Robust, Scalable, and Cost-Effective Surface Carbonized Pulp Foam for Highly Efficient Solar Steam Generation
Bibliographic record
Abstract
Recently, a solar-driven evaporator has been applied in seawater desalination, but the low stability, high cost, and complex fabrication limit its further application. Herein, we report a novel, low-cost, scalable, and easily fabricated pulp-natural rubber (PNR) foam with a unique porous structure, which was directly used as a solar-driven evaporator after facile surface carbonization. This surface carbonized PNR (CPNR) foam without interface adhesion or modification was composed of a top photothermal layer with light absorption ability and a bottom hydrophilic foam layer with a porous and interconnected network structure. Due to the strong light absorption ability (93.2%) of the carbonized top layer, together with the low thermal conductivity (0.1 W m K –1 ) and good water adsorption performance (9.9 g g –1 ) of the bottom layer, the evaporation rate and evaporation efficiency of the pulp foam evaporator under 1 sun of illumination attained 1.62 kg m –2 h –1 and 98.09%, respectively, which were much higher than those of most cellulose-based solar-driven evaporators. Furthermore, the CPNR foam evaporator with high cost-effectiveness presented high light-thermal conversion, heat localization, and good salt rejection properties due to the unique porous structure. Additionally, the CPNR foam evaporator exhibited potential applications in the treatments of simulated sewage, metal ion concentration, and seawater desalination. Its cost-effectiveness was clearly higher than that of most reported evaporators as well. Therefore, this novel, low-cost, and stable pulp foam evaporator demonstrated here can be a very promising solution for water desalination and purification.
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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".