Direct Photopatterning of Colloidal Quantum Dots with Electronically Optimized Diazirine Cross-Linkers
Bibliographic record
Abstract
Colloidal quantum dots (QDs) with a wide color gamut and high luminescent efficiency are promising for next-generation electronic and photonic devices. However, precise and scalable patterning of QDs without degrading their properties and their integration into commercially relevant devices, such as digitally addressable QD light-emitting diode (QLED) displays, remain challenging. Here, we develop electronically optimized diazirine-based cross-linkers for nondestructive, direct photopatterning of QDs and, ultimately, building the active-matrix QLED displays. The key to the cross-linker design is the introduction of electron-donating substituents that permit the formation of ground-state singlet carbenes for air-stable and benign QD photopatterning. Under ambient conditions, these cross-linkers enable the patterning of heavy metal-free QDs at a resolution of over 13,000 pixels per inch using commercial i-line photolithography. The patterned QD layers fully preserved their optical and optoelectronic properties. Pixelated electroluminescent devices with patterned InP/ZnSe/ZnS QD layers show a peak external quantum efficiency of 15.3% and a maximum luminance of about 40,000 cd m –2, outperforming those made by existing QD patterning approaches. We further show the seamless integration of patterned QLEDs with thin-film transistor circuits and the fabrication of dual-color active-matrix displays. These results underscore the importance of designing photochemistry for QD patterning, and promise the implementation of direct photopatterning methods in manufacturing commercial QLED displays and other integrated QD device platforms.
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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 | 0.000 |
| 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 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".