Nanocrystal Quantum Dot Devices: How the Lead Sulfide (PbS) System Teaches Us the Importance of Surfaces
Bibliographic record
Abstract
Semiconducting thin films made from nanocrystals hold potential as composite hybrid materials with new functionalities. With nanocrystal syntheses, composition can be controlled at the sub-nanometer level, and, by tuning size, shape, and surface termination of the nanocrystals as well as their packing, it is possible to select the electronic, phononic, and photonic properties of the resulting thin films. While the ability to tune the properties of a semiconductor from the atomistic- to macro-scale using solution-based techniques presents unique opportunities, it also introduces challenges for process control and reproducibility. In this review, we use the example of well-studied lead sulfide (PbS) nanocrystals and describe the key advances in nanocrystal synthesis and thin-film fabrication that have enabled improvement in performance of photovoltaic devices. While research moves forward with novel nanocrystal materials, it is important to consider what decades of work on PbS nanocrystals has taught us and how we can apply these learnings to realize the full potential of nanocrystal solids as highly flexible materials systems for functional semiconductor thin-film devices. One key lesson is the importance of controlling and manipulating surfaces.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".