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Scanning Electron Microscopy01:07

Scanning Electron Microscopy

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A scanning electron microscope (SEM) is used to study the surface features of a sample by using an electron beam that scans the sample surface in a two-dimensional manner. Typically, areas between ~1 centimeter to 5 micrometers in width can be imaged. SEM can be used to image bacteria, viruses, tissues as well as larger samples like insects. Conventional SEM gives a magnification ranging from 20X to 30,000X and spatial resolution of 50 to 100 nanometers.
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Atomic Emission Spectroscopy: Overview01:20

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Atomic emission spectroscopy (AES) is an analytical technique used to determine the elemental composition of a sample by analyzing the light emitted from excited atoms. In AES, atoms in a sample are excited to higher energy levels by thermal energy from high-temperature sources, such as plasma, arcs, or sparks. When these excited atoms return to lower energy states, they emit light at specific wavelengths characteristic of each element. The resulting atomic emission spectrum, which consists of...
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Electron Microscope Tomography and Single-particle Reconstruction01:07

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Transmission electron microscopy (TEM) can be used to determine the 3D structure of biological samples with the help of techniques such as electron microscope tomography and single-particle reconstruction. While single-particle reconstruction can examine macromolecules and macromolecular complexes in vitro conditions only, tomography permits the study of cell components or small cells in vivo.
Electron Tomography
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Calibration Curves: Linear Least Squares01:20

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A calibration curve is a plot of the instrument's response against a series of known concentrations of a substance. This curve is used to set the instrument response levels, using the substance and its concentrations as standards. Alternatively, or additionally, an equation is fitted to the calibration curve plot and subsequently used to calculate the unknown concentrations of other samples reliably.
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Atomic Emission Spectroscopy: Lab01:29

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AES is a powerful analytical technique, especially effective when used with plasma sources, producing abundant spectra in characteristic emission lines. The Inductively Coupled Plasma (ICP), in particular, yields superior quantitative analytical data due to its high stability, low noise, low background, and minimal interferences under optimal experimental conditions. However, newer air-operated microwave sources are emerging as promising alternatives that could be more cost-effective than...
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π Electron Effects on Chemical Shift: Overview01:27

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An applied magnetic field causes loosely bound π-electrons in organic molecules to circulate, producing a local or induced diamagnetic field over a large spatial volume. As the molecules tumble in solution, the field generated by π-electrons in spherical substituents results in a zero net field. However, the net field generated by π-electrons in non-spherical substituents is not zero. The effect of this induced field depends on the orientation of the molecule with respect to B0,...
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Updated: Jul 26, 2025

Absolute Quantum Yield Measurement of Powder Samples
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Absolute Quantum Yield Measurement of Powder Samples

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Exploring the absolute yield curve of secondary electrons using machine learning methods.

Mehnaz1, Bo Da2, Z J Ding1,3

  • 1Department of Physics, University of Science and Technology of China, Hefei, Anhui 230026, People's Republic of China. zjding@ustc.edu.cn.

Physical Chemistry Chemical Physics : PCCP
|June 21, 2023
PubMed
Summary

Machine learning models predict absolute secondary electron yield (δ) by analyzing material properties and primary electron energy. This approach overcomes experimental data discrepancies and theoretical limitations for electron emission materials.

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Area of Science:

  • Materials Science
  • Surface Science
  • Plasma Physics

Background:

  • Accurate secondary electron yield (δ) data is crucial for electron emission materials applications.
  • Existing experimental data for δ shows significant discrepancies, and theories lack absolute yield prediction capabilities.
  • This limits Monte Carlo simulations and introduces uncertainties in material applications.

Purpose of the Study:

  • To establish a reliable relationship between absolute secondary electron yield (δ), material properties (atomic number Z), and primary electron energy (Ep).
  • To leverage machine learning (ML) for predicting δ based on experimental observations.
  • To address the limitations of current experimental databases and theoretical models.

Main Methods:

  • Application of machine learning (ML) models to analyze experimental secondary electron yield (δ) data.
  • Developing predictive models that correlate δ with atomic number (Z) and primary electron energy (Ep).
  • Utilizing ML to identify reliable data points within scattered experimental measurements.

Main Results:

  • ML models successfully predict the δ(Ep)-curve across a wide energy range (10 eV-30 keV) for various elements.
  • The predictions fall within the uncertainty range of existing experimental data.
  • The models can help in discerning more reliable data from scattered experimental results.

Conclusions:

  • Machine learning offers a powerful approach to predict absolute secondary electron yield (δ) accurately.
  • This method bridges the gap between experimental observations and theoretical understanding.
  • The developed ML models provide a valuable tool for material selection and simulation in electron emission applications.