Scalable and Facile Formation of Microlenses on Curved Surfaces Enabling a Highly Customized Sustainable Solar‐Water Nexus
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it