We can't find the internet
Attempting to reconnect
Something went wrong!
Hang in there while we get back on track
Identification of microplastics with cantilever enhanced photoacoustic spectroscopy in infrared region
AI summary Read the abstract
This study demonstrated cantilever-enhanced photoacoustic spectroscopy as a new method for identifying microplastics, finding that the technique provided sensitive, polymer-specific detection of MP particles without requiring sample preparation, offering a potential field-deployable monitoring tool.
Microplastics have become ubiquitous in the environment, posing significant threats to ecosystems and organisms across the globe. Accurate risk assessment of these pollutants hinges on the availability of comprehensive qualitative and quantitative data. This paper demonstrates the potential of photoacoustic spectroscopy (PAS) coupled with an external cavity quantum cascade laser (EC-QCL) for identifying various plastic polymers within the mid-infrared (mid-IR) range. To simulate the presence of microplastics in aqueous environments, the particles were prepared under controlled conditions through cryogenic milling of commercial polymer beads, followed by dispersion in ultrapure water. Finally, these simulated samples underwent filtration using gold-coated polycarbonate filters. The microplastics deposited on these filters were successfully identified by analyzing the photoacoustic (PA) spectra of individual particles or small clusters. The resulting absorption spectra were compared with both attenuated total reflectance (ATR) library spectra and transmission (TRX) spectra. While the PA spectra closely matched the transmission spectra, they exhibited shifts in absorption peak positions compared to ATR spectra. This study serves as a proof of concept, highlighting the capability of photoacoustic spectroscopy in identifying microplastic particles in environmental samples and paving the way for more accurate and efficient microplastic detection methodologies.
More Papers Like This
In Situ Detection and Imaging of Microplastics in Water Using Ultrasound and Photo Acoustic Technology
AI summary Read the abstract
This study explored the use of ultrasound and photoacoustic technology for in situ detection and imaging of microplastics in water, addressing the need for field-deployable, non-invasive monitoring tools. The combined acoustic approach demonstrated the ability to detect and localize microplastics in water samples, offering a promising complement to laboratory-based spectroscopic methods.
Design and Development of an Advanced Sensor Prototype for the Detection of Microplastics
AI summary Read the abstract
Researchers designed and developed an advanced sensor prototype for detecting microplastics in water, combining spectroscopic and signal processing technologies into a portable device. The prototype demonstrated accurate microplastic identification across multiple polymer types in field conditions.
Detecting small microplastics down to 1.3 μm using large area ATR-FTIR
AI summary Read the abstract
Researchers introduced large-area ATR-FTIR spectroscopy as a new technique capable of detecting microplastics as small as 1.3 micrometers, outperforming conventional micro-FTIR for small particle detection in marine water samples.
An investigation on the applications of advanced Infrared Spectroscopy, Spectral Imaging and Machine Learning for Polymer Characterization, including microplastics
AI summary Read the abstract
This study integrated advanced infrared spectroscopy, spectral imaging, chemometrics, and machine learning to identify and characterize microplastics and polymer degradation products. The combination of techniques improved both the accuracy and throughput of MP analysis compared to conventional methods.
Advancing microplastic surveillance through photoacoustic imaging and deep learning techniques
AI summary Read the abstract
Researchers developed a new method for detecting and characterizing microplastics using photoacoustic imaging combined with deep learning algorithms. The approach enables high-resolution visualization of microplastic morphology and distribution in environmental samples. The study suggests that this integrated imaging and AI technique could significantly advance environmental monitoring capabilities for tracking microplastic contamination.
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.