Corrosion Ferritic Stainless Steel in a Simulated Hydrothermal Liquefaction Bioconversion Aqueous Solution: Effect of Surface Cr Content
Bibliographic record
Abstract
The objective of this study was to determine the effect of surface Cr content on the corrosion of ferritic stainless steels in hot-pressurized alkaline water (310°C at 10 MPa) simulating a hydrothermal liquefaction bioconversion medium. Two methods to increase the Cr surface content were investigated: (i) selecting commercial grades of ferritic stainless steel with an increasing Cr content and (ii) applying a Cr coating (chromizing) to a low-Cr (Type 409) ferritic stainless steel. The observed parabolic-like corrosion kinetics were analyzed and discussed in terms of the structure and composition of the double-layered oxide films that formed. The (surface) Cr content is a critical factor affecting corrosion. Corrosion was reduced by 66% (after 20 d exposure) when increasing the Cr content from 9 wt% (P91) to 21 wt% (SS443) in the commercial grades of ferritic stainless steel. Moreover, corrosion was reduced by 84% (after 20 d exposure) by chromizing the surface of a low-Cr (Type 409) ferritic stainless steel. Improved corrosion protection was attributed to increased Cr incorporation into the inner (barrier) Fe(Fe1-nCrn)2O4 layer, with the formation of a Cr2O3 layer (resulting from chromizing) being most beneficial.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".