Work

Performance improvement of single-molecule sensors through deep learning-based decoding of tunneling signals enables sub-attomolar sensitivity

Year: 2026

Type: article

Abstract: Detecting individual molecules in real time provides high sensitivity for sensing applications. The break junction technique enables highly sensitive single-molecule detection by capturing the specific electronic signatures of individual molecules. However, this method is typically restricted by the requirement for anchoring groups on target analytes. Harnessing intermolecular interactions offers a solution to detect molecules without anchoring groups. Yet the resulting signals are often weak, sparse, and hidden in ensemble analyses. Here, we integrate rationally designed porphyrin-based probes with a time-frequency deep-learning framework to decode these subtle signatures. With this approach, we elevate the detection limit to the sub-attomolar level (10–18 mol L–1) with seconds-scale response time (26 s). Validated across diverse analytes, our strategy consistently enhances sensitivity, confirming its generalizability. This synergistic strategy establishes a paradigm for single-molecule chemical sensing both in methodological and performance dimensions. By transforming fleeting interactions into actionable detection, the approach could provide a robust and broadly applicable framework for environmental monitoring and molecular diagnostic. Single-molecule sensing using the break junction technique typically requires specific anchoring groups. Here, the authors combine porphyrin probes with deep learning to decode weak interactions, thereby achieving rapid detection in the sub-attomolar range for various unanchored molecules.

Source: Nature Communications

DOI: 10.1038/s41467-026-72249-3