Quantitative Examination of Porous Rust on Steel
Bibliographic record
Abstract
Despite innumerable years of research, very little is known about the physical structure of rusts on steel, which are presumed to possess a complex structure of pores of various shapes and sizes. There have been few detailed investigations into rust pore structure and no studies that focus specifically on understanding the role that rust porosity plays on the adhesion and performance of protective organic coatings. It is commonly believed that paints that are formulated for “compromised” (i.e. rusty and salty) steel penetrate surface and through thickness pores in order to stabilise and bind the rust together. However, no convincing experimental evidence exists for such a mechanism. Here we develop a detailed, quantitative, understanding of the porous structure of rust and attempt to directly measure its evolution over time. Characterization of porosity in rust layers is non-trivial, as pore sizes span many orders of magnitude and there is no single technique that can measure across the size range. Thus, a range of complementary techniques have been utilised to measure pore sizes: gas (BET) sorption, mercury porosimetry and helium pycnometry, each of which measures different porosity characteristics across different length scales from the “microporous” (i.e. < 2 nm) through the “mesoporous (2 – 50 nm) to “macropores” of size > 50 nm. We find that for rusts developed using cyclic corrosion testing up to 50% of the porous volume exists in the mesoporous and microporous size ranges with surface areas between 20 and 40 m2 g-1 and that there is significant stratification of porosity between "inner" and "outer" rust layers X-ray computerised tomography (XRCT) has been subsequently used to directly image pores at a voxel size of around 1 micron in order to follow the evolution of the pore structure with time as a function of environmental exposure. The figure shows the experimental protocol whereby projection images of a corroded wire sample are obtained using x-ray absorption contrast and later combined to generate the 3D volume reconstruction. Figure 1
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".