Flakes and Nanoparticles from Waste Ru‐Plated Fashion Items through Food Waste by‐Products
Bibliographic record
Abstract
Abstract Ruthenium is relevant for a broad range of applications, including catalysis and electronics. Like other metals of the platinum group, ruthenium stands out as one of the rarest elements in the Earth's crust. The demand for Ru from the industry is putting pressure on its availability. Hence, its recovery from secondary sources is imperative. Fashion solid residues of the plating industry are an important waste stream for Ru. Within this context, we propose a novel approach to Ru recovery for its safe, sustainable, and economically affordable upcycling. The approach is based on peeling from waste metal wires by a green oxidizing agent, H2O2, in an environment acidic by lactic acid, a by‐product of the food industry. Peeled flakes were characterized by scanning electron microscopy, energy dispersive spectroscopy, X‐ray photoelectron spectroscopy, and atomic force microscopy for their structure and (surface) chemical composition and bonding. Inductively Coupled Plasma Optical Emission Spectroscopy shows the ultra‐low concentration of noble metals in the leachate, thereby suggesting their quantitative recovery in their metallic state. Further, we observed the colloidal nature of the washing water of the peeled flakes. Therefore, we hypothesized the presence of nanoparticles in the washing water and went for their characterization.
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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 | 0.001 |
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".