Colloidal Suspensions of Platinum Group Metal Nanoparticles (Pt, Pd, Rh) Synthesized by Dielectric Barrier Discharge Plasma (DBD)
Bibliographic record
Abstract
Abstract Atmospheric‐pressure plasmas produced by dielectric barrier discharge can be used to grow nanoparticles from aqueous solutions containing ions from the platinum group metals (PGM: Pt, Pd, and Rh). The technology could also be applied to recover PGM from waste solutions. In plasma electrochemistry, PGM solutions act as a liquid electrode, and a counter electrode located near the surface of the liquid is used to generate the plasma (e.g., hydrogen, argon). The treatment synthesizes nanoparticles within minutes, which can be separated from the treated solutions. In the present study, small concentrations of PGM ions (1 × 10−3m) are recuperated from aqueous solutions containing chloride ions. The efficiency of the process is quantified by elemental analysis, and the size of the colloids, measured by dynamic light scattering (DLS) and transmission electron microscopy (TEM). Very high recovery yields are found for palladium (>99%), as well as for rhodium (>75%) and for platinum (>51%). Plasma electrochemistry is a very efficient and rapid process to recuperate PGM ions from water solutions (faster than conventional electrowinning) such as industrial waste, acid leach, and related effluents. The very fine and surfactant‐free nanoparticles could find promising applications as industrial and automotive catalysts.
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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".