There is a strong demand to develop inexpensive, miniaturized gas sensors with significantly improved sensitivity and selectivity. Such Metal Oxide Sensors (MOS) should enable the detection of concentration of gases in air, down to individual gas molecules.
While current MOS sensors can detect a wide range of gases, the ability to discriminate between individual molecular species is mostly still poor.
VS Particle technology enables super fast iterations of material fabrication and testing, which accelerates the extensive screening of next generation MOS materials.
VSParticle technology offers a unique and versatile approach to rapidly produce nanomaterials of high purity with the ability to tune the primary particle size and elemental composition. An application of this technology is the production of gas sensors based on zinc oxide (ZnOx) porous nanostructures for toluene sensing. ZnOx nanostructures produced with the VSParticle VSP-P1 nanoprinter have an excellent sensitivity and response time due to the high surface-to-volume ratio, structured morphology and high purity of the deposited films.
A wide range of MOS sensors can be rapidly produced by our state-of-the-art VSP-P1 nanoprinter. This configuration enables device and material developers to easily print a variety of nanoporous metal oxides, including dopants, and prepare sensors with controlled particle size distribution and layer thickness.
Pure bulk material (or alloys) is used as a source and process parameters (e.g. deposition time, pattern, etc.) can fast and easily be programmed on the VSP-P1 user interface to start the fully automated synthesis and deposition of metal oxide layers directly on a substrate (e.g. on a sensor chip).
This powerful technique enables both gas sensor manufacturers as well as researchers to screen a whole spectrum of sensing materials in significant less time.
VSParticle offers a versatile and unique technology that can be directly transferred and scaled through all three steps of the materials innovation cycle: from trial and error in the lab, to optimization of the production process and large-scale sensor production.