Flow-Through Quantification of Microplastics Using Impedance Spectroscopy

Potential health, environmental implication of microplastics: A review on its detection

Microplastic contamination of terrestrial and aquatic environment has gained immense research attention due to their potential ecotoxicity and biomagnification property when enterer into food chain. Heterogenous nature of microplastics coupled with their ability to combine with other emerging pollutants have increased the severity of this crisis. Existing detection methods often fails to accurately quantify the amount of microplastic components present in environmental and biological samples. Thus, a great deal of research gap always exists in our current understanding about microplastics including the limitations in screening, detection and mitigation. This review work presents a comprehensive out look on the impact of microplastics on both terrestrial and aquatic environment. Furthermore, an in-depth discussion on various microplastic detection techniques recently used for microplastic quantification along with their significance and limitations is summarised in this review. The review also elaborates various physical, chemical and biological methods used for the mitigation of microplastics from environmental samples.

Tailoring porous NiMoO4 nanotube via MoO3 nanorod precursor for environmental monitoring: Electrochemical detection of micro-sized polyvinylchloride

Globally, the hidden contaminants like microplastics (MPs) combined with other harmful substances have agglomerated in rivers and oceans that pose a threat to human health. Thus, evaluating the toxicity of MPs separately and in combination with other pollutants must be done quickly and precisely. This work reports the synthesis of porous NiMoO4 nanotubes (NTs) from the transformation of MoO3 nanorods (NRs) via two steps hydrothermal methods for the effective detection of polyvinyl chloride (PVC) MPs. Transformation of MoO3 NRs to porous NiMoO4 NTs was comprehensively deduced by evaluating the crystalline, structural, compositional and morphological properties. The hydrophobic nature of MoO3 NRs and porous NiMoO4 NTs was proven experimentally and also by DFT calculations. The electrochemical detection of PVC MPs by NiMoO4 NTs was investigated by the CV and EIS measurements. Porous NiMoO4 NTs based electrode expressed the good detection towards PVC MPs with a reasonable sensitivity of ∼1.43 × 10-4 μA/ppm.cm2, a low LOD of ∼18 ppm and R2 = ∼0.9781. EIS results revealed that porous NiMoO4 NTs electrode enabled to deliver sensing response at very low concentration of PVC MPs. Due to their easy interaction with hydrophobic PVC MPs, the hydrophobic NiMoO4 NTs controlled the sensing nature of the material and improved the electrochemical detection at the MP-NiMiO4 NTs interface.

Novel unlabeled electrochemical sensing platform based on highly electroactive Cu-MOF film for nanoplastic detection in water

An unlabeled electrochemical sensing strategy based on electroactive copper-centered metal-organic framework (Cu-MOF) film coupled with multiwalled carbon nanotubes (MWCNTs) was proposed for the rapid assessment of nanoplastic concentration. The sensing interface was fabricated via the electro-deposition of Cu-MOF on the pre-modified MWCNTs using the cathodic reduction method. The exposed copper active sites in Cu-MOF showed excellent electrochemical activity, which was further enhanced due to rapid electron transfer induced by highly conductive MWCNTs. Through the adsorption functionality of Cu-MOF film towards polystyrene (PS) nanoplastics, the rapid recognition for nanoplastics in aqueous solution was achieved, thereby causing the inhibition of the current response. The results showed a robust dependence of the inhibition rate on the PS mass concentration. The proposed detection method was used for the quantitative determination of PS nanoplastics with the sizes of 100 nm, 500 nm, and 1 μm. The applicability of this electrochemical sensing platform was successfully validated in real-world water sample analysis.

Label-free impedimetric analysis of microplastics dispersed in aqueous media polluted by Pb2+ ions

The rapid differentiation between polluted and unpolluted microplastics (MPs) is critical for tracking their presence in the environment and underpinning their potential risks to humans. However, the quantitative analysis of polluted microplastics on the field is limited by the lack of rapid methods that do not need optical analysis nor their capture onto sophisticated electrochemical sensor platforms. Herein, a simple analytical approach for MPs dispersed in aqueous media leveraging electrochemical impedance spectroscopy (EIS) analysis on screen-printed sensors is presented. This method is demonstrated by the EIS-based analysis of two standards of microplastics beads (MPs), one of polystyrene (PS) and one of polystyrene carboxylated (PS-COOH), when exposed to aqueous solutions containing Pb2+ ions. The adsorption of Pb2+ ions on the MPs was quantitatively determined by voltammetric analysis. EIS permitted to rapidly (about 2 minutes) differentiate clean MPs from the Pb2+ polluted ones. These results could constitute a first-step towards the realization of a portable impedimetric sensor for the quantification of microplastics polluted by metal ions in aqueous solutions.

