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Related Concept Videos

Bonding in Metals02:32

Bonding in Metals

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Metallic bonds are formed between two metal atoms. A simplified model to describe metallic bonding has been developed by Paul Drüde called the “Electron Sea Model”. 
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Cycloaddition Reactions: MO Requirements for Thermal Activation01:16

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Thermal cycloadditions are reactions where the source of activation energy needed to initiate the reaction is provided in the form of heat. A typical example of a thermally-allowed cycloaddition is the Diels–Alder reaction, which is a [4 + 2] cycloaddition. In contrast, a [2 + 2] cycloaddition is thermally forbidden.
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2° Amines to N-Nitrosamines: Reaction with NaNO201:20

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Secondary amines react with nitrous acid to form N-nitrosamines, as depicted in Figure 1. Nitrous acid, a weak and unstable acid, is formed in situ from an aqueous solution of sodium nitrite and strong acids, such as hydrochloric acid or sulfuric acid, in cold conditions. In the presence of an acid, the nitrous acid gets protonated. The subsequent loss of water results in the formation of the electrophile known as nitrosonium ion.
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Nitriles to Amines: LiAlH4 Reduction00:55

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Nitriles are reduced to amines in the presence of strong reducing agents like lithium aluminum hydride through a typical nucleophilic acyl substitution. The reaction requires two equivalents of the reducing agent. The reducing agent acts as a source of hydride ions.
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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.
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Network Covalent Solids02:18

Network Covalent Solids

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Network covalent solids contain a three-dimensional network of covalently bonded atoms as found in the crystal structures of nonmetals like diamond, graphite, silicon, and some covalent compounds, such as silicon dioxide (sand) and silicon carbide (carborundum, the abrasive on sandpaper). Many minerals have networks of covalent bonds.
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Updated: Aug 16, 2025

Synthesis of Metal Nanoparticles Supported on Carbon Nanotube with Doped Co and N Atoms and its Catalytic Applications in Hydrogen Production
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Exploring the Optimal Alloy for Nitrogen Activation by Combining Bayesian Optimization with Density Functional Theory

Kazuki Okazawa1, Yuta Tsuji2, Keita Kurino1

  • 1Institute for Materials Chemistry and Engineering and IRCCS, Kyushu University, Nishi-ku, Fukuoka819-0395, Japan.

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|December 19, 2022
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Summary

Researchers used Bayesian optimization and density functional theory to find the best binary alloy catalysts for nitrogen activation. This approach efficiently identified optimal catalysts, significantly improving upon random search methods.

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Supercritical Nitrogen Processing for the Purification of Reactive Porous Materials
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Supercritical Nitrogen Processing for the Purification of Reactive Porous Materials
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Area of Science:

  • Catalysis
  • Materials Science
  • Computational Chemistry

Background:

  • Binary alloy catalysts show promise for enhanced activity in nitrogen activation compared to monometallic catalysts.
  • The vast number of possible binary alloy combinations makes identifying optimal catalysts challenging.

Purpose of the Study:

  • To efficiently search for and identify optimal binary alloy catalysts for nitrogen activation reactions.
  • To compare the efficiency of Bayesian optimization with random search for catalyst discovery.

Main Methods:

  • Utilizing a combination of Bayesian optimization and density functional theory (DFT) calculations.
  • Evaluating catalyst performance based on surface energy and reaction heat for nitrogen dissociation.

Main Results:

  • Bayesian optimization identified a binary alloy catalyst with a surface energy of approximately 0.2 eV/Ų.
  • The optimal catalyst exhibited a low reaction heat for the critical N≡N bond dissociation step.
  • The Bayesian optimization approach proved more efficient than random search in discovering effective catalysts.

Conclusions:

  • Bayesian optimization combined with DFT is an effective strategy for accelerating the discovery of advanced binary alloy catalysts.
  • The identified optimal catalyst offers a promising pathway for efficient nitrogen activation.
  • This methodology significantly reduces the search space for optimal catalyst design.