jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Bright O. Okonkwo | Zimin Li | Li Li | Jianqiu Wang | En‐Hou Han
Source: Corrosion Reviews (article, 2024-12-24)
Field: Materials Science — Materials Chemistry

Research progress on zirconium alloys: applications, development trend, and degradation mechanism in nuclear environment Abstract In this paper, the research progress of zirconium (Zr) alloys is critically reviewed from the aspects of application, development status, and degradation mechanism in a nuclear environment. The review focused on the application of Zr alloys in the nuclear industry, which are widely used due to their low thermal neutron absorption, good corrosion resistance, and excellent mechanical properties. However, with the increasing requirements in the chemical and medical fields, the application of Zr alloys in these non-nuclear fields is growing due to their excellent properties like good corrosion resistance and low thermal expansion coefficient, as summarized in this review. Additionally, the degradation mechanisms of Zr alloy exposed to a corrosive environment, i.e., corrosion and hydrogen uptake, and the role of alloying selection in minimizing these two phenomena is considered in this review, based on pretransition kinetics and the loss of oxide protectiveness at transition. This is corroborated by the discussion on alloying elements with beneficial and detrimental effects on the corrosion performance of Zr alloys, as well as elements with contradicting effects on Zr alloys corrosion performance owing to the discrepancies in literature. Overall, this review can be leveraged in future alloy design to further improve Zr alloys corrosion resistance in nuclear applications, thus ultimately improving their integrity. Materials science ; Zirconium ; Mechanism (biology) ; Degradation (telecommunications) ; Materials processing ; Zirconium alloy ; Metallurgy ; Process engineering ; Computer science ; Engineering Nuclear Materials and Properties ; Nuclear reactor physics and engineering ; Fusion materials and technologies
Authors: Daniel J. Laky | Nicole Cortés | John Eslick | Alexander A. Noring | Naresh Susarla | Chinedu O. Okoli | Miguel Zamarripa | Douglas A. Allan | John Brewer | Arun Iyengar | Maojian Wang | Anthony P. Burgard | David C. Miller | Alexander W. Dowling
Source: Energy & Environmental Science (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Market optimization and technoeconomic analysis of hydrogen-electricity coproduction systems We present an optimization framework to analyze emerging power systems technologies that coproduce power and alternative fuels. Emerging solid oxide-based systems have the potential to enable a reliable, efficient transition to cleaner energy. Coproduction ; Electricity ; Electric power system ; Environmental economics ; Process engineering ; Electricity system ; Hydrogen technologies ; Energy system ; System optimization ; Hydrogen economy ; Business ; Computer science ; Biochemical engineering ; Fuel cells ; Electricity generation ; Hydrogen fuel ; Engineering ; Renewable energy ; Power (physics) ; Economics ; Chemical engineering ; Mathematical optimization ; Electrical engineering Catalytic Processes in Materials Science ; Advancements in Solid Oxide Fuel Cells ; Catalysis and Oxidation Reactions
Authors: Cecilia Herrero | Ludovic Berthier
Source: Physical Review Letters (article, 2024-06-21)
Field: Materials Science — Materials Chemistry

Direct Numerical Analysis of Dynamic Facilitation in Glass-Forming Liquids We propose a computational strategy to quantify the temperature evolution of the timescales and length scales over which dynamic facilitation affects the relaxation dynamics of glass-forming liquids at low temperatures, which requires no assumption about the nature of the dynamics. In two glass models, we find that dynamic facilitation depends strongly on temperature, leading to a subdiffusive spreading of relaxation events which we characterize using a temperature-dependent dynamic exponent. We also establish that this temperature evolution represents a major contribution to the increase of the structural relaxation time. Facilitation ; Relaxation (psychology) ; Exponent ; Dynamic relaxation ; Dynamics (music) ; Materials science ; Statistical physics ; Molecular dynamics ; Glass transition ; Chemical physics ; Physics ; Polymer Material Dynamics and Properties ; Glass properties and applications ; Theoretical and Computational Physics
Authors: Öznur Özaltın
Source: Research in Agricultural Sciences (article, 2024-01-26)
Field: Chemistry — Analytical Chemistry