Mechanically Flexible, Large-Area Fabrication of Three-Dimensional Dendritic Au Films for Reproducible Surface-Enhanced Raman Scattering Detection of Nanoplastics

The escalating crisis of nanoplastic pollution in water and food products demands the development of novel methodologies for detection and recycling. Despite various techniques available, surface-enhanced Raman scattering (SERS) is emerging as a highly efficient technique for the trace detection of micro/nanoplastics. However, the development of highly reproducible and stable, flexible SERS substrates that can be used for sensitive detection in environmental medium remains a challenge. Here, we report a fabrication of large-area, three-dimensional (3D), and highly flexible SERS substrate based on porous dendritic Au films onto a flexible indium tin oxide (ITO) substrate via facile, thermal evaporation of Au over the vacuum-compatible deep eutectic solvent (DES)-coated glass substrate and subsequent direct transfer process. The as-fabricated 3D dendritic Au/ITO flexible substrates can be used for ultrasensitive SERS detection of crystal violet (CV) as probe analyte molecules with the limit of detection (LOD) as low as 6.4 × 10-15 M, with good signal reproducibility (RSD of 11.3%). In addition, the substrate showed excellent sensitivity in detecting nanoplastics such as poly(ethylene terephthalate) (200 nm) and polystyrene (100 nm) with LODs reaching up to 0.051 and 8.2 μg/mL, respectively. This work provides a facile approach for the preparation of highly flexible plasmonic substrates, showing great potential for the SERS detection of a variety of environmental pollutants.

All black: a microplastic extraction combined with colour-based analysis allows identification and characterisation of tire wear particles (TWP) in soils

While tire wear particles (TWP) have been estimated to represent more than 90% of the total microplastic (MP) emitted in European countries and may have environmental health effects, only few data about TWP concentrations and characteristics are available today. The lack of data stems from the fact that no standardized, cost efficient or accessible extraction and identification method is available yet. We present a method allowing the extraction of TWP from soil, performing analysis with a conventional optical microscope and a machine learning approach to identify TWP in soil based on their colour. The lowest size of TWP which could be measured reliably with an acceptable recovery using our experimental set-up was 35 µm. Further improvements would be possible given more advanced technical infrastructure (higher optical magnification and image quality). Our method showed a mean recovery of 85% in the 35-2000 µm particle size range and no blank contamination. We tested for possible interference from charcoal (as another black soil component with similar properties) in the soils and found a reduction of the interference from charcoal by 92% during extraction. We applied our method to a highway adjacent soil at 1 m, 2 m, 5 m, and 10 m and detected TWP in all samples with a tendency to higher concentrations at 1 m and 2 m from the road compared to 10 m from the road. The observed TWP concentrations were in the same order of magnitude as what was previously reported in literature in highway adjacent soils. These results demonstrate the potential of the method to provide quantitative data on the occurrence and characteristics of TWP in the environment. The method can be easily implemented in many labs, and help to address our knowledge gap regarding TWP concentrations in soils.

Supplementary Information

The online version contains supplementary material available at 10.1186/s43591-024-00102-9.

Discrimination of Microplastics and Phytoplankton Using Impedance Cytometry

Both microplastics and phytoplankton are found together in the ocean as suspended microparticles. There is a need for deployable technologies that can identify, size, and count these particles at high throughput to monitor plankton community structure and microplastic pollution levels. In situ analysis is particularly desirable as it avoids the problems associated with sample storage, processing, and degradation. Current technologies for phytoplankton and microplastic analysis are limited in their capability by specificity, throughput, or lack of deployability. Little attention has been paid to the smallest size fraction of microplastics and phytoplankton below 10 μm in diameter, which are in high abundance. Impedance cytometry is a technique that uses microfluidic chips with integrated microelectrodes to measure the electrical impedance of individual particles. Here, we present an impedance cytometer that can discriminate and count microplastics sampled directly from a mixture of phytoplankton in a seawater-like medium in the 1.5-10 μm size range. A simple machine learning algorithm was used to classify microplastic particles based on dual-frequency impedance measurements of particle size (at 1 MHz) and cell internal electrical composition (at 500 MHz). The technique shows promise for marine deployment, as the chip is sensitive, rugged, and mass producible.

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.

Ionic Liquid-Assisted Thermal Evaporation of Bimetallic Ag-Au Nanoparticle Films as a Highly Reproducible SERS Substrate for Sensitive Nanoplastic Detection in Complex Environments

Nanoplastic particles are emerging as an important class of environmental pollutants in the atmosphere that have adverse effects on our ecosystems and human health. While many methods have been developed to quantitatively detect nanoplastics; however, sensitive detection at low concentrations in a complex environment remains elusive. Herein, we demonstrate a greener method to fabricate a surface-enhanced Raman spectroscopy (SERS) substrate consisting of self-assembled plasmonic Ag-Au bimetallic nanoparticle (NP) films for quantitative SERS detection of nanoplastics in complex media. The self-assembly of Ag-Au bimetallic NPs was achieved through thermal evaporation onto a vapor-phase compatible ionic liquid based on deep eutectic solvent over the growth substrate. The finite-difference time-domain simulation revealed that the localized field enhancement is strong in the gaps, which generate uniform SERS "hotspots" in the obtained substrate. Benefiting from highly accessible SERS "hotspots" at the gaps, the SERS substrate exhibits excellent sensitivity for detecting crystal violet with a limit of detection (LOD) as low as 10-14 M and excellent reproducibility (RSD of 5.8%). The SERS substrate is capable of detecting PET nanoplastics with LOD as low as 1 μg/mL and about 100 μg/mL in real samples such as tap water, lake water, diluted milk, and wine. Moreover, we also validated the feasibility of the designed SERS substrate for the practical detection of PET nanoplastics collected from commercial drinking water bottles, and it showed great potential applications for sensitive detection in actual environments.

