Enhanced release of palladium and platinum from catalytic converter materials exposed to ammonia and chloride bearing solutions
Bibliographic record
Abstract
The environmental levels of platinum group elements (PGEs) are steadily rising, primarily due to exhaust emissions of vehicle catalytic converter (VCC) materials containing solid PGEs. Once these VCC materials reach soil and water, the PGEs may be transported in the form of nanoparticles (dimensions 1-100 nm) or they may be mobilized by forming coordination complexes with ligands in the environment. Chloride (Cl-) and ammonia (NH3) are two ligands of particular concern due to their ubiquity as well as their potential to form the chemotherapy drug cisplatin (Pt(NH3)2Cl2) or other potentially bioactive complexes. This initial study examines the release of Pd and Pt into solutions exposed to VCC materials at pH 8 and 25 °C, using elemental analysis of metal content in post-exposure extracts. The solutions had total ammonia nitrogen concentrations (TAN, [NH4+] + [NH3]) of 0 μM, 5.56 μM, 55.6 μM and 1.13 × 105 μM (0 ppm, 0.1 ppm, 1 ppm, and 2147 ppm). The former three represent background environmental levels had a minimal effect on release. However, when combined with 1.13 × 105 μM Cl- (4000 ppm Cl-), 55.6 μM TAN induced a marked increase in metal release (∼41× for Pd). High TAN solutions induced more Pd and Pt release than equimolar NaCl solutions. Materials characterization revealed that ∼4 nm palladium-containing nanoparticles were present, spatially associated with nanoparticles of γ-Al2O3; ceria-zirconia nanoparticles were also present but did not have any metal associated with them. Platinum-containing nanoparticles were not observed.
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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".