RESUMO
Numerous high-performance nanotechnologies have been developed, but their practical applications are largely restricted by the nanomaterials' low stabilities and high operation complexity in aqueous substrates. Herein, we develop a simple and high-reliability hydrogel-based nanotechnology based on the in situ formation of Au nanoparticles in molybdenum disulfide (MoS2)-doped agarose (MoS2/AG) hydrogels for electrophoresis-integrated microplate protein recognition. After the incubation of MoS2/AG hydrogels in HAuCl4 solutions, MoS2 nanosheets spontaneously reduce Au ions, and the hydrogels are remarkably stained with the color of as-synthetic plasmonic Au hybrid nanomaterials (Au staining). Proteins can precisely mediate the morphologies and optical properties of Au/MoS2 heterostructures in the hydrogels. Consequently, Au staining-based protein recognition is exhibited, and hydrogels ensure the comparable stabilities and sensitivities of protein analysis. In comparison to the fluorescence imaging and dye staining, enhanced sensitivity and recognition performances of proteins are implemented by Au staining. In Au staining, exfoliated MoS2 semiconductors directly guide the oriented growth of plasmonic Au nanostructures in the presence of formaldehyde, showing environment-friendly features. The Au-stained hydrogels merge the synthesis and recognition applications of plasmonic Au nanomaterials. Significantly, the one-step incubation of the electrophoretic hydrogels leads to high simplicity of operation, largely challenging those multiple-step Ag staining routes which were performed with high complexity and formaldehyde toxicity. Due to its toxic-free, simple, and sensitive merits, the Au staining integrated with electrophoresis-based separation and microplate-based high-throughput measurements exhibits highly promising and improved practicality of those developing nanotechnologies and largely facilitates in-depth understanding of biological information.
Assuntos
Dissulfetos , Ouro , Hidrogéis , Molibdênio , Molibdênio/química , Dissulfetos/química , Ouro/química , Hidrogéis/química , Nanopartículas Metálicas/química , Eletroforese , Proteínas/análise , Proteínas/químicaRESUMO
Aerobic granular sludge is one of the most promising biological wastewater treatment technologies, yet maintaining its stability is still a challenge for its application, and predicting the state of the granules is essential in addressing this issue. This study explored the potential of dynamic texture entropy, derived from settling images, as a predictive tool for the state of granular sludge. Three processes, traditional thickening, often overlooked clarification, and innovative particle sorting, were used to capture the complexity and diversity of granules. It was found that rapid sorting during settling indicates stable granules, which helps to identify the state of granules. Furthermore, a relationship between sorting time and granule heterogeneity was identified, helping to adjust selection pressure. Features of the dynamic texture entropy well correlated with the respirogram, i.e., R2 were 0.86 and 0.91 for the specific endogenous respiration rate (SOURe) and the specific quasi-endogenous respiration rate (SOURq), respectively, providing a biologically based approach for monitoring the state of granules. The classification accuracy of models using features of dynamic texture entropy as an input was greater than 0.90, significantly higher than the input of conventional features, demonstrating the significant advantage of this approach. These findings contributed to developing robust monitoring tools that facilitate the maintenance of stable granular sludge operations.