Analysis of micro(nano)plastics based on automated data interpretation and modeling: A review

The widespread presence of micro(nano)plastics (MNPs) in the environment threatens ecosystem integrity, and thus, it is necessary to determine and assess the occurrence, characteristics, and transport of MNPs between ecological components. However, most analytical approaches are cost- and time-inefficient in providing quantitative information with sufficient detail, and interpreting results can be difficult. Alternative analyses integrating novel measurements by imaging or proximal sensing with signal processing and machine learning may supplement these approaches. In this review, we examined published research on methods used for the automated data interpretation of MNPs found in the environment or those artificially prepared by fragmenting bulk plastics. We critically reviewed the primary areas of the integrated analytical process, which include sampling, data acquisition, processing, and modeling, applied in identifying, classifying, and quantifying MNPs in soil, sediment, water, and biological samples. We also provide a comprehensive discussion regarding model uncertainties related to estimating MNPs in the environment. In the future, the development of routinely applicable and efficient methods is expected to significantly contribute to the successful establishment of automated MNP monitoring systems.

Rapid sand filtration for <10 μm-sized microplastic removal in tap water treatment: Efficiency and adsorption mechanisms

The omnipresence of microplastics (MPs) in potable water has become a major concern due to their potential disruptive effect on human health. Therefore, the effective removal of MPs in drinking water is essential for life preservation. In this study, tap water containing microplastic <10 μm in size was treated using constructed pilot-scale rapid sand filtration (RSF) system to investigate the removal efficiency and the mechanisms involved. The results show that the RSF provides significant capacity for the removal and immobilization of MPs < 10 μm diameter (achieving 98 %). Results showed that silicate sand reacted with MPs through a cooperative assembly process, which mainly involved interception, trapping, entanglement, and adsorption. The MPs were quantified by Flow cytometry instrument. A kinetics study underlined the pivotal role of physio-chemisorption in the removal process. MP particles smaller than absorbents, saturation of adsorbents, and reactor hydrodynamics were identified as limiting factors, which were alleviated by backwashing. Backwashing promoted the desorption of up to 97 % MPs, conducive for adsorbent active site regeneration. These findings revealed the critical role of RSF and the importance of backwashing in removing MPs. Understanding the mechanisms involved in removing microplastics from drinking water is crucial in developing more efficient strategies to eliminate them.

Underappreciated microplastic galaxy biases the filter-based quantification

Long-term environmental loading of microplastics (MPs) causes alarming exposure risks for a variety of species worldwide, considered a planetary threat to the well-being of ecosystems. Robust quantitative estimates of MP extents and featured diversity are the basis for comprehending their environmental implications precisely, and of these methods, membrane-based characterizations predominate with respect to MP inspections. However, though crucial to filter-based MP quantification, aggregation statuses of retained MPs on these substrates remain poorly understood, leaving us a "blind box" that exaggerates uncertainty in quantitive strategies of preselected areas without knowing overview loading structure. To clarify this uncertainty and estimate their impacts on MP counting, using MP imaging data assembled from peer-reviewed studies through a systematic review, here we analyze the particle-specific profiles of MPs retained on various substrates according to their centre of mass with a fast-random forests algorithm. We visualize the formation of distinct galaxy-like MP aggregation-similar to the solar system and Milky Way System comprised of countless stars-across the pristine and environmental samples by leveraging two spatial parameters developed in this study. This unique pattern greatly challenges the homogeneously or randomly distributed MP presumption adopted extensively for simplified membrane-based quantification purposes and selective ROI (region of interest) estimates for smaller-sized plastics down to the nano-range, as well as the compatibility theory using pristine MPs as the standard to quantify the presence of environmental MPs. Furthermore, our evaluation with exemplified numeration cases confirms these location-specific and area-dependent biases in many imaging analyses of a selective filter area, ascribed to the minimum possibility of reaching an ideal turnover point for the selective quantitive strategies. Consequently, disproportionate MP schemes on loading substrates yield great uncertainty in their quantification processing, highlighting the prompt need to include pattern-resolved calibration prior to quantification. Our findings substantially advance our understanding of the structure, behavior, and formation of these MP aggregating statuses on filtering substrates, addressing a fundamental question puzzling scientists as to why reproducible MP quantification is barely achievable even for subsamples. This study inspires the following studies to reconsider the impacts of aggregating patterns on the effective counting protocols and target-specific removal of retained MP aggregates through membrane separation techniques.

