0
Article Tier 2 Detection Methods Environmental Sources Food & Water Marine & Wildlife Nanoplastics Policy & Risk Sign in to save

Liquid Interfacial Coassembly of Plasmonic Arrays and Trace Hydrophobic Nanoplastics in Edible Oils for Robust Identification and Classification by Surface-Enhanced Raman Spectroscopy

Journal of Agricultural and Food Chemistry 2023 12 citations

AI summary Read the abstract

Researchers developed a surface-enhanced Raman spectroscopy method that uses liquid interface coassembly of gold nanoparticles to detect trace amounts of nanoplastics in edible oils and aqueous environments. The technique achieved detection limits at the microgram-per-milliliter level and, combined with principal component analysis, enabled differentiation and classification of multiple nanoplastic types.

The ubiquity of micro-/nanoplastics poses a visible threat to the environment, aquatic organisms, and human beings and has become a global concern. Here, we proposed a liquid interface-based strategy using surface-enhanced Raman spectroscopy to coassemble nanoplastics and gold nanoparticles into a dense and homogeneous plasmonic array, thereby enabling the rapid and sensitive detection of trace nanoplastics. In addition, due to the uniqueness of the oil-water immiscible two-phase interface, we achieved ideal results for the detection of nanoplastics in a complex matrix (e.g., aqueous environment and edible oil) with a detection limit of μg/mL. With the aid of the principal component analysis algorithm, the differentiation and identification of multiple nanoplastic components (e.g., polystyrene, polyethylene, and polyethylene terephthalate) in aqueous environments and common hazards (e.g., Bap and Phe) in edible oil were achieved. Therefore, our self-assembled plasmonic arrays are expected to be used for monitoring environmental pollution and food safety.

More Papers Like This

Article Tier 2

Integrating Metal–Phenolic Networks-Mediated Separation and Machine Learning-Aided Surface-Enhanced Raman Spectroscopy for Accurate Nanoplastics Quantification and Classification

AI summary Read the abstract

Researchers combined a metal-based separation technique with machine learning and surface-enhanced Raman spectroscopy to detect and classify nanoplastics in environmental samples. The method achieved high accuracy in identifying different types of nanoplastics at very low concentrations. This approach could make it significantly easier and more reliable to monitor nanoplastic contamination in real-world water and soil samples.

Article Tier 2

Detection and classification of microplastics in green tea using SERS with gold nanoparticle substrates integrating chemometrics and deep learning

AI summary Read the abstract

Researchers developed a method using surface-enhanced Raman scattering with gold nanoparticle substrates to detect and classify polystyrene and PET microplastic contamination in green tea powders. They compared chemometric and deep learning classification approaches, finding that partial least squares discriminant analysis achieved the highest accuracy at 100% for most tea varieties. The method offers a practical tool for detecting microplastic contamination in food products.

Article Tier 2

Plasmonic Nanomaterials for Micro- and Nanoplastics Detection

AI summary Read the abstract

This review examines plasmonic noble metal nanomaterials (gold, silver, and their alloys) as analytical tools for detecting and quantifying micro- and nanoplastics in environmental samples. Plasmonic materials exploit surface-enhanced Raman scattering and other optical effects to achieve highly sensitive detection of plastic particles.

Article Tier 2

Surface-enhanced Raman spectroscopy for the detection of microplastics

AI summary Read the abstract

Researchers developed a surface-enhanced Raman spectroscopy method using gold nanoparticles to detect polystyrene microplastics at concentrations as low as 6.5 micrograms per milliliter, offering a new tool for detecting sub-micron plastic pollutants in water.

Article Tier 2

Detection of nanoplastics through low-cost SERS substrates, based on 3D islands of aggregated gold nanoparticles on aluminum foil, for wide ranging applications

AI summary Read the abstract

Researchers developed a low-cost surface-enhanced Raman spectroscopy (SERS) substrate by combining aluminium foil with 3D aggregates of gold nanoparticles stabilised by cucurbit[5]uril, enabling sensitive nanoplastic detection through plasmonic coupling. The substrate achieved trace-level analyte detection and offers a practical, scalable approach for nanoplastic identification across a wide range of environmental and analytical applications.

Research digests by email

When a large batch of papers lands in the Atlas, we read through it and send a short write-up of what stood out.

Email me about

Share this paper