Experience networking and the typical trade fair feeling digitally. Visit us from 19 to 23 October 2020 at the #analyticavirtual – the new online trade fair for laboratory technology, analysis, biotechnology and analytica conference.
We participated to the 37th meeting of the Swiss Group of Mass Spectrometry (SGMS) at the Dorint Resort Blüemlisalp in Beatenberg on 24-25 October 2019.
A ‘Smart’ and Compact TOF Mass Spectrometer Designed for Manufacturability with State-of-the-Art Technology
We present a new compact time-of-flight (TOF) mass spectrometer designed for manufacturability and mass production, using state-of-the-art electronics and software technologies, all making it a competitive alternative to a quadrupole instrument where speed and sensitivity matter.
This new device is based on orthogonal-extraction reflectron TOF architecture (sensor: ~300mm long) and operates at up to 20 kHz, for a complete mass spectrum up to every 0.1 s. Early tests showed 900 M/DM (at 44u, CO2) mass resolution and 105 dynamic range.
Among others, this instrument includes an adapter that allows to combine a commercial quadrupole ion source with the ion source of the TOF mass analyzer, thus allowing for a seamless replacement of an existing slow quadrupole filter with a faster and more sensitive TOF sensor.
The instrument combines selected ideas from space exploration with state-of-the-art technologies. For example, it combines particle swarm optimization algorithms originally developed for the instrument onboard the ESA Rosetta cometary mission (Bieler et al., 2011) with a System-on-Chip (SoC) to autonomously optimize the ion optics without any calibration gas. Moreover, the same capabilities of remote control, data retrieval, and diagnostic required in space are implemented with an HTTP API, making it the first TOF mass spectrometer 100% ready for the Internet of Things (IoT).
Finally, the integration with standard technologies for the storage and visualization of time series, such as InfluxDB and Grafana, and the planned integration with tools such as R, SAS, and Python, open new possibilities for inline measurements, data analytics, and preventive maintenance using machine learning algorithms.