A Fe/Zn Dual Single-Atom Nanozyme with High Peroxidase Activities for Detection of Penicillin G
Bibliographic record
Abstract
Penicillin G (PG) is a common antibiotic, and its accumulation in the environment can pose a threat to the ecological system and ultimately impact human health. Nanozymes have emerged as highly stable enzyme mimics that can be utilized as sensors to achieve the sensitive detection of specific antibiotics. Herein, we report on a dual single-atom Fe/Zn nanozyme (DSAzyme) synthesized from Fe-imidazole as the guest and zeolite imidazole framework-8 as the host. The DSAzyme exhibits intriguing properties that mimic the activities of two natural enzymes: peroxidase and lactamase. Both activities are utilized for the design of a colorimetric sensor for the specific detection of PG: the peroxidase activity enables color generation from 3,3′,5,5′-tetramethylbenzidine and H 2 O 2, and the lactamase activity provides the recognition of PG. The nanozyme consists of many Fe–N 4 and Zn–N 4 site and mechanistic characterizations by experimental investigations and theoretical calculations identify Fe–N 4 as the main active center for the peroxidase activity and Zn–N 4 as the main binding site for PG. The sensor can achieve a limit of detection of 47 nM, is able to detect PG from real-life samples, remains fully functional after 8-month storage, and retain high activities after reuse for fives times. Taken together, our study provides a new approach to the detection of antibiotics in environmental 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) | 0.000 | 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 | 0.001 | 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".