Engineering the Energy Gap of Cupric Oxide Nanomaterial Using Extreme Learning Machine and Stepwise Regression Algorithms

Abstract

CuO is a narrow band gap semiconductor with distinct features that render it indispensable in many industrial and technological applications such as environmental friendly catalysts for organic pollutant removal, sensors, photovoltaic, solar cells, batteries, and storage media among others. Engineering of its energy gap becomes imperative and necessary for tailoring its light absorption capacity to a desired level required for a particular application. Elemental doping mechanisms with accompanied lattice distortion symmetry breaking effectively enhance the optical property of this semiconductor and serve as a major route through which material design is achieved. This work develops an extreme learning machine intelligent predictive (ELM-IP) model and stepwise regression (SWR) based model for estimating energy gap of a doped CuO semiconductor. The developed ELM-IP-Sin model which employs sine activation function performs better than the ELM-IP-Sig model (that utilizes sigmoid activation function) and SWR model with a percentage improvement of 14.15% and 50.05%, respectively, using root mean square error (RMSE) metric, while the developed ELM-IP-Sig model outperforms the SWR-based model. The developed models further investigate the dependence of CuO energy gap on iron and cobalt impurity incorporation, and the obtained results agree well with measured values. The outstanding performance of the developed models is highly meritorious in tailoring the light response capacity of CuO semiconductor for photocatalytic and optoelectronics applications at a reduced cost while the experimental stress is circumvented.