Thermo-physical properties and heat transfer potential of novel silica-ethylene glycol mono nanofluid: Experiments and multi-layer perceptron (MLP) modelling

Prediction of diffuse solar radiation by integrating radiative transfer model and machine-learning techniques

Diffuse radiation is a major component of solar radiation that is important in carbon exchanges and material, energy, and information flows in agricultural ecosystems; however, measuring diffuse radiation is difficult and expensive, leaving only few stations in China that can record diffuse radiation. Therefore, five high-speed and highly accurate hybrid models were developed and compared to simulate diffuse radiation based on the aerosol optical properties and radiation parameters provided by the Aerosol Robotic Network (AERONET), Baseline Surface Radiation Network (BSRN), Wuhan University, Chinese Ecosystem Research Network (CERN), GLASS surface albedo data, and combined radiative transfer model (RTM) with machine learning (ML) models that include random forest (RF), extreme gradient boosting (XGBoost), multi-layer perceptron (MLP), deep neural networks (DNN), and convolutional neural network (CNN). Furthermore, the uncertainty in the simulated diffuse radiation due to the measurement uncertainties of aerosol optical properties and land surface albedo was quantified, and the relative contributions of multiple variables to diffuse radiation were analyzed. The results showed that RTM-RF was the most successful, with determination coefficients (R²) of 0.95, 0.94, and 0.98, and minimum root mean square errors (RMSE) of 9.56, 10.05, and 13.27 W m^-2 at the Lulin, Wuhan, and Xianghe sites, respectively. The largest measurement uncertainty in the aerosol optical depth (AOD) was found at the Lulin site, while that of the single-scattering albedo led to the largest errors in Wuhan and Xianghe. AOD, solar zenith angle (SZA), and single-scattering albedo contributed significantly more than the asymmetry factor, land surface albedo, precipitable water vapor, and ozone. This was especially true for AOD, which was higher than 28 % at all sites. Overall, the proposed RTM-RF method exhibited superior performance, therefore we recommend it for estimating diffuse radiation in China.

Zeta Potential of Nanosilica in 50% Aqueous Ethylene Glycol and in 50% Aqueous Propylene Glycol

A sufficient amount of ionic surfactants may induce a zeta potential of silica particles dispersed in water-glycol mixtures of about 100 mV in absolute value. Nanoparticles of silica were dispersed in 50-50 ethylene glycol (EG)-water and 50-50 propylene glycol (PG)-water mixtures, and the zeta potential was studied as a function of acid, base, and surfactant concentrations. The addition of HCl had a limited effect on the zeta potential. The addition of NaOH in excess of 10-5 M induced a zeta potential of about -80 mV in 50% EG, but in 50% PG the effect of NaOH was less significant. The addition of CTMABr in excess of 10-3 M induced a zeta potential of about +100 mV in 50% EG and in 50% PG. The addition of SDS in excess of 10-3 M induced a zeta potential of about -80 mV in 50% EG and in 50% PG. Long-chained analogs of SDS were even more efficient than SDS, but their application is limited by their low solubility in aqueous glycols.

A Novel Machine Learning Prediction Model for Aerosol Transport in Upper 17-Generations of the Human Respiratory Tract

The main challenge of the health risk assessment of the aerosol transport and deposition to the lower airways is the high computational cost. A standard large-scale airway model needs a week to a month of computational time in a high-performance computing system. Therefore, developing an innovative tool that accurately predicts transport behaviour and reduces computational time is essential. This study aims to develop a novel and innovative machine learning (ML) model to predict particle deposition to the lower airways. The first-ever study uses ML techniques to explore the pulmonary aerosol TD in a digital 17-generation airway model. The ML model uses the computational data for a 17-generation airway model and four standard ML regression models are used to save the computational cost. Random forest (RF), k-nearest neighbour (k-NN), multi-layer perceptron (MLP) and Gaussian process regression (GPR) techniques are used to develop the ML models. The MLP regression model displays more accurate estimates than other ML models. Finally, a prediction model is developed, and the results are significantly closer to the measured values. The prediction model predicts the deposition efficiency (DE) for different particle sizes and flow rates. A comprehensive lobe-specific DE is also predicted for various flow rates. This first-ever aerosol transport prediction model can accurately predict the DE in different regions of the airways in a couple of minutes. This innovative approach and accurate prediction will improve the literature and knowledge of the field.