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 2 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 highthroughput
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.