Efficient and Rapid Synthesis of Radioactive Gold Nanoparticles by Dielectric Barrier Discharge
Bibliographic record
Abstract
A compact bench‐top system based on a dielectric barrier plasma discharge (DBD), enables the rapid, automatable, and continuous‐flow synthesis of gold nanoparticles (AuNPs) and radioactive gold nanoparticles (198AuNPs). AuNPs are used as radiosensitizers in oncology, and 198AuNPs (half‐life: 2.7 d) have been suggested as potential cancer brachytherapy sources. Plasma applied at the surface of a liquid containing gold ions (AuCl4−) and dextran induces the production of AuNPs directly in water. This synthesis is monitored in real time by UV–visible spectrometry: the change of absorbance of the solution provides new insights on the growth dynamics of AuNPs by plasma synthesis. By balancing gold ions and surfactant molecules, particles with a diameter lying in the optimal range for radiosensitizing applications (28 ± 9 nm) are produced. The method yields a reduction of more than 99% of the gold ions within only 30 min of plasma treatment. A postsynthesis ripening of the AuNPs is revealed, monitored by UV–visible spectrometry, and quantified within the first few hours following plasma treatment. Radioactive 198AuNPs are also produced by DBD synthesis and characterized by electron microscopy and single‐photon emission computed tomography imaging. The results confirm the efficiency of DBD reactors for AuNPs synthesis in oncology applications.
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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 | 0.000 | 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) | 0.001 | 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 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".