Polymer/Silica Composite Films as Luminescent Oxygen Sensors
Bibliographic record
Abstract
In this paper, we examine the influence of 10 nm diameter silica nanospheres on oxygen diffusion in films of two different amorphous polymers characterized by a low glass-transition temperature and a high oxygen permeability. The two polymers, poly(dimethylsiloxane) (PDMS) and poly( n -butylamino thionylphosphazene) (C 4 PATP), are useful matrixes for oxygen sensors based upon luminescence quenching. In these applications, the dye platinum octaethylporphine (PtOEP), with a long-lived excited state, is incorporated into the polymer, and the presence of oxygen is registered through a quenching of the dye luminescence. For some sensor applications, these linear polymers themselves are too soft and tacky. Silica as a filler improves the mechanical properties of the matrix but perturbs the measurement of oxygen diffusion and permeation. We show that PtOEP adsorbs to the silica particles in PDMS but remains in the polymer matrix in C 4 PATP. The quenching kinetics of dye fluorescence is complex in PDMS because of contributions of oxygen adsorbed to the silica surface and that dissolved in the polymer matrix. In contrast, the quenching kinetics in C 4 PATP remains almost unaffected by the presence of silica. Time-scan experiments on PDMS films show good accord with Fick's laws of diffusion for films containing up to 30 wt % (16 vol %) silica. The diffusion constant, D O2, is reduced but only by a factor of about two. In C 4 PATP, the time-scan experiments give more complex results because the oxygen adsorbed to the surface of the silica particles serves as an additional reservoir for oxygen in the system. Because the PtOEP remains in the polymer matrix in C 4 PATP, the oxygen adsorbed to the silica does not participate in quenching until it diffuses away from the particles and into the matrix.
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) | 0.000 | 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".