Determination of the Network Structure of Sensor Materials Prepared by Three Different Sol-Gel Routes Using Fourier Transform Infrared Spectroscopy (FT-IR)
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.
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".