Molecularly Engineered Near‐Infrared Light‐Emitting Electrochemical Cells
Bibliographic record
Abstract
Abstract The development of near‐infrared (NIR) luminescent materials has emerged as a promising research field with important applications in solid‐state lighting (SSL), night‐vision‐readable displays, and the telecommunication industry. Over the past two decades, remarkable advances in the development of light‐emitting electrochemical cells (LECs) have stunned the SSL community, which has in turn driven the quest for new classes of stable, more efficient NIR emissive molecules. In this review, an overview of the state of the art in the field of near‐infrared light‐emitting electrochemical cells (NIR‐LEC) is provided based on three families of emissive compounds developed over the past 25 years: i) transition metal complexes, ii) ionic polymers, and iii) host–guest materials. In this context, ionic and conductive emitters are particularly attractive since their emission can be tuned via molecular design, which involves varying the chemical nature and substitution pattern of their ancillary ligands. Herein, the challenges and current limitations of the latter approach are highlighted, particularly with respect to developing NIR‐LECs with high external quantum efficiencies. Finally, useful guidelines for the discovery of new, efficient emitters for tailored NIR‐LEC applications are presented, together with an outlook towards the design of new NIR‐SSL materials.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".