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Properties of Organometallic Compounds

Properties of Organometallic Compounds

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.

Extraction: Advanced Methods

Extraction: Advanced Methods

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...

Crystal Field Theory - Octahedral Complexes

Crystal Field Theory - Octahedral Complexes

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...

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Related Experiment Video

Updated: Jun 21, 2025

Author Spotlight: Experimental Approaches for the Synthesis of Low-Valent Metal-Organic Frameworks from Multitopic Phosphine Linkers

Author Spotlight: Experimental Approaches for the Synthesis of Low-Valent Metal-Organic Frameworks from Multitopic Phosphine Linkers

Published on: May 12, 2023

Deep learning-based recommendation system for metal-organic frameworks (MOFs).

Xiaoqi Zhang¹, Kevin Maik Jablonka^1,2,3, Berend Smit¹

¹Laboratory of Molecular Simulation (LSMO), Institut des Sciences et Ingénierie Chimiques, Ecole Polytechnique Fédérale de Lausanne(EPFL) Rue de l'Industrie 17 CH-1951 Sion Valais Switzerland berend.smit@epfl.ch.

Digital Discovery

|July 12, 2024

Summary

This summary is machine-generated.

This study introduces a novel recommendation system for metal-organic frameworks (MOFs) using an unsupervised machine learning model. It efficiently identifies promising MOF materials for diverse applications like carbon capture and methane storage.

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Synthesis and Characterization of Functionalized Metal-organic Frameworks

Synthesis and Characterization of Functionalized Metal-organic Frameworks

Published on: September 5, 2014

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Last Updated: Jun 21, 2025

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Published on: May 12, 2023

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Synthesis and Characterization of Functionalized Metal-organic Frameworks

Synthesis and Characterization of Functionalized Metal-organic Frameworks

Published on: September 5, 2014

Area of Science:

Materials Science
Computational Chemistry
Machine Learning

Background:

Metal-organic frameworks (MOFs) offer tunable properties for various applications.
Discovering optimal MOFs often requires extensive experimental screening and data labeling.
Developing efficient computational methods is crucial for accelerating MOF discovery.

Purpose of the Study:

To develop an unsupervised machine learning system for recommending metal-organic frameworks (MOFs).
To leverage intrinsic MOF characteristics for material embedding and similarity analysis.
To reduce the burden of exhaustive material labeling in MOF databases.

Main Methods:

Utilized the unsupervised Doc2Vec model for embedding MOFs into a high-dimensional chemical space.
Trained the model on document-structured intrinsic MOF characteristics.
Employed similarity analysis based on user-endorsed MOFs to identify promising candidates.

Main Results:

Successfully embedded MOFs into a chemical space, enabling similarity-based recommendations.
Demonstrated the system's ability to suggest promising MOFs for specific applications.
Showcased adaptability across diverse applications including methane storage, carbon capture, and quantum properties.

Conclusions:

The proposed recommendation system significantly reduces the need for extensive material labeling.
This approach accelerates the identification of suitable MOFs for targeted applications.
The system offers a scalable and efficient method for MOF material discovery.