Peroxidase-Like Behavior of Ni Thin Films Deposited by Glancing Angle Deposition for Enzyme-Free Uric Acid Sensing
Bibliographic record
Abstract
Abstract We present a nanozyme-based biosensor fabricated from nanostructured Ni films deposited onto a silicon wafer by glancing angle deposition (GLAD) for enzyme-free colorimetric monitoring of uric acid (UA), a biomarker for gout, high blood pressure, heart disease, and kidney disease. The helically structured Ni GLAD nanozymes exhibit excellent peroxidase-like activity to accelerate the oxidation reaction of colorless 3,3′,5,5′-tetramethylbenzidine (TMB) to a blue product, oxidized TMB (oxTMB), mediated by H2O2. In the presence of UA, oxTMB is reduced, decreasing the optical absorbance by an amount determined by the concentration of UA in the solution. The nanozyme not only mimics peroxidase but also possesses the notable qualities of reusability, simple operation, and reliability, making it environment-friendly and suitable for on-demand analysis. We optimized essential working parameters (pH, TMB concentration, and H2O2 concentration) to maximize the initial color change of the TMB solution. The catalytic activity of this nanozyme was compared with conventional nanofilms using the Michaelis–Menten theory. Based on this, enzyme-free biosensors were developed for colorimetric detection of UA, providing a wide detection range and a limit of detection (3.3 μM) suitable for measurements of UA concentration in sweat. Furthermore, interference from glucose and urea was studied so as to explore the potential of the biosensor for use in the clinical diagnosis of UA biomarkers.
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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) | 0.002 | 0.001 |
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".