面向微塑料快速检测的新型传感策略与材料设计

陈冉; 孙超; 邹卓; 郭春显; 胡芳馨

doi:10.7536/PC20260110

化学进展 ›› 2026, Vol. 38 ›› Issue (7) :1222-1245 DOI: 10.7536/PC20260110

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面向微塑料快速检测的新型传感策略与材料设计

陈冉 ¹^,^† ,
孙超 ¹^,^† ,
邹卓 ¹^,^† ,
郭春显 ¹ ,
胡芳馨 ¹^,²^,^*

作者信息 +

Novel Sensing Strategies and Material Design for Rapid Detection of Microplastics

Ran Chen ¹^,^† ,
Chao Sun ¹^,^† ,
Zhuo Zou ¹^,^† ,
Chunxian Guo ¹ ,
Fangxin Hu ¹^,²^,^*

Author information +

文章历史 +

摘要

由于来源复杂、环境持久性强以及潜在的生态和健康风险，微塑料已成为全球环境与公共卫生领域的重要关注问题。随着环境样本类型的持续多样化及监测需求的扩大，在复杂基质中实现对微塑料的快速、准确检测已成为一项重大挑战。本文系统综述了微塑料快速检测领域新兴传感策略的最新进展，并探讨了功能材料与界面设计在提升检测性能方面所发挥的关键作用。综述涵盖了光学技术、电化学方法、质谱分析以及基于其他物理领域的检测方法。通过比较不同技术的检测机制、适用颗粒尺寸范围、信息维度及适用场景，可以清晰界定它们在定性鉴定、定量分析以及微塑料结构表征中的互补作用。此外，本文总结了该领域面临的普遍挑战与研究空白，探讨了相关技术的新兴趋势，并勾勒出未来的研究方向。旨在指导将实验室研究成果转化为实际的环境监测与健康风险评估应用。

Abstract

Microplastics have become a global environmental and public health concern due to their complex sources， strong environmental persistence， and potential ecological and health risks. As the types of environmental samples continue to diversify and monitoring demands expand， achieving rapid and accurate detection of microplastics in complex matrices has emerged as a significant challenge. This paper systematically reviews recent advances in emerging sensing strategies for rapid microplastic detection， discussing the critical role of functional materials and interface design in enhancing detection performance. The review encompasses optical techniques， electrochemical methods， mass spectrometry， and detection approaches based on other physical domains. By comparing the detection mechanisms， applicable particle size ranges， information dimensions， and suitable scenarios of different technologies， their complementary roles in qualitative identification， quantitative analysis， and microplastic structural characterization can be clearly defined. Furthermore， the paper summarizes common challenges and research gaps in this field， discusses emerging trends in related technologies， and outlines future research directions. This aims to guide the translation of laboratory research outcomes into practical environmental monitoring and health risk assessment applications.

Contents

1 Introduction

2 Novel sensing strategies for rapid detection of MPs

2.1 Optical sensing strategies

2.2 Electrochemical sensing strategies

2.3 Mass spectrometry and other physical field sensing strategies

3 Challenges and future outlook

3.1 Key challenges in rapid microplastic detection

3.2 Future development trends and technological outlook

4 Conclusion

Graphical abstract

关键词

微塑料 / 快速检测 / 光学传感 / 电化学传感 / 质谱分析

Key words

microplastics / rapid detection / optical sensing / electrochemical sensing / mass spectrometry

引用本文

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陈冉, 孙超, 邹卓, 郭春显, 胡芳馨. 面向微塑料快速检测的新型传感策略与材料设计[J]. 化学进展, 2026, 38(7): 1222-1245 DOI:10.7536/PC20260110

Ran Chen, Chao Sun, Zhuo Zou, Chunxian Guo, Fangxin Hu. Novel Sensing Strategies and Material Design for Rapid Detection of Microplastics[J]. Progress in Chemistry, 2026, 38(7): 1222-1245 DOI:10.7536/PC20260110

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1 Introduction

Microplastics （MPs） are typically defined as plastic particles smaller than 5 mm. Characterized by diverse origins， a wide range of particle sizes， strong environmental persistence， and high mobility， they have become a persistent environmental and health challenge worldwide. Based on origin， MPs can be categorized into primary MPs （e.g.， cosmetic scrub particles， industrial resin pellets） and secondary MPs formed by the fragmentation and degradation of larger plastics under environmental conditions^[1]. Extensive research indicates that MPs are widely distributed across environmental media^[2]， including oceans， rivers， lakes， soil， and the atmosphere， and have even been detected in extreme ecosystems such as deep-sea sediments， groundwater， and polar ice and snow^[3-4]. Furthermore， MPs can enter the human body through drinking water， the food chain， and inhalation of air. Their presence has been detected in blood， placenta， lung tissue， breast milk， and fecal samples^[5-6]. Existing research has revealed associations with inflammatory responses， oxidative stress， immune dysregulation， reproductive toxicity， and neurodegenerative risks^[7-8]， highlighting their significant impact on ecosystem stability and public health^[9-10].

