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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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Formation of Complex Ions03:45

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A type of Lewis acid-base chemistry involves the formation of a complex ion (or a coordination complex) comprising a central atom, typically a transition metal cation, surrounded by ions or molecules called ligands. These ligands can be neutral molecules like H2O or NH3, or ions such as CN− or OH−. Often, the ligands act as Lewis bases, donating a pair of electrons to the central atom. These types of Lewis acid-base reactions are examples of a broad subdiscipline called coordination...
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Related Experiment Video

Updated: May 13, 2025

Co-localizing Kelvin Probe Force Microscopy with Other Microscopies and Spectroscopies: Selected Applications in Corrosion Characterization of Alloys
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From flat to stepped: active learning frameworks for investigating local structure at copper-water interfaces.

Johannes Schörghuber1, Nina Bučková1, Esther Heid1

  • 1Institute of Materials Chemistry, TU Wien, A-1060 Vienna, Austria. georg.madsen@tuwien.ac.at.

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|April 15, 2025
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Summary

Investigating water structure at copper-water interfaces reveals how step density influences atomic arrangements. Machine-learning force fields help analyze complex surfaces, crucial for electrocatalysis applications.

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Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics
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Area of Science:

  • Materials Science
  • Physical Chemistry
  • Computational Chemistry

Background:

  • Solid-liquid interfaces are critical for electrochemical applications like electrocatalysis.
  • Understanding atomic-level interfacial water structure is key to optimizing these processes.
  • Copper-water interfaces serve as a model system for studying these phenomena.

Purpose of the Study:

  • To investigate the impact of varying step densities on interfacial water structure at copper-water interfaces.
  • To develop and apply an active learning framework for training machine-learning force fields (MLFFs).
  • To analyze the structural properties of water in the contact layer using molecular dynamics simulations.

Main Methods:

  • Developed an active learning framework utilizing spatially resolved uncertainties.
  • Trained a machine-learning force field (MLFF) on dispersion-corrected density functional theory data.
  • Employed molecular dynamics simulations to study water density profiles, angular distributions, and 2D pair correlation functions.

Main Results:

  • Observed two water sublayers at the flat Cu(111) surface, consistent with prior studies.
  • Found that high step densities lead to structures dominated by undercoordinated ridge atoms.
  • Identified a crossover point where flat surface behavior is recovered as step density decreases.
  • Characterized four distinct copper environments at interfaces using dimensionality reduction.

Conclusions:

  • Step density significantly alters interfacial water structure at copper surfaces.
  • MLFFs combined with active learning provide a powerful tool for analyzing complex, less idealized interfaces.
  • The findings offer insights into designing better electrocatalytic materials by controlling surface morphology.