Exploration of Organic Acid Chain Length on Water-Soluble Silicon Quantum Dot Surfaces
Bibliographic record
Abstract
Surface functionalization of silicon quantum dots influences oxidation of the silicon core while affording control of physical properties and maintaining optical stability. An effective method for surface modification is photochemical hydrosilylation in which the hydride-terminated Si surface is reacted with an unsaturated C-C bond resulting in a covalent Si-C bond at the surface. The physical properties (e.g., reactivity and solvent compatibility) of the nanocrystals are thus dictated by those of the pendant functional group. Water-soluble nanoparticles can be produced by extending polar functional groups, such as carboxylic acids, from the surface. Previous literature reports have shown acrylic acid to be an attractive starting material for creating water-soluble Si nanocrystals. To date, a detailed study of the effects of differing surface groups (i.e., carboxylic acids of varying carbon chain lengths) has not been offered. Here, we investigate the effects of carboxylic acid surface moieties with increasing carbon chain length on various silicon nanocrystal properties. Oxidative and optical stability was improved by increasing the length of the carbon spacer between the silicon surface and the polar carboxylic acid group. As well, increased chain length was found to enhance nanocrystal dispersibility in polar solvents. Of important note, however, the use of acrylic acid as a precursor led to poly(acrylic acid) formation under the reaction conditions studied, leading to anomalous behavior compared to precursors with longer carbon chains.
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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) | 0.001 | 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".