As awareness of microplastic pollution and its associated health risks continues to deepen， precise and efficient detection technologies hold significant importance for environmental monitoring， pollution control， human exposure assessment， and policy formulation^[11-12]. Faced with diverse sample types， complex environmental matrices， and rapid monitoring demands， detection methods capable of achieving high sensitivity， fast response times， in-situ analysis， and portable integration are emerging as key research and application directions^[13]. Recent rapid advancements in emerging materials science， micro/nano fabrication techniques， microfluidic platforms， electrochemical interface engineering， optical enhancement technologies， and artificial intelligence recognition algorithms have provided a rich technical foundation for developing innovative microplastic sensing systems. Driven by these advancements， a variety of innovative detection strategies have emerged， including precision analytical techniques based on optical signal enhancement^[14]， electrochemical identification methods utilizing sensitive interfaces^[15]， microfluidic platforms with automated processing capabilities^[16]， and high-resolution analytical technologies such as mass spectrometry^[17]. Concurrently， machine learning-driven spectral analysis^[18]， image recognition， and pattern recognition algorithms have further enhanced the efficiency and automation of microplastic identification in complex samples.

Based on the aforementioned research context， this review aims to systematically examine the progress and trends of emerging sensing strategies in the field of MPs detection. To ensure conceptual consistency， MPs are broadly defined to encompass both micron-sized and nanoscale plastic particles. For clarity， when discussing size-dependent properties or nanoscale-specific detection methods， particles with at least one dimension in the nanoscale range （typically < 1 μm） are explicitly termed nanoplastics （NPs）. The analytical feasibility of detection techniques is closely tied to particle size. While most methods are applicable to micrometer-scale particles （>1 μm）， reliable detection of nanoscale particles （<1 μm） remains considerably challenging due to limitations in resolution and signal sensitivity. Unless otherwise specified， the discussion in this paper primarily addresses micrometer-scale microplastics. First， it will comprehensively examine multi-dimensional technical approaches， including optical， electrochemical， mass spectrometry， and other physically driven technologies. The review will elucidate their mechanisms of action， key performance indicators， and applicable scenarios， while summarizing and comparing different detection strategies. Subsequently， aligning with current trends in rapid detection and intelligent monitoring， we discuss key bottlenecks and technical challenges faced by in-situ， rapid， and integrated microplastic detection technologies in practical applications. We also explore potential future breakthrough directions. This review aims to provide theoretical foundations and methodological insights for developing efficient， intelligent， and practically applicable rapid microplastic detection systems.

2 Novel sensing strategies for rapid Detection of MPs

2.1 Optical sensing strategies

Optical sensing technology centers on the interaction between light and matter. By capturing characteristic signals generated during processes such as light absorption， scattering， fluorescence emission， and photothermal effects， it converts the chemical composition and physical morphology information of MPs into quantifiable optical outputs （e.g.， spectra， images， or intensity changes）， thereby enabling qualitative identification and quantitative analysis of MPs. In the field of microplastic detection， optical methods have emerged as a research hotspot due to their non-destructive nature， high sensitivity， high spatial resolution， and capability for online or in-situ detection^[19]. Based on detection mechanisms and signal types， optical sensing strategies primarily include infrared spectroscopy， Raman spectroscopy， and fluorescence imaging. Specifically， infrared spectroscopy-based strategies include attenuated total reflection Fourier transform infrared spectroscopy （FTIR-ATR）^[20]， micro-Fourier transform infrared spectroscopy （μ-FTIR）^[21]， laser direct infrared imaging （LDIR）^[22]， and nano-infrared spectroscopy （Nano-IR）^[23-24]， among other emerging nanoscale infrared techniques. These methods differ in spatial resolution， throughput， and applicable particle size ranges. Raman-related methods encompass traditional Raman microscopy and its advanced variants， such as surface-enhanced Raman scattering （SERS）^[25]， coherent anti-Stokes Raman scattering （CARS）^[26]， and stimulated Raman scattering （SRS）^[27]， enabling enhanced sensitivity， high chemical specificity， and high-speed imaging. Furthermore， fluorescence-assisted sensing strategies-including quantum dot （QDs） labeling^[28]， aggregation-induced emission （AIE） probes^[29]， and stimulated emission depletion microscopy （STED）^[30] have progressively evolved， providing technical support for rapid visualization， enrichment， and semi-quantitative analysis of MPs （Fig.1）. To facilitate systematic comparison， the primary optical sensing strategies for microplastic detection-including infrared， Raman， and fluorescence-based methods-have been summarized and contrasted in Table 1.

