Global analysis of spectrophotometric titration data is a powerful technique for characterizing equilibrated systems, with applications to coordination chemistry, supramolecular chemistry, and nanomolecular fabrication. While binding constants are routinely obtained by nonlinear least-squares regression, it is less well known that the unique stoichiometric ratios and titrant-to-analyte stock solution concentration ratio (the so-called "dangerous parameters") may, in principle, also be obtained simultaneously without prior knowledge. The mathematical uniqueness of the optimized dangerous parameters is established through computational grid searches. Monte Carlo simulations detail the threshold of experimental error that must be met for a reliable optimization. Different computational algorithms are evaluated on the criteria of efficiency, accuracy, and reliability, and are illustrated with examples drawn from both real chemical systems and synthetic data. This technique finds application to equilibrium systems where the binding stoichiometry is complex, or when the concentration of one or more reagents cannot be precisely ascertained. Optimization of stoichiometric parameters provides a computational check to ensure that the best equilibrium model is not overlooked, and optimization of the concentration ratios also provides a useful protocol for diagnosing laboratory errors.

Optimization of dangerous parameters in global analysis of spectrophotometric titration data: Information beyond the binding constant

254th National Meeting (2017)

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