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Anodic Stripping Voltammetry (ASV), Cathodic Stripping Voltammetry (CSV), and Adsorptive Stripping Voltammetry (AdSV) are electrochemical techniques used to determine trace amounts of analytes in solution. These methods involve applying a potential to an electrode and measuring the resulting current.
Anodic Stripping Voltammetry (ASV)
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Ladder Diagrams: Redox Equilibria01:30

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Ladder diagrams are useful tools for understanding redox equilibrium reactions, especially the effects of concentration changes on the electrochemical potential of the reaction. The vertical axis in the redox ladder diagrams represents the electrochemical potential, E. The area of predominance is demarcated using the Nernst equation.
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Standard Electrode Potentials03:02

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On comparing the reactivity of silver and lead, it is observed that the two ionic species, Ag+ (aq) and Pb2+ (aq), show a difference in their redox reactivity towards copper: the silver ion undergoes spontaneous reduction, while the lead ion does not. This relative redox activity can be easily quantified in electrochemical cells by a property called cell potential. This property is commonly known as cell voltage in electrochemistry, and it is a measure of the energy which accompanies the charge...
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Voltammetry: Factors Affecting Measurements01:21

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A current produced due to the redox reactions of the analyte at the working and auxiliary electrodes is called a faradaic current. The reaction can be divided into two types. The current generated due to the reduction of the analyte is called cathodic current, and it carries a positive charge. In contrast, the current produced by analyte oxidation is known as an anodic current, and it has a negative charge. The applied potential at the working electrode determines the faradaic current flow, and...
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Complexometric EDTA Titration Curves

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EDTA titration curves determine the free metal ion concentration. The titration curve represents the change in concentration of free metal ions (p function) as a function of the volume of EDTA added. This curve consists of three regions: before, at, and after equivalence points. Excess free metal ions are present before the equivalence point. Equal concentrations of metal ions and EDTA are present at the equivalence point. After the equivalence point, excess EDTA exists. This means slight...
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Support vector machine for EELS oxidation state determination.

D Del-Pozo-Bueno1, F Peiró1, S Estradé1

  • 1LENS-MIND, Dept. Enginyeries Electrònica i Biomèdica, Universitat de Barcelona, Martí i Franquès 1, E-08028 Barcelona, Spain; Institute of Nanoscience and Nanotechnology (IN2UB), Universitat de Barcelona (UB), E-08028 Barcelona, Spain.

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|December 15, 2020
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Summary
This summary is machine-generated.

Support Vector Machine (SVM) effectively classifies Electron Energy-Loss Spectroscopy (EELS) data, accurately determining oxidation states in iron and manganese oxides despite noise. This advances nanoscale material analysis.

Keywords:
Electron Energy-Loss SpectroscopyIron OxidesMachine LearningManganese OxidesSupport Vector MachineTransition Metals

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Area of Science:

  • Materials Science
  • Spectroscopy
  • Data Analysis

Background:

  • Electron Energy-Loss Spectroscopy (EELS) generates vast datasets for nanoscale material characterization.
  • Analyzing large EELS data volumes for physical properties mapping is challenging.

Purpose of the Study:

  • To assess the effectiveness of the Support Vector Machine (SVM) algorithm for classifying EELS data.
  • To determine the capability of SVM in identifying specific material properties from EELS spectra.

Main Methods:

  • Application of the Support Vector Machine (SVM) algorithm to EELS data analysis.
  • Training SVM models with existing datasets and testing with noisy, real-world data.

Main Results:

  • SVM accurately determined the oxidation states of iron and manganese in oxides.
  • Classification was based on the EELS fine structure (ELNES) of transition metal white lines.
  • SVM demonstrated robust performance even with noisy spectra and instrumental energy shifts.

Conclusions:

  • SVM is a powerful tool for classifying EELS spectra.
  • This method enables precise nanoscale mapping of oxidation states in materials like iron and manganese oxides.