Biomass-based photothermal fabrics and superhydrophobic aerogel for self-floating solar evaporators with high energy efficiency in fresh water production from seawater
Bibliographic record
Abstract
In the context of global water scarcity, solar-powered seawater desalination represents a highly promising approach to addressing the freshwater crisis, offering a green and sustainable solution. However, conventional solar evaporators (SEs) often have issues such as salt deposition and heat loss by conduction, which result in decreased evaporation rates energy efficiency. Moreover, they often require the use of challenging-to-degrade polymer materials as the insulation layers and buoyancy support. In this study, we fabricated a novel energy-efficient SE capable of self-standing/self-floating with biomass materials for biodegradability and sustainability. The photothermal layer of the SE was made with waste cotton cloth modified with CuS for high hydrophilicity and photothermal conversion efficiency, with hierarchical pores for rapid water delivery to the evaporation surface. The supporting base of the SE was made biomass-based superhydrophobic aerogel to provide effective thermal insulation and self-floating ability. The novel design of the SE significantly reduced heat transfer from the top photothermal zone to the bulk water body without affecting rapid water delivery, thereby preventing salt deposition and improving energy efficiency. Under 1 light illumination, the water evaporation rate of the SE reached 2.94 kg m -2 h −1 and 93 % evaporation efficiency. The water harvested via the engineered evaporation system is suitable for direct application in crop irrigation. This type of SEs may have commercial application potential in seawater desalination agricultural planting field, and wastewater treatment.
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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".