New sensor system recognizes molecules without the need for an infrared spectrometer

The new sensor system developed by the Swiss Federal Institute of Technology enables high-precision detection and analysis of molecules without the need for infrared spectrometers. It also opens the way for large-scale imaging analysis based on imaging with the help of artificial intelligence (AI). Learning is a benchmark method for detecting and analyzing organic compounds. But it requires complex operations and large, expensive instruments, so the miniaturization of the equipment is challenging, hindering some industrial and medical applications of infrared spectroscopy, as well as outdoor field data collection, such as the measurement of pollutant concentrations.

In addition, its relatively low sensitivity requires a large sample size and thus limits its wide application. To this end, scientists at the EPFL (Swiss Federal Institute of Technology) (Lausanne, Switzerland) and Australian National University (ANU, Australian National University) have developed a compact, high-sensitivity nanophotonic sensor system that eliminates the need for traditional spectroscopy. Technology can identify the characteristic absorption of molecules. They have used the system for the detection of polymers, pesticides and organic compounds. More importantly, this technology is also compatible with CMOS technology.

Translating the characteristic absorption of a molecule into a "barcode" The chemical bonds in an organic molecule have their specific orientation and mode of vibration, which affects the absorption of light by the molecule, giving each molecule its unique "fingerprint absorption." Infrared spectroscopy detects whether a sample contains a given molecule by detecting whether the sample absorbs the fingerprint characteristic frequency of the molecule. However, this analysis requires large and expensive laboratory instruments. The system developed by EPFL scientists consists of an engineered surface covered with hundreds of tiny sensor systems called Metapixels that generate different "barcodes" for each molecule that is in contact with the surface.

These barcodes can be analyzed and classified on a large scale using advanced pattern recognition and classification techniques such as artificial intelligence neural networks. The research results have been published in the journal Science published in June this year. Developed by EPFL, this groundbreaking sensor system is highly sensitive and miniaturized; it uses nanostructures that capture light at the nanoscale and is therefore extremely sensitive to samples on the system surface. “The molecules we want to detect are nanoscale, so bridging this size gap is an indispensable step,” said Hatice Altug, head of the EPFL Bio-Nano Photonics Lab and co-author of the study. The nanostructures on the surface of the system are divided into hundreds of superpixel groups, each of which resonates at a different frequency.

When a molecule comes into contact with the surface of the system, the characteristic absorption of light by the molecule changes the vibration of all the superpixels it contacts. “It is very important that these superpixels are arranged in such a way that different vibration frequencies are mapped to different areas of the system surface,” said Andreas Tittl, co-author of the study. This results in a pixelated light absorption map that can be translated into a molecular barcode. There is no need to use a spectrum analyzer for the entire process. The potential application of this new system is very wide. “For example, it can be used to make portable medical test equipment that creates barcodes for every biomarker in a blood sample,” said research author Dragomir Neshev.

This technology can also be combined with artificial intelligence to create and process molecular barcode libraries for a wide range of compounds, from proteins and DNA to pesticides and polymers, providing researchers with a new tool for fast and accurate sampling from complex samples. A trace amount of the compound was found.

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