Surface-Modified Substrates for Quantum Dot Inks in Printed Electronics
Bibliographic record
Abstract
Abstract Printed electronics fill the niches for low-cost, flexible devices in electronics. Developing substrates suitable for various printable electronic inks becomes an important topic in both academia and industry. Because of their extraordinary properties like solution processability, colloidal quantum dots (QDs) are gradually emerging in this field as promising candidates for electronic inks. In recent years, researchers have successfully produced high quality PbS QD inks in polar solvents. However, the incorporation of electronic inks onto a well-passivated substrate remains challenging due to the processing incompatibility between polar solvents and hydrophobic substrates. Here, we propose a surface modification strategy by using chlorine to achieve both trap-site suppression and a hydrophilic surface. The chlorine can effectively passivate the surface dangling bonds and charged hydroxyls while creating a hydrophilic surface. On this modified substrate, the contact angle between the water droplet and the SiO2 substrate can be as small as 20° and this strategy is also feasible for other polymer and inorganic substrates. For a proof-of-concept demonstration, we fabricated a PbS QD ink-based field-effect transistor on a Cl-passivated substrate, and the device showed a mobility as high as 4.36 × 10–3 cm2/V s, which indicates effective trap-site suppression. This device also enables the potential of the Cl-passivated substrates for QD inks with water or other polar solvents.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".