Photochemical â©Strategiesâ© for â©the â©Synthesisâ© of â©Advancedâ© Materials
Bibliographic record
Abstract
This thesis describes the study of a variety of nanoscale materials and the development of novel synthetic strategies for their production. While the focus and bulk of this study have been directed specifically at subwavelength lithography, a significant portion of this thesis research involves nanoparticle synthesis, characterization, and functionalization.\n Put in very simple terms, optical lithography is a process where a beam of light, focused in a specific pattern, is used to generate a physical pattern on a solid substrate. This technology forms the basis for almost all microchip production in the world at the present time. As demand for faster and more powerful chips increases, the need to further miniaturize the patterns while minimizing cost has become very important.\n Multiple photochemical systems were developed in the search for non-reciprocal photochemistry at 193 nm to increase the resolution of lithographic processes at that wavelength. One approach, based on anthracene sensitization of sulfonium salts for acid generation, used photochemically reversible 4+4 aromatic cycloaddition reactions to introduce the non-linear photochemistry. A second approach took advantage of the photochemistry of N-methylphenothiazine and provided the first true example of a lithographically-relevant multi-photon acid generating process. \n Since all of the systems we studied used sulfonium salts as the acid generating species, we also looked at the photochemistry of the salts themselves. We evaluated the structural effects of the salts on their direct photochemistry and the implications for sensitized multi-photon photochemistry. We found that the identity of the anion plays a significant role in both processes and propose a new photochemical mechanism for acid generation that involves a charge transfer excitation process.\n We also describe the synthesis and characterization of novel fluorescent silver nanoparticles, both in solution and polymer films. We show that the fluorescent images can be patterned easily and preliminary results show that photolithography based on nanoparticle formation may be possible. This latter approach could provide a facile route to nanoparticle-embedded functional materials. This work with nanoparticles was inspired partly by earlier work, also presented herein, on semiconductor nanoparticles and their interactions with disulfide ligands.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.045 | 0.030 |
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".