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Selective Single-Bacterium Analysis and Motion Tracking Based on Conductive Bulk-Surface Imprinting.

Xiaoyan Liu1, Maojin Tian1,2, Qianer Zhu1

  • 1Institute of Biomedical Engineering, College of Life Science, Qingdao University, Qingdao 266071, China.

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|April 15, 2025
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Summary

This study developed advanced conductive molecular imprinting (MI) for highly selective bacterial detection. A new method precisely tracks single bacteria, improving MI efficiency and enabling sensitive Escherichia coli (E. coli) sensing.

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Area of Science:

  • Electrochemistry
  • Materials Science
  • Biotechnology

Background:

  • Conductive molecular imprinting (MI) offers potential for electrochemical bacterial assays but faces challenges with structural rigidity and imprecise recognition sites.
  • There is a need for surface-activated MI with defined recognition sites and single-bacterium monitoring to verify efficiency.
  • Current methods lack the precision to microscopically verify MI effectiveness in bacterial capture.

Purpose of the Study:

  • To develop an improved conductive molecular imprinting (MI) technique for enhanced bacterial selectivity.
  • To create a single-bacterium tracking method for microscopic verification of MI efficiency.
  • To establish a multidimensional system for characterizing bacterial capture by MI polymers.

Main Methods:

  • Density functional theory was used to predict an ideal monomer for MI.
  • Molecular imprinting was performed using lipopolysaccharides and Escherichia coli (E. coli) as templates.
  • A deep learning-based method was developed for single-bacterial movement trajectory tracking.
  • Electrochemical sensors were fabricated using the prepared MI materials.

Main Results:

  • MI polymers with clear, high-precision recognition sites for E. coli were successfully prepared.
  • The deep learning model effectively tracked single and group bacterial movement paths and velocities.
  • The MI surface capture process for E. coli was systematically monitored and analyzed at the single-cell level.
  • The developed electrochemical sensors achieved sensitive E. coli detection (10 CFU/mL) with 433% increased selectivity.

Conclusions:

  • The study presents a novel approach to activate surface MI with precise recognition sites for bacteria.
  • A deep learning-assisted single-bacterium tracking technique enables microscopic verification of MI efficiency.
  • The developed electrochemical sensors demonstrate rapid, sensitive, and highly selective detection of E. coli.
  • This work paves the way for advanced bacterial imprinting techniques and smart biosensors.