Photothermal Macroporous Lignin Cryogels for Off‐Grid, Continuous Atmospheric Water Collection via Interlayer Heat Recovery
Bibliographic record
Abstract
Abstract Sorption‐based atmospheric water harvesting (SAWH) offers a promising solution to water scarcity in arid and infrastructure‐limited regions, yet achieving both high water productivity and energy efficiency remains a significant challenging. Herein, a lignin‐engineered hygroscopic cryogel is reported with a tailored molecular structure designed to enhance both photothermal conversion and swelling. Compared with kraft lignin (KL), regenerated lignin achieves a photothermal conversion efficiency of 56% (1.68× that of KL) and exhibits about fourfold higher swelling in the hydrogel precursor. After LiCl loading, the composite cryogel reaches 1.81 g water g sorbent −1 at 60% RH, a 1.94× improvement over the KL‐based cryogel. To further increase water yield and energy efficiency, a drum‑type SAWH device is developed that incorporates interlayer heat transfer, recovering waste heat from the upper sorbent bed to drive desorption in a lower layer. This design increases the thermal energy efficiency to 48.4% and enhance the daily water yield by 1.49× in indoor tests. Outdoor trials demonstrate stable operation over ten continuous sorption/desorption cycles, producing 66.15 g of water (1439.04 mL water m solar −2 ), a 31.7% improvement relative to a single‐layer configuration. This work introduces a scalable, off‐grid thermal‐management strategy that significantly improves the efficiency of atmospheric water harvesting in arid environments.
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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".