Intensifying Heat Using MOF‐Isolated Graphene for Solar‐Driven Seawater Desalination at 98% Solar‐to‐Thermal Efficiency
Bibliographic record
Abstract
Abstract Photothermal materials are crucial for diverse heating applications, but it remains challenging to achieve high energy conversion efficiency due to the difficulty to concurrently improve light absorbance and suppress heat loss. Herein, a zeolitic imidazolate framework‐isolated graphene (G@ZIF) nanohybrid is demonstrated that utilizes ultrathin, heat‐insulating ZIF layers, and G@ZIF interfacial nanocavity to synergistically intensify light absorbance and heat localization. Under artificial sunlight illumination (≈1 kW m−2), the G@ZIF film attains a maximum temperature of 120 °C in an open environment with a 98% solar‐to‐thermal conversion efficiency. Importantly, the porous ZIF layer allows small molecules/media to enter and access the embedded hot graphene surface for targeted heat transfer in practical applications. As a proof‐of‐concept, the G@ZIF‐based steam generator realizes 96% energy conversion from light to vapor with near‐perfect desalination and water purification efficiencies (>99.9%). This design is generic and can be extended to other photothermal systems for advanced solar‐thermal applications, including catalysis, water treatments, sterilization, and mechanical actuation.
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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".