In situ growth of gold nanoparticles onto polydopamine-modified MXene to quickly and efficiently degrade dyes
Source abstract
Abstract
As a typical two-dimensional MXene material with abundant oxygen-containing functional groups, Ti3C2 could be used as an excellent carrier for noble metal nanoparticles to effectively avoid its agglomeration. However, Ti3C2 was easily oxidized and deteriorated when stored in a conventional environment, so polydopamine (PDA) was used to modify Ti3C2 to promote uniform loading of noble metal nanoparticles. Based on this, with Ti3AlC2 as the raw material and PDA as the modifier, in situ growth of gold nanoparticles was carried out using chloroauric acid trihydrate as the precursor, and the target nanocomposite catalyst (Au@P-Ti3C2) was then obtained. A sequence of characterizations were employed to analyze chemical structure of the resulting catalyst. The catalytic degradation performances of Au@P-Ti3C2 toward amaranth, methylene blue, chrome black T, and Congo red were systematically investigated, and the results showed that catalytic degradation percentage of Au@P-Ti3C2 for the above dyes could reach 100%. The pseudo-first-order kinetic model could be used to simulate the catalytic reaction process well, and catalytic reaction rate constants of Au@P-Ti3C2(2) for the four pollutants were 0.0411 s−1, 0.0721 s−1, 0.0273 s−1, and 0.0105 s−1, respectively. After 10 cycles of repeated catalysis, catalytic degradation percentage of the composite catalyst toward the four dyes could still remain above 89%. Therefore, this novel, green, and simple method to synthesize Ti3C2-based composite catalyst might provide extremely valuable application prospect for MXene-based catalyst in the field of harmless treatment of industrial dyes.
Source: expertdata · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: MXene and MAX Phase Materials · Nanomaterials for catalytic reactions · Advanced Photocatalysis Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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