journal article·2023-01

Life cycle assessment of LDH-MgFe production for nitrate removal: impacts of synthesis methods

Anamália Ferreira da Silva, José Leandro da Silva Duarte, Rangabhashiyam Selvasembian, Lucas Meili

Journal of Nanoparticle Research· Volume 25· Issue 1
Publisher page

Abstract

Layered double hydroxides (LDHs) form a suitable structure for nitrate removal in aqueous solution. Its synthesis can be obtained by different methods, under different conditions, such as hydrothermal treatment and coprecipitation, a more conventional method. However, the investigation of the possible environmental impacts of each synthetic route has not yet been reported in the literature and, for that, we conducted a simplified life cycle assessment (LCA) of LDH-MgFe production, to identify the main environmental impacts associated with each route of synthesis. The approach was employed on a work scale, investigating coprecipitation, hydrothermal, ultrasound, and microwave methods, with the aid of the openLCA 1.10.3 software, using data from the ecoinvent database v.3, based on the CML 2000 methodology (midpoint). Thus, this study presented the possibility of identifying the main environmental impacts for each synthesis method as a function of the nitrate removal capacity of each produced adsorbent. The results of the LCA comparison showed that the adsorbents obtained by coprecipitation and ultrasound syntheses had the lowest environmental impacts for all tested indicators: acidification potential, eutrophication, ozone layer destruction potential, and human toxicity. In addition, sodium hydroxide and electricity, product and input energy of the LDH production process, would contribute significantly to the increase in the impact on all synthesized adsorbents.
Source: expertdata · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Layered Double Hydroxides Synthesis and Applications · Phosphorus and nutrient management · Advanced Photocatalysis Techniques

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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