According to Web of Science， the number of peer-reviewed research articles published on the keyword topic across all databases （accessed December 8， 2025） is shown in Fig.2. Among these， μ-FTIR and SERS have been the two most widely applied technologies in research over the past five years. The following sections will provide a systematic introduction to these two technologies.

2.1.1 μ-FTIR

μ-FTIR technology locates micrometer-scale individual particles using a microscope assembly， then illuminates the sample with infrared light along the same path. An interferometer collects interference patterns， which are subsequently converted into absorption spectra via a Fourier transform. Accurate polymer identification is achieved by comparing these spectra against standard libraries. With a spatial resolution of 10~20 μm， it offers significant advantages for detecting MPs with particle sizes ≤ 500 μm. This technique provides high chemical identification accuracy， reliably distinguishing polymers based on their unique characteristic absorption peaks. It also yields information on particle size， morphology， and chemical composition， enabling statistical characterization of MPs^[31,35]. Furthermore， as a non-destructive detection method， μ-FTIR is particularly suitable for analyzing sensitive matrices such as environmental samples， food， drinking water， or biological tissues. It performs especially well with transparent or light-colored samples that exhibit low background interference. However， this technique struggles to effectively identify nanoscale plastics or particles smaller than 10 μm. Large-area scanning can be time-consuming， taking several hours， and spectral interference is common in complex matrices like biological tissues or natural particulates^[36]. In recent years， to enhance μ-FTIR performance for high-throughput screening and complex matrix analysis， research has focused on the following three key frontiers.

2.1.1.1 Advancements in high-throughput imaging techniques

LDIR technology based on QCLs significantly reduces scanning time and enhances spectral signal-to-noise ratio through its high-intensity tunable light source， enabling automated full-film scanning of large-sized filter films^[33,37-38]. For instance， Vita et al.^[22] pioneered the application of LDIR for the systematic characterization of MPs in extra virgin olive oil （EVOO）， achieving simultaneous identification and analysis of MPs within the 975~1800 cm^-1 wavenumber range. This approach concurrently captures polymer composition， morphological features， and size distribution information.

2.1.1.2 Development of intelligent spectral analysis algorithms

Faced with massive high-dimensional spectral data generated by large-area imaging， automated recognition algorithms based on machine learning have become a research hotspot^[39-40]. These algorithms effectively eliminate background interference from complex environmental matrices， significantly improving the accuracy and efficiency of polymer classification. For instance， Smolen et al.^[41] developed a similarity-based convolutional neural network model to address challenges in μ-FTIR spectral recognition， such as strong background noise， high data requirements， and difficulty in identifying new categories. This model learns by generating spectral embedding features. Trained solely on clean data， it maintains high classification accuracy （F1-score of 0.905） even for samples with high background noise. It also demonstrates strong cross-domain adaptability and novel category discovery capabilities， enabling efficient and scalable microplastic identification.

2.1.1.3 Optimization of sample pretreatment methods

Recent studies have explored combining chemical （oxidation）， enzymatic， density separation， and flotation techniques to reliably isolate MPs from complex organic matrices while minimizing particle loss and preserving spectral integrity^[42-43]. For instance， Gouda et al.^[44] established an integrated analytical workflow combining oxidation-alkaline tandem digestion， gradient density centrifugation-µ- FTIR， enabling efficient extraction and characterization of MPs in organic fertilizer residues. This method detected MPs ranging from 10~50 µm， identified 19 polymer types， and achieved 78.3% target particle size <200 µm. Although standardization remains limited， these advancements lay the methodological foundation for scaling μ-FTIR from laboratory research to large-scale， high- throughput， reproducible screening of environmental samples.

These developments significantly enhance μ-FTIR’s analytical throughput and intelligence， marking its rapid progression toward high-throughput quantitative analysis for environmental monitoring and risk assessment.

2.1.2 SERS

SERS technology leverages the localized surface plasmon resonance （LSPR） effect generated by metallic nanostructures to amplify the Raman signals of adsorbed molecules by tens to millions of times， thereby significantly enhancing the sensitivity of microplastic detection. Since Raman spectra originate from molecular vibrational energy transitions， SERS provides highly specific fingerprint-like identification of microplastic surface chemical structures and demonstrates potential advantages in detecting particles at the micrometer or even submicrometer scale^[45]. Additionally， due to water molecules’ weak absorption of Raman scattering， SERS possesses inherent advantages for in-situ or on-site monitoring of aqueous samples. However， SERS performance is highly dependent on the uniformity and stability of the enhanced substrate， with signal reproducibility susceptible to batch variations. Furthermore， particularly in environmentally aged microplastic samples， strong fluorescent background interference may obscure target signals. Moreover， SERS primarily reflects surface molecular information and lacks sensitivity to internal composition， limiting its applicability in certain scenarios. Consequently， enhancing SERS usability in real-world environmental samples has become a research priority^[46]. Recent research has primarily focused on the following three areas.

