Photothermal Macroporous Lignin Cryogels for Off‐Grid, Continuous Atmospheric Water Collection via Interlayer Heat Recovery
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.003 | 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 it