Researchers at the University of Cambridge and the University of Warwick have developed a fully 3D-printed quantum dot/graphene-based aerogel sensor for highly sensitive and real-time recognition of formaldehyde at room temperature. Formaldehyde is a known human carcinogen that is a common indoor air pollutant. However, its real-time and selective recognition from interfering gases has thus far remained challenging, especially for low-power sensors suffering from noise and baseline drift.
The new sensor uses artificial intelligence techniques to detect formaldehyde in real time at concentrations as low as eight parts per billion, far beyond the sensitivity of most indoor air quality sensors.
Volatile organic compounds (VOCs) are a major source of indoor air pollution, causing watery eyes, burning in the eyes and throat, and difficulty breathing at elevated levels. High concentrations can trigger attacks in people with asthma, and prolonged exposure may cause certain cancers. Formaldehyde is a common VOC and is emitted by household items including pressed wood products (such as MDF), wallpapers and paints, and some synthetic fabrics. For the most part, the levels of formaldehyde emitted by these items are low, but levels can build up over time, especially in garages where paints and other formaldehyde-emitting products are more likely to be stored.
"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," said Professor Tawfique Hasan from the Cambridge Graphene Center, who led the research.
"We wanted to develop a sensor that is small and doesn't use much power, but can selectively detect formaldehyde at low concentrations," said Zhuo Chen, the paper's first author.
The researchers used aerogels to develop the sensors. By precisely engineering the shape of the holes in the aerogels, the sensors were able to detect the fingerprint of formaldehyde at room temperature.
The proof-of-concept sensors, which require minimal power, could be adapted to detect a wide range of hazardous gases, and could also be miniaturized for wearable and health care applications.
The researchers 3D printed lines of a paste made from graphene and then freeze-dried the graphene paste to form the holes in the final aerogel structure. The aerogels also incorporate tiny semiconductors known as quantum dots.
The sensors they developed were able to detect formaldehyde at concentrations as low as eight parts per billion, which is 0.4 percent of the level deemed safe in UK workplaces. The sensors also work at room temperature, consuming very low power.
"Traditional gas sensors need to be heated up, but because of the way we've engineered the materials, our sensors work incredibly well at room temperature, so they use between 10 and 100 times less power than other sensors," said Chen.
To improve selectivity, the researchers then incorporated machine learning algorithms into the sensors. The algorithms were trained to detect the 'fingerprint' of different gases, so that the sensor was able to distinguish the fingerprint of formaldehyde from other VOCs.
"Existing VOC detectors are blunt instruments—you only get one number for the overall concentration in the air," said Hasan. "By building a sensor that is able to detect specific VOCs at very low concentrations in real time, it can give home and business owners a more accurate picture of air quality and any potential health risks."
The researchers say that the same technique could be used to develop sensors to detect other VOCs. In theory, a device the size of a standard household carbon monoxide detector could incorporate multiple different sensors within it, providing real-time information about a range of different hazardous gases.
The team at Warwick is developing a low-cost multi-sensor platform that will incorporate these new aerogel materials and, coupled with AI algorithms, detect different VOCs.
"By using highly porous materials as the sensing element, we're opening up whole new ways of detecting hazardous materials in our environment," said Chen.
