An Exploration of Enzyme-like Activity of Newly Synthesized Carbon Quantum Dot-Gold Nanoparticle Nanohybrids and Silicene Nanosheets for the Development of an Electrochemically Supported Colorimetric Biosensing Approach for the Detection of H2O2 and Dopamine.
Bibliographic record
Abstract
Over the past few decades, the use of colorimetric testing has been declining for reasons such as low sensitivity and slow incubation times. Scientists have attempted to remedy this by utilizing nanomaterials to provide enzymatic activity forcing the reaction to increase targeted binding and speed. The ability of a nanomaterial to imitate enzymatic activity is referred to as nanozymatic activity. In this thesis, firstly, a new, easy and quick synthesis method for Carbon Quantum Dot-Gold Nanoparticles (CQD-AuNPs) is conveyed. This nanohybrid is then used as a peroxidase-mimic in the oxidation reaction between 3,3′,5,5′-Tetramethylbenzidine (TMB) and hydrogen peroxide (H2O2) where it successfully catalyzed the reaction. This nanozyme was added to reaction mixtures for H2O2 and dopamine (DA) detection and produced incredibly low limit-of-detection (LOD) values alongside increased colorimetric intensity. Furthermore, the nanozyme’s behaviour under an externally applied electric potential was examined. Doing so severely decreased the incubation time and enhanced the detection of H2O2 and DA, thus procuring LOD values as low as 240 nM, and 0.75 mM, respectively. Secondly, new characterization tests were conducted on silicene nanosheets. Formerly, silicene was a material that was studied primarily via theoretical modelling. This thesis utilizes silicene’s exceptional semiconducting properties to promote colorimetric detection of H2O2 and DA. It was evaluated for its peroxidase-like activity in the oxidation reaction of TMB. Under an externally applied electric potential, the incubation time was reduced from 30 minutes to 1.5 minutes and presented lower LOD values - 3.11 M and 2.21M for H2O2 and DA, respectively.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".