Development of Stable Nanosubstrates Using ZnO Nanoparticle-Embedded Polymer Composites for Sensitive Detection of MPT64 Antigens.

Zinc oxide (ZnO) nanowires (NWs) are promising nanomaterials for biomolecule detection because of their high aspect ratio, biocompatibility, and optical properties. However, challenges related to the fabrication and stability of ZnO nanostructures on biocompatible materials such as organic polymers limit biomolecule detection. Here, an 8-well strip ZnO nanosubstrate was developed using a one-step integration fabrication process to overcome the challenges, in which ZnO nanoparticles (NPs) were combined with polypropylene (PP) pellets; the hydrothermal synthesis of ZnO NWs was then performed under controlled heating and agitation conditions. The suitable ZnO NP concentration was determined to create a robust seed layer, thereby forming sea-urchin-shaped ZnO NW structures. Further characterization revealed enhanced biomolecule binding efficiency, reduced autofluorescence, and stability of ZnO nanostructures after a 5 h ZnO NW growth period. This ZnO nanosubstrate was the basis for the development of a highly sensitive immunoassay for Mycobacterium tuberculosis antigen (Mycobacterium protein tuberculosis 64 [MPT64]) detection. In experiments with the recombinant MPT64, our ZnO nanosubstrate-well strip-based assay showed a significantly lower limit of detection (0.977 pg/mL) than conventional immunoplates (62.5 pg/mL) and commercial lateral flow assays (1 ng/mL). The clinical relevance of the ZnO nanosubstrate well strip-based immunoassay was validated by its ability to reliably distinguish between cultured sputum samples from patients with tuberculosis (TB) and those from patients with non-tuberculous mycobacterial infection or negative control subjects. Accordingly, the proposed 8-well strip ZnO nanosubstrate offers a highly reliable platform for immunoassay development, providing enhanced sensitivity and robust performance for TB diagnostics and broader biomolecule detection applications.