Electrochemical in-biosensing computing.

Artificial intelligence (AI)-aided electrochemical biosensing is becoming integral parts in numerous scenarios. However, existing systems generally perform algorithms in external signal processing units. The necessity of analog-to-digital conversion and data transfer results in high complexity, low working efficiency and concern of privacy. In-sensor computing has made great progress in perceiving and processing physical signals, which, nevertheless, faces inherent restriction in biochemical scenarios due to the lack of aqueous compatibility and the necessity of an array. Here, we realized neuromorphic electrochemical in-biosensing computing using just a single photoelectrochemical transistor, which can itself not only perform multi-target biosensing but also constitute a single-layer algorithmic classifier. It is based on a rationally designed multi-gate photoelectrochemical transistor, whose architecture and synaptic memory enable built-in vector-matrix multiplication and light-tunable responsivity. The proof-of-concept is demonstrated by simultaneous sensing and classification of biomarker microRNA fingerprints in real biological samples, which opens the possibilities for next-generation AI-driven electrochemical biosensing with edge computing ability.