Renewable carbon from flax shives with silver nanoparticles biosynthesized using Eichhornia crassipes extract for green electrochemical detection of hydroxychloroquine
Bibliographic record
Abstract
Hydroxychloroquine (HCQ) is an antimalarial drug that was repurposed during the COVID-19 pandemic. However, due to its limited clinical efficacy and notable side effects, its recommendation was later withdrawn. Despite this, HCQ sales increased by over 800% in Brazil, and its presence has since been detected in aquatic environments, raising concerns regarding potential risks to both environmental and human health. In response, a novel green electrochemical sensor was developed for the determination of HCQ in water samples. The sensor is based on a glassy carbon electrode modified with hydrochar derived from flax shives and silver nanoparticles biosynthesized using Eichhornia crassipes (water hyacinth) extract as a reducing and stabilizing agent (GC/HC-AgNPs). The sensor was characterized by scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDS), and cyclic voltammetry (CV). Experimental parameters were optimized, and the device was successfully applied for the detection of HCQ using differential pulse voltammetry (DPV). The sensor exhibited a linear response in the range of 1.0–40.0 µmol L⁻¹, with a limit of detection (LOD) of 0.265 µmol L⁻¹ and a limit of quantification (LOQ) of 0.885 µmol L⁻¹. Excellent repeatability (RSD = 2.27%) and reproducibility (RSD = 5.75%) were achieved. Application in tap and lake water samples resulted in recovery values ranging from 89.5% to 105.0%, and comparative analysis with UV–vis spectroscopy confirmed the accuracy of the proposed method. The sensor further demonstrated good selectivity even in the presence of relevant interfering species. Green chemistry assessment tools confirmed the sustainable character of the developed method. Overall, the proposed sensor represents a promising, effective, and environmentally friendly platform for HCQ monitoring in water matrices.
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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 | 0.001 | 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".