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Related Concept Videos

Voltammetry: Overview01:20

Voltammetry: Overview

2.4K
Voltammetry is an electroanalytical technique in which the current flowing through an electrochemical cell is measured as a function of applied potential, typically under conditions of concentration polarization. The technique provides valuable information about redox-active species, and the current response is plotted as a voltammogram.
A voltammetric cell uses three electrodes: a working electrode, a reference electrode, and an auxiliary electrode. The redox reactions occur in the working...
2.4K
Voltammograms: Overview01:16

Voltammograms: Overview

532
Voltammograms are current plots as a function of applied potential, offering insights into electrochemical systems. The shape of a voltammogram depends on how the current is measured and whether convection (heat transfer by fluid movement) is present or absent.
Shapes of Voltammograms
532
Voltammetry: Factors Affecting Measurements01:21

Voltammetry: Factors Affecting Measurements

390
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...
390
Voltammetric Techniques: Pulse Voltammetry01:17

Voltammetric Techniques: Pulse Voltammetry

991
Differential-pulse voltammetry (DPV) is a type of voltammetry that involves applying a series of voltage pulses to an electrochemical cell while measuring the resulting current. In DPV, the differential pulse or small potential pulses are superimposed on a linear potential sweep. The magnitude of these pulses is typically small, often in the millivolt range. Each voltage pulse lasts a short duration, usually in the order of a few milliseconds, and is applied at regular intervals along the...
991
Voltammetry: Stripping Methods01:13

Voltammetry: Stripping Methods

578
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)
ASV is used to determine metals and metalloids at trace levels. It involves two steps: deposition and stripping. First, a negative potential is applied to the...
578
Voltammetric Techniques: Linear-Scan (E vs Time)01:12

Voltammetric Techniques: Linear-Scan (E vs Time)

715
Polarography is a classical voltammetric technique used to analyze electrochemical reactions. This method applies a linear potential sweep to a dropping mercury electrode (DME), and the resulting current is measured. A dropping mercury electrode is commonly used as the working electrode in polarography. It consists of a capillary tube filled with mercury, where the tiny droplet forms at the tip. This droplet continuously drops from the capillary, creating a new electrode surface for each...
715

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Recent advances and future perspectives for automated parameterisation, Bayesian inference and machine learning in

Luke Gundry1, Si-Xuan Guo1, Gareth Kennedy1

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Advanced data analysis, including machine learning, can revolutionize quantitative voltammetry. These methods offer accurate comparisons between experimental and simulated electrochemical data, enhancing method robustness.

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Area of Science:

  • Electrochemistry
  • Computational Chemistry
  • Analytical Chemistry

Background:

  • Quantitative voltammetry traditionally relies on simpler analytical methods.
  • Complex electrochemical reactions generate data challenging for conventional analysis.
  • Emerging computational tools offer new possibilities for electrochemical data interpretation.

Purpose of the Study:

  • To review the application of advanced data analysis tools in quantitative voltammetry.
  • To highlight the potential of mathematical optimisation, Bayesian inference, and machine learning.
  • To discuss the specific advantages for large amplitude alternating current voltammetry.

Main Methods:

  • Utilizing mathematical optimisation, Bayesian inference, and machine learning algorithms.
  • Implementing these methods with modern computing languages and high-speed computing.
  • Applying Fourier transformation for analyzing higher-order harmonics in voltammetry.

Main Results:

  • Advanced data analysis provides accurate and robust methods for quantitative voltammetry.
  • These techniques enable effective comparison of experimental data with complex electrochemical models.
  • Fourier transformation in large amplitude alternating current voltammetry minimizes background noise.

Conclusions:

  • Advanced computational tools are poised to transform quantitative voltammetry.
  • Accessible algorithms and computing power facilitate routine implementation.
  • Future developments should focus on intelligent, user-friendly data analysis strategies for voltammetry.