Bibliographic record
Abstract
Colloidal quantum dots (QDs) allow for the tuning of dopant concentration as well as flexibility in the engineering of the surrounding medium. This thesis explores the use of magnetic resonance techniques and the development of hardware in order to characterize paramagnetically doped materials, in particular Mn-doped PbS colloidal QDs, and assess their potential for applications in quantum technologies such as quantum information processing (QIP). Colloidal PbS:Mn QDs capped with thioglycerol/dithiolglycerol ligands were synthesised in aqueous solution. Methods of tailoring the Mn-Mn and Mn-1H interactions, with the aim of maximizing phase memory times, were investigated. The distance between spins was optimized by initially, overgrowing the QDs with an undoped shell and secondly, by dispersing the QDs in solution. The use of a deuterated solution was found to further reduce the dephasing effects of Mn-1H interactions. This resulted in unprecedentedly long phase memory (TM ~ 8 μs) and spin–lattice relaxation (T1 ~ 10 ms) time constants for Mn2+ ions at T= 4.5 K, and in the observation of electron spin coherence (TM ~ 1 μs) near room temperature. Further improvements to relaxation times, as well as enhanced optical properties useful for the initialization and readout of spin qubits, were also studied by embedding the QDs in photonic crystals. Magnetic resonance techniques combined with paramagnetic Mn-impurities in PbS QDs are used for sensitive probing of the QD surface and environment. We report inequivalent proton spin relaxations of the capping ligands and solvent molecules. We determine the strengths and anisotropies of the Mn-1H spin interactions, and establish Mn-1H distances with ~1 Å sensitivity. These findings demonstrate the potential of magnetically doped QDs as sensitive magnetic nano-probes and the use of electron spins for surface sensing. We explore a means of characterizing mechanisms responsible for the functionality of paramagnetically doped materials. The development of instrumentation to identify and quantify interactions between paramagnetic and ordered magnetic phases is described. A probe was designed and built with a fast response time and with the aim of facilitating fast field jump experiments to identifying interactions between the different magnetic phases by correlating the response of a sample to mw irradiation with its response to a field jump.
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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) | 0.002 | 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".