Date Fruit Classification by Using Image Features Based on Machine Learning Algorithms The date fruit, scientifically known as Phoenix dactylifera, is a significant dietary component due to its high nutritional value and abundance of essential vitamins and minerals. The process of discerning the classification of this fruit, which exhibits a multitude of variations within its natural domain, needs a specialized skill set. The automated recognition of species based on images of agricultural goods has gained significant prevalence in recent times. In this objective, the present study employed machine learning algorithms to automatically identify seven types of date fruit. In the investigation, decision tree, K-nearest neighbor, artificial neural networks, and support vector machine through their different hyperparameters are employed for the purpose of classifying date fruit. The dataset was divided into ratios of 80% and 20% for training and testing, respectively, and the training process employed the five-fold cross-validation technique to avoid overfitting. In summary, the results indicate that the best algorithm is neural network with a layer size of 25. In this study, this proposed algorithm achieved a test accuracy rate of 93.85%. Given the absence of computational complexity in the investigation, it can be effortlessly incorporated into diverse tools, thereby facilitating the identification of the types of date fruit. Computer science ; Artificial intelligence ; Machine learning ; Image (mathematics) ; Pattern recognition (psychology) ; Algorithm Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI
Authors: Siwatt Pongpiachan | Kanjana Thumanu | Waraporn Tanthanuch | Duangjai Srisamut | Jureerat Pradabsri | Muhammad Zaffar Hashmi | Sun Yan | S. Poshyachinda
Source: Heliyon (article, 2024-10-01)
Field: Chemistry — Inorganic Chemistry

Using synchrotron based ATR-FTIR, EXAFS, and XRF to characterize the chemical compositions of TSP in industrial estate area In Thailand, the Map Ta Phut Industrial Estate (MTPIE), a prominent industrial hub, has substantial environmental and health issues caused by industrial pollution. This study uses advanced synchrotron-based techniques, such as Attenuated Total Reflectance Fourier Transform Infrared (ATR-FTIR), Extended X-ray Absorption Fine Structure (EXAFS), and X-ray Fluorescence (XRF), to fully examine the chemical make-up of total suspended particulate (TSP) in the given area. Notable findings include the detection of remarkably high enrichment factors for magnesium and sulfur, indicating the presence of industrial operations. Additionally, we found that magnetite, which accounts for an average of 40 % of the total iron oxides in the samples, is the main iron oxide. The study also highlights about how calcium carbonate and different organic functional groups are found in large amounts, which shows that industrial emissions and natural sources are connected in a complex way. The findings underscore the susceptibility of children to TSP exposure, revealing increased rates of inhalation and significant health hazards. In order to safeguard public health in industrial areas such as MTPIE, it is imperative to implement more sophisticated pollution control techniques and maintain ongoing environmental monitoring. Synchrotron ; Fourier transform infrared spectroscopy ; Synchrotron radiation ; Analytical Chemistry (journal) ; Extended X-ray absorption fine structure ; Industrial chemistry ; Materials science ; Chemistry ; Mineralogy ; Environmental chemistry ; Chemical engineering ; Physics ; Optics ; Engineering ; Absorption spectroscopy ; Biochemical engineering Radioactive element chemistry and processing ; Analytical chemistry methods development ; Adsorption and biosorption for pollutant removal
Authors: Jing Liu | Ibrahim Al Keyyam | Yangsu Xie | Xinwei Wang
Source: Surface Science and Technology (article, 2024-02-08)
Field: Materials Science — Materials Chemistry

