Assessment of Antioxidant Potential of Carbon-Based Nanomaterials from Different Sources
Bibliographic record
Abstract
Antioxidants regulate oxidative reactions by impeding, delaying, or inhibiting the oxidation of biomolecules. Concerns regarding the toxicity of synthetic antioxidants have driven the search for safer alternatives. In this study, the antioxidant activities of three nontoxic carbon-based nanomaterials—carbon dots from citric acid precursor (CB-Ca), iron-doped carbon dots (CB-Fe) and carbon dots derived from Momordica charantia leaves (CB-Mc)—were investigated using 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical scavenging, hydrogen peroxide (H2O2) scavenging, ferric-reducing antioxidant power, and total antioxidant capacity (TAC) assays. Scavenging activity was carried out at varying concentrations, and half-maximal inhibitory concentration (IC50) was calculated using non-linear regression. Reductive ability and TAC were expressed as mg ascorbic acid equivalents/g nanomaterial. CB-Fe exhibited the most potent DPPH scavenging activity (IC50 = 254.2 ± 37.37 µg/mL), surpassing CB-Mc and CB-Ca by 2- to 3-fold. In contrast, CB-Ca had the highest H2O2 scavenging (IC50 = 84.2 ± 11.87 µg/mL), while CB-Mc had the highest TAC of 77.95 mg ascorbic acid Eq/g. CB-Fe also displayed superior ferric ion reducing capacity. The study concluded that each carbon dot type exhibits unique antioxidant profiles and may offer some special advantages in nanomedicine and other applications.
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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 | 0.000 |
| 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".