Atmospheric corrosion is significantly accelerated by the presence of heat, humidity, corrosive salts, and sunlight. At Kennedy Space Center (KSC), all of these accelerants are present, producing an extremely corrosive environment. Toxicity and environmental impacts of some inorganic corrosion inhibitors have severely limited the use of some of the most effective corrosion inhibitors. Unfortunately, robust, low-toxicity, high-performance organic corrosion inhibitors for coatings are not yet at a stage to replace the most effective inorganic inhibitors.
The screening of organic corrosion inhibitors for their effectiveness is both time-consuming and expensive. The screening of organic corrosion inhibitors for toxicity is also time-consuming and expensive, and requires live animal testing (e.g., mice, rats, minnows, etc.). Virtual screening (e.g., screening and ranking of corrosion inhibitors using computational methods) has the potential to be both rapid and low-cost. Unfortunately, there are few molecular descriptors that can be calculated from the energy-minimized structures of organic compounds that have good predictive value across a wide range of corrosion inhibitor structures.
Using the Laplacian of the electron density at the bond critical point [computed using Bader Quantum Theory of Atoms in Molecules (QTAIM) using the DFT (density functional theory) wave function of the energy-minimized structure], a property descriptor was developed for predicting the inhibition iron-oxide bond strength. This parameter, along with a hydrophobicity (water hating) descriptor known as LogP, was found to successfully predict corrosion inhibition efficiencies for a wide range of corrosion inhibitor structures.
The use of the Laplacian of the electron density of the interaction between the corrosion inhibitor and the metal surface has enabled identification of structures of corrosion-inhibiting molecules that offer high protection for the metal surface. In the model framework, the corrosion inhibition efficiencies can be described using only the Laplacian of the electron density (DSR), the hydrophobicity of the inhibitor (LogP), or a combination of both of these properties.
Thus, under the model framework, there are two major parameters (i.e., LogP and DSR) that can be tuned to increase corrosion inhibitor efficiency. LogP can be increased by making the molecule more hydrophobic. This is most often done by attaching a hydrocarbon tail to a molecule with a headgroup that bonds to the metal surface.
In this effort, new descriptors were identified for relating the structures of organic corrosion inhibitors to their corrosion inhibition efficiencies. These descriptors are based on electron densities (and their derivatives) computed from energy-minimized molecular structures. It was also found that these descriptors, along with an additional descriptor that captures the hydrophobicity of the organic, produced good correlations with inhibitor efficiencies for a wide range of corrosion inhibitor studies reported in the literature. Using hydrophobicity of the organic and a variant of the electron density descriptors developed for corrosion inhibition prediction, very good models were developed for the prediction of toxicity from molecular structure.
This work was done by Ronald Cook and John Micharl Alford of TDA Research, Inc. for Kennedy Space Center. KSC-13691