Quantification of pitting corrosion resistance in stainless steel with hyperspectral imaging in the Vis/NIR range
Source abstract
Abstract
The impact of 316L stainless steel discolouration on chloride induced pitting corrosion was quantified. Several degrees of discolouration were produces by chemical passivation, simulated rouging or anodic polarization. Discolouration was characterized by spectral analysis in the visible and NIR range (400-1000 nm). Pitting corrosion resistance was quantified by pitting potential measurements. The spectra were used to indicate whether pitting or transpassive behaviour would occur using chemometric classification techniques with 88% accuracy. In addition, pitting potentials were predicted combining chemometrics and survival analysis techniques with a 90 mV RMS error in cross-validation. XPS analysis linked pitting resistance with the Cr/Fe balance.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Thermography and Photoacoustic Techniques · Nuclear Physics and Applications · Hydrogen embrittlement and corrosion behaviors in metals
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (36)
Show source-provided references
1. Pitting corrosion of metals: A review of the critical factors10.1149/1.1838615 ↗
2. Pitting corrosion evaluation and inhibition of stainless steels: A review
3. Pitting corrosion evaluation: A review10.1088/1742-6596/1378/2/022088 ↗
4. Localized corrosion and stress corrosion cracking of stainless steels in halides other than chlorides solutions: A review10.1515/corrrev-2019-0061 ↗
5. A point defect model for anodic passive films: I. Film growth kinetics10.1149/1.2127591 ↗
6. A point defect model for anodic passive films: II. Chemical breakdown and pit initiation10.1149/1.2127592 ↗
7. Pit growth studies in stainless steel foils. I. Introduction and pit growth kinetics10.1016/S0010-938X(01)00133-0 ↗
8. Pit growth studies in stainless steel foils. II. Effect of temperature, chloride concentration and sulphate addition10.1016/S0010-938X(01)00134-2 ↗
9. Effects of temperature and chloride concentration on pit initiation and early pit growth of stainless steel10.1149/1.1430415 ↗
10. Correlation between evolution of inclusions and pitting corrosion in 304 stainless steel with yttrium addition10.1038/s41598-018-23273-x ↗
11. Influence of temperature on the localized corrosion of stainless steels10.1007/BF02757532 ↗
12. Improvement of pitting corrosion resistance of type 316L stainless steel by potentiostatic removal of surface MnS inclusions10.1155/2012/482730 ↗
13. ASTM International · 2018
14. Rouging Of stainless steel in wfi systems - examples and present understanding
15. Rouge: The intrinsic phenomenon in 316L stainless steel - A key material for biopharmaceutical facilities
16. Determining acceptable levels of weld discoloration on mechanically polished and electropolished stainless steel surfaces
17. A review of passivity breakdown on metal surfaces: Influence of chloride- and sulfide-ion concentrations, temperature, and pH10.1007/s42247-021-00194-6 ↗
18. Determining the composition of mineral-organic mixes using UV–vis–NIR diffuse reflectance spectroscopy10.1016/j.geoderma.2006.07.004 ↗
19. The use of UV-Vis-NIR reflectance spectroscopy to identify iron minerals10.1002/asna.201211965 ↗
20. Spectroscopic characterization of chromite from the Moa-Baracoa Ophiolitic Massif, Cuba10.1016/j.saa.2004.07.002 ↗
21. Determination of the mineral composition and toxic element contents of propolis by near infrared spectroscopy10.3390/s151127854 ↗
22. Color mechanisms in spinel: A multi-analytical investigation of natural crystals with a wide range of coloration10.1007/s00269-018-1007-5 ↗
23. Handbook of chemometrics and qualimetrics: Part B
24. A system for the intensity calibration of electron spectrometers10.1016/0368-2048(94)02275-5 ↗
25. Simple universal curve for the energy-dependent electron attenuation length for all materials10.1002/sia.5033 ↗
26. ASTM International · 2018
27. Visualising associations between paired ‘omics’ data sets10.1186/1756-0381-5-19 ↗
28. Cao · 2022
29. Cao · 2016
30. Modelling survival data in medical research
31. Survival analysis a self-learning text, third edition
32. Univariate shrinkage in the cox model for high dimensional data10.2202/1544-6115.1438 ↗
33. Deviance residuals-based sparse PLS and sparse kernel PLS regression for censored data10.1093/bioinformatics/btu660 ↗
34. Bertrand · 2018
35. Bertrand · 2023
36. Characterisation of red mud by UV–vis–NIR spectroscopy10.1016/j.saa.2008.06.038 ↗