Evolution of Morphology and Distribution of Salt Crystals on a Photothermal Layer during Solar Interfacial Evaporation
Bibliographic record
Abstract
Solar interfacial evaporation (SIE) by leveraging photothermal conversion could be a clean and sustainable solution to the scarcity of fresh water, decontamination of wastewater, and steam sterilization. However, the process of salt crystallization on photothermal materials used in SIE, especially from saltwater evaporation, has not been completely understood. We report the temporal and spatial evolution of salt crystals on the photothermal layer during SIE. By using a typical oil lamp evaporator, we found that salt crystallization always initiates from the edge of the evaporation surface of the photothermal layer due to the local fast flux of the vapor to the surroundings. Interestingly, the salt crystals exhibit either compact or loose morphology, depending on the location and evaporation duration. By employing a suite of complementary analytical techniques of Raman and infrared spectroscopy and temperature mapping, we followed the evolution and spatial distribution of salt crystals, interfacial water, and surface temperature during evaporation. Our results suggested that the compact crystal structure may emerge from the recrystallization of salt in an initially porous structure, driven by continuous water evaporation from the porous and loose crystals. The holistic view provided in this study may lay the foundation for effective strategies for mitigation of the negative impact of salt crystallization in solar evaporation.
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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".