Prunella vulgaris-phytosynthesized platinum nanoparticles: Insights into nanozymatic activity for H2O2 and glutamate detection and antioxidant capacity
Bibliographic record
Abstract
Artificial nanozymes (enzyme-mimics), specifically metallic nanomaterials, have garnered significant attention recently due to their reduced preparation cost and enhanced stability in a wide range of environments. The present investigation highlights, for the first time, a straightforward green synthesis of biogenic platinum nanoparticles (PtNPs) from a natural resource, namely Prunella vulgaris (Pr). To demonstrate the effectiveness of the phytochemical extract as an effective reducing agent, the PtNPs were characterized by various techniques such as UV–vis spectroscopy, High-resolution Transmission electron microscopy (HR-TEM), zeta-potential analysis, Fourier-transform infrared spectroscopy (FTIR), and Energy dispersive spectroscopy (EDS). The formation of PtNPs with narrow size distribution was verified. Surface decoration of PtNPs was demonstrated with multitudinous functional groups springing from the herbal extract. To demonstrate their use as viable nanozymes, the peroxidase-like activity of Pr/PtNPs was evaluated through a colorimetric assay. Highly sensitive visual detection of H2O2 with discrete linear ranges and a low detection limit of 3.43 μM was demonstrated. Additionally, peroxidase-like catalytic activity was leveraged to develop a colorimetric platform to quantify glutamate biomarker levels with a high degree of selectivity, the limit of detection (LOD) being 7.00 μM. The 2,2-Diphenyl-1-picrylhydrazyl (DPPH) test was used to explore the scavenging nature of the PtNPs via the degradation of DPPH. Overall, the colorimetric assay developed using the Pr/PtNP nanozymes in this work could be used in a broad spectrum of applications, ranging from biomedicine and food science to environmental monitoring.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".