Graphene oxide as an emerging sole adsorbent and photocatalyst: Chemistry of synthesis and tailoring properties for removal of emerging contaminants.

Sahil Thakur, Arisha Bi, Sarfaraz Mahmood, Samriti,Olim Ruzimuradov,Rajeev Gupta,Junghyun Cho,Jai Prakash

Chemosphere(2024)

引用 0|浏览1
暂无评分
摘要
Contaminants of emerging concern (CEC) contain a wide range of compounds, such as pharmaceutical waste, pesticides, herbicides, industrial chemicals, organic dyes, etc. Their presence in the surrounding has extensive and multifaceted effects on human health as they have the potential to persist in the environment, accumulate in biota, and disrupt ecosystems. In this regard, various remediation methods involving different kind of functional nanomaterials with unique properties have been developed. The functional nanomaterials can provide several mechanisms for water pollutant removal, such as adsorption, catalysis, and disinfection, in a single platform. Graphene oxide (GO) is a two-dimensional carbon-based material that has an extremely large surface area and a large number of active sites. Recent advances in synthesising GO have shown great progress in tailoring its various physiochemical, optical, surface, structural properties etc., making it better adsorbent and photocatalysts. In this review, sole adsorbent and standalone photocatalytic performances of GO for the removal of CEC have been discussed in light of tailoring its adsorption and photocatalytic properties through novel synthesis routes and optimizing synthesis parameters. This review also examines various models describing the structure of GO and its surface/structural modifications for improved adsorption and photocatalytic properties. The article provides valuable information for the production of efficient and cost-effective GO-based sole adsorbents and photocatalysts as compared to the traditional materials. Furthermore, future prospective and challenges for sole GO nanostructures to compete with traditional adsorbents and photocatalysts have been discussed providing interesting avenues for future research.
更多
查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要