The fine chemicals and pharmaceutical industries are transforming how their products are manufactured, where economically favorable, from traditional batchwise processes to continuous flow. This evolution is impacting synthetic chemistry on all scales-from the laboratory to full production. This Review discusses the relative merits of batch and micro flow reactors for performing synthetic chemistry in the laboratory.

Applications of microsystems (microreactors) in continuous-flow chemistry have expanded rapidly over the past two decades, with numerous reports of higher conversions and yields compared to conventional batch benchtop equipment. Synthesis applications are enhanced by chemical information gained from integrating microreactor components with sensors, actuators, and automated fluid handling. Moreover, miniaturized systems allow experiments on well-defined samples at conditions not easily accessed by conventional means, such as reactions at high pressure and temperatures. The wealth of synthesis information that could potentially be acquired through use of microreactors integrated with physical sensors and analytical chemistry techniques for online reaction monitoring has not yet been well explored. The increased efficiency resulting from use of continuous-flow microreactor platforms to automate reaction screening and optimization encourages a shift from current batchwise chemical reaction development to this new approach. We review advances in this new area and provide application examples of online monitoring and automation.

Kinetic information is used to determine the optimal reaction conditions, to successfully scale up a reaction from the laboratory to the pilot plant, and to improve process control. Obtaining accurate kinetics using conventional benchtop equipment and techniques, however, requires numerous experiments and can be complicated by sluggish mixing and heat-transfer rates. To improve the speed and efficiency in gathering reaction kinetics, we present an automated, silicon microreactor system that uses a sequential experimentation framework driven by model-based optimization feedback for online reaction rate parameter determination. The method, based on Information Theory and Bayesian Statistics, first selects the appropriate global reaction rate expression. After determining the correct rate law, a D-optimal strategy precisely estimates the pre-exponential and activation energy of the rate constant. The approaches are validated experimentally with a model system, the Diels−Alder reaction of isoprene and maleic anhydride in DMF. The benefits of quickly obtaining this information with an automated microreactor system are further demonstrated by successfully scaling the Diels−Alder reaction by a factor of 500 from a microreactor to a Corning flow reactor.