Perspectives on interfacial thermal resistance of 2D materials: Raman characterization and underlying physics Abstract Interfacial thermal resistance plays a crucial role in efficient heat dissipation in modern electronic devices. It is critical to understand the interfacial thermal transport from both experiments and underlying physics. This review is focused on the transient opto-thermal Raman-based techniques for measuring the interfacial thermal resistance between 2D materials and substrate. This transient idea eliminates the use of laser absorption and absolute temperature rise data, therefore provides some of the highest level measurement accuracy and physics understanding. Physical concepts and perspectives are given for the time-domain differential Raman (TD-Raman), frequency-resolved Raman (FR-Raman), energy transport state-resolved Raman (ET-Raman), frequency domain ET-Raman (FET-Raman), as well as laser flash Raman and dual-wavelength laser flash Raman techniques. The thermal nonequilibrium between optical and acoustic phonons, as well as hot carrier diffusion must be considered for extremely small domain characterization of interfacial thermal resistance. To have a better understanding of phonon transport across material interfaces, we introduce a new concept termed effective interface energy transmission velocity. It is very striking that many reported interfaces have an almost constant energy transmission velocity over a wide temperature range. This physics consideration is inspired by the thermal reffusivity theory, which is effective for analyzing structure-phonon scattering. We expect the effective interface energy transmission velocity to give an intrinsic picture of the transmission of energy carriers, unaltered by the influence of their capacity to carry heat. Raman spectroscopy ; Phonon ; Interfacial thermal resistance ; Raman scattering ; Materials science ; Thermal resistance ; Laser ; Characterization (materials science) ; Laser flash analysis ; Thermal ; Optics ; Thermal conduction ; Optoelectronics ; Nanotechnology ; Condensed matter physics ; Physics ; Thermodynamics Thermal properties of materials ; Thermography and Photoacoustic Techniques ; Thermal Radiation and Cooling Technologies
Authors: Bernd Bayerlein | Markus Schilling | Philipp von Hartrott | Jörg Waitelonis
Source: Scientific Data (article, 2024-04-30)
Field: Materials Science — Materials Chemistry

Semantic integration of diverse data in materials science: Assessing Orowan strengthening This study applies Semantic Web technologies to advance Materials Science and Engineering (MSE) through the integration of diverse datasets. Focusing on a 2000 series age-hardenable aluminum alloy, we correlate mechanical and microstructural properties derived from tensile tests and dark-field transmission electron microscopy across varied aging times. An expandable knowledge graph, constructed using the Tensile Test and Precipitate Geometry Ontologies aligned with the PMD Core Ontology, facilitates this integration. This approach adheres to FAIR principles and enables sophisticated analysis via SPARQL queries, revealing correlations consistent with the Orowan mechanism. The study highlights the potential of semantic data integration in MSE, offering a new approach for data-centric research and enhanced analytical capabilities. SPARQL ; Semantic Web ; Ontology ; Semantic integration ; Computer science ; Materials science ; Information retrieval ; Tensile testing ; Ultimate tensile strength ; RDF ; Semantic Web Stack ; Metallurgy Machine Learning in Materials Science ; Research Data Management Practices ; Scientific Computing and Data Management
Authors: Kıyas Kayaalp
Source: European Food Research and Technology (article, 2024-03-11)
Field: Chemistry — Analytical Chemistry

A deep ensemble learning method for cherry classification Abstract In many agricultural products, information technologies are utilized in classification processes at the desired quality. It is undesirable to mix different types of cherries, especially in export-type cherries. In this study on cherries, one of the important export products of Turkey, the classification of cherry species was carried out with ensemble learning methods. In this study, a new dataset consisting of 3570 images of seven different cherry species grown in Isparta region was created. The generated new dataset was trained with six different deep learning models with pre-learning on the original and incremental dataset. As a result of the training with incremental data, the best result was obtained from the DenseNet169 model with an accuracy of 99.57%. The two deep learning models with the best results were transferred to ensemble learning and a 100% accuracy rate was obtained with the Maximum Voting model. Ensemble learning ; Artificial intelligence ; Deep learning ; Machine learning ; Computer science ; Quality (philosophy) Spectroscopy and Chemometric Analyses ; Identification and Quantification in Food ; Smart Agriculture and AI
Authors: Pedro Castro‐Valdecantos | Gregorio Egea | C. Borrero | Manuel Pérez Ruiz | M. Avilés
Source: Precision Agriculture (article, 2024-07-24)
Field: Chemistry — Analytical Chemistry

