Identifying microplastic litter with Laser Induced Breakdown Spectroscopy: A first approach

In-situ detection of microplastics in the aquatic environment: A systematic literature review

Microplastics are ubiquitous in the aquatic environment and have emerged as a significant environmental issue due to their potential impacts on human health and the ecosystem. Current laboratory-based microplastic detection methods suffer from various drawbacks, including a lack of standardisation, limited spatial and temporal coverage, high costs, and time-consuming procedures. Consequently, there is a need for the development of in-situ techniques to detect and monitor microplastics to effectively identify and understand their sources, pathways, and behaviours. Herein, we adopt a systematic literature review method to assess the development and application of experimental and field technologies designed for the in-situ detection and monitoring of aquatic microplastics, without the need for sample preparation. Four scientific databases were searched in March 2023, resulting in a review of 62 relevant studies. These studies were classified into seven sensor categories and their working principles were discussed. The sensor classes include optical devices, digital holography, Raman spectroscopy, other spectroscopy, hyperspectral imaging, remote sensing, and other methods. We also looked at how data from these technologies are integrated with machine learning models to develop classifiers capable of accurately characterising the physical and chemical properties of microplastics and discriminating them from other particles. This review concluded that in-situ detection of microplastics in aquatic environments is feasible and can be achieved with high accuracy, even though the methods are still in the early stages of development. Nonetheless, further research is still needed to enhance the in-situ detection of microplastics. This includes exploring the possibility of combining various detection methods and developing robust machine-learning classifiers. Additionally, there is a recommendation for in-situ implementation of the reviewed methods to assess their effectiveness in detecting microplastics and identify their limitations.

Capabilities of simultaneous 193 nm - LIBS/LA-ICP-MS imaging for microplastics characterization

Microplastics (MPs) are currently one of the major environmental challenges within our society. With the awareness of the impact of MPs on the environment increasing over the last years, the need for increased monitoring as well as comprehensive analysis to better understand the fate and impact of MPs has become more and more important. A major aspect of MP characterization is the assignment of the polymer type of individual particles. Here, per- and poly-fluoroalkyl substances (PFAS), originating from fluor-containing polymers, have gained a lot of attention due to the severe environmental impact. Additionally, quantitative analysis of the metal content is of great interest in the field, since MPs are prone to either leaching (in)organic additives into the environment or taking up and accumulating hazardous components (e.g., heavy metals). In this work we demonstrate the capabilities of a simultaneous LIBS/LA-ICP-MS setup for the analysis of MPs. In the first part, we demonstrate the potential of targeted LIBS analysis for the imaging of fluor-containing polymers. Using a laser spot size of 5 μm combined with highly sensitive ICCD detection enables analysis of particles in the low μm range. In the second part we combine the polymer-identification capabilities of LIBS with the high sensitivity of ICP-MS to perform matrix-matched quantification of the metal content of individual MPs. In this case we use a spot size of 50 μm facilitating polymer classification with a broadband spectrometer, resulting in detection limits of 0.72 μg/g for Pb and 9.5 μg/g for Sn simultaneously measured using ICP-MS.

A Novel LIBS Sensor for Sample Examinations on a Crime Scene

In this work, we present a compact LIBS sensor developed for characterization of samples on a crime scene following requirements of law enforcement agencies involved in the project. The sensor operates both in a tabletop mode, for aside measurements of swabbed materials or taken fragments, and in handheld mode where the sensor head is pointed directly on targets at the scene. The sensor head is connected via an umbilical to an instrument box that could be battery-powered and contains also a color camera for sample visualization, illumination LEDs, and pointing system for placing the target in focus. Here we describe the sensor's architecture and functionalities, the optimization of the acquisition parameters, and the results of some LIBS measurements. On nano-plotted traces at silica wafer and in optimized conditions, for most of the elements the detection limits, in term of the absolute element masses, were found to be below 10 picograms. We also show results obtained on some representative materials, like fingerprints, swabbed soil and gunshot residue, varnishes on metal, and coated plastics. The last, solid samples were used to evaluate the depth profiling capabilities of the instrument, where the recognition of all four car paint layers was achieved.

