Enhancing Chemiresistive Gas Sensor Performance through Tailored Metal-Organic Framework Architectures

Abstract

Gas sensors are essential tools for safeguarding our environment and human health by detecting volatile organic compounds (VOCs) and toxic gases. Traditional detection methods like gas chromatography and mass spectrometry, while highly accurate, are hindered by their bulky infrastructure, complexity, and time-consuming procedures. In contrast, portable chemiresistive gas sensors, based on semiconducting metal oxides, offer an alternative due to several advantages such as high sensitivity, ease of fabrication, and miniaturization. However, they suffer from poor selectivity and high operating temperatures, limiting their commercial potential.

This thesis represents a comprehensive exploration into the enhancement of chemiresistive gas sensor performance through the utilization of tailored metal organic framework (MOF) architectures, with a primary focus on Zeolitic Imidazole Framework (ZIF-8). This thesis comprises a comprehensive body of work, divided into multiple projects, each contributing to the collective advancement of gas sensing technology. These projects systematically address various aspects of sensor performance, including sensitivity, selectivity, and practical usability.

The core of this research lies in the meticulous selection of materials and the subsequent investigation of how gas molecules interact with these materials. This work encompasses a wide range of sensor designs, ranging from compact structures with varied porosity to hierarchical architectures featuring purposefully engineered porous frameworks. It also introduces machine learning as a valuable tool for advanced gas sensor analysis, thereby bridging the gap between materials science and data-driven analytical approaches. This thesis also explores hybrid nanowire structures integrated with ZIF-8 and conductive MOFs, demonstrating the versatility of MOF-based designs across various sensor platforms.

The central goal of this research is to enhance sensor performance and address critical challenges in gas detection. The key findings include significant improvements in sensor selectivity, sensitivity, and the development of a two-step machine learning model for discriminating gas types and predicting concentrations. One of the key takeaways from this research is the depth of work undertaken to understand the interaction of gas molecules with a variety of materials, fabricated via integrating MOFs with metal oxides, nanowires, and other innovative configurations. The outcomes offer concrete solutions to longstanding challenges in gas detection, such as improved selectivity and sensitivity, along with the ability to operate at lower temperatures.

In conclusion, this research represents a substantial advancement in the field, with immediate implications for environmental preservation and health protection. It highlights the importance of innovative material selection and sensor architectures in advancing gas sensing technology and offers a comprehensive understanding of the intricacies involved in the interaction between gas molecules and variety of materials. It is a significant step towards transforming gas sensors with the potential to address a wide range of industrial challenges and offers practical applications in cost-effective gas detection solutions. Show more