2.1.2.1 Development of functionalized and enriched SERS substrates

Developing SERS-enhancing substrates with selective enrichment or pretreatment capabilities is a key strategy for improving detection sensitivity and reliability^[25,47]. Mechanistically， SERS signal enhancement primarily stems from the electromagnetic enhancement effect induced by LSPR， whose intensity fundamentally depends on the substrate material's structure and composition. Optimizing the geometric shape， size distribution， and composition control of plasmonic nanostructures （such as gold or silver nanoparticles） can effectively modulate local electromagnetic field intensity. Concurrently， the incorporation of flexible or porous support materials helps increase effective surface area and promote target enrichment， thereby further amplifying Raman signals. For instance， Guselnikova et al.^[48] developed a pre-treatment-free SERS sensing platform integrating a layered porous silver foam substrate with a self-attention neural network. This platform enables rapid， high-sensitivity， and high-accuracy detection of six common MPs in complex environmental samples without intricate pre-concentration or pre-treatment steps. It significantly increases microplastic detection throughput while reducing costs， offering a viable solution for field microplastic monitoring. Therefore， in SERS-based microplastic detection systems， the material engineering of the enhancing substrate not only influences the signal amplification factor but also directly determines detection sensitivity， reproducibility， and field application feasibility.

**2.1.2.2 Plasmonic nanoarrays and flexible SERS platforms for in-situ analysis**

Constructing plasmonic nanoarrays or flexible substrates to expand applicability in complex surfaces and in-situ monitoring scenarios^[49-50]； Hong et al.^[51] employed a “drip-coating-peeling” method to fabricate flexible AuNP tape-based SERS sensors. By optimizing nanoparticle concentration and drying temperature to enhance sensitivity， combined with principal component analysis， they achieved rapid in-situ identification of multiple atmospheric MPs.

2.1.2.3 Machine learning-assisted spectral processing and classification

Incorporating machine learning into spectral preprocessing and classification workflows enables automatic denoising and recognition of aged or mixed MPs in complex matrices， thereby enhancing analytical throughput and discrimination accuracy^[52-53]. For instance， Wang et al.^[54] developed the MetalAquaDect platform， integrating silver nanoparticle SERS substrates with LightGBM machine learning models， enabling high-precision real-time monitoring of low-concentration micro- and nanoplastics in complex matrices. This provides an efficient and practical technical solution for real-time environmental microplastic pollution monitoring and risk assessment.

These advancements collectively propel SERS technology toward high-throughput， high-sensitivity， and point-of-care detection capabilities， demonstrating significant application potential for real-time monitoring and risk assessment of environmental microplastic pollution. However， it should be noted that optical sensing strategies， including micro-FTIR and SERS， are inherently constrained by trade-offs between spatial resolution， detection throughput， and matrix tolerance. Spectral interference from natural particulate matter， fluorescent background noise， and the time-consuming nature of large-area scanning continue to limit their robustness in low-abundance and highly heterogeneous environmental samples. Consequently， purely optical methods may face challenges when transitioning from laboratory demonstrations to rapid， large-scale field monitoring scenarios.

In summary， most optical techniques discussed in this section are primarily applicable to micrometer-scale microplastics （>1 μm）， as their detection principles rely on diffraction-limited imaging or holistic spectral response. Reliable detection of nanoparticles （<1 μm） remains challenging without employing signal amplification or indirect labeling strategies. Optical methods have become indispensable tools for microplastic identification due to their advantages in polymer-specific chemical fingerprinting and morphological characterization. However， the practical application of these techniques in rapid field screening and in-situ monitoring is constrained by multiple factors. As systematically summarized in Table 1， various technologies offer complementary strengths. Therefore， while optical methods have established benchmarks for chemical characterization in microplastic detection， their standalone application remains limited in scenarios requiring rapid analysis of low-concentration， highly heterogeneous environmental samples. This underscores the necessity of integrating multimodal technologies. Beyond instrument and methodological limitations， environmental aging represents another significant uncertainty affecting optical detection accuracy. Existing research indicates that microplastics in marine environments undergo substantial alterations in surface functional group composition and molecular vibrational characteristics due to the combined effects of ultraviolet irradiation， mechanical abrasion， and biofouling^[55]. These changes disrupt infrared or Raman spectral fingerprinting， thereby reducing the accuracy of identification based on standard spectral library matching. Although the integration of machine learning algorithms with spectroscopic techniques has enhanced the robustness of complex sample analysis in recent years， and emerging high-resolution detection methods （such as O-PTIR） show great potential in micro-particle detection^[56]， these approaches fundamentally rely on stable， resolvable spectral signals. When aging generates new absorption peaks， broadens characteristic peaks， or weakens peak signal intensity， both the generalization capability of predictive models and the cross-environment comparability of datasets are compromised. Thus， environmental aging not only alters the physicochemical properties of microplastics themselves but also challenges the reliability， reproducibility， and standardization of optical detection signals—a challenge particularly pronounced in rapid environmental screening scenarios.

