Stable Colloidal Quantum Dot Inks Enable Inkjet-Printed High-Sensitivity Infrared Photodetectors
Bibliographic record
Abstract
Colloidal quantum dots (CQDs) have recently gained attention as materials for manufacturing optoelectronic devices in view of their tunable light absorption and emission properties and compatibility with low-temperature thin-film manufacture. The realization of CQD inkjet-printed infrared photodetectors has thus far been hindered by incompatibility between the chemical processes that produce state-of-the-art CQD solution-exchanged inks and the requirements of ink formulations for inkjet materials processing. To achieve inkjet-printed CQD solids with a high degree of reproducibility, as well as with the needed morphological and optoelectronic characteristics, we sought to overcome the mismatch among these processing conditions. In this study, we design CQD inks by simultaneous evaluation of requirements regarding ink colloidal stability, jetting conditions, and film morphology for different dots and solvents. The new inks remain colloidally stable, achieved through a design that suppresses the reductant properties of amines on the dots’ surface. After drop ejection from the nozzle, the quantum dot material is immobilized on the substrate surface due to the rapid evaporation of the low boiling point amine-based compound. Concurrently, the high boiling point solvent allows for the formation of a thin film of high uniformity, as is required for the fabrication of high-performance IR photodetectors. We fabricate inkjet-printed photodetectors exhibiting the highest specific detectivities reported to date (above 10 12 Jones across the IR) in an inkjet-printed quantum dot film. As a patternable CMOS-compatible process, the work offers routes to integrated sensing devices and systems.
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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 | 0.001 |
| 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 | 0.000 |
| 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".