Printable and Machine Intelligent 0D/2D Materials Gas Sensor Arrays

A smart gas sensor array (GSA) powered by machine intelligence is a machine-equivalent olfactory system of mammalian nose. Such systems’ ability to distinguish different species of gases and quantitatively measure trace-level gas concentrations is crucial for pollution monitoring, hazard gas detection, point-of-care disease monitoring, etc. Yet the most widely used gas sensors based on semiconducting metal oxide (SMO) suffer notably from high power consumption, slow response, poor sensitivity, and baseline drift at room temperature. In this thesis, I present printable and hybrid aerogels as high-performance GSAs. These sensors, based on graphene and zero-dimensional (0D) quantum dots (QDs), are capable of intelligent and robust trace-level gas identification. I hybridise graphene aerogels with SMO QDs and formulate 3D-printable inks with a dual-crosslinker design, which enables fine-tuning of inks’ rheology for improved printability and in-situ doping through liquid-phase ligand exchange. I determine key parameters affecting aerogels’ surface porosity, including the ink’s crosslinking degree, nozzle diameter, shear stress during printing, and freeze-drying conditions. I construct multiplexed filament-structured aerogel GSAs with various controllable morphologies and chemical dopants. My sensors achieve a record-high and stable response of 15.23% for 1 part per million (ppm) formaldehyde and an ultralow measured detection limit of 50 parts per billion (ppb), consuming only ∼130 microwatts (μW) power. Combined with dynamic-feature-based machine learning algorithms, my GSAs enable real-time recognition of formaldehyde from interference gases, while ensuring resilience to noise and baseline drift in both simulated and real-world testing, hitherto unachievable for room temperature sensors. Beyond 3D-printed filament-structured aerogel GSAs, I explore hierarchically porous aerogels via freeze-casting and apply Murray’s law for structural optimisation through collaboration. The resulting Murray aerogel gas sensor exhibits dramatically improved sensing dynamics due to enhanced mass transport efficiency. My research framework combines materials engineering, structural design, and computational algorithms to offer unprecedented real-time robust identification capabilities of volatile organic compounds and other hazardous gases. This would provide a novel gas sensing platform to tackle real-life challenges hindering the applications of GSAs for the Internet of Things, wearable electronics, and breath analysis.

Name dc:type.qualificationname

Doctor of Philosophy (PhD)

Level dc:type.qualificationlevel

Doctoral

Grantor dc:publisher.institution

University of Cambridge

Year dc:date.issued

2025

Author dc:creator

• Chen, Zhuo

Advisor dc:contributor.advisor

• Hasan, Tawfique

• Graphene-based aerogel
• Gas sensor arrays
• Machine learning
• Murray’s law

Licence

• https://apollo8-f-pro.lib.cam.ac.uk/bitstreams/952c90cd-65d3-457b-b59e-ea83a6696fed/download
• http://purl.org/NET/rdflicense/allrightsreserved

DOI dc:identifier.doi

https://doi.org/10.17863/CAM.119829

OAI identifier oai:identifier

oai:www.repository.cam.ac.uk:1810/386737