(320a) The Need for Engineering Methods for Electrochemical Systems

Electrochemical phenomena control the existence and movement of charged species in the bulk as well as across interfaces between ionic, electronic, semiconductor, photonic, and dielectric materials. The pervasive occurrence of electrochemical phenomena may be seen in many nanoscale and biological systems, energy systems, microelectronic and photovoltaic devices, and in green processes as well as in natural systems.

Future trends in electrochemical engineering will be influenced by the need to control processes and insure product quality at the molecular scale. Transfer of molecular-scale understanding and discoveries into new and improved products and processes requires integration of system behavior across a range of length- and time-scales. Engineering approaches are needed that couple traditional current- and potential-distribution approaches to molecular-scale events in order to accurately describe and design systems to meet the needs of the next century.

The existing technology base of the electrochemical field is massive and of long-standing. For economic reasons, most traditional large-scale electrolytic processes are driven to transport-limited rates. For many decades, the traditional electrochemical engineering literature therefore focused strongly on understanding how ohmic and mass transport processes influence the potential field between electrodes and the current distribution along electrode surfaces.

Mathematical modeling of transport and reaction processes in electrochemical systems has advanced steadily for many decades. Continuum models based on differential-algebraic equations dominate the extensive literature. Modern sophisticated models typically include simultaneous consideration of thermodynamic, kinetic, and transport phenomena in adjoining phases as well as reactions and other processes at the interface between them. Such models can provide a rational basis for complex engineering applications involving design, optimization, scale-up, optimization, and process control. For many applications, commercial software is available.

Because of the large variety of phenomena that can be taken into account, engineering models can also be used to advance fundamental understanding of such complicated processes as shape evolution of irregular surfaces including dendrites, complex reaction sequences involving additives, phase transformations and surface films, and multiphase phenomena associated with bubbles and wetting, among many other examples.

In addition, there are by now many examples of close integration of numerical and experimental approaches from the start of a project. By pursuing both approaches simultaneously from the outset, these can serve to guide experimental design, to interpret sensitivity data for assessing which among competing effects are the most important, to construct chemical mechanisms and estimate the parameters, to assess uncertainty, and predict behavior of well-characterized model systems.

During the past several decades, the electrochemical field has benefited from a suite of remarkable new tools that provide the ability to create precisely characterized systems for fundamental study; to monitor behavior at unprecedented levels of sensitivity, atomic resolution, and chemical specificity; and to predict behavior with new theories and improved computational abilities. These capabilities have revolutionized fundamental scientific understanding of interfacial and catalytic processes, as well as contributed to the present rapid pace of discovery of novel technological materials and devices for which product quality is determined at the molecular scale.

To drive these new electrochemical discoveries into technology innovation, new engineering methods are needed which take advantage of advances in molecular simulation methods, sensor technology and data-storage, computer speed and memory, and numerical algorithms. New engineering methods are needed for analysis of extremely large experimental data sets, for efficient algorithms which enable optimization and quantification of uncertainty, and for easy-to-use multi-scale methods that link small-scale events to macroscopic processes. The development and reduction to routine use, of such methods will provide engineering tools needed for the next-generation approach to design and control of electrochemical systems, as well as opening the way to exploiting and controlling self-assembly during processing.