Back

Semiconductor augumented valuable chemical photosynthesis from Rhodospirillum rubrum and mechanism study

Wang, L.; Shi, S.; Liang, J.; Wang, B.; Xing, X.; Zeng, C.

2023-04-11 microbiology
10.1101/2023.04.11.532515 bioRxiv
Show abstract

Photosynthetic biohybrid systems based on purple bacteria and semiconducting nanomaterials are promising platforms for sustainable solar-powered chemical production. However, these types of biohybrid systems have not been fully developed to date, and their energy utilization and electron transfer mechanisms are not well understood. Herein, a Rhodospirillum rubrum-CdS biohybrid system was successfully constructed. The photosynthetic activity and photoelectrochemical properties of biohybrid system were analyzed. Chromatographic and spectroscopic studies confirmed the metabolic activities of R. rubrum cells were effectively augmented by surface-deposited CdS nanoparticles and validated with increased H2 evolution, polyhydroxybutyric acid (PHB) production, and solid biomass accumulation. Energy consumption and metabolic profiles of R. rubrum-CdS biohybrid system exhibited a growth phase-dependent behaviour. Photoelectrochemical study confirmed that light-excited electrons from CdS enhanced photosynthetic electron flow of R. rubrum cells. Monochromatic light modulated photoexcitation of biohybrid system was utilized to explore interfacial electron transfer between CdS and R. rubrum cells, and the results showed that CdS enhanced the utilization of blue light by R. rubrum cells. This work investigated the feasibility and prospect of utilizing R. rubrum in semi-artificial photosynthesis of valuable products, and offered insights into the energy utilization and the electron transfer mechanism between nanomaterials and purple bacteria.

Matching journals

The top 16 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.