Joint Mapping of Mobility and Trap Density in Colloidal Quantum Dot Solids
Bibliographic record
Abstract
Field-effect transistors have been widely used to study electronic transport and doping in colloidal quantum dot solids to great effect. However, the full power of these devices to elucidate the electronic structure of materials has yet to be harnessed. Here, we deploy nanodielectric field-effect transistors to map the energy landscape within the band gap of a colloidal quantum dot solid. We exploit the self-limiting nature of the potentiostatic anodization growth mode to produce the thinnest usable gate dielectric, subject to our voltage breakdown requirements defined by the Fermi sweep range of interest. Lead sulfide colloidal quantum dots are applied as the active region and are treated with varying solvents and ligands. In an analysis complementary to the mobility trends commonly extracted from field-effect transistor studies, we focus instead on the subthreshold regime and map out the density of trap states in these nanocrystal films. The findings point to the importance of comprehensively mapping the electronic band- and gap-structure within real quantum solids, and they suggest a new focus in investigating quantum dot solids with an aim toward improving optoelectronic device performance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".