Tuning the Shape and Size of Gold Nanoparticles with Triblock Polymer Micelle Structure Transitions and Environments
Bibliographic record
Abstract
Three block polymers, viz., L31, L64, and P123, were used as reducing agents for the synthesis of gold (Au) nanoparticles (NPs) to determine the effect of their micelle size, structure transitions, and environments on the mechanism of the reduction process leading to the overall morphology of Au NPs. Aqueous phase reduction was monitored with time at constant temperature and under the effect of temperature variation from 20 to 70 °C by simultaneous measurement of UV–visible spectra. The ligand to metal charge transfer (LMCT) band around 300 nm, due to a charge transfer complex formation between the micelle surface cavities and AuCl 4 – ions, and Au NP absorbance around 550 nm, due to the surface plasmon resonance, were simultaneously measured to understand the mechanism of the reduction process and its dependence on the micelle structure transitions and environment of TBPs micelles. L64 micelles showed dramatic shift in the LMCT band from lower to higher wavelength due to an increase in the reduction potential of surface cavities induced by the structure transitions under the effect of temperature variations. This effect was not observed for micelles of either L31 or P123 and is explained on the basis of a difference in their micelle environments. The morphology of Au NPs thus evolved from the reduction process was studied with the help of TEM and SEM studies. Smaller micelle size with few surface cavities, as in L31, produced small NPs in comparison to large micelles with several surface cavities as in P123. Structure transitions of L64 demonstrated direct influence on the final morphology of NPs, and stronger transitions produced fused and deformed NPs in comparison to weaker transitions. The results showed that efficient reduction by the surface cavities and uninterrupted nucleation without structure transitions lead to well-defined morphologies in the presence of P123 micelles.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".