; Boltzmann equation ; Omega ; Physics ; Monolayer ; Scattering ; Materials science ; Quantum mechanics ; Nanotechnology Thermal properties of materials ; Graphene research and applications ; Advanced Thermoelectric Materials and Devices
Authors: Tianyong Zhang | Suisui Wang | Yamei Chai | Jianing Yu | Wenxuan Zhu | Liang Li | Bin Li
Source: The Journal of Physical Chemistry B (article, 2024-07-09)
Field: Materials Science — Polymers and Plastics

Prediction and Interpretability Study of the Glass Transition Temperature of Polyimide Based on Machine Learning with Quantitative Structure–Property Relationship (<i>T</i><sub>g</sub>-QSPR) The glass transition temperature (<i>T</i><sub>g</sub>) is a crucial characteristic of polyimides (PIs). Developing a <i>T</i><sub>g</sub> predictive model using machine learning methodologies can facilitate the design of PI structures and expedite the development process. In this investigation, a data set comprising 1257 PIs was assembled, with <i>T</i><sub>g</sub> values determined using differential scanning calorimetry. 210 molecular descriptors were computed using RDKit, and subsequently, six distinct feature selection methodologies were employed to refine the descriptor set. Quantitative structure-property relationship models targeting <i>T</i><sub>g</sub> (<i>T</i><sub>g</sub>-QSPR) were then constructed using five ensemble learning algorithms and one deep learning algorithm. These models exhibited high predictive accuracy and robustness, with the CATBoost model demonstrating the highest accuracy, achieving a coefficient of determination of 0.823 for the test set, a mean absolute error of 20.1 °C, and a root-mean-square error of 29.0 °C. The study identified the NumRotatableBonds descriptor as particularly influential on <i>T</i><sub>g</sub>, showing a negative correlation with the property. Additionally, the model's accuracy was validated using ten new PI films not included in the original data set, resulting in absolute errors ranging from 2.5 to 26.9 °C and absolute percentage error rates of 1.0-12.8%. These findings underscore the importance of utilizing extensive and diverse data sets for predictive modeling to enhance accuracy and stability. Furthermore, exploring the interpretability of the model and experimentally validating newly synthesized PIs have augmented the practical utility of the model. Under the guidance of the <i>T</i><sub>g</sub>-QSPR models, it will be possible to accelerate the performance prediction and structural design of PIs in the future. Interpretability ; Quantitative structure–activity relationship ; Polyimide ; Glass transition ; Property (philosophy) ; Materials science ; Artificial intelligence ; Thermodynamics ; Machine learning ; Computational chemistry ; Computer science ; Chemistry ; Nanotechnology ; Physics ; Philosophy ; Polymer ; Composite material ; Epistemology Synthesis and properties of polymers ; Epoxy Resin Curing Processes ; Machine Learning in Materials Science
Authors: Qiang Zhang | Xin Li | Long Yu | Lingxiao Wang | Zhiqing Wen | Pengchen Su | Zhenli Sun | Suhua Wang
Source: Journal of Environmental Sciences (article, 2024-01-30)
Field: Materials Science — Materials Chemistry

Machine learning-assisted fluorescence visualization for sequential quantitative detection of aluminum and fluoride ions Fluoride ; Detection limit ; Fluorescence ; Ion ; Computer science ; Principal component analysis ; X-ray fluorescence ; Analytical Chemistry (journal) ; Artificial intelligence ; Chemistry ; Inorganic chemistry ; Optics ; Environmental chemistry ; Chromatography Carbon and Quantum Dots Applications ; Advanced biosensing and bioanalysis techniques ; Biosensors and Analytical Detection
Authors: A. T. Herath | Rhowell N. Tiozon | Tobias Kretzschmar | Nese Sreenivasulu | Peter J. Mahon | Vito M. Butardo
Source: Food Chemistry (article, 2024-08-02)
Field: Chemistry — Analytical Chemistry

Machine learning approach for high-throughput phenolic antioxidant screening in black Rice germplasm collection based on surface FTIR Pigmented rice contains beneficial phenolic antioxidants but analysing them across germplasm collections is laborious and time-consuming. Here we utilised rapid surface Fourier transform infrared (FTIR) spectroscopy and machine learning algorithms (ML) to predict and classify polyphenolic antioxidants. Total phenolics, flavonoids, anthocyanins, and proanthocyanidins were quantified biochemically from 270 diverse global coloured rice collection and attenuated total reflectance (ATR) FTIR spectra were obtained by scanning whole grain surfaces at 800-4000 cm<sup>-1</sup>. Five ML classification models were optimised using the biochemical and spectral data which performed predictions with 93.5%-100% accuracy. Random Forest and Support Vector Machine models identified key FTIR peaks linked to flavonols, flavones and anthocyanins as important model predictors. This research successfully established direct and non-destructive surface chemistry spectroscopy of the aleurone layer of pigmented rice integrated with ML models as a viable high-throughput platform to accelerate the analysis and profiling of nutritionally valuable coloured rice varieties. Germplasm ; Fourier transform infrared spectroscopy ; Polyphenol ; Attenuated total reflection ; Antioxidant ; Chemistry ; Proanthocyanidin ; Food science ; Infrared spectroscopy ; Botany ; Biochemistry ; Biology ; Organic chemistry ; Chemical engineering Spectroscopy and Chemometric Analyses ; Metabolomics and Mass Spectrometry Studies ; GABA and Rice Research
Authors: Dapeng Li | Aijie Liang | Mingwei Zhou | Xianyue Li | Ning Hou
Source: Waste Management (article, 2024-01-13)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Energy utilization of agricultural waste: Machine learning prediction and pyrolysis transformation Biochar ; Pseudocapacitance ; Capacitance ; Straw ; Supercapacitor ; Materials science ; Capacitor ; Porosity ; Process engineering ; Power density ; Biomass (ecology) ; Energy storage ; Pulp and paper industry ; Electrode ; Waste management ; Pyrolysis ; Environmental science ; Power (physics) ; Composite material ; Engineering ; Electrical engineering ; Chemistry ; Agronomy ; Inorganic chemistry ; Thermodynamics ; Voltage Supercapacitor Materials and Fabrication ; Conducting polymers and applications ; Advancements in Battery Materials
Authors: Yali Mao | Shaojie Zhou | Weiyun Tang | Mei Wu | Haochen Zhang | Haiding Sun | Chao Yuan
Source: Journal of Applied Physics (article, 2024-03-01)
Field: Materials Science — Materials Chemistry

