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Related Concept Videos

Complexometric Titration: Ligands00:43

Complexometric Titration: Ligands

1.5K
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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Complexation Equilibria: The Chelate Effect01:19

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In complexation reactions, metal atoms or cations interact with ligands to form donor-acceptor adducts called metal complexes. Ligands that bind through one donor site are monodentate, ligands with two donor sites are bidentate, and those with more than two donor sites are polydentate ligands. For example, ethylene diamine is a bidentate ligand that binds through two nitrogen donor atoms, forming a five-membered ring. EDTA is a polydentate ligand that binds through four oxygen and two nitrogen...
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Color in Coordination Complexes
When atoms or molecules absorb light at the proper frequency, their electrons are excited to higher-energy orbitals. For many main group atoms and molecules, the absorbed photons are in the ultraviolet range of the electromagnetic spectrum, which cannot be detected by the human eye. For coordination compounds, the energy difference between the d orbitals often allows photons in the visible range to be absorbed and emitted, which is seen as colors by the human...
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Crystal Field Theory - Octahedral Complexes02:58

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Crystal Field Theory
To explain the observed behavior of transition metal complexes (such as colors), a model involving electrostatic interactions between the electrons from the ligands and the electrons in the unhybridized d orbitals of the central metal atom has been developed. This electrostatic model is crystal field theory (CFT). It helps to understand, interpret, and predict the colors, magnetic behavior, and some structures of coordination compounds of transition metals.
CFT focuses on...
28.6K
Valence Bond Theory02:42

Valence Bond Theory

9.9K
Coordination compounds and complexes exhibit different colors, geometries, and magnetic behavior, depending on the metal atom/ion and ligands from which they are composed. In an attempt to explain the bonding and structure of coordination complexes, Linus Pauling proposed the valence bond theory, or VBT, using the concepts of hybridization and the overlapping of the atomic orbitals. According to VBT, the central metal atom or ion (Lewis acid) hybridizes to provide empty orbitals of suitable...
9.9K
Metal-Ligand Bonds02:51

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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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Preparation, Purification, and Characterization of Lanthanide Complexes for Use as Contrast Agents for Magnetic Resonance Imaging
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Deep Learning Insights into Lanthanides Complexation Chemistry.

Artem A Mitrofanov1,2, Petr I Matveev1, Kristina V Yakubova2

  • 1Department of Chemistry, Lomonosov Moscow State University, Leninskie Gory, 1 bld.3, 119991 Moscow, Russia.

Molecules (Basel, Switzerland)
|June 2, 2021
PubMed
Summary
This summary is machine-generated.

Deep learning models predict lanthanide complex stability by identifying key molecular structures. This approach clarifies the "black box" of structure-property relationships in chemistry.

Keywords:
complexationdeep learninglanthanides

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Area of Science:

  • Computational Chemistry
  • Materials Science
  • Chemical Physics

Background:

  • Structure-property models in chemistry often function as "black boxes," lacking clear interpretability from molecular structure to target properties.
  • Understanding ligand influence on metal complexation is crucial for designing efficient chemical processes.

Purpose of the Study:

  • To apply deep learning not only for predicting metal complex stability but also for identifying key structural fragments responsible for complexation efficiency.
  • To develop interpretable models for lanthanide-metal complexation using deep learning.

Main Methods:

  • Collected stability data for chemically similar lanthanide ion complexes.
  • Built deep learning models to predict complex stability constants.
  • Decoded the deep learning models to identify critical structural fragments influencing complexation.

Main Results:

  • Achieved good correlation between predicted and experimental stability constants.
  • Identified specific molecular fragments crucial for lanthanide complexation efficiency.
  • Demonstrated that the relative positioning of binding centers significantly impacts complexation constants.

Conclusions:

  • Deep learning offers a powerful tool for creating interpretable structure-property models in chemistry.
  • The study elucidates key structural determinants of lanthanide complexation, advancing the understanding of metal-ligand interactions.
  • This approach provides a pathway to rationally design ligands for targeted metal complexation.