Scalable and Facile Formation of Microlenses on Curved Surfaces Enabling a Highly Customized Sustainable Solar‐Water Nexus
Bibliographic record
Abstract
Solar‐driven water treatment suffers from low efficiency due to the solar energy loss during the energy conversion, especially in the scale‐up operation. One promising solution is using microlenses (MLs) to enhance the photodegradation of organic contaminants in water. However, most MLs fabrications apply to 2D planar surface only, which restricts their potential applications. In this study, a flexible and scalable technology is presented to fabricate MLs on curved surfaces. Precursor microdroplets form in a dilution process and are converted to MLs by photopolymerization. Optical simulations and experiments are combined to establish the correlation between optical properties of MLs and the performance of ML‐functionalized reactors in photodegradation. It is demonstrated that surface MLs on all‐shaped reactors significantly enhance the photodegradation efficiency of organic contaminants under simulated solar light or natural indoor light, with a maximum improvement of 83 folds. The surface coverage and size distribution of MLs can be adjusted by varying the solution concentration and the dilution rate when generating microdroplets. In addition, fabrication of MLs on a larger scale is achieved over an area up to 250 . MLs on 3‐dimensional curved surfaces fabricated by the technique enable significantly enhanced, highly customized, and sustainable solar‐driven water 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".