Toward an All-Optical Fingerprint of Synthetic and Natural Microplastic Fibers by Polarization-Sensitive Holographic Microscopy

Abstract

Microplastic fibers from synthetic textiles have been indicated as a major source of pollution because millions of fibers are released into the environment by laundry washing. In fact, such types of microplastics usually bypass wastewater treatment processes and filters, thus reaching the oceans and other water natural reservoirs in huge quantities. Nowadays, several approaches are available for characterizing microplastics, but unfortunately, there is no unique and standard method for this aim. Among the various methodologies, several microscopy techniques such as optical microscopy (OM), scanning electron microscopy coupled with energy dispersive X-ray (SEM−EDX) analysis, or OM combined with molecular spectroscopy can be used for visual detection and measurements. Our proposal is a non-destructive method based on a digital holographic microscope in a configuration fully sensitive to the polarization of light transmitted by the fibers with a micron dimension. Our aim is to prove that the proposed approach allows for the precise characterization of synthetic and natural fibers in water. The method exploits all the advantages of digital holography such as numerical refocusing, non-invasive testing, and quantitative measurements of the complex wave field by adding access to the polarization of light that conveys meaningful information about materials. We intend to show that a unique all-optical fingerprint can be retrieved using the Jones-matrix formalism for the major classes of synthetic microfilaments used in the textile industry (i.e., polyamide 6, polyamide 6.6, polypropylene, and polyester) and for the most common natural fibers (i.e., wool and cotton) prepared according to previously developed appropriate protocols. Our results show that the proposed technology identifies new features for micron-sized fibers based on optical anisotropy through quantitative digital holography that could open future routes for automatic and all-optical identification of textile contaminants in water.

Preparation and Analysis of Standard Microplastics

“…Figure 6 and b shows the morphology of two nylon fibers (PA6 versus PA 6.6). [15]. They are generally semicrystalline polymers extruded and drawn in various cross-sectional shapes, which can be circular, kidney-shaped or three-lobed with smooth edges.…”

“…Man-made fibers have tripled their market share from 23% in 1965 to nearly 72%. In addition, synthetic fibers have continued to grow to 75%, while cellulosic fibers, for example, have remained constant at about 6.4% [15]. Compared to natural ones, synthetic fibers do not depend on animal breeding or cultivation and are not affected by environmental factors such as seasonality and climate change.…”

Sorting microplastics from other materials in water samples by ultra-high-definition imaging

“…Currently, emerging method to detect MPs utilize hyperspectral imaging of dry MPs [7]. However, other methods such as digital holographic microscopy [8,9], laser speckle pattern [10] and smartphone-enabled method [11] requiring neither sample treatment nor sorting have shown promises for MP detection directly from water. Nevertheless, the water volume probed at once by these devices remains rather small and not convenient for large field study.…”

On the use of machine learning for microplastic identification from holographic phase-contrast signatures

“…Hence, the advent of artificial intelligence (AI) has further enhanced the capabilities of DH for MPs analysis. Additionally, we tackle the problem of discriminating between different microfibers materials, both natural and synthetic [18][19][20], by combining AI techniques with a polarization-resolved DH system [21,22]. By utilizing features derived from the Jones matrix analysis [21] and birefringence maps [22], we can differentiate between synthetic and natural microfibers.…”

“…Additionally, we tackle the problem of discriminating between different microfibers materials, both natural and synthetic [18][19][20], by combining AI techniques with a polarization-resolved DH system [21,22]. By utilizing features derived from the Jones matrix analysis [21] and birefringence maps [22], we can differentiate between synthetic and natural microfibers. The polarization-resolved DH microscope enables the assessment of the birefringence peculiarities of optically anisotropic materials.…”