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Properties of Organometallic Compounds01:23

Properties of Organometallic Compounds

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Organometallic compounds are compounds that contain a carbon–metal bond. Carbon belongs to an organyl group like alkyl, aryl, allyl, or benzyl groups. The metal can be from Group I or Group II of the periodic table, a transition metal, or a semimetal.
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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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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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Chemical bonding theories were pioneered by American chemist Gilbert N. Lewis. He developed a model called the Lewis model to explain the type and formation of different bonds. Chemical bonding is central to chemistry; it explains how atoms or ions bond together to form molecules. It explains why some bonds are strong and others are weak, or why one carbon bonds with two oxygens and not three; why water is H2O and not H4O. 
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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.
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Integrating Data Mining and Natural Language Processing to Construct a Melting Point Database for Organometallic

Jinyoung Jeong1, Taehyun Park1, JunHo Song1

  • 1School of Mechanical Engineering, Soongsil University, 369 Sangdo-ro, Dongjak-gu, Seoul 06978, Republic of Korea.

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|October 1, 2024
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Summary
This summary is machine-generated.

A new database of 1,845 organometallic compounds (OMCs) melting points was created to aid atomic layer deposition (ALD) precursor design. This resource streamlines data collection, accelerating semiconductor industry advancements.

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Area of Science:

  • Materials Science
  • Chemical Engineering
  • Computational Chemistry

Background:

  • Miniaturization of semiconductor devices increases the demand for advanced atomic layer deposition (ALD) technology.
  • Selecting appropriate ALD precursors requires knowledge of their melting points, which should be below the process temperature.
  • Existing data on organometallic compound (OMC) melting points is fragmented, hindering efficient precursor design.

Purpose of the Study:

  • To construct a comprehensive and organized database of melting points for organometallic compounds (OMCs).
  • To facilitate the screening and selection of promising ALD precursors for semiconductor manufacturing.
  • To reduce the time and cost associated with manual data collection and processing for OMC properties.

Main Methods:

  • Compiled a database of 1,845 OMCs, including data for 58 metals and 6 metalloids.
  • Extracted melting point data through automatic extraction from 11 chemical vendor databases (1,434 materials).
  • Utilized natural language processing (NLP) on 2,096 scientific papers to identify melting points for an additional 411 materials with 86.3% accuracy.

Main Results:

  • The database includes OMCs with up to ~250 atoms, with melting points ranging from -170 to 1610 °C.
  • Iron (Fe) is the most common central element (15.0%), followed by Silicon (Si) (11.6%) and Boron (B) (6.7%).
  • A multimodal neural network model showed moderate performance in predicting OMC melting points, validating the database's utility.

Conclusions:

  • The developed database significantly reduces manual data collection efforts for OMC properties.
  • This resource provides crucial information for the efficient screening of ALD precursors.
  • The findings support the advancement of semiconductor technology through improved precursor selection.