Turning Trash to Treasure: The Influence of Carbon Waste Source on the Photothermal Behaviour of Plasmonic Titanium Carbide Interfaces
Bibliographic record
Abstract
Abstract Pyrolysis of carbonaceous waste material has become an attractive method of recycling to generate value added products. Alongside pyrolytic oil and gas fractions, the thermal degradation forms solid pyrolytic char, which can be further processed. Local waste materials, including birch wood residue (BW), Reynoutria japonica stems (KW), spent coffee grounds (CG), tire rubber (TR), and lobster shells (LS) we assessed to form pyrolytic char. Using a simple acid treatment step on the chars, this study has shown successfully incorporate many of them into the low‐temperature synthesis of plasmonic TiC NPs. Each char was shown to display distinctive physical and chemical characteristics, which was exploited to synthesize TiC NPs with unique properties. To study the plasmonic behaviour of each TiC sample, solar driven desalination experiments were conducted. TiC formed from TR char achieved broadband absorbance of ~95 % of the solar spectrum, reaching a near‐perfect solar‐to‐vapor generation efficiency of 95 %, or a water generation rate of 1.40±0.01 kg m −2 h −1 under one‐sun illumination. This makes it the best performing of all chars tested, and among the top performers reported in the literature to date. The evaporators maintain activity over time and under strongly hypersaline conditions.
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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".