A non‐noble metal plasmonic photothermal nanoparticle floating device for efficient interface water evaporation
Bibliographic record
Abstract
Abstract The utilization of solar energy for steam generation presents an eco‐friendly and sustainable strategy to address the challenges linked with water scarcity. Nevertheless, its widespread implementation in industrial production has been significantly hindered by the intrinsic limitation of low evaporation efficiency. Herein, we report a straightforward non‐noble metal plasma photothermal floating device designed for interfacial water evaporation. Precisely, 3 mg of synthesized TiN nanoparticles was uniformly spin‐coated on the carbonized wood dealt with hydrothermal reaction. The experimental results demonstrated a noteworthy photothermal water evaporation efficiency of 93.4% under the irradiation of 1 kW m −2 . Simultaneously, the device exhibited exceptional stable repeatability and salt resistance. The typical ion concentrations (Na + , K + , Ca 2+ , Mg 2+ ) before and after seawater evaporation were monitored and found to have a remarkable ion removal rate of 99.57% ~ 99.94%, which is even lower than the national drinking water hygiene standards. We firmly believe that our work could offers valuable insights for the advancement of large‐scale seawater desalination and crystalline salt recovery applications.
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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 | 0.001 | 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".