Design and Synthesis of Photo-Clickable Au Nanoparticles and Polymers and Their Redox-Active Conjugate Materials
Bibliographic record
Abstract
Functional materials are predicted to have an enormous impact on the field of nanotechnology and these can be expanded by further derivatization of their surfaces on a molecular level to allow for the control of the interactions between material surfaces and their environment. To this end, the cycloaddition chemistry between strained-cyclooctynes and 1,3-dipolar molecules offers a simple, clean, and highly reliable protocol for the modification of material surface functionalities. However, the high reactivity of the strained C-C triple bond of these systems renders the incorporation of these cyclooctyne moieties into materials synthetically challenging or, in some cases, inaccessible. To circumvent this challenge, we seek to employ cyclopropenones as 1) a photo-cage for strained-cyclooctynes to allow for facile incorporation onto material surfaces and 2) a photochemical precursor that, upon decarbonylation, affords the parent strained-alkyne that can be employed in subsequent cycloaddition chemistry. This two-step methodology was applied to the synthesis of photo-clickable Au nanoparticles and norbornene-based polymers. The design, fabrication, and characterization of these materials — Au nanoparticles and polynorbornenes — and their redox-active conjugates will be discussed. Our ability to quantitate surface functionalities and monitor the interfacial chemistry via various spectroscopies (NMR, IR, and UV-Vis) and TGA and TEM will be highlighted. The photoactivated nature of this cycloaddition chemistry will allow for spatial control that will be amenable to the fabrication of nanoelectronic devices. I will also present reflections of my 25+ year relationship with on our Baizer Award winner Professor Flavio Maran.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".