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Statistical Learning-Assisted Dual-Signal Sensing Arrays Based on Conjugated Molecules for Pathogen Detection and
Haorun Song1, Mingyu Li2, Rui Li3
1Department of Chemistry, Beijing Technology and Business University, Beijing 100048, P. R. China.
ACS Applied Materials & Interfaces
|July 29, 2025
Summary
This study introduces a new sensing array using cationic conjugated polymer/silver nanoparticle composites for rapid microbial detection. The dual-signal system enhances diagnostic efficiency for identifying pathogenic microorganisms.
Area of Science:
- Nanotechnology
- Analytical Chemistry
- Microbiology
Background:
- Pathogenic microbial infections present significant risks to human health.
- Current clinical testing methods for microbes face challenges in sample volume processing and detection efficiency.
Purpose of the Study:
- To develop a novel sensing array for rapid and accurate detection and identification of pathogenic microorganisms.
- To overcome limitations in current clinical microbial testing.
Main Methods:
- Development of sensing arrays using cationic conjugated polymer/silver nanoparticle (CCP/Ag) composites.
- Utilizing five distinct conjugated polymers (PFP, PPV, PBF, PMNT, OPV) to create a diverse sensor array.
- Employing fluorescence and colorimetric dual-signal readouts, analyzed by elastic net regression.
Main Results:
- The CCP/Ag composite arrays demonstrated dual-signal readouts (fluorescence and colorimetric) for microbial detection.
- Silver nanoparticles modulated polymer responses via plasmonic effects, enhancing signal changes.
- Elastic net regression effectively distinguished and classified different microbial samples.
Conclusions:
- The developed dual-signal sensing array offers a reliable and straightforward strategy for microbial identification.
- This approach significantly improves diagnostic efficiency and supports rapid, high-throughput analysis.
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