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P Bühlmann

Publications and source records attributed to P Bühlmann.

2 recordsLinked to original sources

Selectivity of potentiometric ion sensors.

Selectivities of solvent polymeric membrane ion-selective electrodes (ISEs) are quantitatively related to equilibria at the interface between the sample and the electrode membrane. However, only correctly determined selectivity coefficients allow accurate predictions of ISE responses to real-world samples. Moreover, they are also required for the optimization of ionophore structures and membrane compositions. Best suited for such purposes are potentiometric selectivity coefficients as defined already in the 1960s. This paper briefly reviews the basic relationships and focuses on possible biases in the determination of selectivity coefficients. The traditional methods to determine selectivity coefficients (separate solution method, fixed interference method) are still the same as those originally proposed by IUPAC in 1976. However, several precautions are needed to obtain meaningful data. For example, errors arise when the response to a weakly interfering ion is also influenced by the primary ion leaching from the membrane. Wrong selectivity coefficients may be also obtained when the interfering agent is highly preferred and the electrode shows counterion interference. Recent advances show how such pitfalls can be avoided. A detailed recipe to determine correct potentiometric selectivity coefficients unaffected by such biases is presented.

Artifacts↗

What is a linear process?

We argue that given even an infinitely long data sequence, it is impossible (with any test statistic) to distinguish perfectly between linear and nonlinear processes (including slightly noisy chaotic processes). Our approach is to consider the set of moving-average (linear) processes and study its closure under a suitable metric. We give the precise characterization of this closure, which is unexpectedly large, containing nonergodic processes, which are Poisson sums of independent and identically distributed copies of a stationary process. Proofs of these results will appear elsewhere.

Linear Models↗