AuCu bimetal nanoclusters as high-performance mimics for ultrasensitive recognition of biomolecules
Bibliographic record
Abstract
Introducing novel mimic materials as alternatives for natural enzymes challenges the analysts. Study on the peroxidase-like materials is an active field in analytical research areas. Herein, Au/Cu bimetal nanoclusters (Au/Cu NCs) are introduced as highly efficient peroxidase mimics, which were investigated using fluorometric and colorimetric techniques. A comprehensive comparison between the catalytic activity of Au, Cu, and their bimetal NCs, with different ratios of Au/Cu was performed using some different peroxidase substrates (including 3,3′,5,5′-tetramethylbenzidine (TMB), o-phenylenediamine dihydrochloride (OPD), and terephthalic acid (TA)). Additionally, different capping agents were applied for the synthesis of NCs, and it was found that penicillamine-capped NCs with 50% Cu have higher activity than other synthesized NCs. Analytical application of the novel mimic for H2O2 detection caused a linear calibration in a wide linear range of 0.001–3 μmol/L, and a great detection limit (3S) of 0.18 nmol/L, using a sensitive fluorescence system. The developed system was also sensitive for recognizing glucose and cholesterol in blood samples, after their enzymatic oxidation and production of H2O2. Detection limits of 55 and 15 nmol/L were obtained for glucose and cholesterol, respectively. The presented method also showed good reliability, which was validated by certified reference materials.
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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) | 0.000 | 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 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".