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Hyperbranched Aggregation-Induced Emission Luminogen-Based Sensor Array for Highly Sensitive Discrimination of

Shiyu Zang1, Xiao Dong2, Haozhi Song1

  • 1School of Chemistry, Dalian University of Technology, Dalian 116024, PR China.

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This summary is machine-generated.

This study presents a novel fluorescent sensor array for simultaneously detecting 15 proteins with high sensitivity. This advancement offers a promising tool for point-of-care disease diagnosis.

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Area of Science:

  • Biochemistry
  • Analytical Chemistry
  • Materials Science

Background:

  • Simultaneous detection of multiple proteins is crucial for accurate disease diagnosis.
  • Existing sensor arrays often lack the required sensitivity and selectivity for complex biological samples.
  • Point-of-care diagnostics demand rapid, sensitive, and reliable protein detection methods.

Purpose of the Study:

  • To develop a highly sensitive fluorescent sensor array for simultaneous discrimination of multiple proteins.
  • To achieve low limits of detection for proteins, enabling early disease prediction.
  • To create a robust platform for point-of-care protein analysis.

Main Methods:

  • Fabrication of a fluorescent sensor array using three hyperbranched aggregation-induced emission (AIE) luminogens.
  • Utilizing multivalent interactions (electrostatic, hydrogen bond, van der Waals forces) for protein binding.
  • Employing linear discrimination analysis (LDA) for data processing and protein identification.

Main Results:

  • Simultaneous discrimination of 15 different proteins with a low limit of detection (0.05 μM).
  • Achieved 100% accuracy in protein detection and differentiation, including mixtures.
  • Demonstrated successful exclusion of interference from amino acids and inorganic salts.
  • Validated performance in complex biological samples like urine and serum.

Conclusions:

  • The developed sensor array offers a simple, cost-effective, and highly sensitive platform for protein analysis.
  • The AIE-based sensor array exhibits excellent selectivity, rapid response, and good repeatability.
  • This technology holds significant potential for point-of-care prediction and diagnosis of protein-related diseases.