Femtosecond laser induced porous surface on polymethyl methacrylate for filmwise condensation to improve solar still productivity
Bibliographic record
Abstract
The decline in freshwater availability has spurred research into employing solar desalination technology . Recent research has concentrated on investigating the use of surface modification to improve the productivity of solar still for desalination . This paper presents the use of femtosecond laser texturing to induce a porous surface on the polymethyl methacrylate (PMMA) cover of a solar still for producing filmwise condensation . Vertical lines 2.5 mm wide were fabricated on the PMMA surface using ultrafast laser texturing, and experiments were conducted using the modified cover on a solar still at a constant basin water temperature. Results show that the static water contact angle measured on the cleaned laser textured surface is hydrophobic. However, the formation of the porous structure leads to a change in wetting state from Cassie-Baxter to Wenzel upon exposure to water vapour . This change in wetting state enables the formation of filmwise condensation under the continuous presence of water vapours. The solar still productivity improves by 15.4 % and 23.1 % using both cleaned and uncleaned laser textured surfaces respectively. The modified surface is stable upon repeated exposure to water vapour , thus proving to be an excellent surface modification method for enhancing PMMA covered solar still performance.
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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".