Combined Effect of Underlayer and Deposition Solution to Optimize the Alignment of Hematite Photoanodes.

This study investigates the optimization of hematite (α-Fe2O3) photoanodes for enhanced photoelectrochemical (PEC) performance and reproducibility, which are crucial for photocatalytic applications. Despite hematite's potential, hindered by inherent limitations, significant improvements were realized by introducing a titanium dioxide (TiO2) underlayer and ethanol-modified deposition. The influence of the deposition methods was understood by potential-dependent photoelectrochemical impedance spectroscopy analysis. The introduction of the TiO2 underlayer effectively increased the density of states, preferable for the electron transport in the bulk hematite, and the ethanol deposition on a TiO2 underlayer led to a stable surface state formation (S1 state) for the photoexcited hole transfer. This analysis illuminated the intricate interplay between electron transport in the bulk and photogenerated hole transfer at the solution interface, thereby facilitating smoother charge transfer. These findings underscore the viability of surface engineering and meticulous process optimization in addressing critical challenges in photocatalyst development.

Identification of the Contributing Factors to the Photoelectric Conversion Efficiency for Hematite Photoanodes by Using Machine Learning.

Hematite has gained significant attention in the field of photocatalysis as one of the most promising materials for the photoanode of photoelectrochemical (PEC) water splitting due to visible light absorption and the abundance of availability. However, its performance improvement process suffers from a serious bottleneck due to "sample variation" and "inactivity". However, the physical origin of them has not yet been elucidated. To address these issues, we have developed a machine learning (ML) strategy using a combination of various analytical data of hematite photoanodes to discern "active/inactive" and identify the dominant factors. For the demonstration purpose of the ML strategy, we picked up one of the dominant factors, the interfacial resistivity between hematite and FTO, which has not generally been explored as a first candidate in the improvement of photocatalytic materials. The operational parameters for the sample preparation were optimized to modify the selected physical property. Along with the improvement of the selected resistivity, we found that the other dominant descriptors related to the properties of bulk hematite and the surface facet were also modified and help improve the PEC performance.

Visualization of Flow-Induced Strain Using Structural Color in Channel-Free Polydimethylsiloxane Devices.

Measuring flow of gases is of fundamental importance yet is typically done with complex equipment. There is, therefore, a longstanding need for a simple and inexpensive means of flow measurement. Here, gas flow is measured using an extremely simple device that consists of an Ar plasma-treated polydimethylsiloxane (PDMS) slab adhered on a glass substrate with a tight seal. This device does not even have a channel, instead, gas can flow between the PDMS and the glass by deforming the PDMS wall, in other words, by making an interstice as a temporary path for the flow. The formation of the temporary path results in a compressive bending stress at the inner wall of the path, which leads to the formation of well-ordered wrinkles, and hence, the emergence of structural color that changes the optical transmittance of the device. Although it is very simple, this setup works sufficiently well to measure arbitrary gases and analyzes their flow rates, densities, and viscosities based on the change in color. It is also demonstrated that this technique is applicable to the flow-induced display of a pattern such as a logo for advanced applications.