Enhancement in sensitivity and detection of luminescent quenching based oxygen sensing by gold nanoparticles
Bibliographic record
Abstract
The field of plasmonics has shown a great promise in the enhancement of luminescence detection. Here, a simple method to enhance oxygen detection by quenching of Ru[(4,7-diphenyl-1,10-anthroline)3]2+ (or Ru[dpp]2+) in a sol-gel matrix by localized surface plasmon resonance (LSPR) of gold nanoparticles (AuNP) is presented. In the experiments, AuNP (10 ± 1.5 nm diameter) were added to a sol that was prepared by hydrolysis of trimethoxysilane, octyltrimethoxysilane and ethanol in the presence of Ru[dpp]2+ luminophore. The resulting sol of the mixture was spincoated on glass and allowed to age in the dark for one week to form the sol-gel film. A control sample was also prepared using the procedure, except that AuNP was not added to the sol. The resulting AuNP embedded sol-gel shows 8.3 times improvement in the baseline (0% O2) intensity (I0) over the control. Moreover, there is a dramatic improvement in the sensitivity from 0.0011 per % O2 in the control to 0.059 per % O2 with AuNP, for O2 level below 15%. Signal to noise ratio also improved, thus leading to a 100-fold improvement in the detection limit. Using phaseluminometry, it was determined that there is a reduction in the luminescence lifetime when AuNP is added to the sol-gel matrix. This reduction in the lifetime can be explained by the near-field interaction between the luminophores and the AuNP.
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 | 0.001 | 0.001 |
| 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".