Hydrothermal fabrication of rectorite based biocomposite modified by chitosan derived carbon nanoparticles as efficient mycotoxins adsorbents
Source abstract
Abstract
Abstract Mycotoxins contamination has been a worldwide long-standing issue, which poses tremendous threats to human health and food security. However, the detoxification of various mycotoxins, especially apolar species, is still a big challenge at present. In this study, carbon nanoparticles derived from biopolymer chitosan (Cts) and rectorite supported Cts carbon nanocomposite (Cts@Rec) were fabricated via hydrothermal treatment as efficient mycotoxin adsorbents for apolar zearalenone (ZER). Microstructural analysis revealed that Cts nanoparticles with smaller sizes uniformly distributed on the surface of rectorite, forming the Cts@Rec biocomposite. The obtained two adsorbents contained high organic carbon contents and large specific surface areas, which exhibited superior adsorption efficiency towards ZER. Kinetic study indicated that the adsorption of ZER on Cts and Cts@Rec reached equilibrium within 120 min, and the adsorption process fitted pseudo-second-order kinetic model better. The adsorption isotherms of ZER on both adsorbents matched obviously with linear model, which implied the existence of adsorption/partition model mechanism. The adsorption capacities were found to be positively correlated to the organic carbon contents and specific surface areas, suggesting that organic matter functioned as partition medium for ZER and the enhanced specific surface areas provided sorption sites for ZER. Furthermore, no desorption happened when changed the simulated pH based on the values from gastric fluid to intestinal fluid, confirming the feasibility of resultant Cts and Cts@Rec biocomposite as mycotoxins adsorbents in vivo.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Mycotoxins in Agriculture and Food · Thallium and Germanium Studies · Selenium in Biological Systems
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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