jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Zheng Li | Ning Xu | Yujing Zhang | Wen Liu | Jiaqian Wang | Meiliang Ma | Xiaolan Fu | Xiaojuan Hu | Wenwu Xu | Zhongkang Han
Source: The Journal of Physical Chemistry Letters (article, 2024-05-28)
Field: Materials Science — Materials Chemistry

Unveiling the Structure of Oxygen Vacancies in Bulk Ceria and the Physical Mechanisms behind Their Formation Understanding the structures of oxygen vacancies in bulk ceria is crucial as they significantly impact the material's catalytic and electronic properties. The complex interaction between oxygen vacancies and Ce<sup>3+</sup> ions presents challenges in characterizing ceria's defect chemistry. We introduced a machine learning-assisted cluster-expansion model to predict the energetics of defective configurations accurately within bulk ceria. This model effectively samples configurational spaces, detailing oxygen vacancy structures across different temperatures and concentrations. At lower temperatures, vacancies tend to cluster, mediated by Ce<sup>3+</sup> ions and electrostatic repulsion, while at higher temperatures, they distribute uniformly due to configurational entropy. Our analysis also reveals a correlation between thermodynamic stability and the band gap between occupied O 2<i>p</i> and unoccupied Ce 4<i>f</i> orbitals, with wider band gaps indicating higher stability. This work enhances our understanding of defect chemistry in oxide materials and lays the groundwork for further research into how these structural properties affect ceria's performance. Chemical physics ; Oxygen ; Materials science ; Nanotechnology ; Crystallography ; Chemistry Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Advancements in Solid Oxide Fuel Cells
Authors: Niken Siwi Pamungkas | Zico Pratama Putra | Hendra Adhi Pratama | Muhammad Yusuf
Source: Journal of Hazardous Materials Advances (article, 2024-11-09)
Field: Chemistry — Inorganic Chemistry

Supervised machine learning-based categorization and prediction of uranium adsorption capacity on various process parameters • ML effectively predicts and classifies uranium adsorption capacity (Qe). • CNN achieved 98% accuracy; Random Forest excelled in Qe prediction. • Initial concentration most influences Qe per permutation importance analysis. • ML models capture complex input-Qe relations, guiding adsorption system design. • Future work: expand datasets, add adsorbents, and use real-time data for models. Existing uranium poses significant dangers to the environment and the general population's health. Within the scope of this study, machine learning techniques were utilized to assess the characteristics that influence uranium adsorption. The adsorbent material, pH, temperature, solid-liquid ratio (SLR), and initial concentration (C i ) values were imported as the feature to evaluate using ML models in this study. The finding concluded that the Convolutional Neural Network (CNN) model performed excellently by attaining an accuracy of 98%, which is very high in classifying low and high levels of adsorption predictions. The Random Forest model was also the best performer for Q e prediction, with the lowest MSE value of 334.45 and the highest Squared-R score of 0.92 for the uranium adsorption process. Permutation Importance analysis indicated that initial concentration had the strongest impact on Q e . Knowing that a good interaction between uranyl ions and adsorbent surface would result in more active sites for trapping radioactive metal, optimizing this parameter can be quite helpful for designing new materials capable of capturing uranium species strongly from solutions. ML model excels at predicting Q e in uranium adsorption and can directly accommodate large datasets and non-linear input parameter-Q e interactions. As noted, the innovation poised to drive ML research has been made possible by recent technological developments in machine learning and can improve while offering new ways of addressing difficult environmental problems. Categorization ; Process (computing) ; Adsorption ; Uranium ; Artificial intelligence ; Computer science ; Machine learning ; Natural language processing ; Process engineering ; Materials science ; Chemistry ; Engineering ; Metallurgy ; Programming language Radioactive element chemistry and processing ; Chemical Synthesis and Characterization ; Minerals Flotation and Separation Techniques
Authors: Fernando Arrais Romero Dias Lima | Marcellus G.F. de Moraes | Martha A. Grover | Amaro Gomes Barreto Junior | Argimiro R. Secchi | Maurício B. de Souza
Source: Industrial & Engineering Chemistry Research (article, 2024-10-29)
Field: Materials Science — Materials Chemistry