2.2 Electrochemical sensing strategies

Electrochemical sensing strategies have emerged as one of the most promising approaches for rapid， portable， on-site detection of MPs. This technology detects MPs by converting their presence， concentration， or physicochemical properties into measurable electrical signals such as current， voltage， or impedance. Compared to spectroscopic or mass spectrometry detection methods， electrochemical sensors offer significant advantages： rapid response， high sensitivity， simple instrumentation， low power consumption， and exceptional scalability for miniaturization， flexible substrate integration， and microfluidic integration. These characteristics make them particularly suitable for continuous monitoring of MPs in water bodies， food processing lines， and indoor/outdoor air^[15]. They can even be integrated into wearable platforms for human exposure assessment. The major electrochemical sensing strategies and their performance features are compared in Table 2. Furthermore， given that electrochemical sensing technology is frequently proposed as a candidate for portable， real-time， and field-deployable microplastic detection， it is particularly necessary to discuss its practical limitations and challenges.

2.2.1 Core mechanisms and strategic framework for electrochemical microplastic detection

Based on sensing principles and design strategies， electrochemical detection of plastics can be broadly categorized into four mechanisms.

2.2.1.1 Direct detection mechanisms based on intrinsic electrochemical responses and interface perturbations

This approach directly utilizes the inherent electrochemical properties of MPs or their physical disturbance of the electrode/solution interface for detection， without requiring labeling or recognition elements. However， most plastics themselves lack electrochemical activity， and their primary detection mechanisms involve perturbations in interface charge transport and changes in impedance response， etc^[57,65]. For example， Kim et al.^[63] developed hydrophobic porous NiMoO₄ nanotube electrodes that successfully detected PVC MPs via cyclic voltammetry （CV） and electrochemical impedance spectroscopy （EIS）， achieving a sensitivity of 1.43×10^-4 μA/（ppm·cm²）（1 ppm=1×10^-6） and a detection limit of 18 ppm， while demonstrating excellent stability in real water bodies. However， this method is typically sensitive to specific plastic types and has limited universality for mixed plastics （Fig.3a）.

2.2.1.2 Signal enhancement strategies driven by electrode modification and interface engineering

To improve detection performance for weakly responsive MPs， interface engineering achieves selective enrichment and signal amplification of target substances by regulating the chemical properties， morphology， and electronic structure of electrode surfaces^[66]. For instance， Kim et al.^[67] prepared biochar from calcined natural starfish （SF-1） and aloe vera （AL-1） and employed it as a sensor electrode for the electrochemical detection of 100 nm polystyrene MPs， where the AL-1 electrode demonstrated high sensitivity of 3.263 μA·L/（µmol∙cm²）， a detection limit of 0.526 nmol/L， and excellent selectivity， stability， and applicability to real samples. Its performance significantly outperformed the SF-1 electrode， providing a low-cost， environmentally friendly solution for microplastic detection （Fig.3b）.

2.2.1.3 Novel device structures and intelligent signal reading modes

To enhance portability and practicality， electrochemical research has expanded into novel device designs， such as integrating triboelectric nanogenerators （TENGs） to construct self-powered sensors and electrochemical luminescence （ECL） technology^[59]. In TENG-based sensors， polymer differentiation stems from their distinct positions within the triboelectric sequence. Different polymers exhibit varying electronic affinities and surface charge transfer tendencies during contact-separation cycles， yielding characteristic output voltage and current signals. By analyzing these electrical response patterns， TENG systems can distinguish polymers with differing triboelectric polarities. Huang et al.^[60] achieved quantitative detection by establishing a linear relationship between the voltage output from liquid-solid triboelectric nanogenerators and microplastic content （mass fraction 0.025%~0.25%）. Further integrating a convolutional neural network model enabled intelligent identification of microplastic types and concentrations， achieving an average accuracy of 86.7% （with 100% accuracy for PS identification）（Fig.3c）. This self-powered， low-power strategy offers a novel pathway for in-situ monitoring and holds promise for resolving complex signal interpretation challenges through artificial intelligence.

2.2.1.4 Specific detection mechanism driven by biological/bionic recognition elements

To enhance selectivity for specific polymers， affinity-sensing interfaces can be constructed by incorporating biological or bionic recognition elements （e.g.， aptamers， peptides）^[68]. Zandieh et al.^[69] screened DNA aptamers with high affinity and selectivity for PVC and PS using SELEX technology， achieving detection limits of 0.5~0.6 mg （Fig. 3d）. Molecular dynamics simulations revealed their binding mechanism via van der Waals forces with plastic surfaces. Such strategies provide effective tools for achieving specific recognition in complex environments.

