Flexible ZnO nanowire platform by metal-seeded chemical bath deposition: Parametric analysis and predictive modeling

Abstract

The growth of zinc oxide nanowires (ZnO NWs) on metal-seeded substrates is crucial for photonics, electronics, and sensing applications. Traditionally, NWs are grown using seed sintering on rigid substrates at high-temperature. However, the rise of flexible electronics, which use substrates unable to withstand high temperatures, has shifted focus to metal-assisted synthesis methods that do not require high-temperature sintering. This method has gained increasing attention due to its compatibility with flexible substrates. This article focuses on understanding the underlying growth mechanisms and achieving controlled growth of ZnO NWs on metal seeded flexible substrates. Furthermore, a parametric analysis is carried out to elucidate the correlation among different growth conditions in the chemical bath deposition (CBD) technique. Through a meticulously planned experimental design, the study investigates the influence of different growth conditions on synthesis outcomes. This leads to the formulation of predictive models using advanced machine learning (ML) methods particularly, artificial neural network (ANN). Following validation and training, the ANN model exhibits a remarkable ability to predict synthesis outcomes, yielding R values of 0.92 for diameter and 0.96 for length of NWs. Notably, the highest aspect ratio (AR) of ∼24 is attained following the growth conditions: 25 mM precursor concentration, 60 min growth time, and a growth temperature of 95 °C. Additionally, this method of growing ZnO NWs on a metal-seeded substrate offers an alternative approach for fabricating nanodevices for various emerging applications.