Neural Network Inverse Model Controllers for Paracetamol Unseeded Batch Cooling Crystallization Crystallization is a common process for purification and product design in the pharmaceutical industrial field. To develop an efficient crystallization process, the generated crystals must present a size distribution respecting the regulatory constraints on product quality. Therefore, a control system is needed to achieve this goal in a crystallization process. Neural network inverse model controllers (NNIMCs) are an efficient control strategy previously used for controlling some chemical processes. Moreover, they are able to calculate the control action faster than the classic model predictive controller (MPC). In this work, a nonlinear model predictive controller (NMPC) was initially applied to a paracetamol batch crystallization process in ethanol. The goal of the NMPC was to maintain the mass and crystal size in the targets by manipulating the temperature. Then, the NMPC was used to simulate controlled batches of the paracetamol crystallization, which were used to generate data for developing NNIMCs. Multilayer perceptron (MLP), regular recurrent neural network (RNN), gated recurrent unit (GRU), and long short-term memory (LSTM) networks were trained to predict the optimum temperature value that maintains the controlled variables in the targets. The controllers’ performance was investigated for different targets, model mismatches, and noisy data. The five controllers could efficiently maintain the mass and crystal size in the targets. The NNIMCs presented lower computational costs and imposed fewer temperature changes than the NMPC when accounting for noise. Furthermore, the NNIMC based on MLP performed best in dealing with model mismatches, being the most efficient controller studied. Therefore, NNIMCs showed a robust and efficient performance for controlling the crystallization process and have the potential to be used to control real crystallization processes. Crystallization ; Inverse ; Artificial neural network ; Computer science ; Process engineering ; Chemistry ; Biological system ; Control theory (sociology) ; Chemical engineering ; Control (management) ; Artificial intelligence ; Organic chemistry ; Mathematics ; Engineering Crystallization and Solubility Studies ; Advanced Control Systems Optimization ; Process Optimization and Integration
Authors: Khalissa Saada | Chouki Farsi | Salah Amroune | Mohamed Fnides | Moussa Zaoui | Hocine Heraiz
Source: Journal of Composite Materials (article, 2024-03-16)
Field: Materials Science — Polymers and Plastics

Examining the bending test properties of bio-composites strengthened with fibers through a combination of experimental and modeling approaches This study explores the relationship between natural fiber filling density (10%, 15%, 25%) and its impact on the bending properties of polymer compounds reinforced with Diss, Sisal and Luffa fibers. Using advanced techniques like fiber analysis and Fourier transform infrared spectrometry (FTIR), the research reveals that a 25% filling density results in the highest stress values (25.61 MPa, 22.21 MPa and 20.88 MPa) for Diss, Sisal and Luffa compounds, respectively, fostering robust bonds in Diss-reinforced polymers. The Artificial Neural Network (ANN) model demonstrates superior predictive capability with correlation coefficients exceeding 0.99 for stress and displacement, outperforming Response Surface Methodology (RSM). Analysis of Variance (ANOVA) underscores the impact of sample section parameters and fiber rate on stress, establishing the significance of type parameters and fiber rate on displacement. This integration of ANN and RSM represents a paradigm shift in predicting bending mechanical properties, advancing our understanding of composite materials for innovative applications. Materials science ; Composite material ; Bending ; Composite number ; Structural engineering Natural Fiber Reinforced Composites ; Textile materials and evaluations ; Innovations in Concrete and Construction Materials
Authors: Wenli Gao | Qianqian Jiang | Ying Guan | Huahong Huang | Shengquan Liu | Shengjie Ling | Liang Zhou
Source: International Journal of Biological Macromolecules (article, 2024-05-07)
Field: Chemistry — Analytical Chemistry

