Covalent Attachment Strategies of Molecular Electrochromes for Enhancing Electrochromic Performance
Bibliographic record
Abstract
Electrochromes are emerging materials for enabling sustainable energy devices such as smart windows and low power-consuming displays along with automotive mirrors. This is owing to their electrochemical activity that results in unique optical transmission, modulating with applied potential. The molecular design rules of electrochromes are well established, consisting of electroactive components such as viologens, rhodamines, and transition metal complexes. While molecular electrochromes offer the advantage of establishing accurate structure/property relationships for tuning the optical transmission contingent on molecular structure, their physisorption on the electrodes limits the performance of electrochromic devices. Indeed, molecular electrochromes suffer from poor performance compared to their polymer counterparts in operating electrochromic devices. This perspective presents approaches to overcome these challenges. Focus is given to various strategies of covalently attaching molecular electrochromes to the device electrode for improving key electrochromic properties of contrast ratio and coloration efficiency. These operating device metrics are improved compared with the physisorption of molecular electrochromes via noncovalent interactions. The overarching goal is to provide useful insight that can be leveraged for the rational design of molecular electrochromes for their covalent attachment to electrodes toward matching device metrics of their polymer counterparts.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".