One-step reactivity-driven synthesis of core–shell structured electrically conducting particles for biomedical applications
Bibliographic record
Abstract
Electrically conductive and functional polymeric nanoparticles have significant potential in biomedical applications such as in sensing and stimulation. Polymeric core-shell particles are usually prepared either through a multiple-step process or by the design of amphiphilic macromolecules. Here we report a simple one-step and one-pot emulsion polymerization method to synthesize the core-shell structured electrically conducting polymer particles based on the difference in comonomer reactivity. The morphology and the surface and bulk chemistry of poly(pyrrole-co-(1-(2-carboxyethyl)pyrrole)) (PPy-co-PPyCOOH) particles formed at different reaction times were analyzed by scanning electron microscopy (SEM), transmission electron microscopy (TEM), X-ray photoelectron spectroscopy (XPS), Fourier transform infrared spectroscopy (FTIR), thermal gravimetric analysis (TGA) and total elemental analysis. The particles were found to be formed by a shell composed of the less conductive but functional PPyCOOH homopolymer, and a core made of the PPy dominated PPy-co-PPyCOOH copolymer of high conductivity. Human serum albumin antibody (anti-HSA) as a model molecule was covalently immobilized onto the particle surface and proven to be reactive to HSA. A five-step schema based on a novel reactivity-driven mechanism was proposed to explain the formation of the core-shell structure. This new strategy therefore provides a simple and general route to prepare core-shell conductive particles with a functional surface, based on the reactivity of comonomers.
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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 | 0.001 | 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) | 0.000 | 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".