Today's KNOWLEDGE Share: Frozen smoke’ sensors detect toxic air in our offices and homes

Today's KNOWLEDGE Share

Researchers from the University of Warwick and the University of Cambridge have developed sensors made from frozen smoke. They claim the sensors can detect extremely low concentrations of formaldehyde.

Significantly, the results may contribute to a new era of air quality monitoring. 

Formaldehyde — what is it?

Formaldehyde is a commonly found air pollutant in indoor environments. Household items like wallpapers, pressed wood products, paints, and tobacco smoke emit it.High concentrations of formaldehyde exposure can lead to respiratory irritation, headaches, respiratory symptoms, and an increased risk of certain cancers.Current indoor air quality sensors lack the sensitivity to detect formaldehyde at such low levels. This is what the researchers focused on. 

The team used 3D printing techniques to develop sensors made from aerogels, also known as frozen smoke. These sensors can detect extremely low levels of formaldehyde in indoor air, which the researchers tested for. 

Aerogels are frozen smoke

Frozen smoke is an apt name for aerogels due to their see-through appearance.These materials exhibit low density, which can be as low as a thousandth of traditional solids, and their highly porous nature.Aerogels are 99.8 percent air, with a network of interconnected nanoparticles forming a highly porous structure, and they possess a high surface area. These properties make aerogels a great candidate for gas-sensing applications.

Their unique structure provides ample sites for gas molecules to interact, improving sensitivity.Through 3D-printing, researcher can tailor aerogels, allowing for precise control over sensor design.

This also helps enhance performance when detecting formaldehyde and other gases at low concentrations.For this, the researchers chose tin dioxide (SnO2), a semiconductor material with excellent sensing properties (especially formaldehyde), allowing for detection even at low concentrations.

3D printing of a hybrid material:

While SnO2 is excellent at detecting formaldehyde at low concentrations, the researchers wanted to enhance it.

They began by creating SnO2 quantum dots using soap-like substances to help with high-pressure, high temperature process. In the paper, the researchers describe this as a ‘surfactant-assisted hydrothermal growth process.’

During this stage, SnO2 mixes with a surfactant to control the size and shape of the resulting nanoparticles.The hot water conditions promote the growth of SnO2 nanoparticles into quantum dot structures with uniform size and distribution.In the next step, the team evenly distribute SnO2 quantum dots on graphene oxide (GO) sheets dispersed in a solution. This serves as the ink for the 3D printing process.

For gas sensing applications, the scientists 3D-print the aerogels on printed circuit board (PCB) substrates in meander (or zig-zag) shapes.The researchers then dope these sensors with metal salt solutions, resulting in a hybrid material: SnO2/rGO 0D-2D material–based aerogels.

Here, the rGO is reduced graphene oxide, which offers high electrical conductivity and large surface areas.This enhances the sensor’s sensitivity and response and also improves the material’s mechanical strength and stability.Next, the combination of quantum dots combined with the SnO20D for 0D (or 0-dimensional) materials. This further enhances the sensing capabilities of the aerogel.

More importantly, their small size allows for precise tuning of electronic properties, enabling selective detection of formaldehyde molecules amidst other gases.

Metal doping introduces additional functionalities to the sensor material. These metal ions can modify the electronic structure of SnO2/rGO, enhancing its sensing performance by increasing sensitivity, selectivity, and stability.

And the 2D or 2-dimensional material is the graphene sheet.

Improving with machine learning

Further, the researchers developed a gas species recognition algorithm based on dynamic feature extraction.They used machine learning algorithms to classify different gases based on their features accurately.This allowed for real-time sensing and recognition, even without reaching a steady state in the sensing response.The sensor achieved a record-high response of 15.23 percent for 1 part per million formaldehyde concentration and an ultralow detection limit of 8.02 parts per billion.

The findings of the study are published in Science Advances.

source:Interesting Engineering