Corrosion of Surface-Treated Type 304 Stainless Steel in Alkaline Subcritical Water
Bibliographic record
Abstract
Surface treatment can reduce corrosion of stainless steel in hot-pressurized (subcritical/supercritical) water and, thus is being considered to control corrosion of austenitic stainless steel Type 304 (Fe-18Cr-8Ni) for application to hydrothermal liquefaction (HTL) conversion of biomass. Typical HTL conversion processes involve hot (250°C to 374°C), pressurized (4 MPa to 22 MPa) subcritical water as the conversion medium with the addition of a homogenous alkaline catalyst. The objective of this research was to determine the relative extent to which well-established surface treatments could reduce corrosion of Type 304 in simulated HTL alkaline water, with the chromia-forming Alloy 33 (Fe-33Cr-32Ni) serving as a comparative baseline. Surface treatments examined include grinding, shot peening, sandblasting, and chemical pickling. Corrosion was assessed using gravimetric measurements made after 10 d of immersion in simulated HTL alkaline water at 310°C and 10 MPa in a static autoclave test system. Analysis of the starting (preimmersion) and corroded (postimmersion) surfaces was conducted using a variety of surface characterization techniques. None of the surface treatments reduced corrosion of Type 304, relative to the mechanically-ground surface, despite achieving the desired outcomes before and during immersion. Alloy 33 is less susceptible to corrosion than Type 304 due to the formation of a more protective Cr2O3 sublayer at the oxide/metal interface.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".