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Novel Machine Learning-Based Method for Estimation of the Surface Area of Porous Silica Particles.

ABSTRACT: This work reports a novel and quick method to estimate the surface area of porous materials. Conventionally, surface area measurement requires the BET method/N2 adsorption experiment which is time-consuming. In this work, we developed a method based on machine learning (ML) and the adsorption of a conductive dye on porous materials. The rate and quantity of dye adsorption, which is characterized by dynamic measurement of conductivity, provide an indirect measure of surface area and zeta potential. An ML-based soft sensor is developed to relate the measured conductivity profiles with surface area and zeta potential. A phenomenological model on dye adsorption is also developed, validated, and used to augment experimental data for training the soft sensor. The developed method was tested for porous silica particles with a range of surface areas (250-1100 m2/g) and zeta potential (-17 mV: -29 mV). The developed soft sensor was able to estimate the surface area and zeta potential quite well. The developed approach and method reduce overall measurement time for surface area from several hours to a few minutes. The method can potentially be implemented in continuous plants producing porous materials like silica.

SUBMITTER: Moghadam RP

PROVIDER: S-EPMC10636746 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature

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Novel Machine Learning-Based Method for Estimation of the Surface Area of Porous Silica Particles.

Moghadam Roja P RP Shukla Chinmay A CA Ranade Vivek V VV

Industrial & engineering chemistry research 20231027 44

This work reports a novel and quick method to estimate the surface area of porous materials. Conventionally, surface area measurement requires the BET method/N<sub>2</sub> adsorption experiment which is time-consuming. In this work, we developed a method based on machine learning (ML) and the adsorption of a conductive dye on porous materials. The rate and quantity of dye adsorption, which is characterized by dynamic measurement of conductivity, provide an indirect measure of surface area and ze ...[more]

PMID: 37969176

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Project description:BackgroundRecently, the dental age estimation method developed by Cameriere has been widely recognized and accepted. Although machine learning (ML) methods can improve the accuracy of dental age estimation, no machine learning research exists on the use of the Cameriere dental age estimation method, making this research innovative and meaningful.AimThe purpose of this research is to use 7 lower left permanent teeth and three models [random forest (RF), support vector machine (SVM), and linear regression (LR)] based on the Cameriere method to predict children's dental age, and compare with the Cameriere age estimation.Subjects and methodsThis was a retrospective study that collected and analyzed orthopantomograms of 748 children (356 females and 392 males) aged 5-13 years. Data were randomly divided into training and test datasets in an 80-20% proportion for the ML algorithms. The procedure, starting with randomly creating new training and test datasets, was repeated 20 times. 7 permanent developing teeth on the left mandible (except wisdom teeth) were recorded using the Cameriere method. Then, the traditional Cameriere formula and three models (RF, SVM, and LR) were used to estimate the dental age. The age prediction accuracy was measured by five indicators: the coefficient of determination (R2), mean error (ME), root mean square error (RMSE), mean square error (MSE), and mean absolute error (MAE).ResultsThe research showed that the ML models have better accuracy than the traditional Cameriere formula. The ME, MAE, MSE, and RMSE values of the SVM model (0.004, 0.489, 0.392, and 0.625, respectively) and the RF model (- 0.004, 0.495, 0.389, and 0.623, respectively) were lower with the highest accuracy. In contrast, the ME, MAE, MSE and RMSE of the European Cameriere formula were 0.592, 0.846, 0.755, and 0.869, respectively, and those of the Chinese Cameriere formula were 0.748, 0.812, 0.890 and 0.943, respectively.ConclusionsCompared to the Cameriere formula, ML methods based on the Cameriere's maturation stages were more accurate in estimating dental age. These results support the use of ML algorithms instead of the traditional Cameriere formula.

Project description:BackgroundThe absence of air pollution monitoring networks makes it difficult to assess historical fine particulate matter (PM2.5) exposures for countries in the areas, such as Kuwait, which are severe impacted by desert dust and anthropogenic pollution.ObjectiveWe constructed an ensemble machine learning model to predict daily PM2.5 concentrations for regions lack of PM2.5 observations.MethodsThe model was constructed based on daily PM2.5, visibility, and other meteorological data collected at two sites in Kuwait. Then, our model was applied to predict the daily level of PM2.5 concentrations for eight airports located in Kuwait and Iraq from 2013 to 2020.ResultsAs compared to traditional statistic models, the proposed machine learning methods improved the accuracy in using visibility to predict daily PM2.5 concentrations with a cross-validation R2 of 0.68. The predicted level of daily PM2.5 concentrations were consistent with previous measurements. The predicted average yearly PM2.5 concentration for the eight stations is 50.65 µg/m3. For all stations, the monthly average PM2.5 concentrations reached their maximum in July and their minimum in November.SignificanceThese findings make it possible to retrospectively estimate daily PM2.5 exposures using the large-scale databases of historical visibility in regions with few particulate matter monitoring stations.Impact statementThe scarcity of air pollution ground monitoring networks makes it difficult to assess historical fine particulate matter exposures for countries in arid areas such as Kuwait. Visibility is closely related to atmospheric particulate matter concentrations and historical airport visibility records are commonly available in most countries. Our model make it possible to retrospectively estimate daily PM2.5 exposures using the large-scale databases of historical visibility in arid regions with few particulate matter ground monitoring stations. The product of such models can be critical for environmental risk assessments and population health studies.

Dataset Information

Novel Machine Learning-Based Method for Estimation of the Surface Area of Porous Silica Particles.

Publications

Novel Machine Learning-Based Method for Estimation of the Surface Area of Porous Silica Particles.

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