Our research is split into 6 interlinked work pacakges
Find out more about each work package in the descriptions below
The key science undertaken is corrosion analysis with in-situ electrochemistry in static and flowing
environments to develop a comprehensive understanding of the link between formation parameters and
characteristics of corrosion product layers. The new methodologies and cutting edge science comes in (a)
application of in-situ synchrotron X-Ray Diffraction (SXRD) coupled with electrochemistry (b) advanced
microscopy transmission and scanning electron (TEM/SEM) and electron diffraction (EBSD)
Major deliverables from WP 1: Quantitative information on corrosion product film composition, porosity,
thickness as a function of water chemistry and flow conditions. Unique simultaneous measurements of
electrochemistry and SXRD.
The experimental work here is focussed on understanding the evolution of pitting. This requires analysis of
the size, geometry and the chemistry within the pit. The galvanic theories of pitting are tested. The new
science applied here is linked to (a) advanced chemical analysis of pit interior chemistry (b) simulation of
pits using micro-patterned surfaces (c) interferometry analysis of pitting statistics to formulate growth
Major deliverables from WP2: Correlation of general and pitting corrosion to link to conditions in WP1.
Simulations of pitting from patterned surfaces for the first time. Models of self-healing capabilities.
We will advance the understanding of the mechanical integrity of the corrosion product layer; this will be
done by implementing new methods for nanoindentation. We will link the mechanical properties to the
This proposal version was submitted by Ben WILLIAMS on 01/09/2016 14:47:07 Brussels Local Time. Issued by the Participant Portal Submission Service.
Neville Part B1 INTELLICORR
macroscopic behaviour we see for removal of corrosion product films from a newly developed flow cell that
can vary shear stress to 600Pa across the surface.
Major deliverables from WP3: A definitive understanding of the link between the corrosion product film
modulus, fracture toughness and hardness and its resistance to removal by mass transfer, shear stress and
We propose that we can reduce the amount of chemical inhibitors employed in many industries by
engineering the naturally formed corrosion product layer. We will employ three novel strategies to do this.
We will employ a silsesquioxane-based nanofiller Octammonium POSS as a nucleation promoter to densify
and enhance the properties of the film. We will investigate the use of using biomineralisation strategies to promote a tailored formation of iron carbonate and we will assess the feasibility of combining inorganic and
organic species to create a self healing composite.
Major deliverables from WP4: A radical departure from corrosion management by inhibitors,
understanding of creating smart nanocomposites, mitigating pitting by creating ultra-dense layers of
Our modelling of the corrosion processes differ to current work by focussing on the surface processes and
not what the chemistry is doing in the bulk solution. This makes a radical difference and will change the way
we approach the understanding of iron carbonate formation. We will advance the modelling by addressing
planar surfaces to validate experimental work from the flow cells and we will model the kinetics of pit
growth by considering the transport of species in a confined pit environment.
Major deliverables from WP 5: An inextricable link between modelling and experimental processes at the
surface. First model to assess supersaturation in a pit
This workpackage involves understanding how implementation of new science can be most effectively
achieved. The tasks here will build on the pure science in WPs 1-5 and will assess economics, environment
and cultural issues related to adoption of the science developed in the industries involved.
Major deliverables from WP 6: An assessment using established scientific principles for the environment
and cost impacts of the science/technology in INTELLICORR