Quantitative Chemical Mapping of Nanostructured “Onionlike” Poly(methyl methacrylate)/Polystyrene Composite Particles by Soft X-ray Microscopy
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it