Green Solvents for Precision Cleaning
Bibliographic record
Abstract
Aerospace machinery used in liquid oxygen (LOX) fuel systems must be precision cleaned to achieve a very low level of non-volatile residue (< 1 mg0.1 m2), especially flammable residue. Traditionally chlorofluorocarbons (CFCs) have been used in the precision cleaning of LOX systems, specifically CFC 113 (C2Cl3F3). CFCs have been known to cause the depletion of ozone and in 1987, were banned by the Montreal Protocol due to health, safety and environmental concerns. This has now led to the development of new processes in the precision cleaning of aerospace components. An ideal solvent-replacement is non-flammable, environmentally benign, non-corrosive, inexpensive, effective and evaporates completely, leaving no residue. Highlighted is a green precision cleaning process, which is contaminant removal using supercritical carbon dioxide as the environmentally benign solvent. In this process, the contaminant is dissolved in carbon dioxide, and the parts are recovered at the end of the cleaning process completely dry and ready for use. Typical contaminants of aerospace components include hydrocarbon greases, hydraulic fluids, silicone fluids and greases, fluorocarbon fluids and greases and fingerprint oil. Metallic aerospace components range from small nuts and bolts to much larger parts, such as butterfly valves 18 in diameter. A fluorinated grease, Krytox, is investigated as a model contaminant in these preliminary studies, and aluminum coupons are employed as a model aerospace component. Preliminary studies are presented in which the experimental parameters are optimized for removal of Krytox from aluminum coupons in a stirred-batch process. The experimental conditions investigated are temperature, pressure, exposure time and impeller speed. Temperatures of 308 - 423 K, pressures in the range of 8.3 - 41.4 MPa, exposure times between 5 - 60 min and impeller speeds of 0 - 1000 rpm were investigated. Preliminary results showed up to 86 cleaning efficiency with the moderate processing conditions of 323 K, 13.8 MPa, 30 min and 750 rpm.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
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".