Macroporous Double-Network Hydrogel for High-Efficiency Solar Steam Generation Under 1 sun Illumination
Bibliographic record
Abstract
Solar steam generation is one of the most promising solar-energy-harvesting technologies to address the issue of water shortage. Despite intensive efforts to develop high-efficiency solar steam generation devices, challenges remain in terms of the relatively low solar thermal efficiency, complicated fabrications, high cost, and difficulty in scaling up. Herein, a double-network hydrogel with a porous structure (p-PEGDA–PANi) is demonstrated for the first time as a flexible, recyclable, and efficient photothermal platform for low-cost and scalable solar steam generation. As a novel photothermal platform, the p-PEGDA–PANi involves all necessary properties of efficient broadband solar absorption, exceptional hydrophilicity, low heat conductivity, and porous structure for high-efficiency solar steam generation. As a result, the hydrogel-based solar steam generator exhibits a maximum solar thermal efficiency of 91.5% with an evaporation rate of 1.40 kg m –2 h –1 under 1 sun illumination, which is comparable to state-of-the-art solar steam generation devices. Furthermore, the good durability and environmental stability of the p-PEGDA–PANi hydrogel enables a convenient recycling and reusing process toward real-life applications. The present research not only provides a novel photothermal platform for solar energy harvest but also opens a new avenue for the application of the hydrogel materials in solar steam generation.
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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".