Current perspectives, challenges, and future directions in the electrochemical detection of microplastics

Microplastics (5 μm) are a developing threat that contaminate every environmental compartment. The detection of these contaminants is undoubtedly an important topic of study because of their high potential to cause harm to ecosystems. For many years, scientists have been assiduously striving to surmount the obstacle of detection restrictions and minimize the likelihood of receiving results that are either false positives or false negatives. This study covers the current state of electrochemical sensing technology as well as its application as a low-cost analytical platform for the detection and characterization of novel contaminants. Examples of detection mechanisms, electrode modification procedures, device configuration, and performance are given to show how successful these approaches are for monitoring microplastics in the environment. Additionally included are the recent developments in nanoimpact techniques. Compared to electrochemical methods for microplastic remediation, the use of electrochemical sensors for microplastic detection has received very little attention. With an overview of microplastic electrochemical sensors, this review emphasizes the promise of existing electrochemical remediation platforms toward sensor design and development. In order to enhance the monitoring of these substances, a critical assessment of the requirements for future research, challenges associated with detection, and opportunities is provided. In addition to-or instead of-the now-in-use laboratory-based analytical equipment, these technologies can be utilized to support extensive research and manage issues pertaining to microplastics in the environment and other matrices.

When microplastics meet electroanalysis: future analytical trends for an emerging threat

Microplastics are a major modern challenge that must be addressed to protect the environment, particularly the marine environment. Microplastics, defined as particles ≤5 mm, are ubiquitous in the environment. Their small size for a relatively large surface area, high persistence and easy distribution in water, soil and air require the development of new analytical methods to monitor their presence. At present, the availability of analytical techniques that are easy to use, automated, inexpensive and based on new approaches to improve detection remains an open challenge. This review aims to outline the evolution and novelties of classical and advanced methods, in particular the recently reported electroanalytical detectors, methods and devices. Among all the studies reviewed here, we highlight the great advantages of electroanalytical tools over spectroscopic and thermal analysis, especially for the rapid and accurate detection of microplastics in the sub-micron range. Finally, the challenges faced in the development of automated analytical methods are discussed, highlighting recent trends in artificial intelligence (AI) in microplastics analysis.

Permittivity-Based Microparticle Classification by the Integration of Impedance Cytometry and Microwave Resonators

Permittivity of microscopic particles can be used as a classification parameter for applications in materials and environmental sciences. However, directly measuring the permittivity of individual microparticles has proven to be challenging due to the convoluting effect of particle size on capacitive signals. To overcome this challenge, a sensing platform is built to independently obtain both the geometric and electric size of a particle, by combining impedance cytometry and microwave resonant sensing in a microfluidic chip. This way the microwave signal, which contains both permittivity and size effects, can be normalized by the size information provided by impedance cytometry to yield an intensive parameter that depends only on permittivity. The technique allows to differentiate between polystyrene and soda lime glass microparticles-below 22 µm in diameter-with more than 94% accuracy, despite their similar sizes and electrical characteristics. Furthermore, it is shown that the same technique can be used to differentiate between normal healthy cells and fixed cells of the same geometric size. The technique offers a potential route for targeted applications such as environmental monitoring of microplastic pollution or quality control in pharmaceutical industry.

A review on constructive classification framework of research trends in analytical instrumentation for secondary micro(nano)plastics: What is new and what needs next?

Secondary micro(nano)plastics generated from the degradation of plastics pose a major threat to environmental and human health. Amid the growing research on microplastics to date, the detection of secondary micro(nano)plastics is hampered by inadequate analytical instrumentation in terms of accuracy, validation, and repeatability. Given that, the current review provides a critical evaluation of the research trends in instrumental methods developed so far for the qualitative and quantitative determination of micro(nano)plastics with an emphasis on the evolution, new trends, missing links, and future directions. We conducted a meta-analysis of the growing literature surveying over 800 journal articles published from 2004 to 2022 based on the Web of Science database. The significance of this review is associated with the proposed novel classification framework to identify three main research trends, viz. (i) preliminary investigations, (ii) current progression, and (iii) novel advances in sampling, characterization, and quantification targeting both micro- and nano-sized plastics. Field Flow Fractionation (FFF) and Hydrodynamic Chromatography (HDC) were found to be the latest techniques for sampling and extraction of microplastics. Fluorescent Molecular Rotor (FMR) and Thermal Desorption-Proton Transfer Reaction-Mass Spectrometry (TD-PTR-MS) were recognized as the modern developments in the identification and quantification of polymer units in micro(nano)plastics. Powerful imaging techniques, viz. Digital Holographic Imaging (DHI) and Fluorescence Lifetime Imaging Microscopy (FLIM) offered nanoscale analysis of the surface topography of nanoplastics. Machine learning provided fast and less labor-intensive analytical protocols for accurate classification of plastic types in environmental samples. Although the existing analytical methods are justifiable merely for microplastics, they are not fully standardized for nanoplastics. Future research needs to be more inclined towards secondary nanoplastics for their effective and selective analysis targeting a broad range of environmental and biological matrices.

