Researchers have developed a sensor made out of ‘frozen smoke’ that uses artificial intelligence techniques to detect formaldehyde in real time at concentrations as little as eight parts per billion, far beyond the sensitivity of most indoor air quality sensors.
The researchers, from the University of Cambridge, developed sensors made out of highly porous materials often called aerogels. By precisely engineering the form of the holes within the aerogels, the sensors were capable of detect the fingerprint of formaldehyde, a typical indoor air pollutant, at room temperature.
The proof-of-concept sensors, which require minimal power, might be adapted to detect a wide selection of hazardous gases, and is also miniaturised for wearable and healthcare applications. The outcomes are reported within the journal Science Advances.
Volatile organic compounds (VOCs) are a significant source of indoor air pollution, causing watery eyes, burning within the eyes and throat, and difficulty respiration at elevated levels. High concentrations can trigger attacks in individuals with asthma, and prolonged exposure may cause certain cancers.
Formaldehyde is a typical VOC and is emitted by home items including pressed wood products (akin to MDF), wallpapers and paints, and a few synthetic fabrics. For probably the most part, the degrees of formaldehyde emitted by these things are low, but levels can construct up over time, especially in garages where paints and other formaldehyde-emitting products usually tend to be stored.
In response to a 2019 report from the campaign group Clean Air Day, a fifth of households within the UK showed notable concentrations of formaldehyde, with 13% of residences surpassing the really useful limit set by the World Health Organization (WHO).
“VOCs akin to formaldehyde can result in serious health problems with prolonged exposure even at low concentrations, but current sensors haven’t got the sensitivity or selectivity to differentiate between VOCs which have different impacts on health,” said Professor Tawfique Hasan from the Cambridge Graphene Centre, who led the research.
“We desired to develop a sensor that’s small and doesn’t use much power, but can selectively detect formaldehyde at low concentrations,” said Zhuo Chen, the paper’s first creator.
The researchers based their sensors on aerogels: ultra-light materials sometimes known as ‘liquid smoke’, since they’re greater than 99% air by volume. The open structure of aerogels allows gases to simply move out and in. By precisely engineering the form, or morphology, of the holes, the aerogels can act as highly effective sensors.
Working with colleagues at Warwick University, the Cambridge researchers optimised the composition and structure of the aerogels to extend their sensitivity to formaldehyde, making them into filaments about thrice the width of a human hair. The researchers 3D printed lines of a paste made out of graphene, a two-dimensional type of carbon, after which freeze-dried the graphene paste to form the holes in the ultimate aerogel structure. The aerogels also incorporate tiny semiconductors often called quantum dots.
The sensors they developed were capable of detect formaldehyde at concentrations as little as eight parts per billion, which is 0.4 percent of the extent deemed secure in UK workplaces. The sensors also work at room temperature, consuming very low power.
“Traditional gas sensors should be heated up, but due to the best way we have engineered the materials, our sensors work incredibly well at room temperature, so that they use between 10 and 100 times less power than other sensors,” said Chen.
To enhance selectivity, the researchers then incorporated machine learning algorithms into the sensors. The algorithms were trained to detect the ‘fingerprint’ of various gases, in order that the sensor was able to differentiate the fingerprint of formaldehyde from other VOCs.
“Existing VOC detectors are blunt instruments — you simply get one number for the general concentration within the air,” said Hasan. “By constructing a sensor that’s capable of detect specific VOCs at very low concentrations in real time, it will possibly give home and business owners a more accurate picture of air quality and any potential health risks.”
The researchers say that the identical technique might be used to develop sensors to detect other VOCs. In theory, a tool the scale of a normal household carbon monoxide detector could incorporate multiple different sensors inside it, providing real-time details about a spread of various hazardous gases. The team at Warwick are developing a low-cost multi-sensor platform that may incorporate these recent aerogel materials and, coupled with AI algorithms, detect different VOCs.
“Through the use of highly porous materials because the sensing element, we’re opening up whole recent ways of detecting hazardous materials in our surroundings,” said Chen.
The research was supported partly by the Henry Royce Institute, and the Engineering and Physical Sciences Research Council (EPSRC), a part of UK Research and Innovation (UKRI). Tawfique Hasan is a Fellow of Churchill College, Cambridge.
