visual discrimination of alcohols using nanoplasmonic sensor array

Methanol is an extremely toxic compound engendering serious human poisoning when it comes to contact with skin, is swallowed or inhaled. numerous methanol poisonings have been reported over the past three years of the pandemic, but notoriously, discrimination of methanol from other alcohols still remains a great challenge. among the plenty of analytical techniques, colorimetric sensor arrays based on the plasmonic nanoparticles hold a great promise in naked-eye discrimination of compounds with high structural similarities and can properly meet the foregoing challenge. herein, we intend to present a colorimetric sensor array based upon the assembly of the gold nanoparticles (aunps) onto two types of bacteria for discrimination and quantification of alcohols in aqueous media. as a matter of fact, the exposure of the bacteria to the alcohols precludes the assembly of aunps, thereby hindering the color variation from red to blue. the structural characteristics of each bacteria differ from one another, resulting in independent responses for each analyte. different experimental conditions comprising the aggregation time, and incubation time were optimized both for the sensing elements. the inspection of visible spectra and rgb data by linear discriminant analysis (lda) revealed the potentiality of the presented approach in the discrimination of unicomponent and bicomponent samples of methanol and ethanol. alternatively, partial least square regression (plsr) was exploited as a multivariate regression technique for calibrating the spectral and rgb data with the concentration of the analytes. after all, the proposed strategy can open up a new avenue in the development of aggregation-based plasmonic sensors.