Transfer learning improves predictions in lignin content of Chinese fir based on Raman spectra Lignin ; Raman spectroscopy ; Chemistry ; Spectral line ; Content (measure theory) ; Analytical Chemistry (journal) ; Physics ; Chromatography ; Mathematics ; Organic chemistry ; Optics Spectroscopy and Chemometric Analyses ; Wood and Agarwood Research ; Fermentation and Sensory Analysis
Authors: Zhipeng Lin | Yuankai Li | Saif A. Haque | Alex M. Ganose | Andreas Kafizas
Source: Journal of Materials Chemistry A (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Insights from experiment and machine learning for enhanced TiO<sub>2</sub> coated glazing for photocatalytic NO<sub><i>x</i></sub> remediation TiO 2 -coated glass for photocatalytic NO x removal was optimised. Correlations between the synthesis parameters, coating properties, and photocatalytic activity was systematically studied through experimentation and machine learning tools for the first time. Photocatalysis ; Glazing ; Materials science ; Coating ; Environmental remediation ; Chemical engineering ; Nanotechnology ; Composite material ; Chemistry ; Engineering ; Catalysis ; Contamination ; Organic chemistry Catalytic Processes in Materials Science ; Pigment Synthesis and Properties ; Advanced Photocatalysis Techniques
Authors: Shaozhou Wang | Yuwei Wan | Ning Song | Yixuan Liu | Tong Xie | Bram Hoex
Source: Scientific Data (article, 2024-01-31)
Field: Materials Science — Materials Chemistry

Automatically Generated Datasets: Present and Potential Self-Cleaning Coating Materials The rise of urbanization coupled with pollution has highlighted the importance of outdoor self-cleaning coatings. These revolutionary coatings contribute to the longevity of various surfaces and reduce maintenance costs for a wide range of applications. Despite ongoing research to develop efficient and durable self-cleaning coatings, adopting systematic research methodologies could accelerate these advancements. In this work, we use Natural Language Processing (NLP) strategies to generate open- and traceable-sourced datasets about self-cleaning coating materials from 39,011 multi-disciplinary papers. The data are from function-based and property-based corpora for self-cleaning purposes. These datasets are presented in four different formats for diverse uses or combined uses: material frequency statistics, material dictionary, measurement value datasets for self-cleaning-related properties and optical properties, and sentiment statistics of material stability and durability. This provides a literature-based data resource for the development of self-cleaning coatings and also offers potential pathways for material discovery and prediction by machine learning. Coating ; Computer science ; Environmental science ; Information retrieval ; Materials science ; Nanotechnology Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Ihtesham Ibn Malek | Koushik Sarkar | Ahmed Zubair
Source: Nanoscale Advances (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Predicting the mechanical properties of pristine and defective carbon nanotubes using a random forest model Data-driven models have lately emerged as a faster and less time-consuming method for computing material properties than computationally expensive conventional molecular dynamics and density functional theory-based simulations. Here, we developed a random forest (RF) model for comprehensively predicting mechanical properties such as stress and Poisson's ratio under varying strain and ultimate tensile strain of pristine and defective carbon nanotubes (CNTs). The variations in stress and Poisson's ratio with the strain of CNTs with a 0.4-2 nm diameter range were calculated by classical molecular dynamics simulations and characterized using parameters extracted from fitting polynomial equations. The fitting parameters and ultimate tensile strength showed distinct dependency on chiral indices, chiral angles, radii, and the presence of defects in CNTs, which constituted the target dataset. The dataset features were selected through principal component analysis, and the correlation with targets was scrutinized. We performed a comparative analysis of different machine learning algorithms for predicting mechanical properties, revealing the RF model as the best-performing algorithm. The RMSE for the stress-strain curve had a maximum value of 0.013 and 0.0143 for pristine and defective CNTs, respectively, while the correlation coefficients were ≫ 0.99 for all CNTs, showcasing the excellent predictive power of the model. The model made excellent predictions of properties for CNTs with diameters >2 nm, which is beyond the training dataset range, demonstrating the robustness of the model as a substitute for MD simulation. The insight gained from this study will benefit the research of nanocomposites, nanoelectronics, and nanomechanical systems incorporating CNTs. Carbon nanotube ; Random forest ; Materials science ; Carbon fibers ; Environmental science ; Biological system ; Nanotechnology ; Composite material ; Computer science ; Artificial intelligence ; Biology Machine Learning in Materials Science ; Neural Networks and Applications ; Carbon Nanotubes in Composites
Authors: Ha-Eun Yang | Nam Wook Kim | Hong‐Gu Lee | Min-Jee Kim | Wan-Gyu Sang | Changju Yang | Changyeun Mo
Source: Frontiers in Plant Science (article, 2024-07-31)
Field: Chemistry — Analytical Chemistry

