Membrane-based microfluidic solvent extraction of Ga-68 from aqueous Zn solutions: towards an automated cyclotron production loop
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Zn]Zn nitrate liquid targets is increasing. However, current purification methods of Ga-68 from the target solution consist of multi-step procedures, thus, leading to a significant loss of activity through natural decay. Additionally, several processing steps are needed to recycle the costly, enriched target material. RESULTS: To eventually allow switching from batch to continuous production, conventional batch extraction and membrane-based microfluidic extraction were compared. In both approaches, Ga-68 was extracted using N-benzoyl-N-phenylhydroxylamine in chloroform as the organic extracting phase. Extraction efficiencies of up to 99.5% ± 0.6% were achieved within 10 min, using the batch approach. Back-extraction of Ga-68 into 2 M HCl was accomplished within 1 min with efficiencies of up to 94.5% ± 0.6%. Membrane-based microfluidic extraction achieved 99.2% ± 0.3% extraction efficiency and 95.8% ± 0.8% back-extraction efficiency into 6 M HCl. When executed on a solution irradiated with a 13 MeV cyclotron at TRIUMF, Canada, comparable efficiencies of 97.0% ± 0.4% were achieved. Zn contamination in the back-extracted Ga-68 solution was found to be below 3 ppm. CONCLUSIONS: Microfluidic solvent extraction is a promising method in the production of Ga-68 achieving high efficiencies in a short amount of time, potentially allowing for direct target recycling.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 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 it