A review on intelligent impedance cytometry systems: Development, applications and advances

Impedance cytometry is a well-established technique for counting and analyzing single cells, with several advantages, such as convenience, high throughput, and no labeling required. A typical experiment consists of the following steps: single-cell measurement, signal processing, data calibration, and particle subtype identification. At the beginning of this article, we compared commercial and self-developed options extensively and provided references for developing reliable detection systems, which are necessary for cell measurement. Then, a number of typical impedance metrics and their relationships to biophysical properties of cells were analyzed with respect to the impedance signal analysis. Given the rapid advances of intelligent impedance cytometry in the past decade, this article also discussed the development of representative machine learning-based approaches and systems, and their applications in data calibration and particle identification. Finally, the remaining challenges facing the field were summarized, and potential future directions for each step of impedance detection were discussed.

Liquid Crystals as Multifunctional Interfaces for Trapping and Characterizing Colloidal Microplastics

Identifying and removing microplastics (MPs) from the environment is a global challenge. This study explores how the colloidal fraction of MPs assemble into distinct 2D patterns at aqueous interfaces of liquid crystal (LC) films with the goal of developing surface-sensitive methods for identifying MPs. Polyethylene (PE) and polystyrene (PS) microparticles are measured to exhibit distinct aggregation patterns, with addition of anionic surfactant amplifying differences in PS/PE aggregation patterns: PS changes from a linear chain-like morphology to a singly dispersed state with increasing surfactant concentration whereas PE forms dense clusters at all surfactant concentrations. Statistical analysis of assembly patterns using deep learning image recognition models yields accurate classification, with feature importance analysis confirming that dense, multibranched assemblies are unique features of PE relative to PS. Microscopic characterization of LC ordering at the microparticle surfaces leads to predict LC-mediated interactions (due to elastic strain) with a dipolar symmetry, a prediction consistent with the interfacial organization of PS but not PE. Further analysis leads to conclude that PE microparticles, due to their polycrystalline nature, possess rough surfaces that lead to weak LC elastic interactions and enhanced capillary forces. Overall, the results highlight the potential utility of LC interfaces for rapid identification of colloidal MPs based on their surface properties.

Highly selective electrochemical impedance spectroscopy-based graphene electrode for rapid detection of microplastics

The widespread occurrence of microplastics (MPs) in aquatic ecosystems that caused environmental pollution has attracted worldwide attention. Herein, graphene electrode was initially derived from petroleum waste. Then the electrochemical responses of the electrode were evaluated using electrochemical impedance spectroscopy (EIS) toward polystyrene (PS) under various optimum conditions. For the quantitative measurement of PS concentration, principal component analysis (PCA) score images displayed that the data points offered the best discrimination of the classes, and singular value decomposition (SVD) showed that a good linearity was achieved between Z"u(1) and lgC_ps in the concentration range of 0.01-25 mg L^-1. Especially for PS with particle size of 1 μm, the highest correlation coefficient (R² = 0.9914) was obtained. The sensor ability to determine the polystyrene concentration in real samples was evaluated with the recovery of 98.4-113.3 % and reliable reproducibility (RSD < 9.72 %). For the quantitative measurement of the particle size of PS, SVD images exhibited that a linearity was obtained between Z'u(1)and lgD_ps in the particle size range of 0.08-20 μm. A good linearity with R² = 0.9877 was obtained when the concentration was 1 mg L^-1. The recovery was in the range of 100.8-118.0 % with the RSD < 6.38 %. Therefore, a novel method is established for the rapid detection of PS MPs.

Microfluidics as a Ray of Hope for Microplastic Pollution

Microplastic (MP) pollution is rising at an alarming rate, imposing overwhelming problems for the ecosystem. The impact of MPs on life and environmental cycles has already reached a point of no return; yet global awareness of this issue and regulations regarding MP exposure could change this situation in favor of human health. Detection and separation methods for different MPs need to be deployed to achieve the goal of reversing the effect of MPs. Microfluidics is a well-established technology that enables to manipulate samples in microliter volumes in an unprecedented manner. Owing to its low cost, ease of operation, and high efficiency, microfluidics holds immense potential to tackle unmet challenges in MP. In this review, conventional MP detection and separation technologies are comprehensively reviewed, along with state-of-the-art examples of microfluidic platforms. In addition, we herein denote an insight into future directions for microfluidics and how this technology would provide a more efficient solution to potentially eradicate MP pollution.

Customizable Machine-Learning Models for Rapid Microplastic Identification Using Raman Microscopy

Raman spectroscopy is commonly used in microplastics identification, but equipment variations yield inconsistent data structures that disrupt the development of communal analytical tools. We report a strategy to overcome the issue using a database of high-resolution, full-window Raman spectra. This approach enables customizable analytical tools to be easily created─a feature we demonstrate by creating machine-learning classification models using open-source random-forest, K-nearest neighbors, and multi-layer perceptron algorithms. These models yield >95% classification accuracy when trained on spectroscopic data with spectroscopic data downgraded to 1, 2, 4, or 8 cm^-1 spacings in Raman shift. The accuracy can be maintained even in non-ideal conditions, such as with spectroscopic sampling rates of 1 kHz and when microplastic particles are outside the focal plane of the laser. This approach enables the creation of classification models that are robust and adaptable to varied spectrometer setups and experimental needs.