Adversarial Data Augmentation and Transfer Net for Scrap Metal Identification Using Laser-Induced Breakdown Spectroscopy Measurement of Standard Reference Materials

In this study, we propose a transfer learning-based classification model for identifying scrap metal using an augmented training dataset consisting of laser-induced breakdown spectroscopy (LIBS) measurement of standard reference material (SRMs) samples, considering varying experimental setups and environmental conditions. LIBS provides unique spectra for identifying unknown samples without complicated sample preparation. Thus, LIBS systems combined with machine learning methods have been actively studied for industrial applications such as scrap metal recycling. However, in machine learning models, a training set of the used samples may not cover the diversity of the scrap metal encountered in field measurements. Moreover, differences in experimental configuration, where laboratory standards and real samples are analyzed in situ, may lead to a wider gap in the distribution of training and test sets, dramatically reducing the performance of the LIBS-based fast classification system for real samples. To address these challenges, we propose a two-step Aug2Tran model. First, we augment the SRM dataset by synthesizing spectra of unobserved types through attenuation of dominant peaks corresponding to sample composition and generating spectra depending on the target sample using a generative adversarial network. Second, we used the augmented SRM dataset to build a robust real-time classification model with a convolutional neural network, which is further customized for the target scrap metal with limited measurements through transfer learning. For evaluation, SRMs of five representative metal types, including aluminum, copper, iron, stainless steel, and brass, are measured with a typical setup to form the SRM dataset. For testing, scrap metal from actual industrial fields is experimented with three different configurations, resulting in eight different test datasets. The experimental results show that the proposed scheme produces an average classification accuracy of 98.25% for the three experimental conditions, as high as the results of the conventional scheme with three separately trained and executed models. Additionally, the proposed model improves the classification accuracy of arbitrarily shaped static or moving samples with various surface contaminations and compositions, and even for differing ranges of charted intensities and wavelengths. Therefore, the proposed Aug2Tran model can be used as a systematic model for scrap metal classification with generalizability and ease of implementation.

Laser-based techniques: Novel tools for the identification and characterization of aged microplastics with developed biofilm

Microplastics found in the environment are often covered with a biofilm, which makes their analysis difficult. Therefore, the biofilm is usually removed before analysis, which may affect the microplastic particles or lead to their loss during the procedure. In this work, we used laser-based analytical techniques and evaluated their performance in detecting, characterizing, and classifying pristine and aged microplastics with a developed biofilm. Five types of microplastics from different polymers were selected (polyamide, polyethylene, polyethylene terephthalate, polypropylene, and polyvinyl chloride) and aged under controlled conditions in freshwater and wastewater. The development of biofilm and the changes in the properties of the microplastic were evaluated. The pristine and aged microplastics were characterized by standard methods (e.g., optical and scanning electron microscopy, and Raman spectroscopy), and then laser-induced breakdown spectroscopy (LIBS) and laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) were used. The results show that LIBS could identify different types of plastics regardless of the ageing and major biotic elements of the biofilm layer. LA-ICP-MS showed a high sensitivity to metals, which can be used as markers for various plastics. In addition, LA-ICP-MS can be employed in studies to monitor the adsorption and desorption (leaching) of metals during the ageing of microplastics. The use of these laser-based analytical techniques was found to be beneficial in the study of environmentally relevant microplastics.

Classification and distribution of freshwater microplastics along the Italian Po river by hyperspectral imaging

In this work, freshwater microplastic samples collected from four different stations along the Italian Po river were characterized in terms of abundance, distribution, category, morphological and morphometrical features, and polymer type. The correlation between microplastic category and polymer type was also evaluated. Polymer identification was carried out developing and implementing a new and effective hierarchical classification logic applied to hyperspectral images acquired in the short-wave infrared range (SWIR: 1000-2500 nm). Results showed that concentration of microplastics ranged from 1.89 to 8.22 particles/m3, the most abundant category was fragment, followed by foam, granule, pellet, and filament and the most diffused polymers were expanded polystyrene followed by polyethylene, polypropylene, polystyrene, polyamide, polyethylene terephthalate and polyvinyl chloride, with some differences in polymer distribution among stations. The application of hyperspectral imaging (HSI) as a rapid and non-destructive method to classify freshwater microplastics for environmental monitoring represents a completely innovative approach in this field.

Micro-nano-sized polytetrafluoroethylene (teflon) particles as a model of plastic pollution detection in living organisms

Micro- and nano-sized particles of polytetrafluoroethylene (PTFE) were used as model (reference) particles to study the biological effects of plastic pollution. Since the PTFE molecule contains fluorine, considered as an "atomic marker" sharply distinguishing it from other common plastics, micro- and nano-particles of PTFE have a specific crystalline structure and are, therefore, well identified by the methods of polarized light microscopy (POL), Raman microspectroscopy (micro-Raman), and energy-dispersive spectroscopy (EDS). Examples of PTFE particles detection in hemolimph of the cockroach Blatella germanica, in hemolimph of the larva and in faecal pellets of imago of a fly Lucilia sp., in the stomach and hingat of brine shrimp Artemia salina, and in association with cell wall of green unicellular alga Chlorococcus sp. are provided. The presented results strongly suggest that PTFE particles can be detected and identified in the biological medium using the method of "atomic markers", polarization microscopy and Raman spectroscopy.