Tiffany E. Sill, Joseph K. Cantrell, Victor Ponce, Caroline G. Valdes, Torrick Fletcher, Kerry Fuller, Sujata Singh, Mohammed Al-Hashimi, Homero Castaneda, Peter M. Johnson* and Sarbajit Banerjee*,
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引用次数: 0
Abstract
Corrosion represents a key impediment to the greater adoption of light metal alloys as alternatives to automotive steels in vehicular applications. Thin nanocomposite coatings generate considerable interest for their potential in aluminum alloy corrosion protection, which is challenging due to the lack of conventional protection mechanisms that are available for other metals. Here, we investigate the thickness-dependent corrosion protection afforded to AA 7075 substrates by poly(ether imide)-based (PEI) coatings. Using electrochemical impedance spectroscopy to monitor ion transport, we observe that with increasing coating thickness, PEI more effectively sequesters ions and enforces permeation selectivity, thereby precluding deleterious substitution processes that dissolve corrosion products. We further explore thickness-dependent modifications to the PEI matrix by incorporation of unfunctionalized exfoliated graphite (UFG) particles to control diffusion processes and co-polymerization with siloxane to manipulate permeation selectivity. Incorporation of UFG platelets can degrade corrosion protection through galvanic coupling with the substrate and enhanced interfacial ion diffusion at lower coating thicknesses. However, interphase development mediated by hydration, network relaxation, and thermal displacement of PEI chains yields a rigid matrix that enhances permeation selectivity and imbues extended tortuosity. This combination results in superior corrosion protection for thicker PEI coatings with embedded UFG platelets under aggressive accelerated corrosion testing conditions. Siloxane co-polymerization, while weakening interfacial adhesion to AA 7075 substrates, facilitates the sequestration of solubilized corrosion products within the matrix under appropriate processing conditions. The results illustrate the importance of understanding the dynamical evolution of polymer secondary structure under aggressive accelerated corrosion testing conditions, point to the specific role of secondary structure and interphasic domains in enforcing permeation selectivity, and establish fundamental thickness limits for retaining effective barrier protection.
期刊介绍:
)ACS Engineering Au is an open access journal that reports significant advances in chemical engineering applied chemistry and energy covering fundamentals processes and products. The journal's broad scope includes experimental theoretical mathematical computational chemical and physical research from academic and industrial settings. Short letters comprehensive articles reviews and perspectives are welcome on topics that include:Fundamental research in such areas as thermodynamics transport phenomena (flow mixing mass & heat transfer) chemical reaction kinetics and engineering catalysis separations interfacial phenomena and materialsProcess design development and intensification (e.g. process technologies for chemicals and materials synthesis and design methods process intensification multiphase reactors scale-up systems analysis process control data correlation schemes modeling machine learning Artificial Intelligence)Product research and development involving chemical and engineering aspects (e.g. catalysts plastics elastomers fibers adhesives coatings paper membranes lubricants ceramics aerosols fluidic devices intensified process equipment)Energy and fuels (e.g. pre-treatment processing and utilization of renewable energy resources; processing and utilization of fuels; properties and structure or molecular composition of both raw fuels and refined products; fuel cells hydrogen batteries; photochemical fuel and energy production; decarbonization; electrification; microwave; cavitation)Measurement techniques computational models and data on thermo-physical thermodynamic and transport properties of materials and phase equilibrium behaviorNew methods models and tools (e.g. real-time data analytics multi-scale models physics informed machine learning models machine learning enhanced physics-based models soft sensors high-performance computing)