Extended Short-Wavelength Infrared Ink by Surface-Tuned Silver Telluride Colloidal Quantum Dots and Their Infrared Photodetection
Bibliographic record
Abstract
Wavelength-tunable infrared materials, particularly those excluding regulated substances, are essential for next-generation optoelectronics. Silver telluride (Ag 2 Te) colloidal quantum dots (CQDs) can be a promising alternative to traditional Pb- or Cd-based narrow-band gap semiconductors due to their low toxicity. However, the strong binding affinity of thiol ligands has limited the broader use of Ag 2 Te CQDs, necessitating more versatile surface chemistries. Here, we synthesized Ag 2 Te CQDs passivated with oleylamine, which facilitated various ligand passivation strategies. The weak bonding strength allows the preparation of X-Ag 2 Te CQD (X = Cl, Br, or I ligands) inks, sensitive to 1.1–2.7 μm infrared radiation. Using the CQD inks, we fabricated extended short-wavelength infrared (eSWIR) CQD photodiodes with two different sizes of CQDs. The resulting Ag 2 Te CQD ink-based eSWIR photodiodes exhibited an external quantum efficiency of 16% at 1.7 μm at room temperature, representing the highest value achieved for nontoxic CQD IR detectors at the wavelength.
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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) | 0.000 | 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".