Dual-NanozymeCascade for System-Wide Specific ColorimetricDetection of Aminoglycoside Antibiotics

Antibiotic contamination has been a significant concern in environmental monitoring. Nanozyme-based colorimetric sensors can provide valuable support for in-field detection. However, the development of sensing elements capable of identifying an entire class of specific antibiotics using a single material poses a considerable challenge. In this work, we present a compartmentalized dual-nanozyme cascade composite (Au@mPDA/PAA-Cu2MI, AmPC) designed for the colorimetric detection of aminoglycoside antibiotics (AGs), and we analyze the catalytic mechanisms of the dual-enzyme system in detail. The AmPC composite possesses both analogue glucose oxidase-like (aGOx-like) and peroxidase-like (POD-like) activities. In this process, AGs with a sugar structure can serve as the initial substrate of the reaction, while the resulting H<sub>2</sub>O<sub>2</sub> acts as the second substrate for colorimetric detection. Using gentamicin (GMC) as a proof of concept, we established a detection range of 0.1–10 μg/mL, with a detection limit (LOD) of 91 ng/mL. In addition, we validated the colorimetric response for other AGs and employed principal component analysis (PCA) for differentiating among various AGs. This approach enables nanozymes to selectively recognize their targets through a cascade mechanism. Furthermore, it facilitates the simultaneous detection and identification of antibiotics, highlighting its potential for in-field applications.

Publication Details

Journal
Figshare
Published
2025-03-11
DOI
https://doi.org/10.1021/acs.analchem.4c06854.s001
Primary Topic
Scientific Computing and Data Management
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Dual-NanozymeCascade for System-Wide Specific ColorimetricDetection of Aminoglycoside Antibiotics

Figshare
Scientific Computing and Data Management
article

Dual-NanozymeCascade for System-Wide Specific ColorimetricDetection of Aminoglycoside Antibiotics

article en

Abstract

Antibiotic contamination has been a significant concern in environmental monitoring. Nanozyme-based colorimetric sensors can provide valuable support for in-field detection. However, the development of sensing elements capable of identifying an entire class of specific antibiotics using a single material poses a considerable challenge. In this work, we present a compartmentalized dual-nanozyme cascade composite (Au@mPDA/PAA-Cu2MI, AmPC) designed for the colorimetric detection of aminoglycoside antibiotics (AGs), and we analyze the catalytic mechanisms of the dual-enzyme system in detail. The AmPC composite possesses both analogue glucose oxidase-like (aGOx-like) and peroxidase-like (POD-like) activities. In this process, AGs with a sugar structure can serve as the initial substrate of the reaction, while the resulting H<sub>2</sub>O<sub>2</sub> acts as the second substrate for colorimetric detection. Using gentamicin (GMC) as a proof of concept, we established a detection range of 0.1–10 μg/mL, with a detection limit (LOD) of 91 ng/mL. In addition, we validated the colorimetric response for other AGs and employed principal component analysis (PCA) for differentiating among various AGs. This approach enables nanozymes to selectively recognize their targets through a cascade mechanism. Furthermore, it facilitates the simultaneous detection and identification of antibiotics, highlighting its potential for in-field applications.

Figshare
Life in Land
Openalex Percentile: Top 38%
Scientific Computing and Data Management
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.