Synergistic charge and proton transfer–assisted lignin-based conductive and superhydrophobic coatings for non-strain raindrop monitoring
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
Developing effective raindrop monitors for complex outdoor environments remains a significant challenge. Herein, an innovative raindrop monitoring mechanism based on synergistic charge and proton transfer was proposed to overcome the limitations of traditional deformation-based raindrop monitors, whose accuracy is compromised by interference from wind and insect micro-vibrations. A span-new conductive and superhydrophobic coating was designed through integrating lignin nanospheres, in situ grown ZnO, in situ polymerized polypyrrole (PPY), and polydimethylsiloxane, transforming natural wood into a raindrop monitor via simple spraying. When raindrops contact the raindrop monitor, the charges and protons in the water vapor penetrate the coating to interact with the surface holes of PPY, thereby causing measurable changes in resistance. The coating exhibits excellent superhydrophobicity (water contact angle of 165.8°) and conductivity (0.52 S/m), precisely responding to raindrops with various volumes, heights, and pH values (response time of 282 ms). Furthermore, the coating demonstrates outstanding environmental stability, including remarkable abrasion resistance (maintaining superhydrophobicity after 30 abrasion cycles), outstanding ultraviolet (UV) resistance (retaining superhydrophobicity for 250 h and 13 h under 15 W/m 2 and 1000 W/m 2 UV irradiation respectively), and high antibacterial property (99.9% against both Escherichia coli and Staphylococcus aureus ), ensuring its long-term stable operation in complex outdoor environments. Given the simple preparation and superior performance, this work provides a foreseeable prosperity for the long-term application of raindrop monitor in complex outdoor environments to promote ecological research.
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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) | 0.001 | 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