Bandgap energy modeling of the deformed ternary GaAs1-uNu by artificial neural networks

Numerical modeling and DFT study for a CsPbCl3 lead-based perovskite solar cell using Zn-doped Cu2O as HTL

“…Hachimi et al [26] investigated CsPbCl3 as an absorber layer in a perovskite solar cell with an FTO/SnO2/CsPbCl3/Cu2O/Au structure and achieved a VOC of 1.80 V, a JSC of 9.14 mA/cm 2 , an FF of 86.06%, and a PCE of 14.20%. The introduction of lattice mismatch has been investigated for its potential to improve the efficiency of perovskite solar cells by causing material deformation and increasing the valence band degeneracy [27][28][29].…”

ResNet14Attention network for identifying the titration end-point of potassium dichromate

“… Low accuracy; affected by the light environment; requires stable solution color changes. Artificial intelligence identification [ [25] , [26] , [27] , [28] , [29] , [30] , [31] ] Autonomous learning of convolutional neural networks. Unaffected by environments; high detection accuracy; fast detection speed; excellent sensitivity; convenient operation.…”

“…Artificial intelligence identification, which replaces the human eye with machine vision [ 25 ], is the third method to automatically identify titration end-point. Recently, convolutional neural networks (CNNs) have performed excellently in computer vision [ 26 ] and are widely applied in various fields [ 27 ]. The CNNs extract image features to self-learn through different structures of hidden layers and obtain quite an effective classification identification [ 28 ].…”