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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".