High surface area nitrogen-functionalized Ni nanozymes for efficient peroxidase-like catalytic activity
Bibliographic record
Abstract
Nitrogen-functionalization is an effective means of improving the catalytic performances of nanozymes. In the present work, plasma-assisted nitrogen modification of nanocolumnar Ni GLAD films was performed using an ammonia plasma, resulting in an improvement in the peroxidase-like catalytic performance of the porous, nanostructured Ni films. The plasma-treated nanozymes were characterized by TEM, SEM, XRD, and XPS, revealing a nitrogen-rich surface composition. Increased surface wettability was observed after ammonia plasma treatment, and the resulting nitrogen-functionalized Ni GLAD films presented dramatically enhanced peroxidase-like catalytic activity. The optimal time for plasma treatment was determined to be 120 s; when used to catalyze the oxidation of the colorimetric substrate TMB in the presence of H2O2, Ni films subjected to 120 s of plasma treatment yielded a much higher maximum reaction velocity (3.7⊆10-8 M/s vs. 2.3⊆10-8 M/s) and lower Michaelis-Menten coefficient (0.17 mM vs. 0.23 mM) than pristine Ni films with the same morphology. Additionally, we demonstrate the application of the nanozyme in a gravity-driven, continuous catalytic reaction device. Such a controllable plasma treatment strategy may open a new door toward surface-functionalized nanozymes with improved catalytic performance and potential applications in flow-driven point-of-care devices.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 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".