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Complexometric Titration: Ligands00:43

Complexometric Titration: Ligands

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Different monodentate and polydentate ligands are used as complexing agents in complexometric titration reactions. The formation of complexes by mono- and bidentate ligands involves two or more intermediate steps, limiting their use as complexing agents. In comparison, polydentate ligands can form complexes with metal ions in a single-step process, facilitating sharper end points. This means polydentate ligands, such as amino carboxylic acid derivatives, are most commonly employed in...
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Metal-Ligand Bonds02:51

Metal-Ligand Bonds

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The hemoglobin in the blood, the chlorophyll in green plants, vitamin B-12, and the catalyst used in the manufacture of polyethylene all contain coordination compounds. Ions of the metals, especially the transition metals, are likely to form complexes.
In these complexes, transition metals form coordinate covalent bonds, a kind of Lewis acid-base interaction in which both of the electrons in the bond are contributed by a donor (Lewis base) to an electron acceptor (Lewis acid). The Lewis acid in...
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Extraction: Advanced Methods00:56

Extraction: Advanced Methods

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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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Correlating Nitrate Adsorption with the Local Environments of FeCoNiCuZn High-Entropy Alloy Catalysts Using Machine Learning.

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Updated: Jan 10, 2026

Two-way Valorization of Blast Furnace Slag: Synthesis of Precipitated Calcium Carbonate and Zeolitic Heavy Metal Adsorbent
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Predicting and Analyzing Nitrate Adsorption on High-Entropy Alloys Based on Pair Distribution Function Using a Hybrid

Truong Nhut Huynh1, Xiang He1, Kim-Doang Nguyen1

  • 1Department of Mechanical and Civil Engineering, Florida Institute of Technology, Melbourne, Florida 32901, United States.

Journal of Chemical Information and Modeling
|November 26, 2025
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Summary

Machine learning models can predict high-entropy alloy (HEA) catalyst performance using pair distribution function (PDF) data. This approach optimizes catalyst design for chemical conversions.

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Area of Science:

  • Materials Science
  • Catalysis
  • Computational Chemistry

Background:

  • High-entropy alloys (HEAs) offer exceptional stability and electronic properties, making them promising catalysts.
  • The complex design space of HEAs necessitates machine learning for optimizing catalytic performance.
  • Structural features are crucial for machine learning accuracy in HEA catalyst development.

Purpose of the Study:

  • To investigate the use of pair distribution function (PDF) data as input features for machine learning-based optimization of HEA catalysts.
  • To develop a hybrid machine learning framework for predicting catalytic activity using PDF data.
  • To assess the performance of this framework compared to conventional machine learning algorithms.

Main Methods:

  • Principal Component Analysis (PCA) was employed to reduce the dimensionality of PDF data.
  • A hybrid framework combining a transformer-based model and a large language model (LLM) was utilized.
  • The framework predicted the Gibbs free energy of nitrate adsorption on FeCoNiCuZn HEA surfaces.

Main Results:

  • The hybrid framework accurately predicted Gibbs free energy of nitrate adsorption using PCA-reduced PDF data.
  • Performance significantly surpassed conventional algorithms like random forest, support vector regression, and gradient boosting.
  • LLM integration further improved prediction accuracy and provided interpretable insights.

Conclusions:

  • PCA-reduced PDF data can serve as effective input features for machine learning models in HEA catalyst design.
  • The developed hybrid transformer-LLM framework enables accurate prediction and optimization of HEA catalysts.
  • This approach facilitates the predictive design of HEA-based catalysts with enhanced activity and selectivity.