Exploring the potential of photoluminescence spectroscopy in combination with Nile Red staining for microplastic detection

Highlights

• PL spectroscopy with NR staining is a promising approach to detect microplastics.

• Four NR-based staining protocols were compared and an optimized protocol proposed.

• The effects of different staining parameters were investigated spectroscopically.

Abstract

The significant amount of plastic litter in the form of microplastics (size <5 mm) is garnering attention owing to its potential threat to marine life. Reliable, cost- and time-efficient analysis methods for monitoring microplastic abundance globally are still missing. Several studies proposed a fast detection method by binding the solvatochromic dye Nile Red on the surface of microplastics and using fluorescence microscopy for their detection. All the staining approaches reported so far differ in terms of Nile Red concentration, solvents, and staining procedure. Here, we compare the staining protocols published prior to 2019 and propose an optimized staining protocol. Furthermore, we explore the potential of Nile Red staining in combination with photoluminescence spectroscopy to identify the polymer type and to distinguish plastics from non-plastics.

Introduction

According to Lebreton and Andrady (2019) as of 2015, between 60 and 99 million metric tons (Mt) of mismanaged plastics waste were produced globally, and it is estimated by 2025 these numbers might hike up to 230 Mt. It was estimated in 2010 that 4 to 12 Mt. of this non-degradable plastic debris enter the marine environment (Jambeck et al., 2015). Many studies have already addressed the presence and impact of large plastic debris (Koelmans, 2015; Jambeck et al., 2015; Li et al., 2016): Marine litter is reported to impact 663 species (Secretariat of the Convention on Biological Diversity and the Scientific and Technical Advisory Panel—GEF, 2012), especially by ingestion and entanglement. Although the presence of small plastic fragments was first reported in the 1970s (Carpenter and Smith, 1972), microplastic research only gained momentum when extensive amounts of microplastics were found in sediment and plankton samples from the North-East Atlantic in 2004 (Thompson et al., 2004)

Microplastics can be defined as synthetic organic polymers in the size range of 0.001 mm–5 mm (GESAMP, 2015). Microplastics are classified into two types based on their origin: (a) primary microplastics – plastic particles intentionally engineered to this size class (e.g. microbeads, pellets), (b) secondary microplastics – gradually degraded plastic particles originating due to fragmentation or weathering of macroplastics.

The problem is that even small aquatic organisms like zooplankton could ingest small microplastic particles (Besseling et al., 2017; Koelmans, 2015; Wagner et al., 2018). Apart from physical hazards on aquatic animals, plastics as persistent, non-biodegradable material may have direct effects on the food web as transport vectors for persistent organic pollutants (Besseling et al., 2017; Koelmans et al., 2016; Moore, 2008; Rios et al., 2007; Toussaint et al., 2019; Verschoor et al., 2016). Besides understanding how microplastics can affect different living beings, monitoring microplastic abundance is needed, in order to assess its impact on the environment. Furthermore, efficient and reliable detection allows for the determination of high concentration hotspots.

Detection of microplastics was often performed by visual inspection using a microscope, which was shown to be error-prone and can lead to overestimation or underestimation (Lenz et al., 2015). For material identification, spectroscopic methods such as Raman and FTIR are commonly applied (Dümichen et al., 2017; Hanvey et al., 2017; Kniggendorf et al., 2019; Wander et al., 2020; Wright et al., 2019). However, these spectroscopic methods are expensive and require laborious sample purification, which led to a parallel interest in alternative detection methods such as cytometry (Sgier et al., 2016), thermal degradation (Dümichen et al., 2017) and fluorescence microscopy (Maes et al., 2017). Among these methods, fluorescence microscopy has the potential for fast detection as it involves a simple step of fluorescence labeling with a solvatochromic dye. In 2010, the use of a lipophilic dye to stain microplastics was suggested for the first time (Andrady, 2010). Followed by this, several different Nile Red-based staining protocols for the fluorescence labeling of microplastics were reported so far (Cole, 2016; Erni-Cassola et al., 2017; Hengstmann and Fischer, 2019; Lv et al., 2019; Maes et al., 2017; Prata et al., 2020, Prata et al., 2019; Shim et al., 2016; Stanton et al., 2019; Tamminga et al., 2017).

Nile Red is a benzophenoxazine dye extensively used in biological imaging (Martinez and Henary, 2016). It is known to have a high quantum yield in non-polar media and a wide solvatochromic range due to both its high chromophore sensitivity to solvent polarity and its dielectric constant (Martinez and Henary, 2016). This feature of Nile Red as a probe to distinguish plastic samples based on their polarity has been explored by the existing protocols using fluorescence microscopy (Cole, 2016; Maes et al., 2017). In these published protocols the micrographs of fluorescing dyed virgin microplastics were sufficient to successfully categorize the plastics based on their polarity (polar/less polar) (Cole, 2016; Maes et al., 2017). Yet, the exact information of the material type could not be determined.

Previous studies considered only the color of the emission in the fluorescence images. In this work, we additionally investigate the spectral emission by using photoluminescence (PL) spectroscopy. By studying the effect of staining parameters (dye concentration, type of solvent, staining temperature, and staining time) on the PL, we developed an optimized staining protocol. To pinpoint interprotocol differences, we compared our protocol with the existing protocols published prior to 2019. Finally, we investigated whether our optimized protocol can differentiate between (i) plastic types and (ii) plastics and natural materials.