Rapid and Efficient Collection of Platinum from Karstedt’s Catalyst Solution via Ligands-Exchange-Induced Assembly
Bibliographic record
Abstract
Reported herein is a novel strategy for the rapid and efficient collection of platinum from Karstedt’s catalyst solution. By taking advantage of a ligand-exchange reaction between alkynols and the 1,3-divinyltetramethyldisiloxane ligand (M Vi M Vi ) that coordinated with platinum (Pt(0)), the Karstedt’s catalyst particles with a size of approximately 2.5 ± 0.7 nm could be reconstructed and assembled into larger particles with a size of 150 ± 35 nm due to the hydrogen bonding between the hydroxyl groups of the alkynol. In addition, because the silicone-soluble M Vi M Vi ligand of the Karstedt’s catalyst was replaced by water-soluble alkynol ligands, the resultant large particles were readily dispersed in water, resulting in rapid, efficient, and complete collection of platinum from the Karstedt’s catalyst solutions with platinum concentrations in the range from ∼20 000 to 0.05 ppm. Our current strategy not only was used for the rapid and efficient collection of platinum from the Karstedt’s catalyst solutions, but it also enabled the precise evaluation of the platinum content in the Karstedt’s catalysts, even if this platinum content was extremely low (i.e., 0.05 ppm). Moreover, these platinum specimens that were efficiently collected from the Karstedt’s catalyst solutions could be directly used for the evaluation of platinum without the need for pretreatment processes, such as calcination and digestion with hydrofluoric acid, that were traditionally used prior to testing via inductively coupled plasma mass spectrometry in conventional methods.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".