Polypyridyl ligands as a versatile platform for solid-state light-emitting devices
Bibliographic record
Abstract
The replacement of inorganic semiconductors with molecule-based compounds for applications in current-to-light conversion has led to a significant increase in interdisciplinary collaborations worldwide, affording new improved organic-light emitting diodes (OLEDs) ripe for commercial applications, as well as light-emitting electrochemical cells (LECs) that have recently started to head to the market. This review highlights the role that transition metal coordination complexes (TMCs) have played in advancing the field of molecular electronics, from early conception to the advanced development of several polypyridyl complexes currently pursued for both OLED and LEC concepts. In this context, the design and synthesis of Ir(iii), Pt(ii), Cu(i) and Ag(i) complexes as the emissive components of OLEDs and LECs are thoughtfully presented. We discuss how molecular design is pivotal for fine-tuning color and optimizing power efficiencies, highlighting the key roles of the metal, cyclometalate, and ancillary polypyridyl ligands. We provide insight into the strategies exploited for the development of new, improved emitters and their fabrication into OLEDs/LECs with high external quantum efficiencies and stabilities. In addition, we have surveyed the remarkable photophysical properties of third generation TMCs capable of undergoing thermally activated delayed fluorescence (TADF). Since previous reviews of TADF materials are strongly biased towards organic-based systems, this overview compliments other synopses of light emitting TADF materials. Finally, we shed light onto the conceptual challenges that still need to be overcome to advance the rational design of TMC-based TADF emitters with tunable ligands and the subsequent fabrication of OLEDs/LECs, which are tailor-made for each specific application.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".