Recombinant Nus-organophosphorus hydrolase (OPH) immobilized carbon quantum dot (CQD) loaded thin films for efficient...

The toxicity of organophosphate (OP) insecticides and pesticides is increasing in the human population owing to their indiscriminate use globally. The need to achieve food security for the rising population is the main reason for the excessive use of such chemical compounds. The hazardous effects of OP pesticides can be mitigated only through conscientious efforts in detecting them in various food and water resources. In the present study, we developed carbon quantum dots (CQDs) and recombinant Nus-organophosphorus hydrolase (Nus-OPH) enzyme co-immobilized chitosan polymer film-based biosensors for the detection of OP compounds, such as coumaphos (CP), methyl parathion (MP) and ethyl paraoxon (EP), present in river water and household water resources using a fiber optic spectrophotometer. The E. coli strains were utilized for the expression of the Nus-OPH enzyme, purified and subsequently immobilized on the chitosan thin films containing CQDs that were synthesized from triethylene diamine and phthalic acid through microwave irradiation. The thin-film biosensors composed of CQDs-Nus-OPH were characterized utilizing several methods like XRD, XPS, TEM, CLSM, and enzyme activity measurements. The biosensing studies of CP, EP, and MP were performed by recording fluorescence changes using reflectance probe-mediated measurements in a fiber optic spectrophotometer. This study determined that the CQDs-Nus-OPH thin film-based biosensor has the potential to detect/analyze EP, CP, and MP in the 0-100 µM range with the limits of detection of 145 ppb, 178 ppb, and 188 ppb, respectively, within a time frame of 5 minutes. The accuracy of the biosensor in the OP-spiked water matrices was found to be 100%-102%, indicating that the CQDs-Nus-OPH thin-film biosensors can detect the analyte accurately. These results indicate that this biosensor platform shows potential for organophosphate detection, although further studies are needed to improve specificity and analyte discrimination.