Controlling the stability and energy alignment of semicondcutor nanoparticles is crucial for their many applications. In this talk, I will present some of our progresses in this aspect through both rational design and machine learning. As an example, when a semiconductor is in contact with an electrolyte, its flat-band potential (EFB) is an important quantity for determining band edge positions in photoelectrochemistry.Oxide semiconductors generally have a EFB shift of-59 mV in aqueous solutions when the pH is increased by one unit as aconsequence of surface deprotonation. Many of the most desirable redox reactions, such as the reduction of CO2 to HCOOH, or 2H+ to H2, also show the same dependence on pH due to the involvement of protons. Therefore, pH cannot be used to tune the relative energy alignment between the electrode and the electrolytes. Through rationaly design, we have demonstrate via Mott-Schottky measurement that sensitized NiO with a membrane-inspired design of dye molecule (BH4) can decouple the pH dependence of EFB. The EFB of BH4 sensitized NiO films showed little change as a function of pH, whereas less hydrophobic dye (P1) sensitized NiO and the bare NiO follow the Nernst shift with respect to pH. The protective layer arises from the hydrophobicity as well as the strong π- π interactions between dye molecules on metal oxide surface,which wereidentifiedfrom molecular dynamic simulations and spectroscopic techniques.

Interface synthesis and machine learning for controlling stability and energy alignment of nanoparticles

2019 Spring National Meeting

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