Plasmonic group IV transition metal carbide interfaces for solar-driven desalination
Bibliographic record
Abstract
To combat the dwindling supply of freshwater, solar-driven desalination using plasmonic nanomaterials has emerged as a promising and renewable solution. Effective materials must exhibit high solar-to-vapor conversion efficiencies, be inexpensive, chemically stable, and maintain performance over time. Refractory plasmonic carbide nanomaterials are exciting candidates that could meet these demands but have not been as widely explored. Here, we investigate plasmonic carbide interfaces made of TiC, ZrC, and HfC nanoparticles loaded onto to a mixed cellulose ester (MCE) membrane gain insight into their solar-vapor generation and desalination potential. Evaporation rates and efficiencies were determined for tap water and saltwater with varying salt concentrations. Desalination using Atlantic Ocean water under 1 sun intensity yielded rates of 1.26 ± 0.01, 1.18 ± 0.02, and 1.40 ± 0.01 kg m-2 h-1, with efficiencies of 86, 80, and 96% for TiC, ZrC, and HfC, respectively, under 1 sun illumination. Carbide interfaces effectively removed salt and metal ions from the water and were able to reject salt over extended periods of desalination and high salt concentrations of up to 35%. The effect of ambient temperature and relative humidity on the desalination process was also investigated which showed that the evaporation rates and efficiencies decrease with increasing humidity and decreasing room temperature. However, the performance of HfC was less affected by the changes in the ambient conditions compared to TiC and ZrC.
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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".