Oxygen Sensors Based on Mesoporous Silica Particles on Layer-by-Layer Self-assembled Films
Bibliographic record
Abstract
We describe photoluminescent (PL) oxygen sensors based upon phosphorescent dyes adsorbed into the pores of mesoporous silica particles at submonolayer coverage on a layer-by-layer self-assembled film. Eight transition metal dyes (four Pt and Pd porphyrin complexes and four ruthenium complexes) were investigated through monitoring the changes in PL intensity and lifetime upon varying oxygen pressure. The intensity Stern−Volmer (SV) plots were curved. In most systems, the intensity SV plots match the lifetime SV plots, with deviations seen only at high oxygen pressures where static quenching may play a role. The curved intensity Stern−Volmer plots could be fitted with two phenomenological models, a two-site model, and a model based on a Freundlich binding isotherm for oxygen. The Gaussian distribution model was less successful in fitting the data. The most important result to emerge from these data is that the unquenched lifetime of the dye itself is not a sufficient scaling parameter to reduce all of the SV plots to a common line. A second scaling parameter was necessary. This parameter, which measures the capture radius for quenching ( R eff ), times the efficiency of quenching per encounter α ranged from 0.38 nm for PdOEP and 0.42 for PtTPP to 1.12 for Ru(bpy) 3 ]Cl 2 and 1.25 for Ru(phen) 3 ]Cl 2 relative to an assumed value of α R eff = 1.0 nm for PtOEP.
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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) | 0.000 | 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 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".