Machine Learning in Computational Design and Optimization of Disordered Nanoporous Materials

Materials, Год журнала: 2025, Номер 18(3), С. 534 - 534

Опубликована: Янв. 24, 2025

This review analyzes the current practices in data-driven characterization, design and optimization of disordered nanoporous materials with pore sizes ranging from angstroms (active carbon polymer membranes for gas separation) to tens nm (aerogels). While machine learning (ML)-based prediction screening crystalline, ordered porous are conducted frequently, porosity receive much less attention, although ML is expected excel field, which rich ill-posed problems, non-linear correlations a large volume experimental results. For micro- mesoporous solids carbons, silica, aerogels, etc.), obstacles mostly related navigation available data transferrable easily interpreted features. The majority published efforts based on obtained same work, datasets often very small. Even limited data, helps discover non-evident serves material production optimization. development comprehensive databases low-level structural sorption characteristics, as well automated synthesis/characterization protocols, seen direction immediate future. paper written language readable by chemist unfamiliar science specifics.

Язык: Английский

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Efficient Adsorption Capacity of MgFe-Layered Double Hydroxide Loaded on Pomelo Peel Biochar for Cd (II) from Aqueous Solutions: Adsorption Behaviour and Mechanism

Molecules, Год журнала: 2023, Номер 28(11), С. 4538 - 4538

Опубликована: Июнь 3, 2023

A novel pomelo peel biochar/MgFe-layered double hydroxide composite (PPBC/MgFe-LDH) was synthesised using a facile coprecipitation approach and applied to remove cadmium ions (Cd (II)). The adsorption isotherm demonstrated that the Cd (II) by PPBC/MgFe-LDH fit Langmuir model well, behaviour monolayer chemisorption. maximum capacity of determined be 448.961 (±12.3) mg·g-1 from model, which close actual experimental 448.302 (±1.41) mg·g-1. results also chemical controlled rate reaction in process PPBC/MgFe-LDH. Piecewise fitting intra-particle diffusion revealed multi-linearity during process. Through associative characterization analysis, mechanism involved (i) formation or carbonate precipitation; (ii) an isomorphic substitution Fe (III) (II); (iii) surface complexation functional groups (-OH); (iv) electrostatic attraction. great potential for removing wastewater, with advantages synthesis excellent capacity.

Язык: Английский

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Exploring Principal Component Analysis for Enhanced Insights into Physical and Operational Characteristics of Palladium-Based Membrane Composites: Advancing Hydrogen (H2) Energy Potential to Revolutionize the Energy Sector

Processes, Год журнала: 2025, Номер 13(1), С. 192 - 192

Опубликована: Янв. 11, 2025

In this study, we used Principal Component Analysis (PCA) to evaluate the physical and operational properties of palladium (Pd)-based membrane composites, focusing on variables like temperature, differential pressure (ΔP), thickness, hydrogen (H2) permeability, H2 flux. The analysis revealed that first two principal components explained 53.16% total variance, indicating moderate explanatory power. Interdependencies were observed among flux, while ΔP functioned independently. This study found similarities membranes, such as eco-friendly chitosan-based which performed comparably conventional options Pd–PSS Pd–Cu/αAl2O3. Overall, PCA proved be an invaluable tool for uncovering hidden patterns, optimizing experimental processes, deepening understanding Pd-based membranes. findings underscore PCA’s potential enhance material performance promote sustainable alternatives, with practical benefits advancing separation technologies. illustrates how data-driven approaches can refine drive innovation in design.

Язык: Английский

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Investigation on the Effect of Three Different Nonthermal Sterilization Methods on Volatile Organic Compounds in Safflower Using HS-GC-IMS

ACS Omega, Год журнала: 2025, Номер 10(4), С. 3838 - 3850

Опубликована: Янв. 23, 2025

During the transportation, storage, and processing of safflower, it is susceptible to contamination by microorganisms, which may seriously affect quality safety flowers. Therefore, sterilization an important step in ensuring safety, quality, stability safflower products. In this study, headspace gas chromatography-ion mobility spectrometry (HS-GC-IMS) was utilized compare volatile organic compounds (VOCs) samples before after with three nonthermal technologies (60Co irradiation sterilization, ultraviolet ozone sterilization). A total 70 VOCs were detected all samples. According two-dimensional three-dimensional difference contrast spectra fingerprint results HS-GC-IMS, processed methods varied. By conducting principal component analysis (PCA), cluster (CA), partial least-squares regression (PLS-DA) on VOCs, found that 3-methyl-2-butenal, 2-heptanone, 4-methyl-2-pentanone main contributors differences between groups. HH-01 (not sterilized) differed significantly from HH-03 (UV HH-04(ozone least HH-02(60Co sterilized), suggesting 60Co sterilized had effect safflower. technology recommended sterilize safflowers large-scale production. This study provides a scientific basis for future high-quality The demonstrate HS-GC-IMS can provide strong technical support identification authenticity assessment

