A Highly Sensitive Chitosan-Based SERS Sensor for the Trace Detection of a Model Cationic Dye
Bibliographic record
Abstract
The rapid detection of contaminants in water resources is vital for safeguarding the environment, where the use of eco-friendly materials for water monitoring technologies has become increasingly prioritized. In this context, the role of biocomposites in the development of a SERS sensor is reported in this study. Grafted chitosan was employed as a matrix support for Ag nanoparticles (NPs) for the surface-enhanced Raman spectroscopy (SERS). Chitosan (CS) was decorated with thiol and carboxylic acid groups by incorporating S-acetyl mercaptosuccinic anhydride (SAMSA) to yield CS-SAMSA. Then, Ag NPs were immobilized onto the CS-SAMSA (Ag@CS-SAMSA) and characterized by spectral methods (IR, Raman, NIR, solid state 13C NMR with CP-MAS, XPS, and TEM). Ag@CS-SAMSA was evaluated as a substrate for SERS, where methylene blue (MB) was used as a model dye adsorbate. The Ag@CS-SAMSA sensor demonstrated a high sensitivity (with an enhancement factor ca. 108) and reusability over three cycles, with acceptable reproducibility and storage stability. The Raman imaging revealed a large SERS effect, whereas the MB detection varied from 1–100 μM. The limits of detection (LOD) and quantitation (LOQ) of the biocomposite sensor were characterized, revealing properties that rival current state-of-the-art systems. The dye adsorption profiles were studied via SERS by fitting the isotherm results with the Hill model to yield the ΔG°ads for the adsorption process. This research demonstrates a sustainable dual-function biocomposite with tailored adsorption and sensing properties suitable for potential utility in advanced water treatment technology and environmental monitoring applications.
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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) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".