Innovative and Energy Efficient Smart Window Based on Nanomaterial Technologies
Bibliographic record
Abstract
Strategies for incorporating energy-efficiency requirements into building standards have been implemented by governments in developed countries in order to introduce the concept of green nanotechnology. Substituting regular glass windows in residential/commercial buildings with smart windows is the objective. This paper describes the development of innovative nanomaterial based membranes/coatings for smart windows that would work as electric dimming glass. Currently, curtains and blinds function to block sun light; eliminating such elements is of importance due to its limited functionality (e.g. blocking UV prevents sun light illumination), health (e.g. dust and germ collection especially in hospitals), disposal/recycling issues, cost, and maintenance. The proposed smart window is expected to block harmful UV light and provide a controlled privacy. Electrochromic smart windows are already in use and are able to vary their throughput of visible light and solar energy by application of electrical voltage and are able to provide energy efficiency and indoor comfort in buildings. These smart windows comprises of electrochromic materials such as LixWO2.89 and HxNiO2 as cathodic and anodic oxide films, respectively, and other complex polymers, which are complicated to create, expensive and some are hazardous in nature. Nanocellulose (achieved from wood/pulp product) is already being used in flexible electronics, so nanomaterial membrane for smart window is a probable alternative. This paper presents an innovative technology for smart-windows, utilizing nanocellulose fiber (abundantly available) doped with conductive nanoparticles (work as dimming glass on voltage application), mixed with minute amount of electrochromic material form thin film membranes.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".