Hybrid Nanofiber-Based Atmospheric Water Harvesters: Sunlight-Driven Operation in Low-Humidity and Low-Illumination Environments
Bibliographic record
Abstract
Aerogels incorporating hygroscopic salts have been widely explored for atmospheric water harvesting (AWH). However, the scalability of these sorbents remains limited due to their reliance on energy-intensive and time-consuming drying methods such as lyophilization or supercritical drying. Here, we present a simple and scalable approach to drying hydrogels with desirable AWH properties using a freezing process followed by solvent exchange and thawing at room temperature. Our system consists of cellulose and silica nanofibers, forming hybrid xerogels with ultralow density (10.86 ± 0.32 mg cm –3 ), high specific surface area (104.22 m 2 g –1 ), excellent water stability, and mechanical strength. By incorporating carbon-based photothermal materials and lithium chloride as a hygroscopic salt, the xerogels achieve exceptional water uptake capacities ranging from 0.90 to 3.21 g g –1 across a relative humidity (RH) range from 15 to 75%. Under natural sunlight, the AWH xerogel produces water at a rate of 1.17 g g –1 day –1 . These results highlight a sustainable and scalable AWH strategy, leveraging ambient-dried xerogels as an energy-efficient solution to mitigate water scarcity.
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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 | 0.001 |
| 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".