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      Graphite-like carbon nitride-laden gold nanoparticles as signal amplification label for highly sensitive lateral flow immunoassay of 17β-estradiol

      , , , , , , , ,
      Food Chemistry
      Elsevier BV

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          Porous graphitic carbon nitride synthesized via direct polymerization of urea for efficient sunlight-driven photocatalytic hydrogen production.

          Energy captured directly from sunlight provides an attractive approach towards fulfilling the need for green energy resources on the terawatt scale with minimal environmental impact. Collecting and storing solar energy into fuel through photocatalyzed water splitting to generate hydrogen in a cost-effective way is desirable. To achieve this goal, low cost and environmentally benign urea was used to synthesize the metal-free photocatalyst graphitic carbon nitride (g-C₃N₄). A porous structure is achieved via one-step polymerization of the single precursor. The porous structure with increased BET surface area and pore volume shows a much higher hydrogen production rate under simulated sunlight irradiation than thiourea-derived and dicyanamide-derived g-C₃N₄. The presence of an oxygen atom is presumed to play a key role in adjusting the textural properties. Further improvement of the photocatalytic function can be expected with after-treatment due to its rich chemistry in functionalization.
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            Mechanism insight into rapid photocatalytic disinfection of Salmonella based on vanadate QDs-interspersed g-C3N4 heterostructures

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              Lateral Flow Immunoassay Based on Polydopamine-Coated Gold Nanoparticles for the Sensitive Detection of Zearalenone in Maize

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                Author and article information

                Journal
                Food Chemistry
                Food Chemistry
                Elsevier BV
                03088146
                June 2021
                June 2021
                : 347
                : 129001
                Article
                10.1016/j.foodchem.2021.129001
                33503575
                dd1cc565-69f8-4387-8edd-99a4cea32071
                © 2021

                https://www.elsevier.com/tdm/userlicense/1.0/

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