Ambient‐Dried MOF/Cellulose‐Based Aerogels for Atmospheric Water Harvesting and Sustainable Water Management in Agriculture
Bibliographic record
Abstract
Abstract Atmospheric water harvesting (AWH) is a promising approach to address water scarcity; however, achieving scalable and efficient materials remains a critical challenge. Herein, we present ambient‐dried aerogels composed of biobased materials (cellulose nanofibers and sodium alginate), integrated with metal–organic frameworks (MOFs) and hygroscopic salts for effective AWH. A key innovation in this system is the functional incorporation of MOFs into the aerogel scaffolds, where they enhance water capture at low relative humidity (RH) and contribute to improved salt stabilization. The biobased matrix facilitates ambient drying, while promoting efficient water transport and absorption. The prepared aerogels demonstrate a competitive water uptake of 0.32 g/g at 25% RH and 3.52 g/g at 90% RH within 12 h. When coated with a carbon nanotube (CNT) layer, the aerogels achieve a solar‐driven evaporation efficiency of ≈70%. As proof of concept, the aerogels were used to create microclimates inside a terrarium, where atmospheric water absorbed by the system was released under solar irradiation to sustain a plant growth for two weeks. This stategy can be extended to greenhouses, leveraging high humidity and waste heat for enhanced water regeneration, alongside ventilation systems to optimize water collection efficiency, representing a transformative opportunity for sustainable agriculture.
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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".