Determination of the Network Structure of Sensor Materials Prepared by Three Different Sol-Gel Routes Using Fourier Transform Infrared Spectroscopy (FT-IR)
Why this work is in the frame
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Bibliographic record
Abstract
Solid acid-base sensor materials were prepared by encapsulating three pH indicators (alizarin red, brilliant yellow, and acridine) within a silica matrix using a sol-gel approach through three different routes: (1) non-hydrolytic, (2) acid-catalyzed, and (3) base-catalyzed. Raman and Fourier transform infrared spectroscopies were used to evaluate the silica-indicator interactions. Because vibrational bands assigned to functional groups present in the indicator molecules were not detected, the main silica stretching mode νSi-O between approximately 1300 and 1000 cm(-1) was used to detect the presence of our indicators within the silica matrix. The large band centered at 1100 cm(-1) was deconvoluted into four components corresponding to the longitudinal optic and transversal optic modes of the silicon monoxide (SiO)4 and (SiO)6 siloxane rings. Using the component area of each mode, it was possible to calculate the percentage of each structure. Such percentages ranged from 49% to 70% (SiO)6 for the analyzed samples, within a confidence level of 95% (p = 0.05). (The confidence limits were 53-62%.) These results could be related to the pH indicator content, indicating that the quantity of the encapsulated molecule affects the (SiO)6 percentage values. In addition, a comparison with the radius of gyration obtained by small angle X-ray scattering was done. These results indicate that the analyte accesses the receptor elements through the passages between the siloxane rings but not through the siloxane rings themselves.
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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 | 0.000 |
| 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 it