Detection of fusarium wilt-induced physiological impairment in strawberry plants using hyperspectral imaging and machine learning Abstract Strawberry ( Fragraria x ananassa ) is a crop affected by various soil-borne fungal pathogens with mostly non-specific foliar symptoms and often requiring laboratory isolation for correct diagnosis. Moreover, these nonspecific foliar symptoms, appreciated by the human eye, appear after some time following infection by the pathogen. Early detection of plant diseases is one of the primary objectives in agriculture because it may contribute to identifying more tolerant cultivars in breeding programs and optimise pesticide use in agricultural production with earlier applications in emerging disease foci. New technologies, such as remote sensing and machine learning (ML) algorithms, have arisen as potential tools to improve the ability to detect and classify different crop diseases. The combined use of hyperspectral imagery and ML algorithms were investigated to detect and classify the physiological stress caused by early infections of Fusarium wilt in strawberry plants. Six ML models, namely artificial neural network, decision tree, K-nearest neighbour, support vector machine, multinomial logistic regression and Naïve Bayes were developed to estimate physiological stress associated with Fusarium wilt disease. The results showed that stomatal conductance (g s ) and photosynthesis ( A ) declined even without visual symptoms of the disease. Among the six ML models evaluated, the artificial neural network model showed the highest classification performance with an overall accuracy of 81%, regardless of the physiological parameter utilized for model training. Moreover, the artificial neural network accurately predicted the absolute values of both physiological parameters (g s and A) based on the complete spectral signature from visually healthy foliar tissue, achieving coefficients of determination of 84% and 81%, respectively. Consequently, ML models utilizing physiological response data and hyperspectral imaging exhibited remarkable robustness, facilitating the estimation of Fusarium wilt severity in strawberry plants even without visual symptoms. Hyperspectral imaging ; Fusarium ; Fusarium wilt ; Biology ; Botany ; Artificial intelligence ; Computer science ; Fusarium oxysporum Spectroscopy and Chemometric Analyses ; Leaf Properties and Growth Measurement ; Smart Agriculture and AI
Authors: Georgios Tziotzios | Xanthoula Eirini Pantazi | Charalambos Paraskevas | Christos Tsitsopoulos | Dimitrios Valasiadis | Elpida Nasiopoulou | Michail Michailidis | Athanassios Molassiotis
Source: Horticulturae (article, 2024-03-06)
Field: Chemistry — Analytical Chemistry

Non-Destructive Quality Estimation Using a Machine Learning-Based Spectroscopic Approach in Kiwifruits The current study investigates the use of a non-destructive hyperspectral imaging approach for the evaluation of kiwifruit cv. “Hayward” internal quality, focusing on physiological traits such as soluble solid concentration (SSC), dry matter (DM), firmness, and tannins, widely used as quality attributes. Regression models, including partial least squares regression (PLSR), bagged trees (BTs), and three-layered neural network (TLNN), were employed for the estimation of the above-mentioned quality attributes. Experimental procedures involving the Specim IQ hyperspectral camera utilization and software were followed for data acquisition and analysis. The effectiveness of PLSR, bagged trees, and TLNN in predicting the firmness, SSC, DM, and tannins of kiwifruit was assessed via statistical metrics, including R squared (R²) values and the root mean square error (RMSE). The obtained results indicate varying degrees of efficiency for each model in predicting kiwifruit quality parameters. The study concludes that machine learning algorithms, especially neural networks, offer substantial accuracy, surpassing traditional methods for evaluating kiwifruit quality traits. Overall, the current study highlights the potential of such non-destructive techniques in revolutionizing quality assessment during postharvest by yielding rapid and reliable predictions regarding the critical quality attributes of fruits. Horticulture ; Quality (philosophy) ; Artificial intelligence ; Mathematics ; Machine learning ; Pattern recognition (psychology) ; Computer science ; Biology ; Physics Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Meat and Animal Product Quality
Authors: Jingwen Zhu | Guozhi Ji | Bingyu Chen | Bo Yan | Feiyue Ren | Ning Li | Xuchun Zhu | Shan He | Zhishen Mu | Hongzhi Liu
Source: Frontiers in Nutrition (article, 2024-12-09)
Field: Chemistry — Analytical Chemistry

