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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 source (direct Gemma or distilled Codex), 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".