In addition to biomolecular recognition elements such as aptamers and peptides， molecularly imprinted polymers （MIPs） are also crucial artificial recognition materials in microplastic sensing systems. MIPs form recognition cavities with complementary structures through template-guided polymerization. Their selectivity stems from precise control over the composition of functional monomers and crosslinking structures， exemplifying the typical structure-property relationship in materials. Compared to biological components， MIPs exhibit superior chemical stability and environmental tolerance， making them suitable for complex environmental samples^[70]. When integrated with electrochemical or optical platforms， MIP-based sensing interfaces enable selective enrichment and detection of specific polymers such as PET and PS^[71]. Consequently， MIP engineering provides a crucial material design pathway for constructing highly stable and specific microplastic sensing systems.

2.2.2 Key technical bottlenecks and application challenges in electrochemical MPs detection

Despite the promising prospects of electrochemical sensing， chemical inertness， compositional diversity， and environmental aging of MPs themselves present multiple challenges for their detection， ranging from fundamental methodologies to engineering applications.

2.2.2.1 The absence of standardized protocols compromises signal comparability and model universality

Currently， this field lacks unified standards for sample pretreatment， measurement parameters， signal analysis， and quantitative methods. This results in poor comparability among different research datasets， hindering the establishment of universal and reliable detection models. The dielectric constants of different polymers （e.g.， PET ≈ 3.0~3.3， PE ≈ 2.3， PS ≈ 2.6） and their vastly different electronic structures （e.g.， weak signals from saturated-chain PP/PE versus enhanced signals in benzene-ring-containing PS due to π-π interactions） result in significantly divergent response behaviors at the electrode interface. Furthermore， existing research indicates that environmental aging induces a series of changes， including surface morphology alteration， biofilm formation， chemical oxidation， and deterioration in thermodynamic and mechanical properties^[72-73]. These changes significantly impact charge transfer pathways， complicating and destabilizing signal characteristics^[74-75] （Fig.4a~c）. Consequently， without standardized methods and certified reference materials， the mutual recognition and reproducibility of cross-platform data struggle to meet engineering requirements.

2.2.2.2 Interference and misidentification caused by complex environmental matrices

Dissolved organic matter （DOM） in real samples readily adsorbs onto electrode surfaces， leading to electrode passivation and elevated background noise， which significantly reduces detection sensitivity and quantitative accuracy^[76]. Simultaneously， non-target coexisting substances （e.g.， algae， biogenic debris） cause signal crosstalk. Even with high-discrimination methods like impedance spectroscopy， leading to signal crosstalk and false-positive responses in impedance- and voltammetry-based measurements^[77] （Fig.4d~f）. These matrix interferences collectively limit sensor reliability in real-world environments， necessitating urgent solutions through anti-fouling interface design， signal separation algorithms， or multimodal fusion strategies.

2.2.2.3 Practical application orientation： insufficient sensor engineering and long-term stability

Currently， the development and validation of electrochemical MPs sensors remain largely confined to controlled laboratory settings， with performance evaluations primarily conducted in spiked samples or simplified aqueous solutions. In contrast， systematic validation under complex environmental conditions， such as high-salinity seawater or chemically variable wastewater， remains insufficient， particularly for long-term continuous operation^[78]. Recent studies integrating bioelectrochemical interfaces have further revealed that electrode surfaces readily form biofilms and undergo interfacial remodeling， leading to signal drift and compromising system stability^[79] （Fig.4g~j）. Therefore， bridging the gap between laboratory prototypes and deployable， durable sensing systems requires not only enhanced analytical performance but also breakthroughs in materials engineering， device encapsulation， and system-level integration.

2.2.2.4 Design of highly specific recognition elements and environmental adaptability challenges

Currently， most electrochemical sensors rely on non-specific physical adsorption， making it difficult to accurately distinguish between different polymers. Although research has explored strategies such as screening DNA aptamers or binding peptides to construct biometric interfaces targeting specific plastics （e.g.， PS， PP） to enhance detection specificity^[80]， significant challenges remain in advancing practical environmental applications. These challenges include the binding specificity， structural stability， and environmental tolerance of such biometric components. The chemical inertness and structural similarity of microplastics pose a unique challenge. Developing recognition elements that stably operate in complex environments and accurately distinguish multiple polymers requires far greater design complexity， screening scale， and stability than systems targeting active small molecules or biomarkers^[68] （Fig.4k）.

