Intrinsic Enzyme-like Activities of Cerium Oxide Nanocomposite and Its Application for Extracellular H<sub>2</sub>O<sub>2</sub> Detection Using an Electrochemical Microfluidic Device
Bibliographic record
Abstract
Abstract Artificial enzyme mimics have gained considerable attention for use in sensing applications due to their high stability and outstanding catalytic activity. We show that cerium oxide nanosheets (NSs) exhibit triple-enzyme mimetic activity. The oxidase-, peroxidase-, and catalase-like activities of the proposed nanoparticles are demonstrated using both colorimetric and electron paramagnetic resonance (EPR) spectroscopy. On the basis of the excellent catalytic activity of cerium oxide NSs toward hydrogen peroxide, an electrochemical approach for the high-throughput detection of H2O2 in living cells was established. This report presents an analytical microfluidic chip integrated with a cerium oxide NS mimic enzyme for the fabrication of a simple, sensitive, and low-cost electrochemical sensor. Three Au microelectrodes were fabricated on a glass substrate using photolithography, and the working electrode was functionalized using cerium oxide NSs. The operation of this biosensor is based on cerium oxide NSs and presents a high sensitivity over a wide detection range, between 100 nM and 20 mM, with a low detection limit of 20 nM and a high sensitivity threshold of 226.4 μA·cm–2·μM–1. This microfluidic sensor shows a strong response to H2O2, suggesting potential applications in monitoring H2O2 directly secreted from living cells. This sensor chip provides a promising platform for applications in the field of diagnostics and sensing.
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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".