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Details of Grant 

EPSRC Reference: EP/W024284/1
Title: Design theory-based nanostructured leaf-vein networks for selective VOC sensing
Principal Investigator: Hasan, Dr T
Other Investigators:
Occhipinti, Dr LG
Researcher Co-Investigators:
Project Partners:
Centre for Process Innovation CPI (UK) Flusso Limited Sorex Sensors Ltd
Trinity College Dublin University of Namur (FUNDP)
Department: Engineering
Organisation: University of Cambridge
Scheme: Standard Research
Starts: 01 August 2022 Ends: 31 July 2025 Value (£): 420,381
EPSRC Research Topic Classifications:
Instrumentation Eng. & Dev. Manufacturing Machine & Plant
Materials Synthesis & Growth
EPSRC Industrial Sector Classifications:
Manufacturing Environment
Related Grants:
EP/W022451/1 EP/W023229/1
Panel History:
Panel DatePanel NameOutcome
08 Jun 2022 Engineering Prioritisation Panel Meeting 8 and 9 June 2022 Announced
Summary on Grant Application Form
The importance of indoor air quality monitoring to safeguard the health of children and vulnerable adults in the UK cannot be overstated. A primary source of indoor air pollution is everyday household products and materials. Many emit harmful non-methane volatile organic compounds (VOCs), such as formaldehyde, toluene and phthalates. Even in minute concentrations, these specific compounds can induce a variety of respiratory, neurological, endocrine disorders over prolonged low-level exposures. However, current environmental sensors, including those commercialised by major semiconductor integrated device manufacturers and by specialised gas sensor manufacturers (e.g . Bosch Sensortech, Sensirion, AMS, and others), cannot specifically detect these different toxic gases at an acceptable concentration level and are unable to provide any helpful preventive guidance.

The challenges faced by current-generation low-cost VOC sensors arise from empirically optimised sensing films for common sensor architectures. This approach has strong drawbacks as it does not have an overarching design consideration for the optimum permeation of gases or analytes through the sensing material for a maximised response. Crucially, these sensors are non-specific and can only detect the total concentration of VOCs (TVOCs), i.e. the total concentration of a subset of airborne VOCs present in the air, as an overall measure of indoor air quality. However, different TVOC measurement methods depend on VOCs' mixture and can yield substantially different estimated TVOC concentrations. Notably, the toxicity thresholds of the individual VOCs differ by orders of magnitude; the total concentration, therefore, does not provide any useful measure of total toxicity.

We will design material building blocks engineered to offer a maximum and selective response to target gas molecules to address this challenge. Then, in an ambitious step, through solution-phase additive manufacturing techniques, we will create large-scale self-assembly of these building blocks to obtain a nano- and micro-level structure mimicking the hierarchy of length scales found in xylems and leaf veins in plants. With multiple levels of interconnected channels, this universal structure has evolved over many million years to ensure mass transport (i.e. fluid permeation) with minimum energy expenditure through the preservation of volumetric flow rate. Our approach will therefore allow highly optimum through-flow of gases to the engineered building blocks, providing a fast, highly sensitive and selective response to these toxic gases. The highly repeatable nature of our additively manufactured sensing thin-film with self-assembled blocks will enable unprecedented device-to-device uniformity. We will exploit this to create a new generation of training algorithms to significantly reduce the traditional sensor training time and cost. We envisage that our materials design and manufacturing pathway based on natural laws will offer x10 to x100 times the state-of-the-art toxic VOC sensors' performance, making indoor air quality monitoring affordable and reliable.

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