Fabricating a Three-Dimensional Surface-Enhanced Raman Scattering Substrate Using Hydrogel-Loaded Freeze-Induced Silver Nanoparticle Aggregates for the Highly Sensitive Detection of Organic Pollutants in Seawater
Sensors (Basel, Switzerland)（2025）
Abstract
Surface-enhanced Raman scattering (SERS) spectroscopy faces challenges in achieving both high sensitivity and reproducibility for the detection of real samples, particularly in high-salinity matrices. In this study, we developed a high-performance, salt-resistant three-dimensional (3D) SERS substrate by integrating physically induced colloidal silver nanoparticle aggregates (AgNAs) with an agarose hydrogel. AgNAs were prepared using a freeze–thaw–ultrasonication method to minimize interference in SERS signals while significantly enhancing the detection efficiency of trace pollutants. The incorporation of the agarose hydrogel not only improved the substrate’s pollutant enrichment capability, but also effectively prevented the aggregation and sedimentation of AgNAs in salt solutions. The developed SERS substrate exhibited an ultralow detection limit of 10−12 M for Nile Blue (NB), with a 100-fold increase in sensitivity compared to colloidal AgNAs and drop-cast AgNAs solid substrates. The analytical enhancement factor (AEF) for malachite green (MG) achieved a value of 1.4 × 107. Furthermore, the substrate demonstrated excellent signal uniformity, with a relative standard deviation (RSD) of 6.74% within a 200 μm × 200 μm detection area and also show a satisfactory RSD of only 9.38% within a larger area of 1 mm × 1 mm. Notably, the 3D SERS substrate exhibited excellent stability under high-salinity conditions (0.5 M NaCl) and successfully detected a model pollutant (MG) in real seawater samples using the standard addition method. This study provides a novel strategy for highly sensitive SERS detection of trace pollutants in saline environments, offering promising applications in environmental monitoring and marine pollution analysis.
MoreTranslated text
Key words
surface-enhanced Raman scattering (SERS),3D SERS substrate,hydrogel,highly sensitive,uniformity,seawater detection
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined