3D Printed Cellulose Nanofiber Aerogel Scaffold with Hierarchical Porous Structures for Fast Solar‐Driven Atmospheric Water Harvesting
Bibliographic record
Abstract
Abstract Hygroscopic salt‐based composite sorbents are considered ideal candidates for solar‐driven atmospheric water harvesting. The primary challenge for the sorbents lies in exposing more hygroscopically active sites to the surrounding air while preventing salt leakage. Herein, a hierarchically structured scaffold is constructed by integrating cellulose nanofiber and lithium chloride (LiCl) as building blocks through 3D printing combined with freeze‐drying. The milli/micrometer multiscale pores can effectively confine LiCl and simultaneously provide a more exposed active area for water sorption and release, accelerating both water sorption and evaporation kinetics of the 3D printed structure. Compared to a conventional freeze‐dried aerogel, the 3D printed scaffold exhibits a water sorption rate that is increased 1.6‐fold, along with a more than 2.4‐fold greater water release rate. An array of bilayer scaffolds is demonstrated, which can produce 0.63 g g−1 day−1 of water outdoors under natural sunlight. This article provides a sustainable strategy for collecting freshwater from the atmosphere.
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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".