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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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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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The bond between aggregate particles and the cement matrix is significantly influenced by the shape and surface texture of the aggregates. High-strength concretes benefit from a rougher texture, which leads to stronger bonding due to greater adhesion. Angular aggregates with larger surface areas also enhance this bond. The bonding quality, however, is complex to assess as no universally accepted test exists. Good bonding is indicated when a crushed concrete specimen shows some aggregate...
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Extraction: Advanced Methods00:56

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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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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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Biasing of Metal-Semiconductor Junctions01:27

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Biasing metal-semiconductor junctions involves applying a voltage across the junction. Specifically, the metal is connected to a voltage source, while the semiconductor is grounded. This technique is essential for controlling the direction and magnitude of current flow in electronic devices, including diodes, transistors, and photovoltaic cells.
In Schottky junctions, where the semiconductor is n-type, applying a positive voltage to the metal relative to the semiconductor reduces its Fermi...
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Updated: Jul 12, 2025

Co-localizing Kelvin Probe Force Microscopy with Other Microscopies and Spectroscopies: Selected Applications in Corrosion Characterization of Alloys
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Metal-Metal Bonding Process Research Based on Xgboost Machine Learning Algorithm.

Jingpeng Feng1,2, Lihua Zhan1,2, Bolin Ma1,2

  • 1State Key Laboratory of Precision Manufacturing for Extreme Service Performance, Central South University, Changsha 410083, China.

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Summary

Optimizing metal-metal bonding uses machine learning and finite element models. This approach enhances tensile-shear strength by 14% through optimized process parameters like roughness and lap length.

Keywords:
Xgboost machine learning algorithmfinite element modelsinterpretation toolkit SHAPprocess parameter optimizationsingle-lap joints

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Area of Science:

  • Materials Science
  • Mechanical Engineering
  • Computational Science

Background:

  • Optimizing bonding process parameters conventionally involves extensive experiments and complex data analysis.
  • New technologies are essential for understanding intricate relationships between process parameters and bonding performance.

Purpose of the Study:

  • To improve metal-metal bonding performance using advanced computational and machine learning techniques.
  • To identify and optimize critical process parameters influencing bonding strength.

Main Methods:

  • Utilized Single Lap Joint (SLJ) experiments for data generation.
  • Employed Finite Element Models (FEMs) to simulate and validate bonding behavior.
  • Applied the Xgboost machine learning (ML) algorithm for parameter optimization and analysis.
  • Used SHAP (Shapley additive explanations) for interpreting parameter importance.

Main Results:

  • The Xgboost model, trained on 70 runs, demonstrated effective prediction capabilities.
  • Parameter importance for tensile-shear strength (TSS) was ranked: roughness, adhesive layer thickness, and lap length.
  • Optimized parameters included roughness (0.89 μm), adhesive layer thickness (0.1 mm), and lap length (27 mm).
  • Experimental validation showed a 14% increase in TSS with optimized parameters.

Conclusions:

  • Machine learning methods offer a more accurate and intuitive understanding of process parameters' impact on TSS.
  • The integrated approach of SLJ experiments, FEMs, and ML provides an efficient pathway for optimizing bonding processes.