Positively Charged Gold Quantum Dots: An Nanozymatic “Off-On” Sensor for Thiocyanate Detection
Bibliographic record
Abstract
The concentration of thiocyanate (SCN−) in bodily fluids is a good indicator of potential and severe health issues such as nasal bleeding, goiters, vertigo, unconsciousness, several inflammatory diseases, and cystic fibrosis. Herein, a visual SCN− sensing method has been developed using the enzyme-like nature of positively charged gold quantum dots (Au QDs) mixed with 3,3′,5,5′-tetramethylbenzidine (TMB) and hydrogen peroxide (H2O2). This research also reports a new method of synthesizing positively charged Au QDs directly from gold nanoparticles through a hydrothermal process. Microscopic imaging has showed that the Au QDs were 3–5 nm in size, and the emission wavelength was at 438 nm. Au QDs did not display any enzyme-like nature while mixed up with TMB and H2O2. However, the nanozymatic activity of Au QDs appeared when SCN− was included, leading to a very low detection limit (LOD) of 8 nM and 99–105% recovery in complex media. The steady-state kinetic reaction of Au QDs showed that Au QDs had a lower Michaelis–Menten constant (Km) toward H2O2 and TMB, which indicates that the Au QDs had a higher affinity for H2O2 and TMB than horseradish peroxidase (HRP). A mechanism study has revealed that the scavenging ability of hydroxyl (•OH) radicals by the SCN− group plays an important role in enhancing the sensitivity in this study. The proposed nanozymatic “Off–On” SCN− sensor was also successfully validated in commercial milk samples.
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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 | 0.001 | 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) | 0.001 | 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 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".