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Abstract In this work, we demonstrate the development of a rapid and label-free electrochemical biosensor to detectListeria monocytogenesusing a novel stimulus–response thiomer nanobrush material. Nanobrushes were developed via one-step simultaneous co-deposition of nanoplatinum (Pt) and alginate thiomers (ALG-thiomer). ALG-thiomer/Pt nanobrush platform significantly increased the average electroactive surface area of electrodes by 7 folds and maintained the actuation properties (pH-stimulated osmotic swelling) of the alginate. Dielectric behavior during brush actuation was characterized with positively, neutral, and negatively charged redox probes above and below the isoelectric point of alginate, indicating ALG-thiomer surface charge plays an important role in signal acquisition. The ALG-thiomer platform was biofunctionalized with an aptamer selective for the internalin A protein onListeriafor biosensing applications. Aptamer loading was optimized and various cell capture strategies were investigated (brush extended versus collapsed). Maximum cell capture occurs when the ALG-thiomer/aptamer is in the extended conformation (pH > 3.5), followed by impedance measurement in the collapsed conformation (pH < 3.5). Low concentrations of bacteria (5 CFU mL−1) were sensed from a complex food matrix (chicken broth) and selectivity testing against other Gram-positive bacteria (Staphylococcus aureus) indicate the aptamer affinity is maintained, even at these pH values. The new hybrid soft material is among the most efficient and fastest (17 min) forL. monocytogenesbiosensing to date, and does not require sample pretreatment, constituting a promising new material platform for sensing small molecules or cells.
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This content will become publicly available on July 23, 2025
Bacterial Wastewater-Based Epidemiology Using Surface-Enhanced Raman Spectroscopy and Machine Learning
Abstract Wastewater-based epidemiology (WBE) is a powerful tool for monitoring community disease occurrence, but current methods for bacterial detection suffer from limited scalability, the need fora prioriknowledge of the target organism, and the high degree of genetic similarity between different strains of the same species. Here, we show that surface-enhanced Raman spectroscopy (SERS) can be a scalable, label-free method for detection of bacteria in wastewater. We preferentially enhance Raman signal from bacteria in wastewater using positively-charged plasmonic gold nanorods (AuNRs) that electrostatically bind to the bacterial surface. Transmission cryoelectron microscopy (cryoEM) confirms that AuNRs bind selectively to bacteria in this wastewater matrix. We spike the bacterial speciesStaphylococcus epidermidis, Staphylococcus aureus, Serratia marcescens, andEscerichia coliand AuNRs into filter-sterilized wastewater, varying the AuNR concentration to achieve maximum signal across all pathogens. We then collect 540 spectra from each species, and train a machine learning (ML) model to identify bacterial species in wastewater. For bacterial concentrations of 109cells/mL, we achieve an accuracy exceeding 85%. We also demonstrate that this system is effective at environmentally-realistic bacterial concentrations, with a limit of bacterial detection of 104cells/mL. These results are a key first step toward a label-free, high-throughput platform for bacterial WBE.
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- Award ID(s):
- 2233672
- PAR ID:
- 10541886
- Publisher / Repository:
- bioRxiv
- Date Published:
- Format(s):
- Medium: X
- Institution:
- bioRxiv
- Sponsoring Org:
- National Science Foundation
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