When cellulose nanocrystals meet graphene oxide: Structurally enhanced aerogels for efficient solar steam generation and water purification
Bibliographic record
Abstract
• Cellulose nanocrystal-reduced graphene oxide aerogels (CGAs) were fabricated. • The CGAs show excellent chemical and environmental stability. • The CGAs have a unique microstructure from unidirectional freeze-drying. • The CGAs are effective for water purification through solar steam generation. Pollution, population growth, and climate change are intensifying global freshwater shortages. Traditional methods of collecting freshwater, such as rainwater harvesting and sewage treatment, have high energy consumption, limiting their implementation in underdeveloped regions. On the other hand, solar-driven seawater evaporation offers a promising solution, purifying water using naturally abundant solar energy. Reduced graphene oxide (rGO) is considered as a strong candidate for this purpose due to its broad spectral absorption. However, its hydrophobicity hinders its direct use in solar steam generators. Here, we report the preparation of a series of cellulose nanocrystal (CNC)-rGO aerogels (CGAs) through a one-pot hydrothermal gelation process, followed by unidirectional freeze-drying. The introduction of CNCs enhances the hydrophilicity and structural stability of the resulting CGAs. The CGAs also feature uniform and unidirectional channels that promote efficient water transport, as confirmed by scanning electron microscopy (SEM) and X-ray microtomography (XMT). The CGAs have an optimized water evaporation rate of 1.80 kg m −2 h −1 under 1 sun irradiation, with 90 % solar-to-vapor energy efficiency. Moreover, durability and seawater desalination tests provide additional evidence for the practicality of CGAs in water purification. It is anticipated the implementation of CGAs will expedite the application of solar steam generators in practical scenarios.
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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".