Toward the Ultimate Limit of Connectivity in Quantum Dots with High Mobility and Clean Gaps
Bibliographic record
Abstract
Colloidal quantum dots (CQDs) are highly versatile nanoscale optoelectronic building blocks, but despite their materials engineering flexibility, there is a considerable lack of fundamental understanding of their electronic structure as they couple within thin films. By employing a joint experimental-theoretical study, we reveal the impact of connectivity in CQD assemblies, going beyond the single CQD picture. High-resolution transmission electron microscopy (HR-TEM) demonstrates connectivity motifs across different CQD sizes and length scales and provides the necessary perspective to build robust computational models to systematically study the achievable degree of connectivity in these materials. We focused on state-of-the-art surface ligand treatments, taking into account both the degree of connectivity and nanocrystal orientation, and performed ab initio simulations within the phonon-assisted hopping regime. Importantly, both the TEM studies and our simulation results revealed morphological and electronic defects that could dramatically reduce optoelectronic performance, and yet would not have been captured within a single CQD model that neglects connectivity. We calculate carrier mobility in the presence of such defect states and conclude that the best-achievable CQD assemblies for optoelectronics will require a modest degree of fusing via the {001} facet, followed by atomic ligand passivation to generate a clean band gap and unprecedentedly high charge transport.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".