A Charge-Orbital Balance Picture of Doping in Colloidal Quantum Dot Solids
Bibliographic record
Abstract
We present a framework--validated using both modeling and experiment--to predict doping in CQD films. In the ionic semiconductors widely deployed in CQD films, the framework reduces to a simple accounting of the contributions of the oxidation state of each constituent, including both inorganic species and organic ligands. We use density functional theory simulations to confirm that the type of doping can be reliably predicted based on the overall stoichiometry of the CQDs, largely independent of microscopic geometrical bonding configurations. Studies employing field-effect transistors constructed from CQDs that have undergone various chemical treatments, coupled with Rutherford backscattering and X-ray photoelectron spectroscopy to provide compositional analysis, allow us to test and confirm the proposed model in an experimental framework. We investigate both p- and n-type electronic doping spanning a wide range of carrier concentrations from 10(16) cm(-3) to over 10(18) cm(-3), and demonstrate reversible switching between p- and n-type doping by changing the CQD stoichiometry. We show that the summation of the contributions from all cations and anions within the film can be used to predict accurately the majority carrier type. The findings enable predictable control over majority carrier concentration via tuning of the overall stoichiometry.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".