Influence of Surface Morphology on the Colloidal and Electronic Behavior of Conjugated Polymer−Silica Microspheres
Bibliographic record
Abstract
A template approach to the synthesis of a series of conjugated polymer-mesoporous silica composite microspheres is described. Poly(3,4-ethylenedioxythiophene) (PEDOT), poly(thiophene), and poly( N-methylpyrrole) composites were prepared. The surface morphology of the samples was analyzed by scanning electron microscopy, and it was found that well-defined, monodisperse colloidal materials could only be prepared when the monomer is insoluble in the polymerization medium. The filling of the mesopores was systematically varied from 0% to 100%, and powder X-ray diffraction and nitrogen adsorption studies were used to confirm the pore filling. Thermogravimetric analysis shows that the polymer loading tracks the monomer loading in an asymptotic fashion. Conductivity measurements show that the conductivity of the PEDOT materials is relatively constant at high polymer loadings but decreases exponentially at low loadings. Measurements of the electrophoretic mobility were made in order to explain this behavior. These data suggest that, at high polymer loadings, the particle surface is characteristic of the polymer, while at low polymer loadings it is characteristic of the silica host. These results identify important design criteria for the template synthesis of a variety of new colloidal materials. Importantly, these optimized parameters may open the door to the preparation of colloids and colloidal crystals of previously unprocessable materials.
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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 | 0.001 |
| 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".