Nature Communications, a new strategy for gas sensing!

Background

Catalytic surface functionalization of nanostructured metal oxides is a promising and versatile approach for various chemical research areas, including heterogeneous catalysts and catalysis-based sensors. Using this surface functionalization, researchers have extensively explored catalysis-based electrosensing on nanostructured metal oxide surfaces for various volatile organic compounds (VOCs). Catalysis-based molecular sensors have attracted extensive attention due to their ability to tune sensitivity and selectivity through catalytic events on the sensor surface, which is difficult to achieve on bare metal oxide sensor surfaces. Among the numerous surface functionalization methods of nanostructured metal oxides, organic molecular surface modification is one of the most facile methods, exploiting the intermolecular interactions and affinity between the target analytes and the surface-modified molecules. The effects of such organic molecular surface modifications on molecular selective sensing data are usually interpreted as attractive or repulsive intermolecular interactions between the analyte and the modified organic. For example, (3-aminopropyl)trimethoxysilane (APTMS) self-assembled monolayer (SAM) modified multi-ZnO nanowire sensors have been shown to have enhanced sensitivity to acetone by tuning the depletion layer thickness on the semiconductor ZnO surface and the electrical interaction between the -NH ₂ groups of the APTMS SAM and the C=O of acetone.

However, considering that the purpose of catalytic-based metal oxide sensor surfaces is to maintain and/or increase the surface density of metal oxide catalytic sites, organic molecular surface modification is a clear deviation from the general design principle based on catalytic mechanisms. This is because organic surface modification inherently reduces the catalytic site density on metal oxide surfaces. On the other hand, recent studies on heterogeneous catalysts have shown that organic molecular surface modification can direct the pathways of catalytic reactions on metal oxide surfaces. For example, electronic tuning of oxide surfaces using SAMs has been demonstrated to affect the dehydration activity of alcohols by modulating transition state structures and activation energies. In catalytic processes on metal oxide surfaces and in catalytic-based VOC electrical sensing, the “catalyst deactivation effect” is an inherent and unavoidable problem. This effect occurs when catalytic sites are deactivated by residual analytes and/or catalytic products on the surface during operation, significantly reducing catalytic performance. Although the use of UV or high-temperature annealing has been proposed to mitigate these surface deactivation effects, such harsh and destructive surface treatments are clearly not suitable for catalyst surfaces modified with organic materials. Among the various surface chemistries, carboxylates are particularly important for two main reasons. First, carboxylates are very common in chemical sensors and heterogeneous catalysis, as organic compounds composed of carbon, hydrogen, and oxygen are eventually oxidized to carboxylic acids in an oxidative environment. Second, carboxylates form stronger bonds with typical metal oxide catalysts, leading to significant catalyst deactivation by blocking active catalytic sites on the metal oxide surface.

Highlights of this article

1. This work highlights the underappreciated role of van der Waals interactions between hydrophobic aliphatic alkyl chains and hydrophilic ZnO surface in mitigating catalyst deactivation during aliphatic aldehyde sensing.

2. By immobilizing octadecylphosphonic acid (ODPA) on the ZnO nanowire sensor, the recovery time of nonanal detection can be significantly shortened without affecting the sensitivity.

3. The desorption temperature of carboxylates on ODPA-modified ZnO decreased to below 150 °C, while that on bare ZnO remained above 300 °C, indicating that the catalyst deactivation was significantly reduced.

Graphical analysis