It has been estimated that the ongoing COVID‐19 pandemic has exceedingly in‐ creased the demand for the use of plastics . As of 23 August 2021, more than 8.4 million tons of pandemic‐associated plastic debris were released into the oceans . Among them, most of the plastic debris was microplastics with a size smaller than 5 mm [3,4], and this can be classified into primary and secondary microplastics. Generally, primary microplas‐ tics are the microscopic plastics that were intentionally made small (e.g., microbeads used in cosmetics) [5–7], while secondary microplastics are particles resulting from the break‐ down of macroscopic pieces due to the conjoint environmental effects (i.e., photo‐oxida‐ tion, hydrolysis, microorganism degradation, mechanical shear, etc.) [8,9]. Despite the fact that a variety of plastic types have been identified in microplastics, most of the microplas‐ tics in seawater originate from packaging materials (e.g., polyethylene, and polypropyl‐ ene) . Owing to their small density, these microplastics tend to float on the surface of the water, thus they can spread worldwide (in opposition to denser plastics that tend to settle down) and are more difficult to remove [11,12].
Nevertheless, the current understanding of plastic pollution in terms of the quantity, type, lifetime, and associated health effects largely remains unknown. As a result, micro‐ plastic separation and identification serve as an important approach to providing evi‐ dence and metrics of the pressing environmental issues caused by plastic pollution . For example, worldwide microplastic assessment is possible to identify hot pollution spots and determine the historical trends which may lead to novel strategies for fighting debris spread [14–16]. At present, quite a few identification techniques have been ex‐ plored for microplastic identification. Among them, common methods include visual inspection, Fourier‐transform infrared spectroscopy (FTIR), Raman spectroscopy and scanning electron microscopy (SEM) [17,18]. Despite their effectiveness, these techniques, except visual inspection, rely on expensive apparatus and time‐consuming detection methods that are limited to trained personnel, thus hindering the expansion of these meth‐ ods for high‐throughput detection [19–21]. As a result, visual inspection, though not as effective as other sophisticated counter‐ parts, is still widely applied for faster recognition [22,23]. Currently, a variety of sampling and identification technologies have been used to improve the performance of visual in‐ spection, of which a commonly used one is the combination of filtration and staining [24,25]. However, filtration often leads to false positives due to potential interference from organic matters in the samples [26,27]. More importantly, its performance is highly reliant on the size of the filters, thus limiting its capabilities in sampling small microplastics. In addition, particles are also prone to adhere to the filters, resulting in ineffective separation of the microplastics for identification [28,29]. In addition, staining of the microplastics re‐ lies on staining agents that turn microplastics into prominently visible particles [30,31]. Currently, it is unsurprising that quite a few staining agents (e.g., Rhodamine B, Rose Bengal, Trypan Blue, etc.) have been explored for this purpose. Among them, Nile Red is a hydrophobic fluorophore and was reported to be one of the most effective agents due to its favorable binding performance with lipophilic substances [30,32,33]. Nevertheless, the focus of current studies on microplastic staining and identification has been largely given to batch‐by‐batch or case‐by‐case analysis [25,34–36]. Therefore, sample collection is still inevitable and remains a time‐consuming step in the whole sam‐ pling process. Moreover, temporal information is hardly achievable. Given the need of acquiring in‐depth studies of microplastic pollution in oceans and other water bodies, long‐term monitoring or continuous monitoring is essential and a low‐cost, simple and effective method should be developed. Thanks to the burgeoning developments of micro‐ fluidic technologies overthe past decade, microfluidic devices can be a promising solution to address this need due to their powerful particle control capabilities and the ease of integration in modern electronic systems [37,38]. For example, microfluidics has been used for long‐term monitoring of algae in the past . Tumor responses to hypoxia con‐ ditions were also analyzed continuously in a microfluidic platform . Indeed, micro‐ plastic identification is also not a new research area for microfluidics [41,42], yet to the best knowledge of the authors, microfluidics has not been applied for long‐term micro‐ plastics assessment and we believe the combination of low‐cost Nile Red staining and microfluidic fluid control would provide a novel venue to confront the ever‐deteriorating plastic issues without complicated analysis and costly instrumentations. Low‐cost fabri‐ cation methods such as 3D printing and molding can be applied to further minimize the cost associated with this method, and the miniaturized devices may also be integrated into the monitoring stations near seashores, along with data collection of other water qual‐ ity metrics on a continuous basis.
Herein, we further explored the staining capabilities of Nile Red through a microflu‐ idic device capable of continuously staining microplastics for rapid identification. The proposed device has two inlets for respective Nile Red and sample injection (Figure 1A), and a serpentine channel that allows for sufficient mixing of the staining agent and the sample. In this paper, we studied the effects of dominating parameters on the identifica‐ tion performance, including Nile Red concentration, temperature, and residency time. Note that prior to performing microfluidic studies, static studies that resemble traditional Nile Red staining processes were adopted and served as a baseline for comparison.
Figure 1. Schematic illustration of the staining processes studied in this paper. (A) Process of micro‐ fluidics‐based continuous staining of microplastics using Nile Red. (B) Process of static staining of microplastics. Compared to microfluidic staining, the static process is laborious as it requires mul‐ tiple batches and manual operation.
