Molecular surface programming of rectifying junctions between InAs colloidal quantum dot solids
Bibliographic record
Abstract
Heavy-metal-free III–V colloidal quantum dots (CQDs) show promise in optoelectronics: Recent advancements in the synthesis of large-diameter indium arsenide (InAs) CQDs provide access to short-wave infrared (IR) wavelengths for three-dimensional ranging and imaging. In early studies, however, we were unable to achieve a rectifying photodiode using CQDs and molybdenum oxide/polymer hole transport layers, as the shallow valence bandedge (5.0 eV) was misaligned with the ionization potentials of the widely used transport layers. This occurred when increasing CQD diameter to decrease the bandgap below 1.1 eV. Here, we develop a rectifying junction among InAs CQD layers, where we use molecular surface modifiers to tune the energy levels of InAs CQDs electrostatically. Previously developed bifunctional dithiol ligands, established for II-VI and IV-VI CQDs, exhibit slow reaction kinetics with III-V surfaces, causing the exchange to fail. We study carboxylate and thiolate binding groups, united with electron-donating free end groups, that shift upward the valence bandedge of InAs CQDs, producing valence band energies as shallow as 4.8 eV. Photophysical studies combined with density functional theory show that carboxylate-based passivants participate in strong bidentate bridging with both In and As on the CQD surface. The tuned CQD layer incorporated into a photodiode structure achieves improved performance with EQE (external quantum efficiency) of 35% (>1 μm) and dark current density < 400 nA cm −2 , a >25% increase in EQE and >90% reduced dark current density compared to the reference device. This work represents an advance over previous III-V CQD short-wavelength IR photodetectors (EQE < 5%, dark current > 10,000 nA cm −2 ).
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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".