High-throughput near-infrared spectroscopy for detection of major components and quality grading of peas Pea (<i>Pisum sativum</i> L.) is a nutrient-dense legume whose nutritional indicators influence its functional qualities. Traditional methods to identify these components and examine the relationships between their contents could be more laborious, hindering the quality assessment of the varieties of peas. This study conducted a statistical analysis of data about the sensory and physicochemical nutritional attributes of peas acquired using traditional techniques. Additionally, 90 sets of spectral data were obtained using a portable near-infrared spectrometer, which were then integrated with chemical values to create a near-infrared model for the basic ingredient content of peas. The correlation analysis revealed significant findings: pea starch displayed a substantial negative correlation with moisture, crude fiber, and crude protein, while showing a highly significant positive correlation with pea seed thickness. Furthermore, pea protein exhibited a significant positive correlation with crude fiber and crude fat. Cluster analysis classified all pea varieties into three distinct groups, successfully distinguishing those with elevated protein content, high starch content, and low-fat content. The combined contribution of PC1 and PC2 in the principal component analysis (PCA) was 51.2%. Partial least squares regression (PLSR) and other spectral preprocessing methods improved the predictive model, which performed well with an external dataset, with calibration coefficients of 0.89-0.99 and prediction coefficients of 0.71-0.88. This method enables growers and processors to efficiently analyze the composition of peas and evaluate crop quality, thereby enhancing food industry development. Throughput ; Grading (engineering) ; Spectroscopy ; Infrared spectroscopy ; Infrared ; Environmental science ; Computer science ; Materials science ; Analytical Chemistry (journal) ; Remote sensing ; Chemistry ; Optics ; Engineering ; Telecommunications ; Physics ; Environmental chemistry ; Geology Spectroscopy and Chemometric Analyses ; Meat and Animal Product Quality ; Food composition and properties
Authors: Eun Bin Kim | Yoo Sang Baek | Onseok Lee
Source: Skin Research and Technology (article, 2024-04-01)
Field: Chemistry — Analytical Chemistry

RETRACTED: Parameter‐based transfer learning for severity classification of atopic dermatitis using hyperspectral imaging The present study not only demonstrates the feasibility of the severity classification of AD based on hyperspectral images, but also showcases combinations and research scalability for domain exploration. Hyperspectral imaging ; Atopic dermatitis ; Transfer of learning ; Artificial intelligence ; Dermatology ; Pattern recognition (psychology) ; Medicine ; Computer science Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Infrared Thermography in Medicine
Authors: Brian H. Toby
Source: Journal of Applied Crystallography (article, 2024-01-26)
Field: Materials Science — Materials Chemistry

A simple solution to the Rietveld refinement recipe problem Rietveld refinements are widely used for many purposes in the physical sciences. Conducting a Rietveld refinement typically requires expert input because correct results may require that parameters be added to the fit in the proper order. This order will depend on the nature of the data and the initial parameter values. A mechanism for computing the next parameter to add to the refinement is shown. The fitting function is evaluated with the current parameter value set and each parameter incremented and decremented by a small offset. This provides the partial derivatives with respect to each parameter, along with information to discriminate meaningful values from numerical computational errors. The implementation of this mechanism in the open-source <i>GSAS-II</i> program is discussed. This new method is discussed as an important step towards the development of automated Rietveld refinement technology. Recipe ; Simple (philosophy) ; Rietveld refinement ; Mathematics ; Materials science ; Calculus (dental) ; Computer science ; Chemistry ; Crystallography ; Crystal structure ; Philosophy ; Epistemology ; Medicine X-ray Diffraction in Crystallography ; Nuclear Physics and Applications ; Machine Learning in Materials Science