Novel Solvent and Dispersant Systems for Fluoropolymers and Silicones
Bibliographic record
Abstract
Prior to their current phase-out, chlorofluorocarbons (CFCs) were widely used as processing solvents for various materials. CFCs were well suited for many medical applications owing to their high solvency, nonflammability, good materials compatibility, and low toxicity. The uses for CFCs include a silicone deposition solvent, a fluoropolymer dispersion liquid, and processing solvents. However, the Montreal Protocol phase-out of ozone-depleting substances has required that alternative dispersants and solvents be found. Limitations of most available alternatives include flammability, low volatility, poor solvency, and poor materials compatibility. Perfluorocarbons (PFCs) have many functional properties that are similar to those of CFCs but they do not cause ozone depletion. Therefore they can be excellent replacements for CFCs in many demanding, high-performance applications. In addition to being non-ozone-depleting, PFCs are essentially nontoxic, nonflammable, nonvolatile organic compounds, easily dried, and available in a wide range of boiling points, and they provide excellent materials compatibility. Although PFCs exhibit poor solubility with most compounds, their ability to dissolve or disperse halogenated compounds is excellent. In addition, these
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.007 |
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".