Biosynthesized silver nanoparticles anchored on a carbon material derived from maple leaves for the development of a green non-enzymatic biosensor for creatinine sensing
Bibliographic record
Abstract
• A eco-friendly non-enzymatic biosensor for creatinine determination was developed; • The carbon material was derived from maple leaves and used to anchor silver nanoparticles; • The biosynthesized silver nanoparticles was prepared from fresh grass; • The sensor has potential for high alignment with green chemistry principles. Creatinine (CRE) is a byproduct of creatine and phosphocreatine breakdown in muscles, produced at a relatively constant rate and excreted by the kidneys, making it a critical biomarker for assessing renal function. This study reports the development of a novel, eco-friendly non-enzymatic biosensor for CRE determination in synthetic urine. A carbon material was derived from maple leaves and used to anchor biosynthesized silver nanoparticles (HC-AgNPs) prepared from fresh grass. This composite was employed to modify a glassy carbon electrode (GC/HC-AgNPs) for CRE detection. Due to CRE's strong affinity for specific metals, the reduction in silver oxidation peaks served as an indicator of CRE presence in solution. The synthesized composites were characterized by scanning electron microscopy, energy-dispersive spectroscopy, cyclic voltammetry, and spectrophotometry. The sensor exhibited a linear response range of 100–500 µmol L⁻¹, with detection and quantification limits of 26.1 and 86.1 µmol L⁻¹, respectively, using square wave voltammetry. Recovery rates in synthetic urine were of 105.60% and 106.89%, with selectivity experiments revealing recovery percentages exceeding 92% for tested molecules. This sustainable and cost-effective biosensor aligns with green chemistry principles, offering a promising alternative for CRE detection.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 | 0.001 | 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".