Язык: Английский

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Determination and comparison of lipid profiles of Chinese green tea varieties using untargeted lipidomics analysis combined with chemometrics

Food Chemistry, Год журнала: 2025, Номер 477, С. 143467 - 143467

Опубликована: Фев. 18, 2025

Язык: Английский

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Assessment of metals/metalloids and organic pollutants in road dust: a case study of diverse land uses

Stochastic Environmental Research and Risk Assessment, Год журнала: 2025, Номер unknown

Опубликована: Март 28, 2025

Язык: Английский

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Application of Unsupervised Learning for the Evaluation of Aerogels’ Efficiency towards Dye Removal—A Principal Component Analysis (PCA) Approach

Gels, Год журнала: 2023, Номер 9(4), С. 327 - 327

Опубликована: Апрель 12, 2023

Water scarcity is a growing global issue, particularly in areas with limited freshwater sources, urging for sustainable water management practices to insure equitable access all people. One way address this problem implement advanced methods treating existing contaminated offer more clean water. Adsorption through membranes technology an important treatment technique, and nanocellulose (NC)-, chitosan (CS)-, graphene (G)- based aerogels are considered good adsorbents. To estimate the efficiency of dye removal mentioned aerogels, we intend use unsupervised machine learning approach known as "Principal Component Analysis". PCA showed that chitosan-based ones have lowest regeneration efficiencies, along moderate number regenerations. NC2, NC9, G5 preferred where there high adsorption energy membrane, porosities could be tolerated, but allows lower efficiencies contaminants. NC3, NC5, NC6, NC11 even low surface area. In brief, presents powerful tool unravel towards removal. Hence, several conditions need when employing or manufacturing investigated aerogels.

Язык: Английский

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Application of Principal Component Analysis for the Elucidation of Operational Features for Pervaporation Desalination Performance of PVA-Based TFC Membrane

Processes, Год журнала: 2024, Номер 12(7), С. 1502 - 1502

Опубликована: Июль 17, 2024

Principal Component Analysis (PCA) serves as a valuable tool for analyzing membrane processes, offering insights into complex datasets, identifying crucial factors influencing performance, aiding in design and optimization, facilitating monitoring fault diagnosis. In this study, PCA is applied to understand operational features affecting pervaporation desalination performance of PVA-based TFC membranes. PCA-biplot representation reveals that the first two principal components (PCs) accounted 62.34% total variance, with normalized permeation selective layer thickness (Pnorm), water flux (P), temperature (T) contributing significantly PC1, while salt rejection dominates PC2. Membrane clustering indicates distinct influences, membranes grouped based on correlation factors. Excluding outliers increases variance 74.15%, showing altered arrangements. Interestingly, adopted strategy showed high discrepancy between P Pnorm, indicating relevance comparing PVA specific layers those none. results Pnorm more important than features, highlighting its significance both research practical applications. Our findings show even know remains key property; critical developing high-performance, efficient, economically viable Subsequent without (M1 M6) (M7 M11) highlights higher influence variables, understanding membranes’ behavior suitability under different conditions. Overall, effectively delineates characteristics potential applications This study would confirm applicability approach efficiency via these

Язык: Английский

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An autoencoder-based arithmetic optimization clustering algorithm to enhance principal component analysis to study the relations between industrial market stock indices in real estate

Expert Systems with Applications, Год журнала: 2024, Номер unknown, С. 126165 - 126165

Опубликована: Дек. 1, 2024

Язык: Английский

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Uncovering Top-Tier Machine Learning Classifier for Drinking Water Quality Detection

Опубликована: Авг. 24, 2023

Water quality assessments are crucial for human health and environmental safeguards. The utilization of a subset artificial intelligence such as Machine Learning (ML) presents significant impacts to enhance the prediction classification water quality. In this research, set diverse ML algorithms was evaluated handle comprehensive dataset measurements over an extended period. aim develop robust approach accurately forecasting This employed machine learning classifiers Logistic Regression (LR), Support Vector (SVM), Stochastic Gradient Descent (SGD), K-Nearest Neighbors (KNN), Gaussian Process Classification (GPC), Naive Bayes (GNB), Random Forest (RF), Decision Tree (DT), XGBoost, Multilayer Perceptron (MLP). parameters assessed pH, hardness, solids, chloramines, sulfate, conductivity, organic carbon, trihalomethanes turbidity. XGBoost model exhibited highest accuracy 89.47% among Stacked Ensemble Classifiers (SEC) improved further 92.98%. findings suggest that SEC hold promise reliable approaches in contrast intelligence.

Язык: Английский

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