In summary， electrochemical sensing strategies are typically more suitable for smaller particle sizes， as signal generation relies on interfacial interactions or catalytic amplification rather than direct optical resolution. This makes them promising for scaling up to nanoscale detection， though sensitivity and selectivity remain largely dependent on the design of interfacial materials. Electrochemical sensing strategies demonstrate significant advantages in portability， response speed， low power consumption， and ease of miniaturization， positioning them as a highly promising technical approach for continuous field monitoring of MPs. However， the signal response of these methods fundamentally originates from the adsorption behavior of microplastic particles at the electrode interface or perturbations in interfacial electrochemical properties—an indirect signal transduction process that struggles to directly characterize the polymeric chemical fingerprint of MPs. These inherent limitations make it challenging for electrochemical sensors to achieve stable and reliable polymer-specific recognition when applied independently. It is noteworthy that these limitations can be broadly categorized into engineering-related constraints and inherent mechanistic boundaries. While issues such as matrix interference， signal drift， and reproducibility can be partially mitigated through advances in electrode materials and device engineering， the absence of polymer-specific electrochemical signatures fundamentally stems from the indirect nature of electrochemical signal generation. This inherent constraint indicates that electrochemical sensing alone is insufficient for reliable polymer identification， while simultaneously highlighting the necessity of multimodal detection strategies. Therefore， electrochemical strategies are better suited as rapid screening or auxiliary sensing modules within integrated detection systems rather than as standalone qualitative identification tools.

2.3 Mass spectrometry and other physical field sensing strategies

Mass spectrometry （MS）， with its high resolution， high sensitivity， and robust chemical structure analysis capabilities， has become a crucial tool for analyzing the chemical composition of MPs. Compared to optical and electrochemical methods， mass spectrometry can directly resolve polymer degradation products and molecular fragments， avoiding misinterpretations caused by spectral overlap. Furthermore， mass spectrometry is frequently employed as a reference method in the development of novel sensors， machine learning model training， and field data validation. Meanwhile， physical field-sensing technologies such as nanoparticle tracking analysis （NTA） and micro-computed tomography （Micro-CT） provide powerful tools for characterizing the size distribution， morphology， and three-dimensional structure of microplastic particles. It should be noted that the term “rapid detection” in this paper does not imply uniform analysis times across all techniques. For technologies such as pyrolysis-gas chromatography-mass spectrometry （Py-GC-MS）， recent advances in achieving rapid analysis have primarily been realized through simplified sample preparation， automated sample introduction systems， and optimized thermal programs. Compared to traditional workflows， these measures significantly reduce total turnaround time. Although their absolute analysis times may still exceed those of optical or electrochemical methods， these improvements represent meaningful acceleration within the mass spectrometry analytical framework. In contrast， physical-field-based techniques discussed in this review， such as Micro-CT， primarily serve as supplementary or validation tools rather than standalone solutions for large-scale rapid screening.

This section will systematically review the latest advances， technical features， and application prospects of mass spectrometry and other physical field techniques in microplastic detection. Representative mass spectrometry methods and physical field-based techniques are compared in Table 3.

2.3.1 Methodological advances in MS-based microplastic analysis

Unlike optical and electrochemical sensing strategies， recent advances in MS-based microplastics analysis have been largely driven by methodological optimization. Improvements in thermal programming， simplified sample pretreatment， and automated sample introduction processes have significantly enhanced analytical efficiency and reproducibility. Mass spectrometry achieves highly accurate identification and quantitative analysis of MPs’ chemical composition by ionizing samples and separating them based on mass-to-charge ratio. Within existing MPs detection methodologies， Py-GC-MS has become one of the most widely applied and highly standardized reference analytical methods due to its combination of high chemical resolution， robust quantitative capabilities， and adaptability to complex samples. Additionally， methods such as matrix-assisted laser desorption/ionization time-of-flight mass spectrometry （MALDI-TOF-MS）^[89] and liquid chromatography-mass spectrometry （LC-MS）^[90] are increasingly being employed for studying surface degradation products， low-molecular-weight oligomers， and additive migration behavior associated with MPs. However， their application in routine quantitative detection remains relatively limited. Therefore， the following discussion will focus on the mechanism of action， technical advantages and challenges， as well as recent advances in Py-GC-MS.

