Bibliographic record
Abstract
Abstract Windows play significant roles in commercial and residential buildings and automobiles, which direct and control light illumination, thermal insulation, natural ventilation, and aesthetics. Various approaches are attempted to make windows “smart” by tailoring their transparency and thermal insulation in response to environmental changes. Hence, there has been much effort to develop smart windows that can dynamically modulate the transmission and reflectance of the visible light and solar radiance into buildings according to weather conditions or personal preferences. Development of smart window materials is also beneficial to applications including wearable sensors, energy harvesting and storage, and medical devices. By carefully matching the refractive indices of nanoparticle (NPs) and polymer matrix, surface chemistry, and their mechanical properties, particle‐embedded polymer composites can exhibit synergistic effects with improved chemical and mechanical stability, enhanced dispersion of NPs, and optimized and stimuli‐responsive optical properties. Here, an overview of recent progresses in the development of smart windows based on electro‐, thermo‐, and mechanoactuations is provided. Additional functionalities, e.g., flexibility, stretchability, and mechanical/chemical stability, can also be achieved by careful choices of NPs and polymers.
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 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".