Grave-to-cradle upcycling of Ni from electroplating wastewater to photothermal CO2 catalysis
Bibliographic record
Abstract
Abstract Treating hazardous waste Ni from the electroplating industry is mandated world-wide, is exceptionally expensive, and carries a very high CO 2 footprint. Rather than regarding Ni as a disposable waste, the chemicals and petrochemicals industries could instead consider it a huge resource. In the work described herein, we present a strategy for upcycling waste Ni from electroplating wastewater into a photothermal catalyst for converting CO 2 to CO. Specifically, magnetic nanoparticles encapsulated in amine functionalized porous SiO 2 , is demonstrated to efficiently scavenge Ni from electroplating wastewater for utilization in photothermal CO 2 catalysis. The core-shell catalyst architecture produces CO at a rate of 1.9 mol·g Ni −1 ·h −1 (44.1 mmol·g cat −1 ·h −1 ), a selectivity close to 100%, and notable long-term stability. This strategy of upcycling metal waste into functional, catalytic materials offers a multi-pronged approach for clean and renewable energy technologies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 | 0.000 |
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 teacher head, 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".