Quantitative Chemical Mapping of Nanostructured “Onionlike” Poly(methyl methacrylate)/Polystyrene Composite Particles by Soft X-ray Microscopy
Bibliographic record
Abstract
In earlier work, we have produced micrometer-sized, monodisperse, poly(methyl methacrylate)/polystyrene (PMMA/PS) composite particles having a multilayered structure. A challenge to developing applications for these multilayered particles is the quantitative, spatially resolved chemical analysis of their onionlike layered structure, where some layers are thinner than 100 nm. Scanning transmission X-ray microscopy (STXM) has a spatial resolution of better than 50 nm and sufficient chemical contrast to both observe and quantitate the composition of individual layers without staining. The results of quantitative chemical analysis of the particles and standard P(S- co -MMA) random copolymers by STXM are shown to be in good agreement with 1 H NMR measurements of their average composition. Results from chemical quantification of the individual layers of the onionlike particles by STXM are reported. Analyses of samples prepared with and without embedding in epoxy are compared. Our analysis takes into account the finite spatial resolution of the STXM. The extent of intermixing between layers was estimated by comparing the measured results to those generated from an assumed instrumental response function and trial compositional structures. This analysis suggests that the individual layers are relatively pure, although small amounts of intermixing (<15%) are also consistent with the results. Possible origins of intermixed layers are indicated and ways to achieve more pure layers are suggested.
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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".