Nanocrystals for Photocatalysis and Imaging Applications
Bibliographic record
Abstract
Nanocrystals, contrary to the bulk counterparts, can exhibit size-dependent optical, electronic, magnetic, and catalytic properties. These materials can be tailored for specific applications including sensing, bioimaging, drug delivery, optoelectronics, and catalysis. This dissertation explores two types of nanocrystals, namely CdS-based quantum dots (QDs) and DNA-conjugated gold nanoparticles (DNA-AuNPs), as photocatalysts for reductive organic transformations and high mass probes for the emerging Imaging Mass Cytometry bioanalytical platform, respectively. \n\nQDs are zero-dimensional semiconductor nanocrystals with attractive properties arising from quantum confinement effects. The tunable absorption and emission profiles make QDs desirable candidates in display technologies, lasing, and solar energy applications. On the other hand, non-radiative photophysical processes enhanced by quantum confinement in QDs are underutilised and often perceived as undesirable. One such process – Auger relaxation – can produce hot electrons with high reducing power and can be amplified further by doping the nanocrystal with Manganese (II). Part one of this thesis examines the photoreduction capabilities of Mn2+-doped CdS/ZnS core/shell QDs (Mn:CdS/ZnS QDs). The doped QDs were implemented as photocatalytic coatings on reaction vessels, and several model organic reactions were evaluated including the 6-electron reduction of nitrobenzene to aniline that reached an overall internal quantum efficiency of ~3%. The findings demonstrate several-fold increase in the photoreduction efficiency of Mn:CdS/ZnS over undoped CdS/ZnS QDs, and the film set up allows for facile post-reaction workup and a range of solvent compatibility. Additionally, surface characterizations were performed to probe the changes and address the reusability of the QDs. Lastly, the initial implementation of QD coatings in flow reactors showed success. This work presents new opportunities and diversifies the toolbox of heterogeneous photocatalysts for prospective use in organic reactions. \n\nImaging Mass Cytometry (IMCTM) is a multiparametric imaging technique that utilizes metal-tagged antibodies as probes for investigating subcellular components via mass spectrometry. However, low-abundant cellular components can generate weak or no signals due to the small number of antibodies that bind to them. In the second part of this thesis, DNA-functionalized gold nanoparticles, each comprising >10 000 Au atoms, were examined as high mass probes for targeting low abundant microRNA. The interaction between the DNA strands on the AuNPs and microRNA-210, a biomarker for preeclampsia and hypertensive diseases, leads to the accumulation of DNA-AuNPs in cells as readily imaged with IMC. The results from IMC corroborated with traditional fluorescence-based methods, but with an enhanced sensitivity of a thousand-fold. This work is the first demonstration that DNA-AuNP can serve as high mass probes in IMC for detecting low-abundant nucleic acids.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".