Turning Trash to Treasure: The Influence of Carbon Waste Source on the Photothermal Behaviour of Plasmonic Titanium Carbide Interfaces
Bibliographic record
Abstract
Pyrolysis of carbonaceous waste materials has emerged as an effective recycling method to generate value-added products. In addition to producing pyrolytic oil and gas, the thermal degradation process yields solid pyrolytic char, which can be further processed. In this study, local waste materials, birch wood residue, Japanese knotweed stems, spent coffee grounds, tire rubber, and lobster shells were assessed for their potential to form pyrolytic char. After a simple acid treatment, many of these chars were successfully incorporated into solid-state synthesis of plasmonic titanium carbide (TiC) nanoparticles (NPs). Each char exhibited unique physical and chemical properties, which were leveraged to synthesize TiC NPs with distinct characteristics. To evaluate the plasmonic behavior of these TiC samples, solar-driven desalination experiments were performed. Notably, TiC derived from tire rubber demonstrated a high broadband absorbance and achieved a solar-to-vapor generation efficiency of 95%, corresponding to an evaporation rate of 1.40 ± 0.01 kg m⁻² h⁻¹ under one-sun illumination. This performance is the highest among all chars tested and ranks among the top reported values in the literature. Additionally, the evaporation interface maintained its performance over multiple cycles and under highly 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".