ArticleRSC advances2026
Artificial neural network model of the capacity of diethylenetriamine functionalised zinc oxide-apricot stone shell nanocomposite for sequestering 2,4-dichlorophenoxyacetic acid from wastewater.
Article in RSC advances, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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4 authors.
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Abstract
A novel nanocomposite, diethylenetriamine functionalised zinc oxide-apricot stone shell (ZnO@Ap/DETA), has been synthesised for the efficient sequestration of 2,4-dichlorophenoxyacetic acid (2,4-D). The capacity and removal efficiency were evaluated using an artificial neural network (ANN). The characterisation of ZnO@Ap/DETA was performed to determine its physicochemical properties through various instrumental techniques. The nanocomposite displayed a more uniform mesoporous structure, having pores with an average size of 34.065 nm and a significantly enhanced surface area of 26.56 m
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