Probing Quantum Dynamics and Spectral Broadening in Semiconductor Nanocrystals With Nonlinear Spectroscopies
Bibliographic record
Abstract
Semiconductor nanocrystals (NCs) are becoming increasingly entrenched in modern consumer technologies. Their versatility and capability to produce light emitting devices with high photoluminescence quantum yield, solar cells with excellent power conversion efficiencies, and extendibility to many other applications and industries including biotechnology have made NCs an exciting area of research and engineering. To realize the next generation of devices that use NCs effectively, it is important to understand how these nanoscale materials function at the quantum level using an array of techniques that can probe the electronic structure, morphology and size distributions, and ultrafast carrier dynamics that result in their remarkable macroscopic behavior. In this thesis I use two-dimensional electronic spectroscopy (2DES) to examine these properties and aid synthetic efforts to optimize these materials for various applications. In chapter 3, InP quantum dots are examined using many techniques includes 2DES to explain their broad emission spectra by understanding the electronic structure of emissive defects in the nanocrystalline lattice. Chapter 4 presents work in developing a novel synthesis of methylammonium lead-halide perovskites and using 2DES to probe the ultrafast dynamics of these NCs that are used in effective solar harvesting devices. In chapter 5, thin films of CdSe nanoplatelets are probed using 2DES with various pump laser powers, both at room temperature and 77K to gain insight into these materials that are used in low-threshold laser applications. Finally, chapter 6 concludes the thesis by proposing some future directions for our group as we continue to explore the quantum dynamics and electronic structure of interesting nanomaterials.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".