Terahertz Detection of Acid Blue 113 Dye Using Hybrid Hydrogels
Bibliographic record
Abstract
Abstract The detection of pollutant dyes in the environment, particularly in waterways, can be extended and potentially simplified using terahertz spectroscopy. The use of hydrogels to absorb these contaminants from water and create solid samples with moderate transparency at terahertz frequencies evidently facilitates spectroscopic analysis. In this study, we demonstrate that chitosan and poly(vinyl alcohol) hydrogels, as well as their cross-linked and nanocomposite hybrid blends, efficiently capture the acid blue 113 azo dye (AB113). We show that terahertz transmittance and refractive index measurements conducted on these hydrogel materials offer an effective alternative method for detecting water contaminants, especially azo dyes. The terahertz transmittance spectra provide evidence of azo dye molecules within the hydrogel membranes. Additionally, considering the alterations in the hydrogels’ refractive index due to the sorption of AB113 dye molecules, we derived an analytical model to accurately estimate the amount of dye sorbed by the polymeric networks. The findings of this study establish a practical and promising approach for both qualitative and quantitative terahertz detection of AB113 dye using hybrid hydrogels. A detailed comparison with optical and infrared spectroscopy is also provided for reference.
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How this classification was reachedexpand
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) | 0.000 | 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 teacher head, 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".