Digital filtering dissemination for optimizing impedance cytometry signal quality and counting accuracy

Improving biosensor performance which utilize impedance cytometry is a highly interested research topic for many clinical and diagnostic settings. During development, a sensor's design and external factors are rigorously optimized, but improvements in signal quality and interpretation are usually still necessary to produce a sensitive and accurate product. A common solution involves digital signal processing after sample analysis, but these methods frequently fall short in providing meaningful signal outcome changes. This shortcoming may arise from a lack of investigative research into selecting and using signal processing functions, as many choices in current sensors are based on either theoretical results or estimated hypotheses. While a ubiquitous condition set is improbable across diverse impedance cytometry designs, there lies a need for a streamlined and rapid analytical method for discovering those conditions for unique sensors. Herein, we present a comprehensive dissemination of digital filtering parameters applied on experimental impedance cytometry data for determining the limits of signal processing on signal quality improvements. Various filter orders, cutoff frequencies, and filter types are applied after data collection for highest achievable noise reduction. After designing and fabricating a microfluidic impedance cytometer, 9 µm polystyrene particles were measured under flow and signal quality improved by 6.09 dB when implementing digital filtering. This approached was then translated to isolated human neutrophils, where similarly, signal quality improved by 7.50 dB compared to its unfiltered original data. By sweeping all filtering conditions and devising a system to evaluate filtering performance both by signal quality and object counting accuracy, this may serve as a framework for future systems to determine their appropriately optimized filtering configuration.

Antibody-functionalized aluminum oxide-coated particles targeting neutrophil receptors in a multifrequency microfluidic impedance cytometer

Personalized diagnostics of infectious diseases require monitoring disease progression due to their ever-changing physiological conditions and the multi-faceted organ system mechanisms involved in disease pathogenesis. In such instances, the recommended clinical strategies involve multiplexing data collection from critical biomarkers related to a patient's conditions along with longitudinal frequent patient monitoring. Numerous detection technologies exist both in research and commercial settings to monitor these conditions, however, they fail to provide biomarker multiplexing ability with design and data processing simplicity. For a recently conceived multiplexing biomarker modality, this work demonstrates the use of electrically sensitive microparticles targeting and identifying membrane receptors on leukocytes using a single detection source, with a high potential for multiplexing greater than any existing impedance-based single-detection scheme. Here, polystyrene microparticles are coated with varying thicknesses of metal oxides, which generate quantifiable impedance shifts when exposed to multifrequency electric fields depending on the metal oxide thickness. Using multifrequency impedance cytometry, these particles can be measured and differentiated rapidly across one coplanar electrode scheme. After surface-functionalizing particles with antibodies targeting CD11b and CD66b receptors, the particles are combined with isolated neutrophils to measure receptor expression. A combination of data analysis techniques including multivariate analysis, supervised machine learning, and unsupervised machine learning was able to accurately differentiate samples with up to 91% accuracy. This proof-of-concept study demonstrates the potential for these oxide-coated particles for enumerating specific leukocytes enabling multiplexing. Further, additional coating thicknesses or different metal oxide materials can enable a compendium of multiplexing targeting resource to be used to develop a high-multiplexing sensor for targeting membrane receptor expression.

Emerging electrochemical techniques for identifying and removing micro/nanoplastics in urban waters

The ubiquitous micro/nanoplastics (MPs/NPs) in urban waters are priority pollutants due to their toxic effects on living organisms. Currently, great efforts have been made to realize a plastic-free urban water system, and the identification and removal of MPs/NPs are two primary issues. Among diverse methods, emerging electrochemical techniques have gained growing interests owing to their facile implementation, high efficiency, eco-compatibility, onsite operation, etc. Herein, recent progress in the electrochemical identification and removal of MPs/NPs in urban waters are comprehensively reviewed. The electrochemical sensing of MPs/NPs and their released pollutants (e.g., bisphenol A (BPA)) has been analyzed, and the sensing principles and the featured electrochemical devices/electrodes are examined. Afterwards, recent applications of electrochemical methods (i.e., electrocoagulation, electroadsorption, electrokinetic separation and electrochemical degradation) in MPs/NPs removal are discussed in detail. The influences of critical parameters (e.g., plastics' property, current density and electrolyte) in the electrochemical identification and removal of MPs/NPs are also analyzed. Finally, the current challenges and prospects in electrochemical sensing and removal of MPs/NPs in urban waters are elaborated. This review would advance efficient electrochemical technologies for future MPs/NPs pollutions management in urban waters.

