Radiative Cooling Sorbent towards High Performance All Weather Ambient Water Harvesting
Bibliographic record
Abstract
Abstract Emerging atmospheric water harvesting (AWH) technologies promise water supply to underdeveloped regions that have no access to liquid water resources. The prevailing AWH systems, including condensation- or sorption-based, mostly rely on a single mechanism and thus have a limited range of working conditions and inferior performance. In this study, we synergistically integrate multiple mechanisms, including thermosorption effect, radiative cooling, and multiscale cellulose-water interactions, and demonstrate a low-cost (~ 4 USD kg− 1) and high performance (up to 6.75 L kg− 1 day− 1 in field tests) AWH system requiring zero active energy input. The proposed system consists of a highly scalable and sustainable cellulose scaffold impregnated with hygroscopic deliquescent lithium chloride (LiCl) salt. Cellulose scaffold and coated LiCl synergistically interact with water at different scales from molecular, to nanometer, and micrometer scales, providing a fast harvesting rate and high yield over a wide operation range. Iterating radiative cooling and solar heating workflow achieves simultaneous enhancement of water capture and release through the so-called temperature-swing strategy. The captured water in return facilitates radiative cooling due to the intrinsically high infrared (IR) emissivity of the LiCl-cellulose composite. With our simple yet effective material design, the AWH working range extends to lower than ~ 10% relative humidity (RH), and the water uptake in the controlled lab test reaches 16 kg kg− 1 at 90% RH (sample at 19°C, i.e. 6°C below 25°C ambient temperature). In addition, we propose a theoretical model capable of elucidating the experimental water uptake curves and demonstrating the synergy among cellulose-LiCl-water-energy interaction. The promising performance emphasizes the potential of involving multiple AWH mechanisms and points to an alternative pathway to stimulate future improvement.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".