AI sensor made from ‘frozen smoke’ detects indoor air pollutants

This sensitivity surpasses that of many current indoor air quality sensors, heralding a new era in environmental monitoring. The team from the University of Cambridge crafted these sensors from aerogels, a highly porous substance, enabling precise detection of formaldehyde at ambient temperature by meticulously shaping the aerogel's cavities.

These prototype sensors, which operate on minimal power, show promise for broad application, including the potential for miniaturisation for wearable and healthcare uses. The findings were detailed in the journal Science Advances.

Volatile organic compounds (VOCs) pose a significant indoor air pollution risk, leading to symptoms such as watery eyes, throat irritation, and breathing difficulties at high levels. Long-term exposure to certain VOCs like formaldehyde, commonly found in household items such as MDF, wallpapers, paints, and some synthetic fabrics, can have severe health implications, including triggering asthma attacks and increasing cancer risk.

A 2019 Clean Air Day campaign report highlighted that a fifth of UK households exhibit noticeable formaldehyde levels, with 13% exceeding World Health Organization (WHO) recommended limits. "VOCs such as formaldehyde can lead to serious health problems with prolonged exposure even at low concentrations, but current sensors don’t have the sensitivity or selectivity to distinguish between VOCs that have different impacts on health," noted Professor Tawfique Hasan from the Cambridge Graphene Centre, the research lead.

Zhuo Chen, the paper's first author, expressed the aim to create a compact sensor with low power consumption yet capable of selectively detecting formaldehyde at minimal concentrations. By leveraging the unique structure of aerogels, which are over 99% air, the team engineered an effective sensor platform. Collaborating with Warwick University, the researchers enhanced the aerogel's sensitivity to formaldehyde, incorporating quantum dots into the structure and utilising machine learning to distinguish formaldehyde's unique 'fingerprint' among other VOCs.

"Our sensors work incredibly well at room temperature, so they use between 10 and 100 times less power than other sensors," Chen explained. This breakthrough not only enables real-time, specific VOC detection but also represents a step forward in environmental health monitoring. Professor Julian Gardner from Warwick University indicated the development of a low-cost, multi-sensor platform incorporating this novel material, capable of detecting various VOCs in conjunction with AI algorithms.

Supported by the Henry Royce Institute and the Engineering and Physical Sciences Research Council (EPSRC), this research opens new avenues for detecting hazardous materials, offering a finer resolution in assessing air quality and health risks.