Metal-Ion Optical Fingerprinting Sensor Selection via an Analyte Classification and Feature Selection Algorithm.

ABSTRACT: Accurate analyte classification remains a significant challenge in sensor technologies. We present the Analyte Classification and Feature Selection Algorithm (ACFSA), a computational tool designed to identify optimal sensor combinations from unique fingerprint patterns for analyte classification. We applied the ACFSA to a library of peptide-corona-functionalized single-walled carbon nanotubes (SWCNTs), developed as a near-infrared fluorescent, semiselective fingerprinting sensor set for detecting heavy metal ions. Inspired by natural metal-ion complexation sites, each SWCNT sensor in this library features a unique peptide sequence containing various amino acids for metal binding, revealing diverse optical response patterns to the various metal ions tested. The sensor library was further di

SUBMITTER: Petresky G 

PROVIDER: S-EPMC12044593 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature