Heterogeneous Integration of Colloidal Quantum Dot Inks on Silicon Enables Highly Efficient and Stable Infrared Photodetectors
Bibliographic record
Abstract
Integrating lead sulfide (PbS) colloidal quantum dots (CQDs) with crystalline silicon (c-Si) has been proven to be an effective strategy in extending the sensitivity of Si-based photodetectors into infrared regime. Here, we demonstrate the successful integration of PbS CQD inks with Si and construct a highly efficient heterojunction infrared photodiode operating in the range from 800 up to 1500 nm. Thanks to the well-passivated Si surface by a two-step chlorination/methylation method and high-quality CQD inks, the heterojunction photodiode yields a low density of trap states, as validated by transient photovoltage and photocurrent measurements. With an insertion layer of a p-type CQD capped with 1,2-ethanedithiol ligands, the built-in electric field is much enhanced, leading to improved charge extractions. As a result, we have obtained an external quantum efficiency (EQE) of 44% at the excitonic wavelength of 1280 nm. The EQE values are maintained without detectable degradation through the course of more than 600 h, achieving superior device stability. In contrast to commercial solutions, which require high-temperature epitaxial deposition of germanium (Ge) or III–V compounds, the presented single-step spin-coating process of CQD inks also enables large-area integration on Si.
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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".