Dopant-Assisted Matrix Stabilization Enables Thermoelectric Performance Enhancement in n-Type Quantum Dot Films
Bibliographic record
Abstract
Efficient thermoelectric generators require further progress in developing n-type semiconductors that combine low thermal conductivity with high electrical conductivity. By embedding colloidal quantum dots (CQDs) in a metal halide matrix (QDMH), the metal halide matrix can enhance phonon scattering, thus suppressing thermal transport; however, simultaneously achieving high electrical conductivity in such systems has previously been limited by the deleterious impact of a large density of interfaces on charge transport. Therefore, new strategies are needed to improve charge carrier transport without sacrificing matrix-enabled low thermal transport. Here, we report the use of chemical doping in the solution state to improve electron transport while maintaining low thermal transport in QDMH films. By incorporating cesium carbonate (Cs 2 CO 3 ) salts as a dopant prior to matrix formation, we find that the dopant stabilizes the matrix in colloidal inks and enables efficient n-type doping in QDMH films. As a result, this strategy leads to an enhanced n-type thermoelectric behavior in solution-processed QDMH films near room temperature, with a thermal conductivity of 0.25 W m –1 K –1 —significantly lower than in prior films based on organic-ligand-cross-linked CQD films (>0.6 W m –1 K –1 ) and spark-plasma-sintered CQD systems (>1 W m –1 K –1 ). This study provides a pathway to developing efficient n-type thermoelectric materials with low thermal conductivity using single-step deposition and low-temperature processing.
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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".