Thermo-adaptive interfacial solar evaporation enhanced by dynamic water gating
Bibliographic record
Abstract
Abstract Solar-driven evaporation has emerged as a sustainable approach for water generation and purification. However, the undesirable heat loss leads to low energy conversion efficiency that limits water generation and impedes the scalability of this technology. Here, we developed a bilayer-structured solar evaporator (SDWEs) by engineering the fluidic flow within two water transport channels. A porous polydopamine (PDA) coating layer served as photothermal section and water supply microchannels, while the thermo-responsive sporopollenin layer on the bottom skeleton of the foam acted as a switchable water gating layer. Through confocal laser microscopy and micro-CT characterization, we demonstrated that this structural design enabled the selective and directional water transport. Noteworthy, this unique fluidic flow could facilitate the continuous supply of thin water layers and reduce the latent heat required for water evaporation. Therefore, the optimized p-SDWE sample achieved a high-water evaporation rate of 3.58 kg m−2 h−1 using 93.9% solar energy from 1 sun irradiation, and successfully delivered 18–22 liters of purified water per square meter of SDWE per day when treating brine water. This work elucidated the functions of water transport at the interface within the solar evaporator and presented a novel strategy for high-performance solar-driven water 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".