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The difference between the two peak potentials (E p), ΔE p, is of particular interest. ΔE p = E pa - E pc > 0. This difference mainly results from the effects of analyte diffusion rates. In the ideal case of a reversible 1e- couple (i.e., Nernstian), ΔE p is 57 mV and the full-width half-max of the forward scan peak is 59 mV. Typical values ...
In electrochemistry, the electrochemical potential of electrons (or any other species) is the total potential, including both the (internal, nonelectrical) chemical potential and the electric potential, and is by definition constant across a device in equilibrium, whereas the chemical potential of electrons is equal to the electrochemical ...
Chronoamperometry is the technique in which the current is measured, at a fixed potential, at different times since the start of polarisation. Chronoamperometry is typically carried out in unstirred solution and at the fixed electrode, i.e., under experimental conditions avoiding convection as the mass transfer to the electrode.
To apply a Q test for bad data, arrange the data in order of increasing values and calculate Q as defined: Q = gap range {\displaystyle Q={\frac {\text{gap}}{\text{range}}}} Where gap is the absolute difference between the outlier in question and the closest number to it.
Accuracy is also used as a statistical measure of how well a binary classification test correctly identifies or excludes a condition. That is, the accuracy is the proportion of correct predictions (both true positives and true negatives) among the total number of cases examined. [10] As such, it compares estimates of pre- and post-test probability.
Free-energy perturbation (FEP) is a method based on statistical mechanics that is used in computational chemistry for computing free-energy differences from molecular dynamics or Metropolis Monte Carlo simulations. The FEP method was introduced by Robert W. Zwanzig in 1954. [1]
Hence, when a result of statistical analysis is termed an “exact test” or specifies an “exact p-value”, this implies that the test is defined without parametric assumptions and is evaluated without making use of approximate algorithms. In principle, however, this could also signify that a parametric test has been employed in a situation ...
In statistics, Grubbs's test or the Grubbs test (named after Frank E. Grubbs, who published the test in 1950 [1]), also known as the maximum normalized residual test or extreme studentized deviate test, is a test used to detect outliers in a univariate data set assumed to come from a normally distributed population.