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Related Concept Videos

Metal-Semiconductor Junctions01:24

Metal-Semiconductor Junctions

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The contact of metal and semiconductor can lead to the formation of a junction with either Schottky or Ohmic behavior.
Schottky Barriers
Schottky barriers arise when a metal with a work function (Φm) contacts a semiconductor with a different work function (Φs). Initially, electrons transfer until the Fermi levels of the metal and semiconductor align at equilibrium. For instance, if Φm > Φs, the semiconductor Fermi level is higher than the metal's before contact. The...
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Biasing of Metal-Semiconductor Junctions01:27

Biasing of Metal-Semiconductor Junctions

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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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Alkali Metals03:06

Alkali Metals

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Group 1 elements are soft and shiny metallic solids. They are malleable, ductile, and good conductors of heat and electricity. The melting points of the alkali metals are unusually low for metals and decrease going down the group, while the density increases going down the group with the exception of potassium (Table 1).
Table 1: Properties of the alkali metals
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Semiconductors01:22

Semiconductors

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There is variation in the electrical conductivity of materials - metals, semiconductors, and insulators that are showcased with the help of the energy band diagrams.
Metals such as copper (Cu), zinc (Zn), or lead (Pb) have low resistivity and feature conduction bands that are either not fully occupied or overlap with the valence band, making a bandgap non-existent. This allows electrons in the highest energy levels of the valence band to easily transition to the conduction band upon gaining...
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Calculating Equilibrium Concentrations02:05

Calculating Equilibrium Concentrations

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Being able to calculate equilibrium concentrations is essential to many areas of science and technology—for example, in the formulation and dosing of pharmaceutical products. After a drug is ingested or injected, it is typically involved in several chemical equilibria that affect its ultimate concentration in the body system of interest. Knowledge of the quantitative aspects of these equilibria is required to compute a dosage amount that will solicit the desired therapeutic effect.
A more...
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Oxidation Numbers03:14

Oxidation Numbers

42.8K
In redox reactions, the transfer of electrons occurs between reacting species. Electron transfer is described by a hypothetical number called the oxidation number (or oxidation state). It represents the effective charge of an atom or element, which is assigned using a set of rules.
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Related Experiment Video

Updated: Feb 4, 2026

Silicon Metal-oxide-semiconductor Quantum Dots for Single-electron Pumping
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Research on a Mixed Gas Recognition and Concentration Detection Algorithm Based on a Metal Oxide Semiconductor

Yonghui Xu1, Xi Zhao2, Yinsheng Chen3

  • 1School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, China. xyh@hit.edu.cn.

Sensors (Basel, Switzerland)
|October 3, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces an advanced hybrid gas detection method using Kernel Principal Component Analysis (KPCA) and K-nearest neighbour (KNN) for accurate identification and concentration measurement of mixed gases.

Keywords:
gas detectiongas identificationkernel principal component analysismultivariate relevance vector machinesensor array

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Area of Science:

  • Chemical Sensing
  • Machine Learning Applications
  • Environmental Monitoring

Background:

  • Machine olfactory systems often struggle with low accuracy in identifying and quantifying hybrid gases.
  • Existing methods like Principal Component Analysis (PCA) and Independent Component Analysis (ICA) have limitations in handling complex gas mixtures.

Purpose of the Study:

  • To develop a novel, highly accurate method for hybrid gas identification and concentration detection.
  • To improve the performance of machine olfactory systems for real-world applications.

Main Methods:

  • Utilized Kernel Principal Component Analysis (KPCA) for nonlinear feature extraction of mixed gases.
  • Employed K-nearest neighbour (KNN) algorithm for gas identification and classification.
  • Applied Multivariable Relevance Vector Machine (MVRVM) for nonlinear regression to determine gas concentrations.

Main Results:

  • Achieved a hybrid gas identification accuracy of 98.33%, significantly outperforming PCA (5.83% lower) and ICA (14.16% lower).
  • Reduced average relative errors for CO and CH₄ concentration detection to 5.58% and 5.38%, respectively.
  • Demonstrated the method's effectiveness using CO and CH₄ as experimental samples.

Conclusions:

  • The proposed KPCA-KNN-MVRVM method offers superior accuracy and reduced error in hybrid gas sensing.
  • This approach significantly enhances the capabilities of machine olfactory systems for precise gas analysis.