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Controlled-Potential Coulometry: Electrolytic Methods01:17

Controlled-Potential Coulometry: Electrolytic Methods

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Controlled-potential coulometry, also known as potentiostatic coulometry, employs a three-electrode system in which the working electrode's potential is precisely regulated using a potentiostat. Platinum working electrodes are utilized for positive potentials, while mercury pool electrodes are favored for extremely negative potentials. The platinum counter electrode is separated from the analyte using a membrane or salt bridge to avoid interference in the analysis.
The chosen potential...
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Controlled-Current Coulometry: Overview01:27

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Controlled current coulometry, also known as amperostatic coulometry, is a technique used in electrochemical analysis to measure the quantity of a substance through the controlled passage of current. It involves the application of a constant current to an electrochemical cell containing the analyte of interest. As the current flows through the cell, the analyte undergoes a redox reaction at the electrode surface, resulting in a charge transfer. By monitoring the time required for a certain...
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Electrodeposition01:08

Electrodeposition

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Electrodeposition is a technique used to separate an analyte from interferents by electrochemical processes. Here, the analyte is a metal ion that can be deposited on an electrode immersed in the sample solution. The electrochemical setup consists of an anode and a cathode. When an electric current is applied to the setup, oxidation occurs at the anode. At the cathode, which consists of a large metal surface, metal ions undergo reduction and deposit onto the surface.
Electrodeposition can...
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Controlled-Current Coulometry: Coulometric Titration01:18

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Coulometric titrations are a form of titrimetric analysis where the reagent is generated electrically, and its amount is evaluated based on current and generating time. The electron serves as the standard reagent. The procedure is similar to conventional titrations, such as endpoint detection.
The fundamental requirements for coulometric titrations are (1) 100% efficiency in the reagent-generating electrode reaction and (2) a stoichiometric and preferably rapid reaction between the generated...
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Precipitation and coprecipitation methods can be used to separate a mixture of ions in a solution. In qualitative inorganic analysis, ions that form sparingly soluble precipitates with the same reagent are separated based on the differences in solubility products. For example, consider the separation of Cu(II) and Fe(II) ions by precipitation as insoluble sulfides. First, copper(II) sulfide is precipitated by the addition of acidic H2S, where the dissociation of H2S is suppressed. Adding H2S...
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Coulometry is one of the rapid, most accurate, and precise analytical techniques that determine the quantity of an analyte by measuring the electrical charge needed for its complete electrolysis without using any analytical standards. The total charge passed during electrolysis correlates with the analyte amount by Faraday's laws of electrolysis. For accurate coulometric measurements, a charge equal to Faraday's constant multiplied by the number of electrons involved in the relevant...
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Quality control prediction of electrolytic copper using novel hybrid nonlinear analysis algorithm.

Yuzhen Su1, Weichuan Ye2,3, Kai Yang4,5

  • 1Department of Quality Management, Inspection and Testing, Yibin University, Yibin, 644000, Sichuan, People's Republic of China.

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|October 16, 2023
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Summary

Advanced hybrid models significantly improve electrolytic copper quality prediction. A novel random forest-relevance vector machine approach offers superior accuracy and reduced error for quality management.

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Area of Science:

  • Materials Science
  • Data Science
  • Chemical Engineering

Background:

  • Traditional models like linear regression and neural networks show limitations in predicting electrolytic copper quality.
  • Accurate estimation of electrolytic copper quality is crucial for effective industrial management.

Purpose of the Study:

  • To develop and evaluate advanced hybrid models for enhanced prediction of electrolytic copper quality.
  • To identify key factors influencing electrolytic copper quality using data-driven approaches.

Main Methods:

  • Utilized Maximum Information Coefficient (MIC) to analyze non-linear correlations between 19 factors and 5 quality indicators.
  • Employed Random Forest (RF) algorithm to determine principal factors affecting copper quality.
  • Developed hybrid models: Particle Swarm Optimization-Least Square Support Vector Machine (PSO-LSSVM) and RF-Relevance Vector Machine (RF-RVM).

Main Results:

  • RF identified five principal factors influencing electrolytic copper quality, confirmed by MIC analysis.
  • The RF-RVM model achieved predictive accuracy comparable to PSO-LSSVM and superior to traditional models.
  • The RF-RVM model demonstrated a predictive error index below 5%, significantly outperforming the standalone RVM model.

Conclusions:

  • The study unveils complex non-linear patterns in electrolytic copper quality influenced by multiple factors.
  • The advanced RF-RVM hybrid model effectively overcomes the deficiencies of conventional predictive models.
  • Findings provide valuable insights for optimizing electrolytic copper quality management through sophisticated modeling techniques.