Selective colorimetric detection of Pb<sup>2+</sup> using Ag nanoparticles encapsulated in CMC
Bibliographic record
Abstract
This study presents a novel approach for the selective colorimetric sensing of Pb2+ in aqueous solutions. Green-synthesized silver nanoparticles in a carboxymethyl cellulose matrix (AgNPs/CMC) were created using a UV-irradiation technique. The successful formation of AgNPs/CMC was thoroughly examined using UV–vis, X-ray photoelectron spectroscopy, X-ray diffraction, transmission electron microscope, and Fourier transform infrared spectroscopy. AgNPs enhance Pb2+ detection through a synergistic effect with CMC’s complexation ability, enabling sensitive and accurate detection of environmentally relevant Pb2+ concentrations (5.0 × 10−5 to 1.0 × 10−7 M). Upon adding Pb2+, the UV absorption intensity of the longitudinal band diminished and blueshifted, with an observable color change. This indicates that AgNPs assembled side by side due to electrostatic interactions between the positive charge of Pb2+ and the negative charges of OH and carboxyl groups in CMC. The observed shift in plasmon absorption resulted from the organization of AgNPs at a supramolecular level. The unique coordination behavior of Pb2+ enables the formation of a stable supramolecular complex, leading to plasmon coupling and a visible color change. This suggests that the interaction between Pb2+ and CMC likely occurs through a T-Pb2+-T bond, causing the aggregation of AgNPs.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".