Ranolazine-Functionalized Copper Nanoparticles as a Colorimetric Sensor for Trace Level Detection of As3+
Bibliographic record
Abstract
This study involves environmentally friendly synthesis of copper nanoparticles in aqueous medium without inert gas protection, using ranolazine as a capping material. UV-Visible (UV-Vis) spectrometry showed that ranolazine-derived copper nanoparticles (Rano-Cu NPs) demonstrate a localized surface plasmon resonance (LSPR) band at 573 nm with brick-red color under optimized parameters, including pH, reaction time, and concentrations of copper salt, hydrazine hydrate, and ranolazine. The coating of ranolazine on the surface of Cu NPs was studied via Fourier transform infrared (FTIR) spectroscopy. Scanning electron microscopy (SEM) revealed that Rano-Cu NPs consist of spherical particles. X-ray diffraction (XRD) verified that Rano-Cu NPs are crystalline in nature. Atomic force microscopy (AFM) showed that the average size of Rano-Cu NPs was 40 ± 2 nm in the range of 22–95 nm. Rano-Cu NPs proved to be highly sensitive as a selective colorimetric sensor for As3+ via color change from brick red to dark green, in the linear range of 3.0 × 10−7 to 8.3 × 10−6 M, with an R² value of 0.9979. The developed sensor is simple, cost effective, highly sensitive, and extremely selective for As3+ detection, showing a low detection limit (LDL) of 1.6 × 10−8 M. The developed sensor was effectively tested for detection of As3+ in some water samples.
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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 | 0.000 |
| 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 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".