In Situ Fluorescent Illumination of Microplastics in Water Utilizing a Combination of Dye/Surfactant and Quenching Techniques

Although plastics have benefited our lives in terms of cost and convenience, the disposal of end-of-life plastics poses environmental problems, such as microplastics (MPs). Although the separation (e.g., filtration) and staining of MPs with fluorescent dye/solvent are generally accepted steps to observe MPs in an environmental matrix, in this study, an in situ selective fluorescent illumination of the MPs in water was attempted with the aid of surfactant. Nonpolar fluorescent dye in combination with surfactant affords nanometer-sized dye particles in water, which adsorb on MPs and penetrate the polymer matrix for effective staining and stable fluorescent behaviors. The effects of different staining parameters, including different dyes, surfactants, staining temperatures, staining times, dye/surfactant ratios, dye/MP ratios, and MP concentrations in aqueous solutions were investigated to better understand staining conditions. More interestingly, non-adsorbed free dye molecules in the staining solution were almost completely fluorescence-quenched by introducing the quenching agent, aniline, while the fluorescence intensity of the stained MP was maintained. By staining MPs with a dye/surfactant combination and subsequently quenching with aniline, in situ selective fluorescent illumination of the MPs in water was successfully achieved, which may eliminate the tedious separation/filtration procedure of MPs to accomplish the quick detection or monitoring of MPs.

Advanced microplastic monitoring using Raman spectroscopy with a combination of nanostructure-based substrates

Micro(nano)plastic (MNP) pollutants have not only impacted human health directly, but are also associated with numerous chemical contaminants that increase toxicity in the natural environment. Most recent research about increasing plastic pollutants in natural environments have focused on the toxic effects of MNPs in water, the atmosphere, and soil. The methodologies of MNP identification have been extensively developed for actual applications, but they still require further study, including on-site detection. This review article provides a comprehensive update on the facile detection of MNPs by Raman spectroscopy, which aims at early diagnosis of potential risks and human health impacts. In particular, Raman imaging and nanostructure-enhanced Raman scattering have emerged as effective analytical technologies for identifying MNPs in an environment. Here, the authors give an update on the latest advances in plasmonic nanostructured materials-assisted SERS substrates utilized for the detection of MNP particles present in environmental samples. Moreover, this work describes different plasmonic materials-including pure noble metal nanostructured materials and hybrid nanomaterials-that have been used to fabricate and develop SERS platforms to obtain the identifying MNP particles at low concentrations. Plasmonic nanostructure-enhanced materials consisting of pure noble metals and hybrid nanomaterials can significantly enhance the surface-enhanced Raman scattering (SERS) spectra signals of pollutant analytes due to their localized hot spots. This concise topical review also provides updates on recent developments and trends in MNP detection by means of SERS using a variety of unique materials, along with three-dimensional (3D) SERS substrates, nanopipettes, and microfluidic chips. A novel material-assisted spectral Raman technique and its effective application are also introduced for selective monitoring and trace detection of MNPs in indoor and outdoor environments.

Graphical abstract

Backscattering-Based Discrimination of Microparticles Using an Optofluidic Multiangle Scattering Chip

In this research, we designed and fabricated an optofluidic chip for the detection and differentiation of single particles via the combination of backscattered (BSC) and forward-scattered (FSC) or side-scattered (SSC) light intensity. The high sensitivity of BSC light to the refractive index of the particles enabled an effective approach for the differentiation of individual particles based on the type of material. By recording BSC as well as FSC and SSC light intensities from single particles, transiting through the illumination zone in a microfluidic channel, the size and type of material could be detected simultaneously. The analysis of model samples of polystyrene (PS), as a primary microplastic particle, and silica microspheres showed substantially higher BSC signal values of PS because of a larger refractive index compared to the silica. The scatter plots correlating contributions of BSC (FSC-BSC and SSC-BSC) allowed a clear differentiation of PS and silica particles. To demonstrate the great potential of this methodology, two "real-life" samples containing different types of particles were tested as application examples. Commercial toothpaste and peeling gel products, as primary sources of microplastics into effluents, were analyzed via the optofluidic chip and compared to results from scanning electron microscopy. The scattering analysis of the complex samples enabled the detection and simultaneous differentiation of particles such as microplastics according to their differences in the refractive index via distinctive areas of high and low BSC signal values. Hence, the contribution of BSC light measurements in multiangle scattering of single particles realized in an optofluidic chip opens the way for the discrimination of single particles in a liquid medium in manifold fields of application ranging from environmental monitoring to cosmetics.

Recent Advances in Spectroscopic Techniques for the Analysis of Microplastics in Food

Microplastic pollution has become a worldwide concern in aquatic and terrestrial environments. Microplastics could also enter the food chain, causing potential harm to human health. To facilitate the risk assessment of microplastics to humans, it is critically important to have a reliable analytical technique to detect, quantify, and identify microplastics of various materials, sizes, and shapes from environmental, agricultural, and food matrices. Spectroscopic techniques, mainly vibrational spectroscopy (Raman and infrared), are commonly used techniques for microplastic analysis. This review focuses on recent advances of these spectroscopic techniques for the analysis of microplastics in food. The fundamental, recent technical advances of the spectroscopic techniques and their advantages and limitations were summarized. The food sample pretreatment methods and recent applications for detecting and quantifying microplastics in different types of food were reviewed. In addition, the current technical challenges and future research directions were discussed. It is anticipated that the advances in instrument development and methodology innovation will enable spectroscopic techniques to solve critical analytical challenges in microplastic analysis in food, which will facilitate the reliable risk assessment.

