Solid-state device design methods for the realization of high-efficiency and -intensity electrochemical luminescence
Bibliographic record
Abstract
In this study we investigate the degree to which the design methods of solid-state band-diagrams might be extended and applied to realize high performance electrochemical luminescence (ECL) heterostructures. Taking quantum dots (QDs) as a model system, we first build up a description of their ECL properties in the single-particle picture utilizing the Gerischer-Hopfield framework of electron transfer. This is then applied to the construction of single- and two-probe electrode designs, where it is shown that the internal quantum efficiencies of individual QDs can theoretically reach performance metrics competitive with solid-state devices. These near-ballistic designs}, characterized by single step carrier transfer events at each electrode, are demonstrated to only attain such high efficiencies through the use of band gap (heterostructure) engineering in the electrodes. Subsequently, we propose a further diffusive operating design composed of an electrolyte infused network of QDs, forming a functional material whereby carriers hop between QD sites. The operation of this diffusive ECL heterostructure is further analyzed to possess maximum theoretical light emission intensities and efficiencies rivalling solid-state devices. Ultimately, this work underscores the potential utility of applying band diagram based design methods and electrochemical approaches when seeking to enhance the efficiency of QD emitters for a wide range of processable electronics applications.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".