High Throughput Approaches to Engineer Fluorescent Nanosensors

Optical sensors/probes are powerful tools to identify and image (biological) molecules. Because of their optoelectronic properties, nanomaterials are often used as building blocks. Such nanosensors are assembled from an optically sensitive nanomaterial, a (biological) recognition unit, and linker chemistry that connects them. To transduce the chemical interaction with the analyte into an optical signal, the interplay between surface chemistry and nanomaterial photophysics has to be optimized. Understanding these aspects promises major opportunities for tailored sensors with optimal performance. However, this requires methods to create and explore the wide range of possible chemical permutations. Indeed, many current approaches are limited in throughput. This affects the chemical design space that can be studied, the application of machine learning approaches as well as fundamental mechanistic understanding. Here, we provide an overview of selection-limited and synthesis-limited approaches to create and identify molecular nanosensors. We discuss bottlenecks and highlight opportunities of non-classical recognition strategies such as corona phase molecular recognition as well as the requirements for high throughput and scalability. Fluorescent carbon nanotubes are powerful building blocks for sensors and their huge chemical design space makes them an ideal platform for high throughput approaches. Therefore, they are the focus of this article, but the insights are transferable to any nanosensor system. Overall, this perspective aims to provide a fresh perspective to overcome current challenges in the nanosensor field.