PARTICLE LEACHING FROM POLYMERIC COATINGSA Combined Experimental and Simulation Study

Eugenio Bonetti –AkzoNobel, Malmö, SE and CEAS, The University of Manchester, UK Ander Cervellera-Dominguez – AkzoNobel, Sassenheim, NED and School of Materials, The University of Manchester, UKPeter Visser – AkzoNobel, Sassenheim, NEDSimon Gibbon – AkzoNobel, Felling, UKProfessor Xiaorong Zhou – School o Materials, The University of Manchester, UKFlor R. Siperstein – CEAS, The University of Manchester, UK

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IntroductionActive corrosion protection by organic coatings in AerospaceCorrosion in modern world• Corrosion cost: 2.5 trillion US$ - 4% percent of the global Gross Domestic Product (GDP)• 20-35% of this loss could have been saved by implementing better corrosion preventing

practices.

In the aerospace industry corrosion impacts• Cost • Aircraft availability • Safety• Social

German, T. & Section, N. Corrosion news. 140 146 (2018).

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October 1992, The Netherlands:Fatigue corrosion cracking

https://aviation-safety.net/database/record.php?id=19921004-2

April 1988, Hawaii: Fatigue corrosion crackinghttps://faculty.up.edu/lulay/me401/aloha_flight_243_a_new_direction.pdf

Organic coatings provide corrosion protection

Key steps for corrosion inhibition via leaching

1. Water uptake2. Dissolution of active pigment3. Transport and delivery of active species through

the polymeric matrix to de defect area4. Fast, effective, and irreversible passivation

O Gharbi et al. Npj Materials Degradation, 2, 12 (2018)

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IntroductionActive corrosion protection by organic coatings in Aerospace

Challenge

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Develop coatings that are:

Environmentally friendly

Sustainable

Efficient

Cost effective

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Approach

Replacing toxic chemicals (chromates)

Use renewable materials

Understand and optimise performance

Minimise use of expensive materials

UNDERSTAND LEACHING OF INHIBITORS FROM ORGANIC COATINGS

UNDERSTAND THE RELATIONSHIP BETWEEN LEACHING AND PERFORMANCE

IntroductionLithium leaching technology protection concept

Provide fast, effective, and irreversible corrosioninhibition

1. Leaching of lithium ions2. Lithium ion transport to defect area3. Formation of a protective layer on the

aluminium substrate

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Visser et al. Faraday Discuss. 180, 511-526 (2015)

Fitting with physical model• Oxide layer provides corrosion protective properties

Visser et al. Journal ofThe Electrochemical Society, 164 (7) C396-C406 (2017)

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The effect of the PVC Systems of study and microstructure analysis

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Epoxy model system with PVCs between 7.5 – 37.5%Pigment ratios constant

Before exposure

30 PVC

30 PVC 30 PVC

30 PVC

After 2 weeks exposure to 3.5% NaCl solution

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The effect of the PVC Lithium leaching and corrosion protection properties

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0

0.5

1

1.5

2

2.5

0 100 200 300 400 500 600

mg/

L

Time (h)

Leaching rate of lithium from the matrix

7.5 PVC

15 PVC

22.5 PVC

30 PVC

37.5 PVC

37.5 PVC22.5 PVC7.5 PVC

Epoxy model system with PVCs between 7.5 – 37.5%Pigment ratios constant

0

0.1

0.2

0.3

0.4

0.5

0.6

7.5%PVC

15%PVC

22.5%PVC

30%PVC

37.5%PVC

MΩ

Oxide layer resistance Polarization resistance

Active corrosion properties

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The effect of the PVC

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Higher pigment volume concentration result in higher leaching rates and faster exhaustion of the system• Larger network of interconnected pigment clusters

Increase of pigment volume concentration decrease the barrier properties of the coating• Formation of defects through the polymeric matrix which enhance water permeation

Slower dissolution rate leads to higher Roxid and Rpol – Better active corrosion protection

Can we control the rate of release?

Can we control the amount released?

Develop a model that describes the leaching of inhibitors with sufficient

accuracy to explore the formulation space and inform what are the desirable features or properties of the coating and inhibitor

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Release of corrosion inhibitors

Binder properties

Particles microstructure

Pigment volume concentration

Inhibitor solubility

Fillers

Inhibitor dissolution rate

Cellular Automata Model is a good compromise to model the release of inhibitors:

Can capture different physical phenomena Can be related to real physical properties of the system Computationally efficient

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Approach

Microstructure generation Release simulation

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Microstructure generation• Pigment volume concentration• Particle size distribution

Permeable heterogeneities

Clusters formation

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Release simulation

DissolutionPolymer

Solid inhibitor

Water

Dissolved inhibitor

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Release simulation

DissolutionPolymer

Solid inhibitor

Water

Dissolved inhibitor

Diffusion

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Results

Random distribution of inhibitor particles results in unique connectivity profiles.Averages are needed over multiple configurations to represent real systems

Total and connected inhibitor concentration Release curves

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Effect of PVC

0

500000

1000000

1500000

2000000

2500000

3000000

3500000

4000000

0 5 10 15 20 25

Num

ber o

f cel

ls re

leas

ed

Time / days

PVC = 30%

PVC = 22.5%

PVC = 15%

PVC = 7.5%

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Effect of solubility

Solubility

S = 0.13 g/cm3

S = 0.013 g/cm3

S = 0.0013 g/cm3

0

0.00002

0.00004

0.00006

0.00008

0.0001

0.00012

0.00014

0.001 0.01 0.1 1 10 100

Mas

s re

leas

ed p

er u

nit a

rea

/ g c

m-2

Time / log(days)

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Effect of particle distribution

Spatial heterogeneity can affect the connectivity between inhibitor particles

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Effect of particle distribution

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PVC = 7.5%

PVC = 15%

PVC = 22.5%

PVC = 30%

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

0 5 10 15 20 25

Con

cent

ratio

n / g

cm

-3

Time / days

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Comparison with experimental data

Simulated data

Experimental data

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Conclusions

• The release of corrosion inhibitors from primer coatings can be modelled using the CA approach

• Models can show the influence of microstructure and pigment properties on the release, enabling control on the factors that affect the process

• Simulations can provide important insight on the structure-property relationship in complex coatings to enable optimal formulation design

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Acknowledgments

Dr. Yanwen Liu

Ms Maria Georgiades

Assistance given by the IT Services and the use of the Computational Shared Facility at the University of Manchester.

University of Manchester – AkzoNobel Corrosion Protection Partnership

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 721451

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Cellular Automata model

Discrete approach Transition rules Locality

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