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Compared with pure water, the solubility of an ionic compound is less in aqueous solutions containing a common ion (one also produced by dissolution of the ionic compound). This is an example of a phenomenon known as the common ion effect, which is a consequence of the law of mass action that may be explained using Le Chȃtelier’s principle. Consider the dissolution of silver iodide:
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Updated: Sep 17, 2025

Optimized Procedure for Determining the Adsorption of Phosphonates onto Granular Ferric Hydroxide using a Miniaturized Phosphorus Determination Method
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Multivariate explainable machine learning assists in redefining high-performance phosphate removal by layered double

Peng Zhang1, Yafei Li2, Silu Huo3

  • 1College of Environmental Science and Engineering, MOE Key Laboratory of Pollution Processes and Environmental Criteria, Nankai University, Tianjin 300350, China; Hubei Key Laboratory of Mineral Resources Processing and Environment, School of Resources and Environmental Engineering, Wuhan University of Technology, Wuhan 430070, China.

Water Research
|June 28, 2025
PubMed
Summary
This summary is machine-generated.

Machine learning optimizes layered double hydroxides (LDHs) for phosphate removal. An MNRF model identified key factors, guiding the synthesis of high-performance LDHs for effective water treatment and eutrophication prevention.

Keywords:
AdsorptionMachine learningPhosphate adsorptionPhosphate removal

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Area of Science:

  • Environmental Science
  • Materials Science
  • Data Science

Background:

  • Excessive phosphorus discharge causes eutrophication, threatening water quality and ecosystems.
  • Layered double hydroxides (LDHs) show promise for phosphate removal but require optimized design and application.
  • Complex interactions between LDH properties and operational parameters hinder performance optimization.

Purpose of the Study:

  • To develop and apply an optimized multilevel nested random forest (MNRF) model for analyzing and predicting phosphate adsorption performance of LDHs.
  • To identify critical structural features and operational parameters influencing phosphate adsorption capacity (PAC) and phosphate removal efficiency (PRE).
  • To provide a data-driven guide for designing high-performance LDHs for sustainable water treatment.

Main Methods:

  • Utilized an optimized multilevel nested random forest (MNRF) model for systematic analysis and prediction of LDH phosphate adsorption.
  • Employed multivariate interpretability techniques including Shapley values and partial dependence plots to assess feature importance.
  • Conducted experimental validation to confirm model predictions and guide LDH synthesis.

Main Results:

  • The MNRF model accurately predicted PAC and PRE, identifying key factors influencing dephosphorization.
  • Decisive structural features include metal type, synthesis temperature, and time; critical operational parameters are initial concentration, dosage, and pH.
  • Model-guided Mg-Al LDH achieved a PAC of 98.32 mg g⁻¹, while Ca-Fe LDH exceeded 93% PRE for low-concentration phosphate removal.

Conclusions:

  • Machine learning, specifically the MNRF model, offers an innovative approach to optimize LDHs for enhanced phosphate removal.
  • Identification of key factors enables targeted design of LDHs for specific water treatment scenarios.
  • This study advances sustainable water treatment by providing a framework for developing high-performance adsorbents.