High-Efficiency Photothermal Water Evaporation under Low-Intensity Sunlight Using Wood Biochar Monolith
Bibliographic record
Abstract
Utilizing energy directly from the sun, solar water evaporation drives the global hydrological cycle and produces freshwater from saline water in the oceans and on land. As water is a poor solar absorber, a photothermal material is needed to facilitate the conversion of photons to thermal energy and increase the efficiency of solar desalination. However, the current photothermal materials are less efficient and expensive to be manufactured. Inspired by nature, we created a new photothermal material called a wood biochar monolith (WBM) by carbonizing wood using the pyrolysis process at 1000 °C and subsequently steaming at high pressure. Under low light intensity (193 W/m 2 ), the light to vapor efficiency of maple WBM is more than 100%. The outstanding performance of WBM is attributed to (1) the facilitated water transport in the hierarchical, open-pore network preserved from the wood precursor in WBM and (2) the reduced evaporation enthalpy of confined water in WBM and the high broadband sunlight absorptivity of WBM. Moreover, the high evaporation rate causes the temperature of WBM to be lower than that of the surrounding water, enabling thermal energy harvesting by WBM from water and making a light-to-vapor efficiency of >100% feasible. This discovery offers opportunities for developing low-cost, high-performance water desalination or humidification devices deployable in remote areas with nonconcentrated natural sunlight.
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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".