Biodegradable gas sensors

Research Theme

Autonomous Sensing Platforms

Sensor Signal Processing

Aim

To develop a low-power, organic photovoltaic (OPV)-integrated, disposable sensor platform that detects trace vapours associated with explosives and common environmental gases (NOx, NH₃, CO and VOCs) for resilient, distributed monitoring.

Objectives

1. Design and print a multi-modal sensing array (chemiresistive) with selective coatings for nitroaromatics, taggants and key environmental gases.
2. Develop an OPV power-management system and low-power electronics to enable duty-cycled, ≥24-hour autonomous operation under realistic illumination.
3. Develop machine learning and pattern-recognition methods to classify sensor responses, enhance selectivity, and minimise false alarms under variable environmental conditions.
4. Validate performance with safe simulants in the lab.

Description

This project will develop a compact, low-power sensor node that integrates printed organic photovoltaics (OPV) with a multi-modal array of disposable chemiresistive and electrochemical sensing elements to detect trace vapours from nitroaromatics, explosive taggants, and key environmental gases including NOx, ammonia, carbon monoxide and common VOCs. Functionalized carbon-nanomaterial and molecularly imprinted polymer coatings will provide selective uptake, while machine learning and pattern-recognition algorithms will fuse multi-sensor responses to maximise sensitivity, discriminate analytes, and reduce false alarms. To ensure sustainability, devices will be designed to safely disintegrate into soil after use, building on Dr Panidi’s expertise in organic electronics. A key challenge for low-cost sensors is accuracy in complex, dynamic environments; to address this, deep learning approaches such as convolutional neural networks (CNNs) will enhance feature extraction and enable robust, real-time pollutant classification, drawing on Dr Yang’s expertise. This project couples sustainable sensor design with AI-driven analytics, directly supporting DSTL’s priorities in scalable, secure environmental sensing for public health and national resilience.

Minimum entry qualification - an Honours degree at 2:1 or above (or International equivalent) in a relevant science or engineering discipline, possibly supported by an MSc Degree. Further information on English language requirements for EU/Overseas applicants.

Full funding is available for this position.