Nanotech Sensor Turns Molecular Fingerprints Into Bar Codes

Imaging-based molecular barcoding with pixelated dielectric metasurfaces

A new system developed at EPFL can detect and analyze molecules with very high precision and without needing bulky equipment. It opens the door to large-scale, image-based detection of materials aided by artificial intelligence. The research has been published in Science. (source: EPFL)

August 27, 2018 | Source: Ecole polytechnique fédérale de Lausanne, actu.epfl.ch, 6 July 2018, Laure-Anne Pessina

Infrared spectroscopy is the benchmark method for detecting and analyzing organic compounds. But it requires complicated procedures and large, expensive instruments, making device miniaturization challenging and hindering its use for some industrial and medical applications and for data collection out in the field, such as for measuring pollutant concentrations. Furthermore, it is fundamentally limited by low sensitivities and therefore requires large sample amounts.

However, scientists at EPFL’s School of Engineering and at Australian National University (ANU) have developed a compact and sensitive nanophotonic system that can identify a molecule’s absorption characteristics without using conventional spectrometry. The scientists have already used their system to detect polymers, pesticides and organic compounds. What’s more, it is compatible with CMOS technology.

Their system consists of an engineered surface covered with hundreds of tiny sensors called metapixels, which can generate a distinct bar code for every molecule that the surface comes into contact with. These bar codes can be massively analyzed and classified using advanced pattern recognition and sorting technology such as artificial neural networks. This research – which sits at the crossroads of physics, nanotechnology and big data – has been published in Science.

There are a number of potential applications for this new system. “For instance, it could be used to make portable medical testing devices that generate bar codes for each of the biomarkers found in a blood sample,” says Dragomir Neshev, another coauthor of the study.

Artificial intelligence could be used in conjunction with this new technology to create and process a whole library of molecular bar codes for compounds ranging from protein and DNA to pesticides and polymers. That would give researchers a new tool for quickly and accurately spotting miniscule amounts of compounds present in complex samples.