Dynamically Adaptive Wrinkle‐Structured Light‐Regulating Films for Energy‐Efficient Buildings
Bibliographic record
Abstract
Abstract Energy‐efficient buildings utilize smart windows with switchable optical properties to minimize energy consumption for lighting and cooling. However, most existing dynamically switchable materials depend on external energy sources, such as electricity, for activation. In this work, a moisture‐responsive light‐regulating film is presented composed of a cellulose nanofibril (CNF) layer and a polyvinyl alcohol (PVA)‐glycerol layer, fabricated through a scalable casting method. The film harnesses moisture‐induced differences in modulus and swelling behavior between its layers, allowing the CNF layer to form a wrinkled surface under dry conditions that flattens upon moisture exposure. This results in a regulation of the sunlight, achieving tunable light diffusion for daylight harvesting, thermal regulation, and privacy control. Compared to conventional glass, the light‐regulating film reduces indoor temperatures by up to 8.4 °C on sunny days in hot summer while maintaining sufficient light on rainy days. Simulations indicate potential energy savings of up to 7.0 MJ m −2 annually for a typical medium office building in Miami, a city in the U.S. This work offers a sustainable and energy‐efficient solution for modern building applications.
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.
How this classification was reachedexpand
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) | 0.001 | 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 teacher head, 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".