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Updated: Aug 6, 2026

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Monitoring Protein Adsorption with Solid-state Nanopores
Published on: December 2, 2011
Chaotropic Surface Chemistry for Protein Profiling with Solid-State Micropores
Shoji Kashiwabara1, Md Sifat Islam1, Makusu Tsutsui1
1SANKEN, The University of Osaka, 8-1 Mihogaoka, Osaka 567-0047, Ibaraki, Japan.
ACS Sensors
|July 21, 2026
Summary
This study introduces a novel protein sensing method that uses chemical properties, not just size, to detect sequence variations. This approach enables accurate biomarker profiling without complex nanofabrication.
Area of Science:
- Biochemistry
- Nanotechnology
- Analytical Chemistry
Background:
- Proteins' biological functions are determined by their amino acid sequences, with variations linked to diseases and treatment outcomes.
- Conventional nanopore analysis of proteins faces limitations due to issues like stochastic capture and rapid translocation.
- Precise control over pore dimensions is crucial for traditional nanopore-based protein analysis.
Purpose of the Study:
- To develop a scalable method for sequence-sensitive protein analysis that bypasses the need for sub-nanometer pore control.
- To convert subtle, sequence-dependent protein surface chemistry into amplified, particle-scale transport signatures.
- To enable accurate discrimination of protein variants using readily fabricated micro-scale pores.
Main Methods:
- Immobilizing monoclonal antibody variants with distinct amino acid sequences on polymeric microparticles.
- Measuring particle-protein complex passage through silicon nitride (SiNx) micropores.
- Utilizing chaotropic chemistry (guanidine hydrochloride) to modulate surface charge and reshape the electrokinetic landscape.
- Analyzing resistive pulse waveforms generated during particle translocation using machine learning.
Main Results:
- Chaotropic perturbation effectively modulated the electrokinetic properties around particle-protein complexes in a variant-dependent manner.
- Distinct resistive pulse waveforms were generated, allowing for near-perfect discrimination of antibody variants.
- The method demonstrated sequence sensitivity by differentiating antibody variants with minor sequence variations.
- Machine learning algorithms successfully classified the antibody variants based on their unique transport signatures.
Conclusions:
- This novel approach shifts protein sensing from geometrical constraints in nanopores to chemical amplification in micrometer-scale pores.
- The method offers a scalable and accessible route for sequence-sensitive biomarker profiling.
- It eliminates the requirement for advanced nanofabrication, making protein analysis more widely applicable.

