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Metal ions can be separated from one another by complexation with organic ligands–the chelating agent– to form uncharged chelates. Here, the chelating agent must contain hydrophobic groups and behave as a weak acid, losing a proton to bind with the metal. Since most organic ligands used in this process are insoluble or undergo oxidation in the aqueous phase, the chelating agent is initially added to the organic phase and extracted into the aqueous phase. The metal-ligand complex is...
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Adenosine triphosphate, or ATP, is considered the primary energy source in cells. However, energy can also be stored in the electrochemical gradient of an ion across the plasma membrane, which is determined by two factors: its chemical and electrical gradients.
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Related Experiment Video

Updated: Dec 12, 2025

Stable Aqueous Suspensions of Manganese Ferrite Clusters with Tunable Nanoscale Dimension and Composition
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Manganese (Mn) removal prediction using extreme gradient model.

Suraj Kumar Bhagat1, Tiyasha Tiyasha1, Tran Minh Tung1

  • 1Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City, Viet Nam.

Ecotoxicology and Environmental Safety
|August 14, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces an Extreme Gradient Boosting (XGBoost) model for predicting Manganese (Mn) removal efficiency. The XGBoost model demonstrated superior performance compared to other machine learning methods, with

Keywords:
Environmental assessmentMn removal predictionRandom forestRemoval efficiencyXGBoost model

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Area of Science:

  • Environmental Science and Engineering
  • Computational Chemistry
  • Water Treatment Technologies

Background:

  • Manganese (Mn) is a critical heavy metal (HM) regulated by the WHO, impacting ecosystem health.
  • Accurate prediction of Mn removal is essential for effective water treatment and environmental management.
  • Understanding the influence of various chemical and physical factors on Mn removal is crucial.

Purpose of the Study:

  • To develop and evaluate an Extreme Gradient Boosting (XGBoost) model for predicting Manganese (Mn) removal.
  • To assess the predictability of Mn removal using influencing factors: D2EHPA, Time, H2SO4, NaCl, and EDTA.
  • To compare the performance of XGBoost against Multilinear Regression (MLR), Support Vector Machine (SVM), and Random Forest (RF) models.

Main Methods:

  • Statistical analysis of Mn removal data to understand stochastic behavior.
  • Application of Principal Component Analysis (PCA) biplot to identify predictor importance.
  • Implementation and validation of XGBoost model against MLR, SVM, and RF using R² and RMSE metrics.

Main Results:

  • The XGBoost model significantly outperformed MLR, SVM, and RF, achieving the highest R² (0.75) and lowest RMSE (2.23) on the test set.
  • Model performance ranking: XGBoost > RF > SVM > MLR.
  • The 'Time' predictor was identified as the most influential factor in the XGBoost model's Mn removal prediction.

Conclusions:

  • XGBoost is a highly efficient, stable, and reliable model for predicting Manganese removal.
  • The study highlights the effectiveness of data-driven approaches in optimizing water treatment processes.
  • Understanding predictor importance, particularly 'Time', can guide process optimization for enhanced Mn removal.