Visible-light driven Photoelectrochemical Immunosensor Based on SnS2@mpg-C3N4 for Detection of Prostate Specific Antigen
Abstract Herein, a novel label-free photoelectrochemical (PEC) immunosensor based on SnS2@mpg-C3N4 nanocomposite is fabricated for the detection of prostate specific antigen (PSA) in human serum. Firstly, mesoporous graphite-like carbon nitride (mpg-C3N4) with carboxyl groups is synthesized successf...
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| Autores principales: | , , , , , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Nature Portfolio
2017
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/1cf27f06ec424772b4624b4cc372a39d |
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| Sumario: | Abstract Herein, a novel label-free photoelectrochemical (PEC) immunosensor based on SnS2@mpg-C3N4 nanocomposite is fabricated for the detection of prostate specific antigen (PSA) in human serum. Firstly, mesoporous graphite-like carbon nitride (mpg-C3N4) with carboxyl groups is synthesized successfully which possesses high specific surface area and large pore volume. Then, SnS2 as a typical n-type semiconductor with weak photoelectric conversion capability is successfully loaded on carboxylated mpg-C3N4 to form a well-matched overlapping band-structure. The as-synthesized SnS2@mpg-C3N4 nanocomposite performs outstanding photocurrent response under visible-light irradiation due to low recombination rate of photoexcited electron-hole pairs, which is transcend than pure SnS2 or pure mpg-C3N4. It is worth noting that SnS2@mpg-C3N4 nanocomposite is firstly employed as the photoactive material in PEC immunosensor area. The concentration of PSA can be analyzed by the decrease in photocurrent resulted from increased steric hindrance of the immunocomplex. Under the optimal conditions, the developed PEC immunosensor displays a liner photocurrent response in the range of 50 fg·mL−1 ~ 10 ng·mL−1 with a low detection limit of 21 fg·mL−1. Furthermore, the fabricated immunosensor with satisfactory stability, reproducibility and selectivity provides a novel method for PSA determination in real sample analysis. |
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