Prediction of protein content in paddy rice (Oryza sativa L.) combining near-infrared spectroscopy and deep-learning algorithm Rice is a staple crop in Asia, with more than 400 million tons consumed annually worldwide. The protein content of rice is a major determinant of its unique structural, physical, and nutritional properties. Chemical analysis, a traditional method for measuring rice's protein content, demands considerable manpower, time, and costs, including preprocessing such as removing the rice husk. Therefore, of the technology is needed to rapidly and nondestructively measure the protein content of paddy rice during harvest and storage stages. In this study, the nondestructive technique for predicting the protein content of rice with husks (paddy rice) was developed using near-infrared spectroscopy and deep learning techniques. The protein content prediction model based on partial least square regression, support vector regression, and deep neural network (DNN) were developed using the near-infrared spectrum in the range of 950 to 2200 nm. 1800 spectra of the paddy rice and 1200 spectra from the brown rice were obtained, and these were used for model development and performance evaluation of the developed model. Various spectral preprocessing techniques was applied. The DNN model showed the best results among three types of rice protein content prediction models. The optimal DNN model for paddy rice was the model with first-order derivative preprocessing and the accuracy was a coefficient of determination for prediction, R<sub>p</sub> <sup>2</sup> = 0.972 and root mean squared error for prediction, RMSEP = 0.048%. The optimal DNN model for brown rice was the model applied first-order derivative preprocessing with R<sub>p</sub> <sup>2</sup> = 0.987 and RMSEP = 0.033%. These results demonstrate the commercial feasibility of using near-infrared spectroscopy for the non-destructive prediction of protein content in both husked rice seeds and paddy rice. Oryza sativa ; Rice protein ; Mean squared error ; Partial least squares regression ; Brown rice ; Husk ; Preprocessor ; Support vector machine ; Biological system ; Mathematics ; Agronomy ; Computer science ; Algorithm ; Artificial intelligence ; Chemistry ; Statistics ; Botany ; Food science ; Biology Spectroscopy and Chemometric Analyses ; GABA and Rice Research ; Smart Agriculture and AI
Authors: Sakthiprasad Kuttankulangara Manoharan | Rajesh Kannan Megalingam | A Gopika | Govind Jogesh | Kumar Aryan | Akhil Revi Kunnambath
Source: Smart Agricultural Technology (article, 2024-09-19)
Field: Chemistry — Inorganic Chemistry

AI based early identification and severity detection of nutrient deficiencies in coconut trees Coconut trees are vital in various agricultural and economic sectors. Their susceptibility to nutrient deficiencies poses a significant threat to growth and productivity. Traditional nutrient assessment methods are time and labour-intensive, relying on manual inspection and chemical testing. There has been no significant research on detecting nutrient deficiencies in coconut trees. Additionally, assessing deficiency, and severity automatically and recommending suitable fertilizers remain unexplored. This research leverages the YOLOv9 model to identify macro and micronutrient deficiencies in coconut trees and proposes an Image Analysis based Severity Detection (IASD), to determine the severity of these deficiencies. Along with these a Severity Index Calculation Model (SICM) is also introduced that calculates the Severity Index (SI) of these deficiencies. For each identified deficiency, the appropriate fertilizer and its application quantity are suggested. Four deep learning models—RetinaNet, Faster Regional Convolutional Neural Network (Faster R-CNN), You Only Look Once version 5 (YOLOv5), and version 9 (YOLOv9) —were compared for the prediction of nutrient deficiencies in coconut trees using a dataset of 5,720 images of nutrient-deficient coconut tree leaves. YOLOv9 outperformed other models with Accuracy, Precision, and Recall values of 80 %, 98.59 %, and 80.37 %, respectively. Manual verification ensured the correctness of IASD and SICM predictions during model creation, providing farmers and agricultural professionals with a precise, automated tool for managing coconut plantations. Identification (biology) ; Nutrient ; Biology ; Botany ; Ecology Coconut Research and Applications ; Date Palm Research Studies ; Oil Palm Production and Sustainability
Authors: K. Vijayalakshmi | Radha Sankararajan | Muthumeenakshi Kailasam | B. S. Sreeja
Source: Crystal Research and Technology (article, 2024-01-18)
Field: Chemistry — Electrochemistry

