Chiral single-walled carbon nanotubes as chiral selectors in multimode enantioselective sensors

Single-walled carbon nanotube-(7,6) chirality was used for the design of multimode enantioselective sensors using different carbon matrices such as graphene paste, graphite paste, and carbon nanopowder-based paste. l- and d-malic acids were used as model analytes. The responses of the multimode sensors were evaluated for potentiometric and differential pulse voltammetry (DPV) modes. When carbon nanopowder was used as matrix, the multimode sensor was enantioselective for d-malic acid in the concentration range 10(-3) to 10(-15) mol/L for the potentiometric mode and 10(-5) to 10(-8) mol/L for the DPV mode. The graphite paste-based sensor was enantioselective for l-malic acid in the ranges: 10(-10) to 10(-13) for the potentiometric mode and 10(-4) to 10(-7) mol/L for the DPV mode. The sensors based on graphene and chiral single-walled carbon nanotubes were enantioselective for d-malic acid, and a response was obtained only in the DPV mode. Accordingly, the matrix influenced both the enantioselectivity and the sensitivity of the measurements. The application of the sensors was for the enantioanalysis of malic acid in wines and apple juice samples. The proposed method is fast and reliable and allows the quantification of l- and d-malic acids using electrochemical methods based on different principles, from the real samples after a buffering of the samples. The enantioanalysis of malic acid in wine and juice samples was performed with high recoveries (higher than 90.00%) and low relative standard deviation (RSD) (%) values (lower than 1.00%).

Automated summary

Single-walled carbon nanotubes with (7,6) chirality were used to design multimode enantioselective sensors with different carbon matrices, showing enantioselectivity for l- and d-malic acids. The sensitivity and selectivity of the sensors were influenced by the matrix, allowing for analysis of malic acid in wine and juice samples with high recoveries and low RSD values.