Ultrafast python-integrated single-entity electrochemical sensor for detecting glycated albumin

This study reports a rapid single-entity electrochemistry (SEE) approach for detecting glycated albumin (GA), a biomarker primarily used for short-term glycaemic monitoring and for clinical situations in which glycated haemoglobin (HbA1c) may be unreliable. Silver nanoparticles (AgNPs) were functionalised with 4-mercaptophenylboronic acid (MPBA) to enable cis-diol affinity interactions with glycated moieties. The MPBA-AgNP conjugates were mixed directly with serum samples, and random nanoparticle collisions at an ultramicroelectrode-generated chronoamperometric transient. A Python-based workflow extracted and classified collision events using peak morphology, and quantification was performed by counting GA-associated events within a 150 s acquisition window. The method provided a linear response from 0.1% to 50% GA (R² = 0.9972 ± 0.0019) and operated in a non-immobilised, mix-and-measure format. These results highlight an ultrafast SEE readout based on single-event counting and interpretable transient features, supporting rapid GA assessment in serum matrices.