Machine Learning Assisted Metal Oxide‐Bismuth Oxy Halide Nanocomposite for Electrochemical Sensing of Heavy Metals in Aqueous Media Abstract Heavy metal in excess quantity is one of the major inorganic pollutants in water. It causes several hazards to human life and ecosystem. It exists in traces in most of the commonly available drinking water sources from lakes, ponds, wells, etc., However, their presence in treated water is relatively significant. As the treated water is primarily used for agricultural purposes, it is necessary to monitor and measure their concentration. This requires sensing of metals in aqueous medium with good sensitivity and stability. Recently, nanosensors coupled with electrochemical transducer is preferred for analyzing heavy metal in aqueous solutions. In this work, Silver oxide‐bismuth oxy bromide coated with nafion is proposed as an electrochemical sensor for detection of heavy metal ions in aqueous solution. Cyclic voltammetry (CV) behavior of the proposed electrode is observed in different electrolytes. Further, Differential Pulse Voltammetry (DPV) study shows that current increases with trace nickel and copper metal ions of different concentration. Further, machine learning (ML) algorithms such as Naïve Bayes, ANN, SVM and decision trees are employed for nickel ions to train the cyclic voltammetry data and evaluate its performance. Naïve Bayes algorithm provides the best accuracy of 93.2% among all the models. Differential pulse voltammetry ; Cyclic voltammetry ; Metal ions in aqueous solution ; Aqueous solution ; Bismuth ; Inorganic chemistry ; Electrochemical gas sensor ; Materials science ; Oxide ; Electrochemistry ; Metal ; Chemistry ; Electrode ; Metallurgy Electrochemical Analysis and Applications ; Electrochemical sensors and biosensors ; Advanced Chemical Sensor Technologies
Authors: Shitong Zhang | Yanjing He | Zhengqing Zhang | Chongli Zhong
Source: Green Chemical Engineering (article, 2024-02-02)
Field: Chemistry — Inorganic Chemistry

Machine learning aided investigation on the structure-performance correlation of MOF for membrane-based He/H2 separation The separation of He/H2 using membrane technology has gained significant interest in the field of He extraction from natural gas. One of the greatest challenges associated with this process is the extremely close kinetic diameters of the two gas molecules, resulting in low membrane selectivity. In this study, we investigated the structure-performance relationship of metal-organic framework (MOF) membranes for He/H2 separation through molecular simulations and machine learning approaches. By conducting molecular simulations, we identified the potential MOF membranes with high separation performance from the Computation-Ready Experimental (CoRE) MOF database, and the diffusion-dominated mechanism was further elucidated. Moreover, random forest (RF)-based machine learning models were established to identify the crucial factors influencing the He/H2 separation performance of MOF membranes. The pore limiting diameter (PLD) and void fraction (φ), are revealed as the most important physical features for determining the membrane selectivity and He permeability, respectively. Additionally, density functional theory (DFT) calculations were carried out to validate the molecular simulation results and suggested that the electronegative atoms on the pore surfaces can enhance the diffusion-based separation of He/H2, which is critical for improving the membrane selectivities of He/H2. This study offers useful insights for designing and developing novel MOF membranes for the separation of He/H2 at the molecular level. Membrane ; Gas separation ; Selectivity ; Molecule ; Diffusion ; Molecular dynamics ; Chemical physics ; Separation process ; Chemistry ; Density functional theory ; Materials science ; Computational chemistry ; Chromatography ; Thermodynamics ; Catalysis ; Organic chemistry ; Physics Metal-Organic Frameworks: Synthesis and Applications ; Membrane Separation and Gas Transport ; Advancements in Battery Materials