Bioinspired and Biodegradable Hydrothermally Treated Cellulose Nanocrystal Aerogels for High Efficiency Solar Steam Generation and Sustainable Water Purification
Bibliographic record
Abstract
Abstract Access to clean water is crucial for human survival, yet a significant portion of the global population continues to face challenges from water scarcity and contamination. Traditional water purification methods such as desalination and distillation are energy‐intensive, necessitating the adoption of green alternatives to reduce greenhouse gas emissions and mitigate global warming. High‐efficiency solar steam generation has emerged as a promising solution. Although many solar evaporators have shown impressive evaporation rates, construction of these using only sustainable materials remains challenging. Inspired by the microstructure of natural wood, a series of hydrothermally‐treated cellulose nanocrystal aerogels (HTCAs) is proposed as efficient and eco‐friendly solar steam generators. The HTCAs are prepared through a one‐pot hydrothermal treatment, avoiding the use of hazardous chemicals, and entirely based on sustainable cellulose nanocrystals. They present low tortuosity porous microstructures and exhibit high evaporation rate (1.70 kg m −2 h −1 under 1 sun irradiation) with low water evaporation enthalpy (841 ± 35 J g −1 ). Integration with advanced water purification techniques also demonstrates their effectiveness in removing contaminants. This strategy mimics the microstructure of wood through unidirectional freeze‐drying and offers a sustainable pathway to biodegradable solar steam generators, potentially alleviating the global water scarcity within the carbon neutrality framework.
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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".