Probing interfacial interactions of nafion ionomer: Thermal expansion of nafion thin films on substrates of different hydrophilicity/hydrophobicity
Bibliographic record
Abstract
ABSTRACT Interfacial interactions of Nafion ionomer with superhydrophilic (Pt, Au), hydrophilic (SiO2), and hydrophobic (graphene, octyltrichlorosilane [OTS]‐modified SiO2) is investigated, using in situ thermal ellipsometry, by quantification of substrate‐ and thickness‐dependent thermal properties of the ultrathin Nafion films of nominal thickness ranging 25–135 nm. For sub‐50 nm thin Nafion films, the thermal expansion coefficient of films decreased in the order of most hydrophobic to most hydrophilic substrate: OTS > graphene > SiO2 > Au > Pt, implying weaker interpolymer and polymer–substrate interactions for films on hydrophobic substrates. Expansion coefficient of films on SiO2, graphene, and OTS‐modified SiO2 decreased with thickness whereas that of films on Au and Pt substrates increased with thickness. Above ~100 nm of thickness, films on all substrates converged toward a common value representative of bulk Nafion. Thermal transition temperature was found to be higher for films on hydrophilic SiO2 than that for films on hydrophobic graphene and OTS‐modified SiO2 but was not discernible for films on Au and Pt substrates. © 2019 Wiley Periodicals, Inc. J. Polym. Sci., Part B: Polym. Phys. 2019, 57, 343–352
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.
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) | 0.001 | 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".