Targeted Analysis of Microplastics Using Discrete Frequency Infrared Imaging

An analytical strategy to improve sample throughput with discrete frequency infrared image-based targeted analysis of microplastics using a laser direct infrared chemical imaging system was successfully developed and implemented. Leveraging a quantum cascade laser as a light source, the system could lock the frequency at predetermined wavelengths and use a discrete frequency infrared imaging technique to identify particles with absorption at desired wavelengths. In this way, targeted analysis can be achieved by selectively characterizing these particles. In the concept demonstration study, the targeted analysis was able to identify 87.7% of spiked polyethylene particles by scanning only 20% of the particles in the sample. The technique substantially improves sample throughput by at least a factor of 4 under conditions used. In the tests performed with real environmental samples, the targeted analysis workflow correctly identified eight types of common microplastics by only investigating around 60% of the particles and less than 30% of the sample area. Results obtained demonstrated that this scanning strategy is a game changer to enhance sample throughput in microplastic analysis. The technique has the potential of being applied to other infrared-based analytical platforms.

Deep Learning Assisted Microfluidic Impedance Flow Cytometry for Label-free Foodborne Bacteria Analysis and Classification. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021;2021:7087-7090. [PMID: 34892734 DOI: 10.1109/embc46164.2021.9630684]

According to the urgent need for rapid detection and identification of foodborne bacteria to prevent public health event, a microfluidic electrical impedance flow cytometry assisted with convolutional neural network (ConvNet) based deep learning algorithm was proposed in this study to analyze the impedance signals of bacteria. With the assistance of the deep learning algorithm, Escherichia coli (EPEC), Salmonella enteritidis (SE) and Vibrio parahaemolyticus (VP) were identified with an accuracy of 100%. The proposed impedance based analysis system can be potentially applied for pre-classification of different subtypes of bacteria in a label-free manner.Clinical Relevance-The whole platform can be miniaturized and applied for point-of-care testing (POCT) of pathogenic bacteria detection.

Microfluidic impedance cytometry for single-cell sensing: Review on electrode configurations

Single-cell analysis has gained considerable attention for disease diagnosis, drug screening, and differentiation monitoring. Compared to the well-established flow cytometry, which uses fluorescent-labeled antibodies, microfluidic impedance cytometry (MIC) offers a simple, label-free, and noninvasive method for counting, classifying, and monitoring cells. Superior features including a small footprint, low reagent consumption, and ease of use have also been reported. The MIC device detects changes in the impedance signal caused by cells passing through the sensing/electric field zone, which can extract information regarding the size, shape, and dielectric properties of these cells. According to recent studies, electrode configuration has a remarkable effect on detection accuracy, sensitivity, and throughput. With the improvement in microfabrication technology, various electrode configurations have been reported for improving detection accuracy and throughput. However, the various electrode configurations of MIC devices have not been reviewed. In this review, the theoretical background of the impedance technique for single-cell analysis is introduced. Then, two-dimensional, three-dimensional, and liquid electrode configurations are discussed separately; their sensing mechanisms, fabrication processes, advantages, disadvantages, and applications are also described in detail. Finally, the current limitations and future perspectives of these electrode configurations are summarized. The main aim of this review is to offer a guide for researchers on the ongoing advancement in electrode configuration designs.

Field-Portable Microplastic Sensing in Aqueous Environments: A Perspective on Emerging Techniques

Microplastics (MPs) have been found in aqueous environments ranging from rural ponds and lakes to the deep ocean. Despite the ubiquity of MPs, our ability to characterize MPs in the environment is limited by the lack of technologies for rapidly and accurately identifying and quantifying MPs. Although standards exist for MP sample collection and preparation, methods of MP analysis vary considerably and produce data with a broad range of data content and quality. The need for extensive analysis-specific sample preparation in current technology approaches has hindered the emergence of a single technique which can operate on aqueous samples in the field, rather than on dried laboratory preparations. In this perspective, we consider MP measurement technologies with a focus on both their eventual field-deployability and their respective data products (e.g., MP particle count, size, and/or polymer type). We present preliminary demonstrations of several prospective MP measurement techniques, with an eye towards developing a solution or solutions that can transition from the laboratory to the field. Specifically, experimental results are presented from multiple prototype systems that measure various physical properties of MPs: pyrolysis-differential mobility spectroscopy, short-wave infrared imaging, aqueous Nile Red labeling and counting, acoustophoresis, ultrasound, impedance spectroscopy, and dielectrophoresis.