Plasmonic Glasses and Films Based on Alternative Inexpensive Materials for Blocking Infrared Radiation
Bibliographic record
Abstract
The need for energy-saving materials is pressing. This Letter reports on the design of energy-saving glasses and films based on plasmonic nanocrystals that efficiently block infrared radiation. Designing such plasmonic composite glasses is nontrivial and requires taking full advantage of both material and geometrical properties of the nanoparticles. We compute the performance of solar plasmonic glasses incorporating a transparent matrix and specially shaped nanocrystals. This performance depends on the shape and material of such nanocrystals. Glasses designed with plasmonic nanoshells are shown to exhibit overall better performances as compared to nanorods and nanocups. Simultaneously, scalable synthesis of plasmonic nanoshells and nanocups is technologically feasible using gas-phase fabrication methods. The computational simulations were performed for noble metals (gold and silver) as well as for alternative plasmonic materials (aluminum, copper, and titanium nitride). Inexpensive plasmonic materials (silver, copper, aluminum, and titanium nitride) show an overall good performance in terms of the commonly used figures of merit of industrial glass windows. Together with numerical data for specific materials, this study includes a set of general rules for designing efficient plasmonic IR-blocking media. The plasmonic glasses proposed herein are good candidates for the creation of cheap optical media, to be used in energy-saving windows in warm climates' housing or temperature-sensitive infrastructure.
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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 | 0.000 | 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 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".