Hydrofluoroethers (HFEs): A History of Synthesis
Bibliographic record
Abstract
An overview of the synthesis and applications of hydrofluoroethers (HFEs) is presented, structurally defined within this chapter as –(CF2)1–4–O–alkyl. First, an exhaustive summary of the commercial uses of HFEs is given along with the synthetic steps to achieve the incorporation of an HFE within its specific applications. Particular applications included are herbicides, insecticides, growth promoters in agriculture, blowing agents, cleaners, heat transfer fluids, protective coatings, and electrolytes in industry, and anesthetics, anti-inflammatories, and autoimmune and antitumor agents in medicine, to name just a few. Beyond applications, it is also important to understand the life cycle of HFEs in the environment; therefore, both biological and chemical reactivities of HFEs are provided. In addition, conventional strategies for the preparation of HFEs are outlined with many examples of, but not limited to, alkylation of fluorinated alkoxides, Suzuki–Miyaura coupling, oxycuperation of fluoroalkenes, and fluorination to thionyl esters with a wide range of fluorinating agents. The latter part of the chapter provides synthetic methodology to incorporate functionality on the fluorinated portion of the HFE ranging from amines to sulfonyl fluorides, and also including di- to multifunctional reports of HFEs. This overview summarizes current fields, emphasizing structural variety, end-group functionalities, and a range of synthetic methodologies to inform researchers on the manner in which new materials for current and future needs can be created.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".