A Universal Perovskite Nanocrystal Ink for High‐Performance Optoelectronic Devices
Bibliographic record
Abstract
Semiconducting lead halide perovskite nanocrystals (PNCs) are regarded as promising candidates for next-generation optoelectronic devices due to their solution processability and outstanding optoelectronic properties. While the field of light-emitting diodes (LEDs) and photovoltaics (PVs), two prime examples of optoelectronic devices, has recently seen a multitude of efforts toward high-performance PNC-based devices, realizing both devices with high efficiencies and stabilities through a single PNC processing strategy has remained a challenge. In this work, diphenylpropylammonium (DPAI) surface ligands, found through a judicious ab-initio-based ligand search, are shown to provide a solution to this problem. The universal PNC ink with DPAI ligands presented here, prepared through a solution-phase ligand-exchange process, simultaneously allows single-step processed LED and PV devices with peak electroluminescence external quantum efficiency of 17.00% and power conversion efficiency of 14.92% (stabilized output 14.00%), respectively. It is revealed that a careful design of the aromatic rings such as in DPAI is the decisive factor in bestowing such high performances, ease of solution processing, and improved phase stability up to 120 days. This work illustrates the power of ligand design in producing PNC ink formulations for high-throughput production of optoelectronic devices; it also paves a path for "dual-mode" devices with both PV and LED functionalities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".