The robotic microscope that detects asbestos


By LabOnline Staff
Monday, 20 March, 2017


Istock 000015475981medium

Frontier Microscopy has developed an automated microscopy analysis system for the asbestos air monitoring industry. The analysis program, named Marvin, uses artificial intelligence and a robotic microscope to screen air samples for asbestos fibres in a fraction of the time it takes humans.

The company’s co-founder and managing director, Jordan Gruber, explained that current asbestos screening processes require people to analyse samples, record the data on paper and then manually enter the information into a computer. According to Gruber, this process is too slow and could lead to potentially avoidable exposure.

In contrast, Marvin’s microscope is a custom-built robotic device that does not include an eyepiece but instead takes up to 100 images across an entire sample in less than a minute. These images are then automatically uploaded to the software, where they are analysed for traces of asbestos, before being stored on the cloud and formatted into a suitable document for relevant government bodies or companies.

A recent graduate of the Southstart accelerator program, Frontier Microscopy recently received $50,000 in funding from the South Australian Government’s Early Commercialisation Fund and is still continuing to look for more opportunities. The company will make Marvin available worldwide in the middle of the year.

This is a modified version of a story published by The Lead South Australia under Creative Commons.

Image credit: ©iStockphoto.com/Alistair Forrester Shankie

Related News

Calibration standard moves to final stage of revision

The International Organization for Standardization (ISO) is revising ISO/IEC 17025, General...

Quantum technology for imaging life at the nanoscale

Researchers have demonstrated a way to detect and image electronic spins non-invasively with...

A concord of chemistry and crystallography

Researchers have released a theoretical approach to explain inconsistencies between...


  • All content Copyright © 2017 Westwick-Farrow Pty Ltd