Advances in electrochromic device technology through the exploitation of nanophotonic and nanoplasmonic effects
Bibliographic record
Abstract
Abstract Research regarding electrochromic (EC) materials, such materials that change their color upon application of an electrochemical stimulus, has been conducted for centuries. However, most recently, increasing efforts have been put into developing novel solutions to utilize these on‐off switching materials in advanced nanoplasmonic and nanophotonic devices. Due to the significant change in dielectric properties of oxides such as WO 3 , NiO, Mn 2 O 3 and conducting polymers like PEDOT:PSS and PANI, EC materials have transcended beyond simple smart window applications and are now found in plasmonic devices for full‐color displays and enhanced modulation transmission and photonic devices with ultra‐high on‐off ratios and sensing abilities. Advancements in nanophotonic ECDs have further decreased EC switching speed by several orders of magnitude, allowing integration in real‐time measurement and lab‐on‐chip applications. The EC nature of such nanoscale devices promises low energy consumption with low operating voltages paired with bistability and long lifetimes. We summarize these novel approaches to EC device design, lay out the current short comings and draw a path forward for future utilization.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".