Strong nanozymatic activity of silicene quantum dots: Enhanced sensitivity for the selective detection of H2O2 and dopamine in complex media
Bibliographic record
Abstract
• A new method for synthesizing SiQDs was introduced. • The nanozymatic activity of SiQDs was reported for the first time. • The peroxidase-like activity of SiQDs was used to determine H 2 O 2 and dopamine. • Dopamine detection was validated in a blood matrix. Nanozymes have recently gained popularity because of their desirable properties such as excellent stability, minimal cytotoxicity, high catalytic activity and enhanced cost-effectiveness. This work reports, for the first time, the nanozymatic nature (peroxidase-like enzyme activity) of silicene quantum dots (SiQDs) using a simple hydrothermal synthesis method. The study utilized the nanozymatic activity of SiQDs to develop a colorimetric biosensor for H 2 O 2 and dopamine detection. The developed method was based on the oxidation of the chromogenic substance 3,3′,5,5′-Tetramethylbenzidine (TMB) by the hydroxyl radicals generated from the decomposition of H 2 O 2, resulting in a color change that enabled colorimetric detection in the presence of SiQDs. The SiQDs acted as a catalyst for this reaction by supplying more active sites on its surface for the interaction between H 2 O 2 and TMB. Various factors affecting the reaction yield were considered and optimized. The proposed method was applied for H 2 O 2 and dopamine detection, and the limits of detection (LOD) were found to be 0.0113 mM and 0.5521 µM, respectively, proving the proposed method’s high sensitivity. The selectivity of the developed method for dopamine detection was examined by studying the effect of potential interfering substances, the method demonstrated high selectivity for dopamine detection in the presence of other interfering analytes. Additionally, the method was successfully applied for dopamine detection in a simulated blood matrix with a LOD of 1.6982 µM, indicating the superiority of the method for dopamine detection in complex media.
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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 | 0.001 | 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".