2. Materials and Methods In this paper, the process of static microplastic identification using Nile Red was car‐ ried out without a filter. Specifically, the staining Nile Red solution was added directly into an Eppendorf tube containing the microplastics sample and placed inside an oven (Figure 1B). On the other hand, microfluidic experiments followed a similar procedure: mixing Nile Red and microplastics in the device (which was placed inside an oven). Since the mixing process is passively induced without human operation, this process holds promise for continuous staining.
2.1. Nile Red Preparation The staining solution was prepared by dissolving Nile Red (technical grade, N3013, Sigma‐Aldrich, St. Louis, MO, USA) in methanol to different concentrations. We have con‐ sidered the limit of solubility of Nile Red in methanol (1 mg/mL) to be the stock solution for further dilution, from which the solution was diluted into 50X, 100X, 250X, 500X, and 1000X samples.
2.2. Microplastics Sample Preparation In this paper, lab‐prepared and commercially available microplastics were adopted in lieu of naturally formed microplastics. More specifically, microspheres made of poly‐ ethylene (PE), ranging from 10–45 μm (Cospheric, Inc., Santa Barbara, CA, USA) were applied to determine the optimal parameters for staining. Other plastics including the mi‐ crospheres made of polystyrene (PS) with sizes from 9.5–11.5 μm (Cospheric, Inc., Santa Barbara, CA, USA), cotton and acrylic fabric acquired from clothing, polypropylene (PP) and non‐spherical PE prepared from plastic storage containers were also applied to test the versability of the proposed method. All the samples were mixed with deionized (DI) water prior to staining. Note that commercial microspheres were diluted in a concentra‐ tion of 10 mg/mL, while the other samples were diluted to 1 mg/mL, which is because the commercial particles were more available than the ones obtained from other sources.
2.3. Static Experiments To prepare the samples for static experiments, 100 μL of Nile Red solution was thor‐ oughly mixed with 100 μL of PE microplastic solution inside an Eppendorf tube, followed by baking inside an oven (Quincy Lab, model 10, Burr Ridge, IL, USA). On the other hand, all static experiments were performed using PE microspheres. To investigate the effect of Nile Red concentration on staining performance, different concentrations were tested: 100X, 250X, 500X, and 1000X; in addition, different temperatures (i.e., 25, 40, 50, 60, 70, and 80 °C) were applied to study the effects of temperature. All the samples were placed inside the oven for 10 min, and analysis was conducted immediately after baking.
2.4. Microfluidic Experiments To create the microfluidic devices, soft lithography, a commonly used method in mi‐ crofluidics, was applied. Specifically, a 3D printer(CADWorks 3D, μMicrofluidics edition, Toronto, ON, Canada) was used to create the molds for casting polydimethylsiloxane (PDMS) to obtain the final devices. After curing the PDMS mixture in an oven overnight at 65 °C, a corona treater (BD‐20AC Laboratory Corona Treater, Electro‐Technic Products, Chicago, IL, USA) was used to permanently bond the device onto a glass slide. Finally, the device was placed inside the oven and a syringe pump (Fusion 200, Chemyx Inc., Staf‐ ford, TX, USA) was used to run the samples as well as the staining agents inside the de‐ vice.
2.5. Sample Observation For both static and microfluidic staining, an inverted microscope (Zeiss Axio Vert.A1) was used. To visualize the fluorescent signal from the samples, an illumination system (X‐Cite mini+, Excelitas, Waltham, MA) with a wavelength of 365 nm was used. All the images were recorded using a camera attached to the microscope (VEO E310L, Phantom, Wayne, NJ, USA). ImageJ (https://imagej.nih.gov/ij/, accessed on 10 November 2021) was used to analyze and quantify the results. Each experiment was performed four times for statistical analysis. We have not filtered the particles prior to observation, in‐ stead, we have directly placed a droplet of the diluted sample on top of a glass slide.
3.1. Static Results
It is worth mentioning that high concentration Nile Red can lead to undesired aggre‐ gation [43,44], which may clog microfluidic channels and mask signals from stained mi‐ croplastics. Moreover, the aggregation may destroy the samples into unrealistic micro‐ plastics (once aggregated the original size and shape are lost) and induce misleading con‐ clusions [45,46]. We have observed that aggregations occurred for Nile Red solutions di‐ luted up to 50X. Therefore, Nile Red solutions diluted to a minimum of 100X were used in our experiments to guarantee that no induced aggregations would happen. Figure 2 illustrates how aggregation occurs over time. Specifically, 50X Nile Red solution was placed onto a glass slide containing PE microspheres, and the aggregation process was recorded at 3000 fps. Figure 2A shows the initial frame (0.0003 s), it is possible to observe that particles are separated. The other images show the subsequent frames (from 0.0006 s to 0.0013 s), where the aggregation is shown. In this image, it is possible to see how fast aggregations are induced in microplastics due to the excess of Nile Red. Furthermore, the original features of the particles are lost, if someone were to study the size distribution or the shape of this sample, the outcome would certainly not be accurate due to the aggrega‐ tion.