Nonflammable, Nonaqueous, Low Atmospheric Impact, High Performance Cleaning Solvents
Bibliographic record
Abstract
For many years, chlorofluorocarbon (CFC) and chlorocarbon solvents have played an important part in aerospace operations. These solvents found extensive use as cleaning and analysis (EPA) solvents in precision and critical cleaning. However, CFCs and chlorocarbon solvents have deleterious effects on the ozone layer, are relatively strong greenhouse gases, and some are suspect or known carcinogens. Because of their ozone-depletion potential (ODP), the Montreal Protocol and its amendments, as well as other environmental regulations, have resulted in the phaseout of CFC-113 and 1,1,1-trichloroethane (TCA). Although alternatives have been recommended, they do not perform as well as the original solvents. In addition, some analyses, such as the infrared analysis of extracted hydrocarbons, cannot be performed with the substitute solvents that contain C-H bonds. CFC-113 solvent has been used for many critical aerospace applications. CFC-113, also known as Freon (registered) TF, has been used extensively in NASA's cleaning facilities for precision and critical cleaning, in particular the final rinsing in Class 100 areas, with gas chromatography analysis of rinse residue. While some cleaning can be accomplished by other processes, there are certain critical applications where CFC-113 or a similar solvent is highly cost-effective and ensures safety. Oxygen system components are one example where a solvent compatible with oxygen and capable of removing fluorocarbon grease is needed. Electronic components and precision mechanical components can also be damaged by aggressive cleaning solvents.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".