Corrosion under Hot Dilute Acidic Pre-Hydrolysis Biorefining Process
Bibliographic record
Abstract
Abstract Hot dilute acidic pre-hydrolysis biorefining is a pre-treatment technology recently developed for converting raw biomass materials into sugar streams and other valuable intermediate chemicals at elevated temperatures. However, corrosion database of steels and alloys in hot dilute acidic solutions are very limited, resulting in the cost-effective selection of materials of construction difficult. Corrosion studies were thus performed to identify suitable alloys of construction and advance the understanding of how alloying elements (e.g., Cr and Mo) present in steels and alloys affect the formation and properties of surface oxides. In this paper, the corrosion performance of three alloys (UNS S31603, UNS S32101 and UNS N06625) in hot dilute sulfuric acids are introduced. The alloys exhibited active general corrosion and even pitting in the hot acidic solutions. Alloy 625 has better resistance to the hot dilute acid compared to SS 316L and duplex 2101. This may be attributed to the higher contents of Mo in the alloy. Long-term tests indicate that their corrosion rates are gradually increased with time. The introduction of 100 ppm Cl- from raw biomass feedstocks into the acid solution only has marginal effect on corrosion.
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 | 0.000 |
| 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".