All Wood‐Based Evaporator via Cell Wall Regulating for Integrated Water and Energy Generation
Bibliographic record
Abstract
Abstract Solar‐powered interfacial evaporators (SPIEs) derived from natural wood present a sustainable solution to global water scarcity. However, the full‐component utilization of wood and the precise engineering of its microstructure remain underexplored. Here, response surface methodology is coupled with cell wall engineering to regulate the dissolution‐regeneration of cellulose in situ in the cell wall, constructing micro/nanofibrillar networks within wood and facilitating water transport. Lignin, self‐extracted from natural wood, is employed as a photothermal material, enabling integrated photothermal layer design and achieving a photothermal conversion efficiency of 91.25%. The resulting wood‐based SPIE (W‐SPIE) delivers a high evaporation rate of 2.07 kg m −2 h −1 . Concurrent water‐electricity co‐generation is achieved during water harvesting, yielding 254.52 mV and 1.07 µA, with a sustained evaporation rate of 1.92 kg m −2 h −1 . Life‐cycle assessment indicates a 30.99% reduction in carbon emissions relative to petroleum‐derived hydrogel evaporators. This W‐SPIE demonstrates an effective, carbon‐conscious approach for simultaneous clean water production and energy harvesting, offering a viable strategy for addressing escalating global water and energy demands through full valorization of renewable wood resources.
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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) | 0.002 | 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".