Materials Compatibility Issues With Biomass-Derived Oils
Bibliographic record
Abstract
Production of liquid fuels and higher value chemicals from biomass provides a means to lessen our dependence on fossil fuels and, consequently, contributes to a reduction in production of greenhouse gases.However, thermochemically derived products from biomass contain large quantities of oxygen-containing compounds, and there are undesirable characteristics associated with these biomass-derived oils.The carboxylic acids, particularly formic acid, are corrosive to many common structural alloys, and other compounds, particularly ketones, cause degradation of many of the common elastomeric materials used for seals in liquid systems.New analysis techniques, including separations and structurally descriptive mass spectrometry, have been developed to characterize these bio-oils, laboratory corrosion studies have been conducted to assess the effects of the bio-oils on both metallic and nonmetallic materials, and examinations have been conducted on metallic samples and components exposed in operating liquefaction facilities.Results indicate preferential internal oxidation occurs in some 300 series stainless steels and degradation of many elastomeric materials is found after exposure in a mixture containing partially hydrotreated bio-oil.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".