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Untapping multiparametric 2D luminescence sensing through MACHine LEarning and Spectral Sorting

CORDIS · observation · Publication date unknown

Untapping multiparametric 2D luminescence sensing through MACHine LEarning and Spectral Sorting Cellular organisms are complex machines whose health and functioning are dictated by parameters including temperature, oxygen concentration, and pH. Luminescence nanosensing is a technology that promises all the features required to reliably monitor these parameters at the intracellular level: minimal invasiveness, remote working principle, and submicrometric spatial resolution. These features are ensured by the use of sub-micrometric sensors (particles) whose luminescence is sensitive to changes in the parameters to be sensed. Yet, despite the hype about luminescence nanosensing, its reliability in the study of cells is limited by interparticle variability in optical properties and sensing performance, simultaneous response to several parameters (cross-sensitivity), and lack of a measurement technology that enables fast 2D mapping of multiple parame

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recordType

award

status

SIGNED

region

EU

value

1500000

unit

EUR