Critical micelle concentration (CMC) is a known indicator for surfactants such as corrosion inhibitors ability to partition from two phase systems such as oil and water. Most corrosion inhibitors are surface active and at critical micelle concentration, the chemical is partitioned to water, physadsorb on metallic surfaces and form a physical barrier between steel and water. This protective barrier thus prevents corrosion from taking place on the metal surface When the applied chemical concentration is equal or higher than the CMC, the chemical is available in aqueous phase, thus preventing corrosion. Therefore, it was suggested that CMC can be used as an indicator of optimal chemical dose for corrosion control1. The lower the CMC of a corrosion inhibitor product, the better is this chemical for corrosion control as the availability of the chemical in the aqueous phase increase and therefore, can achieve corrosion control with less amount of chemical. In this work, this physical property (CMC) was used as an indicator to differentiate corrosion inhibitor performance.
The corrosion inhibitor formulations were built out by using combinatorial chemical methods and the arrays of chemical formulations were screened by utilizing high throughput robotics, using CMC as the selection guide. To validate the concept, several known corrosion inhibitor formulas were selected to optimize their efficacy. Each formula contained several active ingredients and a solvent package. These raw materials were blended in random but in a control, manner using combinatorial methodologies. Instead of rapidly blending a large number of formulations using robotics, the design of control (DOE) methods were utilized to constrain the number of blends.
Once the formulations were generated by DOE method, using Design Expert software that can effectively explore a desired space. The development of an equally robust prescreening analysis was also developed. This was done by using the measurements of CMC with a high-throughput screening methodology. After formulation of a vast array of formulation by using Design Expert software, the products were screened for by CMC using automated surface tension workstation. Several formulations with lower CMC than the reference products were selected.
The selected corrosion inhibitor formulations were identified and blended in larger scales. The efficacy of these products was tested by classical laboratory testing methods such as rotating cylinder electrode (RCE) and rotating cage autoclave (RCA) to determine their performance as anti-corrosion agents. These tests were performed against the original reference corrosion inhibitor.
The testing indicated that several corrosion inhibitor formulations outperform the original blend thus validating the proof of concept.
For details:
Development of New Corrosion Inhibitors Using Robotics with High Throughput Experimentation Methods
