Automated Stopped-Flow Systems for Fast Reaction-Rate Methods

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Abstract
The stopped-flow technique for the rapid mixing of chemical reagents has gained widespread importance in the measurement of the rates of rapid chemical reactions. Reaction-rate information can be obtained routinely on reactions with half-lives as short as a few milliseconds by using stopped-flow mixing in conjunction with a rapid reaction monitoring technique, such as UV-visible spectrophotometry. Stopped-flow mixing is most frequently used to obtain fundamental information about rapid chemical reactions (rate law information, rate constants, activation energies, etc.). However, in recent years the stopped-flow technique has been shown to be a valuable tool for analytical purposes. There are several reasons why the stopped-flow technique has considerable potential in the analytical laboratory. First, with moderately rapid reactions, stopped-flow mixing can provide analytical information in a very short time, often in a few seconds or less. The high information throughput provided by stopped-flow systems is particularly attractive because the demand for analytical information in such critical areas as clinical chemistry and environmental chemistry is rapidly growing and threatening to outpace the ability of the laboratory to supply the desired information. A second reason why stopped-flow mixing should gain increasing acceptance in analytical chemistry is the extremely small solution volumes required to obtain analytical information. Often, reaction-rate or endpoint methods can be carried out with sample volumes as small as a few microliters. The stopped-flow technique can also be completely automated to eliminate manual manipulations of reagents and to provide rapid and reproducible mixing of reactants, These latter features are, of course, desirable even for measurements on reactions which are normally considered slow. Finally, the increasing use of minicomputers and microprocessors with stopped-flow systems for control, data acquisition, and data processing should lead to a higher level of automation with significant increases in measurement throughput, accuracy, and precision.