There is currently no affordable, at-home device that can detect spoiled milk faster than the human nose. While technology allows us to observe distant stars and single atoms, our sense of smell remains highly accurate in identifying odors. However, portable sensors known as e-noses that mimic the olfactory system have the potential to identify volatile compounds in the air.
Harvard’s John A. Paulson School of Engineering and Applied Sciences (SEAS) has developed a new e-nose that can quickly and accurately detect volatile compounds, such as volatile organic compounds (VOCs). This sensor is unique in that it combines both passive and active sensing in a single device and uses machine learning to identify various molecules and their mixtures.
The sensor works by simulating the “sniffing” mechanism used by living organisms. It generates inhale-exhale patterns and utilizes dynamic mass transport processes. This innovative approach significantly enhances the detection capabilities of the sensor. It has broad applications, including disease diagnostics, monitoring air quality, hazardous waste detection, and detecting food spoilage.
Volatile organic compounds (VOCs) are emitted into the air by both natural and human-made sources. They are present in everyday products such as cosmetics and furniture and are also emitted from activities like agriculture, car exhaust, industrial processes, and wildfires. VOCs can range from harmless to harmful, making it crucial to monitor their presence, concentration, and identity. Recent research has shown that measuring VOCs in breath and urine can help detect certain cancers in the early stages.
Current devices used to detect and quantify VOCs in the air are often bulky and require specialized knowledge to operate and interpret. E-noses offer a portable, practical, and easy-to-read alternative. Rather than replicating millions of individual receptors in the nose, Harvard’s e-nose uses a single sensor to detect the optical properties of different VOC molecules.
The sensor consists of alternating layers of silica and titania nanoparticles with tiny pores in between. It sits atop a small glass box, and a small amount of liquid containing VOCs is added. As the liquid evaporates, the gas enters the pores of the sensor, where it adsorbs and condenses. These processes cause the color of the sensor to change. By analyzing the dynamic spectral fingerprint of each VOC, the researchers trained a machine learning algorithm to identify specific compounds, determine their concentration, and predict physical properties of volatile liquids.
Machine learning plays a crucial role in enhancing the capabilities of these sensors. With larger datasets, more sophisticated models can be developed, enabling finer discrimination of different compounds. The development of portable and easy-to-manufacture e-nose sensors holds promise for various applications, from monitoring air quality to detecting hazardous waste and even diagnosing diseases through breath analysis.
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