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The purpose of this paper is to study the electronic transport performance of Ag-ZnO film under dark and UV light conditions.

Ag-doped ZnO thin films were prepared on fluorine thin oxide (FTO) substrates by sol-gel method. The crystal structure of ZnO and Ag-ZnO powders was tested by X-ray diffraction with Cu Kα radiation. The absorption spectra of ZnO and Ag-ZnO films were recorded by a UV–visible spectrophotometer. The micro electrical transport performance of Ag-ZnO thin films in dark and light state was investigated by photoassisted conductive atomic force microscope (PC-AFM).

The results show that the dark reverse current of Ag-ZnO films does not increase, but the reverse current increases significantly under illumination, indicating that the response of Ag-ZnO films to light is greatly improved, owing to the formation of Ohmic contact.

To the best of the author’s knowledge, the micro electrical transport performance of Ag-ZnO thin films in dark and light state was firstly investigated by PC-AFM.

The purpose of this study is to adapt the incompressible smoothed particle hydrodynamics (ISPH) method with artificial intelligence to manage the physical problem of double diffusion inside a porous L-shaped cavity including two fins.

The ISPH method solves the nondimensional governing equations of a physical model. The ISPH simulations are attained at different Frank–Kamenetskii number, Darcy number, coupled Soret/Dufour numbers, coupled Cattaneo–Christov heat/mass fluxes, thermal radiation parameter and nanoparticle parameter. An artificial neural network (ANN) is developed using a total of 243 data sets. The data set is optimized as 171 of the data sets were used for training the model, 36 for validation and 36 for the testing phase. The network model was trained using the Levenberg–Marquardt training algorithm.

The resulting simulations show how thermal radiation declines the temperature distribution and changes the contour of a heat capacity ratio. The temperature distribution is improved, and the velocity field is decreased by 36.77% when the coupled heat Cattaneo–Christov heat/mass fluxes are increased from 0 to 0.8. The temperature distribution is supported, and the concentration distribution is declined by an increase in Soret–Dufour numbers. A rise in Soret–Dufour numbers corresponds to a decreasing velocity field. The Frank–Kamenetskii number is useful for enhancing the velocity field and temperature distribution. A reduction in Darcy number causes a high porous struggle, which reduces nanofluid velocity and improves temperature and concentration distribution. An increase in nanoparticle concentration causes a high fluid suspension viscosity, which reduces the suspension’s velocity. With the help of the ANN, the obtained model accurately predicts the values of the Nusselt and Sherwood numbers.

A novel integration between the ISPH method and the ANN is adapted to handle the heat and mass transfer within a new L-shaped geometry with fins in the presence of several physical effects.