Stable, Cost‐Effective TiN‐Based Plasmonic Nanocomposites with over 99% Solar Steam Generation Efficiency
Bibliographic record
Abstract
Abstract Plasmonic nanoparticles (NPs), such as Au, Ag, and Cu, are considered as promising photothermal materials and attract extensive attention for freshwater production by solar steam generation. However, high cost, narrow absorption range and/or poor stability greatly limit their practical applications. Herein, a high‐efficiency solar energy conversion material consisting of low‐cost non‐metal, extremely thermally‐stable plasmonic TiN NPs and hydrophilic semi‐reduced graphene oxide (semi‐rGO), with broadband solar absorption capability, by a fast in situ microwave reduction method is prepared. The 2D semi‐rGO serves as a support for the loading of plasmonic NPs, and meanwhile accelerates the transport and evaporation of water due to its hydrophilicity. Then, decoration of plasmonic TiN NPs further enhances the solar photon absorption and hydrophilicity while suppressing the heat loss, thanks to the layered structure of TiN/semi‐rGO, improving overall solar energy utilization. Owing to the enhanced absorption and unique layered nanostructure with strong interfacial interaction, the optimal sample of TiN/semi‐rGO‐25% absorber achieves a high and stable water evaporation rate of ≈1.76 kg m −2 h −1 with an energy efficiency as high as 99.1% under 1 sun illumination. Furthermore, this plasmonic TiN/semi‐rGO absorber is capable of producing high‐quality freshwater from sustainable seawater desalination and wastewater purification processes.
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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".