Magnetic Droplet Microfluidics as a Platform for the Concentration of [<sup>18</sup>F]Fluoride and Radiosynthesis of Sulfonyl [<sup>18</sup>F]Fluoride
Bibliographic record
Abstract
The radioisotope 18F is often considered the best choice for positron emission tomography (PET) owing to its desirable chemical and radiochemical properties. However, nucleophilic 18F-fluorination of large, water-soluble biomolecules, based on C-F bond formation, has traditionally been difficult. Thus, several aqueous fluorination approaches that offer significant versatility in radiopharmaceutical synthesis with sensitive targeting vectors have been developed. Furthermore, because 18F decays rapidly, production of these 18F-labeled compounds requires an automated process to reduce production time, reduce radiation exposure, and minimize losses due to the transfer of reagents during tracer synthesis. Herein, we report the use of magnetic droplet microfluidics (MDM) as a means to concentrate [18F]fluoride from the cyclotron target solution, followed by the synthesis of an 18F-labeled compound on a microfluidic platform. Using this method, we have demonstrated 18F preconcentration in a small-volume droplet through the use of anion exchanging magnetic particles. By using MDM, the preconcentration step took approximately 5 min, and the [18F]fluoride solution was preconcentrated by 15-fold. After the preconcentration step, an 18F-labeling reaction was performed on the MDM platform using the S-F bond formation in aqueous conditions to produce an arylsulfonyl [18F]fluoride compound which can be used as a prosthetic group to label PET targeting ligands. The high radiochemical purity of 95±1% was comparable to the 96% previously reported using a conventional method. In addition, when MDM was used, the total synthesis time was improved to 15 min with lower reagent volumes (50-60 μL) used.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".