Dense ionization and subsequent non-homogeneous radical-mediated chemistry of femtosecond laser-induced low density plasma in aqueous solutions: synthesis of colloidal gold
Bibliographic record
Abstract
The "cold" low density plasma channels generated by the filamentation of powerful femtosecond laser pulses in aqueous solutions constitute a source of dense ionization. Here, we probed the radiation-assisted chemistry of water triggered by laser ionization via the radical-mediated synthesis of nanoparticles in gold chloride aqueous solutions. We showed that the formation of colloidal gold originates from the reduction of trivalent ionic gold initially present in solution by the reactive radicals (e.g. hydrated electrons) produced upon the photolysis of water. We analyzed both the reaction kinetics of the laser-induced hydrated electrons and the growth kinetics of the gold nanoparticles. Introduction of radical scavengers into the solutions and different initial concentrations of gold chloride provided information about the radical-mediated chemistry. The dense ionization results in the second order cross-recombination of the photolysis primary byproducts. Competition with recombination imposes the non-homogeneous interaction of reactive radicals with solute present in irradiated aqueous solutions. Such a laser-induced non-homogeneous chemistry suggests similarities with the radiation chemistry of water exposed to conventional densely ionizing radiation (high dose rate, high linear energy transfer).
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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".