Despite extensive research, eliminating hexavalent chromium-based inhibitors from aerospace coatings remains challenging due to a lack of understanding of coating degradation during aircraft service. This study addresses the issue by investigating the protective mechanisms and aging processes of chromate-containing coatings on aircraft components after service for over 35 years. Four aircraft parts underwent visual inspection, disassembly, and analysis using scanning electron microscopy (SEM) and X-ray Photoelectron Spectroscopy (XPS). While most coating areas remained intact after extended use, three distinct degradation modes were identified: tip erosion, corrosion around rivets, and corrosion around fasteners at the leading edge. These findings reveal the complexity of corrosion protection, emphasizing that hexavalent chromium-containing coatings may not offer comprehensive protection at local design heterogeneities. The study also highlights the need to revisit traditional laboratory analysis protocols based on accelerated corrosion testing of oversimplified sample configurations, given the revealed end-of-service failure mechanisms.@en

The substitution of chromate-containing structural coating systems in aviation with alternatives complying with nowadays strict environmental, health and safety regulations remains a formidable challenge. This complexity is partly due to the absence of a standardized from-test-to-market methodology, including a performance comparison between chromate-containing and alternative coating systems. To address this gap, the present study delves into the identification of crucial degradation factors that merit inclusion in such a methodology. Concurrently, it investigates the protective mechanisms inherent in chromate-containing coating systems and proposes improvements that can be applied to alternative coating systems. This study entails a comprehensive post-service examination of the degradation of paint applied to an aircraft component with over 35 years of service, employing electrochemical, microscopic and spectroscopic techniques. The findings underscore the role of thermo-oxidation as a significant degradation factor in the aging process of such coatings. Furthermore, the investigation elucidates a notable phenomenon in which aluminium ions within the coating pores form an aluminium hydroxide gel onto which chromate adsorbs. This process contributes to an increase in pore resistance upon exposure to electrolyte, leading to a self-healing barrier effect within the coating. Remarkably, this self-healing mechanism continues to offer long-term protection even when the coating matrix is sub-optimally cured due to application errors. Furthermore, this study reveals that the significant changes in capacitance during immersion testing result primarily from inhibitor leaching, emphasizing the effectiveness of combining Electrochemical Impedance Spectroscopy (EIS) with Scanning Electron Microscopy (SEM) analysis for studying coating degradation.

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Background: The COVID-19 pandemic continues to overwhelm intensive care units (ICUs) worldwide, and improved prediction of mortality among COVID-19 patients could assist decision making in the ICU setting. In this work, we report on the development and validation of a dynamic mortality model specifically for critically ill COVID-19 patients and discuss its potential utility in the ICU. Methods: We collected electronic medical record (EMR) data from 3222 ICU admissions with a COVID-19 infection from 25 different ICUs in the Netherlands. We extracted daily observations of each patient and fitted both a linear (logistic regression) and non-linear (random forest) model to predict mortality within 24 h from the moment of prediction. Isotonic regression was used to re-calibrate the predictions of the fitted models. We evaluated the models in a leave-one-ICU-out (LOIO) cross-validation procedure. Results: The logistic regression and random forest model yielded an area under the receiver operating characteristic curve of 0.87 [0.85; 0.88] and 0.86 [0.84; 0.88], respectively. The recalibrated model predictions showed a calibration intercept of −0.04 [−0.12; 0.04] and slope of 0.90 [0.85; 0.95] for logistic regression model and a calibration intercept of −0.19 [−0.27; −0.10] and slope of 0.89 [0.84; 0.94] for the random forest model. Discussion: We presented a model for dynamic mortality prediction, specifically for critically ill COVID-19 patients, which predicts near-term mortality rather than in-ICU mortality. The potential clinical utility of dynamic mortality models such as benchmarking, improving resource allocation and informing family members, as well as the development of models with more causal structure, should be topics for future research.

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