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<p>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<sup>–18</sup> mol L<sup>–1</sup>) 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.</p>

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月21日

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  • Xinhuan Zhang,
  • Ziyang Wang,
  • Ping Duan,
  • Xijuan Wang,
  • Yiming Chen,
  • Yuxiao Chen,
  • Manqiu Ma,
  • Bailin Gao,
  • Chuanxiang Chen,
  • Zhichao Pan,
  • Chuancheng Jia,
  • Saisai Yuan

摘要

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.