Deep learning-based data processing method for transient thermoreflectance measurements Pump–probe thermoreflectance has been commonly applied for characterizing the thermal properties of materials. Generally, a reliable and efficient non-linear fitting process is often implemented to extract unknown thermal parameters during the pump–probe thermoreflectance characterizations. However, when it comes to processing large amounts of data acquired from similar structural samples, non-linear fitting process appears to be very time-consuming and labor-intensive to search for the best fitting for every testing curve. Herein, we propose to apply deep learning (DL) approach to nanosecond transient thermoreflectance technique for high-throughput experimental data processing. We first investigated the effect of training set parameters (density and bounds) on the predictive performance of the DL model, providing a guidance to optimize the DL model. Then, the DL model is further verified in the measurement of the bulk sapphire, SiC, diamond samples, and GaN-based multilayer structures, demonstrating its capability of analyzing the results with high accuracy. Compared to the conventional non-linear fitting method (such as Global Optimization), the computation time of the new model is 1000 times lower. Such a data-driven DL model enables the faster inference and stronger fitting capabilities and is particularly efficient and effective in processing data acquired from wafer-level measurements with similar material structures. Computer science ; Wafer ; Curve fitting ; Algorithm ; Computation ; Process (computing) ; Transient (computer programming) ; Data set ; Materials science ; Experimental data ; Artificial intelligence ; Machine learning ; Optoelectronics ; Mathematics Thermal properties of materials ; Heat Transfer and Optimization ; Heat Transfer and Boiling Studies
Authors: Shuailin You | Ying Fan | Yeyun Chen | Xiran Jiang | Wanting Liu | Xin-yue Zhou | Jin Zhang | Jing Zheng | Huazhe Yang | Xu Hou
Source: Cell Reports Physical Science (article, 2024-07-25)
Field: Materials Science — Materials Chemistry

Advancements and prospects of deep learning in biomaterials evolution In recent decades, significant strides have been made in advancing biomaterials for biomedical applications. Ideal biomaterials necessitate suitable mechanical properties, excellent biocompatibility, and specific bioactivities. However, the design and preparation of materials with these essential characteristics pose formidable challenges, persisting as significant issues in the field. The development and optimization of high-performance biomaterials, along with the construction of composites and hybrids with diverse biofunctions, present promising strategies for enhancing therapeutic and diagnostic procedures. However, reliance on traditional "trial and error" methods for acquiring a substantial volume of experimental data proves to be laborious, time consuming, and unreliable. An emerging and promising approach involves the successful application of artificial intelligence (AI), specifically deep learning (DL), to investigate and optimize the preparation and manufacturing techniques for various biomaterials. DL, as an automated and intelligent tool within the AI domain, finds widespread application in devising, analyzing, and optimizing different biomaterials. Through the "experiment-AI" technique, DL predicts the potential feature information and performance of biomaterials, showcasing remarkable potential in biomaterial research and development. This review comprehensively explores the application of DL-based technologies in the biomedical field, emphasizing cutting-edge advantages and providing insights and recommendations to enhance the efficacy of such approaches in biomaterials. Computer science ; Nanotechnology ; Biochemical engineering ; Artificial intelligence ; Data science ; Engineering ; Materials science Machine Learning in Materials Science ; Advanced Neural Network Applications ; Additive Manufacturing and 3D Printing Technologies
Authors: Xiao Zong | Shuangshuang Wu | Kunji Lin | Jintao Zhang | Yehua Li | Donglin Lu | Xin Deng | Sheng‐Guo Lu | Jihua Qiu | Yuanzhi Shao | Shanghua Wu
Source: Ceramics International (article, 2024-04-13)
Field: Materials Science — Materials Chemistry

Advanced ceramics with integrated structures and functions: Machine learning prediction and experimental verification Materials science ; Ceramic ; Artificial intelligence ; Machine learning ; Computer science ; Composite material Electronic and Structural Properties of Oxides ; Machine Learning in Materials Science ; Advanced ceramic materials synthesis