Spherical Architecture of Bamboo Lignocellulosic Aerogels for Enhanced Solar‐Driven Evaporation and Antisalt Accumulation
Bibliographic record
Abstract
Abstract Environmental challenges, including shortages of clean fuel and freshwater, have a significant impact on human health and economic development. The bio‐based evaporator harnessing solar energy for clean‐water harvesting holds great promise for sustainable photothermal conversion. Inspired by bamboo, a fabrication strategy is developed for spherical architecture featuring a centrosymmetric radial channel, which transforms lignocellulosic aerogel through freezing and hydrogen bond regulation. The lignocellulosic aerogel features porous surface and centrosymmetric radial channel, which facilitates efficient photothermal conversion, water vapor transport, and antisalt accumulation. The aerogel evaporator achieves an efficient evaporation rate of 1.49 ± 0.05 kg m −2 h −1 at 1 kW m −2 , representing a 4.23 enhancement factor. Owing to its stable self‐floating, self‐cleaning and balanced sensitivity for unrestricted rotation in all directions, the aerogel evaporator demonstrates remarkable adaptability to diverse solar angles and water salinity. Even at a 0° angle and 20 wt.% NaCl, the evaporator retains 97.3% and 78.9% efficiency compared to DI water at 90°. Furthermore, the self‐cleaning properties of the evaporation surfaces effectively mitigate the effects of salt accumulation during seawater evaporation. This research introduces an innovative aerogel methodology that integrates spherical lignocellulosic aerogel in bamboo design, thereby enhancing the stability and sustainability of solar‐driven evaporation.
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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) | 0.000 | 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".