<sup>18</sup>F-Labeling of Radiotracers Functionalized with a Silicon Fluoride Acceptor (SiFA) for Positron Emission Tomography
Bibliographic record
Abstract
The para-substituted di-tert-butylfluorosilylbenzene structural motif known as the silicon-fluoride acceptor (SiFA) is a useful tag in the radiochemist's toolkit for incorporating radioactive [18F]fluoride into tracers for use in positron emission tomography. In comparison to conventional radiolabeling strategies, isotopic exchange of fluorine-19 from SiFA with [18F]fluoride is carried out at room temperature and requires minimal reaction participants. The formation of by-products is thus negligible, and purification is greatly simplified. However, while the precursor molecule used for labeling and the final radiolabeled product are isotopically discrete, they are chemically identical and are thus inseparable during purification procedures. The SiFA tag is also susceptible to degradation under the basic conditions arising from the processing and drying of [18F]fluoride. The '4 drop method', wherein only the first 4 drops of eluted [18F]fluoride are used from the solid-phase extraction, reduces the amount of base in the reaction, facilitates lower molar amounts of precursor, and reduces degradation.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.010 |
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".