A review of hydroxyapatite synthesis for heavy metal adsorption assisted by machine learning.

Heavy metals (HMs) represent a persistent and significant threat to aquatic ecosystems. Hydroxyapatite (HAp) has emerged as a utilized material in the remediation of environmental HMs, owing to its exceptionally high porosity, expansive surface area, and the presence of three-dimensional ordered channels. An in-depth study of the synthesis strategy of HAp and its adsorption properties can help reduce the cost of remediating HMs in aquatic environments and alleviate the water shortage. In this paper, we reviewed 466 works of literature on the adsorption of heavy metals by HAp based on the Web of Science database between 2013 and 2023 that focused on the adsorption of heavy metals by HAp. We meticulously synthesized the findings related to the synthesis conditions-namely precipitation, hydrothermal, and calcination-as well as the characterization parameters and the adsorption capacity of HAp for heavy metals such as Pb, Cd, Cu, Zn, and Ni. Synthesizing advanced materials by reducing the number of experiments is essential to accelerate material development. Machine learning (ML) holds significant promise in material discovery and performance enhancement. We have consolidated the qualitative and quantitative relationships between HAp synthesis conditions, characterization parameters, and heavy metal adsorption capacity across previous studies, utilizing both the Statistical Package for Social Sciences (SPSS) and ML techniques. Building on the most recognized heavy metal adsorption mechanisms, we have evaluated the influence of characterization parameters on adsorption performance. We have outlined the optimal synthetic conditions for enhancing the adsorption of Pb, Cd, Cu, Zn, and Ni through precipitation, hydrothermal, and calcination methods, offering a practical guide for the targeted synthesis of HAp tailored to specific heavy metal adsorption capacities.