ArticleNature communications2026
Performance improvement of single-molecule sensors through deep learning-based decoding of tunneling signals enables sub-attomolar sensitivity.
Article in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
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Authors and funding
12 authors.
Funding
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
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Registered trials
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