0
Article ? AI-assigned paper type based on the abstract. Classification may not be perfect — flag errors using the feedback button. Tier 2 ? Original research — experimental, observational, or case-control study. Direct primary evidence. Marine & Wildlife Nanoplastics Sign in to save

Quantitative detecting low concentration polystyrene nanoplastics in aquatic environments via an Ag/Nb2CT (MXene) SERS substrate

Talanta 2024 22 citations ? Citation count from OpenAlex, updated daily. May differ slightly from the publisher's own count. Score: 55 ? 0–100 AI score estimating relevance to the microplastics field. Papers below 30 are filtered from public browse.
Lekai Chang, Shuli Bai, Ping Wei, Ping Wei, Xingyue Gao, Jinfeng Dong, Bingpu Zhou, Chao Peng, Jianbo Jia, Tiangang Luan

Summary

Researchers fabricated an Ag nanoparticle-decorated MXene composite SERS substrate that detects polystyrene nanoplastics down to 10 mg/mL in lake water with high accuracy and recovery rates of 95–107%, and can distinguish nanoplastic types in mixtures using their Raman fingerprint spectra.

In this study, the plasmonic Ag nanoparticles (Ag NPs) were uniformly anchored on the high conductivity NbCT (MXene) nanosheets to construct an Ag/NbCT substrate for surface-enhanced Raman spectroscopy (SERS) detection of polystyrene (PS) nanoplastics. The KI addition (0.15 mol/L), the volume ratio between substrate colloid and nanoplastic suspension (2:1), and the mass ratio of NbCT in substrate (14%) on SERS performance were optimized. The EM hot spots of Ag/NbCT are significantly enlarged and enhanced, elucidated by FDFD simulation. Then, the linear relationship between the PS nanoplastics concentration with three different sizes (50, 300, and 500 nm) and the SERS intensity was obtained (R > 0.976), wherein, the detection limit was as low as 10 mg/mL for PS nanoplastic. Owing to the fingerprint feature, the Ag/NbCT-14% substrate successfully discerns the mixtures from two-component nanoplastics. Meanwhile, it exhibits excellent stability of PS nanoplastics on different detection sites. The recovery rates of PS nanoplastics with different sizes in lake water ranged from 94.74% to 107.29%, with the relative standard deviation (RSD) ranging from 2.88% to 8.30%. Based on this method, the expanded polystyrene (EPS) decomposition behavior was evaluated, and the PS concentrations in four water environments were analyzed. This work will pave the way for the accurate quantitative detection of low concentration of nanoplastics in aquatic environments.

Sign in to start a discussion.

More Papers Like This

Article Tier 2

Quantification of trace polystyrene nanoplastics in aquatic environments using hybrid substrates of gold-loaded dendritic mesoporous silica and silver-decorated graphene nanosheets for surface-enhanced Raman scattering analysis

Researchers developed a surface-enhanced Raman scattering (SERS) detection platform using a hybrid gold-silica and silver-graphene substrate to detect polystyrene nanoplastics in water at concentrations as low as 0.1 μg/mL, achieving 91–109% recovery rates in real lake, ocean, and polluted ditch water samples.

Article Tier 2

Identification of polystyrene nanoplastics using surface enhanced Raman spectroscopy

Researchers demonstrated for the first time that surface-enhanced Raman spectroscopy (SERS) using silver nanoparticles can identify polystyrene nanoplastics as small as 50 nm in real water samples, providing a rapid detection method that bypasses conventional sample preparation and could advance environmental monitoring of nanoplastics previously invisible to standard analytical techniques.

Article Tier 2

One-step detection of nanoplastics in aquatic environments using a portable SERS chessboard substrate

Researchers developed a portable surface-enhanced Raman scattering (SERS) detection platform that captures and identifies nanoplastics from water samples in under one minute using silver nanoparticle-enhanced filter substrates, achieving a detection limit of 0.001 mg/mL for polystyrene nanoplastics across sizes from 30 to 1000 nm.

Article Tier 2

Highly sensitive superhydrophobic SERS substrate combined with machine learning for precise identification and classification of nanoplastics

Researchers fabricated a superhydrophobic surface-enhanced Raman scattering (SERS) substrate that concentrates nanoplastics in a tiny detection zone, then combined it with machine learning to identify seven types of nanoplastics in real lake water with 99.88% accuracy, offering a practical high-throughput environmental monitoring approach.

Article Tier 2

Breaking the Size Barrier: SERS-Based Ultrasensitive Detection and Quantification of Polystyrene Plastics in Real Water Samples

Researchers developed a surface-enhanced Raman spectroscopy (SERS) method capable of detecting and quantifying polystyrene plastic particles of various sizes — including nanoplastics — in real environmental water samples at ultrasensitive concentrations.

Share this paper