The core principle of Py-GC-MS lies in decomposing high-molecular-weight polymers into characteristic low-molecular-weight fragments through high-temperature pyrolysis. These pyrolysis products are subsequently separated by gas chromatography （GC） and introduced into a tandem mass spectrometer （MS） for fingerprint-based identification and quantification. The analytical accuracy of this method heavily relies on precise control of pyrolysis conditions and systematic construction of a pyrolysis product spectral library^[91]. Thanks to this mechanism， Py-GC-MS can effectively distinguish polymer types with similar molecular weights and highly overlapping optical spectra. In terms of performance advantages， Py-GC-MS not only directly distinguishes polymers with highly similar structures but also demonstrates high robustness toward environmentally aged， composite-contaminated， and multi-component coexisting microplastic systems， enabling quantitative analysis at the mass fraction level^[92]. This grants it significant advantages in studying complex matrix samples^[80]. For instance， Lou et al.^[93] established a quantitative detection method for mixed MPs based on Py-GC-MS combined with 1，2，4-trichlorobenzene （TCB） extraction. Through parameter optimization and quantitative indicators， the method achieved a relative standard deviation （RSD） close to or below 10% and with average recoveries ranging from 107.3% to 110.4%. This validated method is suitable for large-scale analysis of complex solid matrix samples， enabling reliable identification and quantitative analysis of target polymers in mineralized waste from landfills. However， Py-GC-MS still has inherent limitations that are difficult to overcome. Its pyrolysis process is irreversibly destructive， unable to preserve the original morphology， size， and spatial distribution information of microplastic particles. Additionally， the instrument’s large size， high cost， and stringent operational requirements make it impractical for direct use in field or in-situ monitoring. Therefore， within the current microplastics detection framework， Py-GC-MS is better suited as a highly definitive chemical benchmark and calibration standard for emerging rapid sensing technologies， rather than as a standalone solution for field or real-time monitoring.

To address the demand for rapid on-site screening， recent studies have systematically optimized Py-GC-MS at the methodological level. On one hand， introducing rapid heating pyrolysis programs enables pyrolysis within seconds， significantly reducing single-sample analysis time while ensuring complete pyrolysis. On the other hand， simplifying sample pretreatment procedures minimizes sample transfer and handling steps， thereby enhancing overall detection throughput. Li et al.^[94] proposed a combined pretreatment workflow integrating tetramethylammonium hydroxide （TMAH） digestion with dichloromethane （DCM） dissolution. Coupled with a 590 ℃， 18-second pyrolysis program in Py-GC-MS， this approach streamlined pretreatment steps and minimized sample handling， thereby significantly enhancing the detection throughput of MPs in solid matrices （Fig.5a）. At the application level， Py-GC-MS research has progressively shifted its focus from early polymer qualitative identification toward method standardization， high-throughput automation， and cross-technology integration^[95-96]. Simultaneously， the integration of machine learning algorithms for automated identification and classification of pyrolysis spectra has significantly enhanced the efficiency and accuracy of multi-component microplastic identification in complex environmental samples. Nijenhuis et al.^[97] focused on analyzing PE， PVC， and PET MPs in human blood. They optimized the Py-GC-MS detection workflow by simplifying pretreatment through direct membrane filtration， enabling automated sample introduction via an Auto-Shot Sampler， and implementing intelligent processing with PARADISe software for automatic feature extraction and machine learning-based feature selection. This approach enhanced detection throughput and quantitative accuracy （Fig.5b and c）. Overall， these advances are gradually expanding the application scope of the Py-GC-MS method， driving the realization of faster， higher-throughput microplastic analysis.

2.3.2 Other physical field sensing strategy

Detection technologies based on physical field principles provide powerful complementary means for characterizing the particle size distribution， number concentration， and three-dimensional structure of MPs. Depending on the nature of the applied physical field， these methods can be broadly categorized into statistical analysis techniques based on particle motion and imaging techniques based on penetration effects. By analyzing particle motion behavior， scattering characteristics， or structural responses within specific physical fields， these approaches enable quantitative characterization of MPs’ physical properties. This is crucial for comprehensively understanding MPs’ migration and transformation processes in the environment， as well as their potential ecological risks.

NTA is currently the most mature and representative technique among physical-field-based particle analysis methods， and has been widely adopted as a quantitative sensing tool for nanoscale MPs. Based on the principle of Brownian motion， NTA tracks the real-time movement trajectories of individual particles in liquid phases. By combining these with the Stokes-Einstein equation， it derives the hydrodynamic diameter of particles， thereby determining the particle size distribution and number concentration within a sample. Compared to ensemble-averaging methods like dynamic light scattering （DLS）， NTA offers single-particle resolution， demonstrating higher sensitivity and statistical reliability for polydisperse systems， as well as low-abundance nanoscale MPs^[98].

In MPs research， NTA has been widely employed to evaluate the generation behavior and particle size evolution characteristics of nanoscale MPs in environmental samples and simulated aging processes. It enables rapid statistical analysis of particle size and concentration within a short timeframe， demonstrating significant potential for rapid screening. Sujathan et al.^[87] systematically evaluated NTA’s performance in both fluorescent and non-fluorescent modes. They successfully quantified polystyrene MPs and NPs ranging from 0.1~5 μm in diameter within systems containing coexisting clay minerals such as kaolinite， montmorillonite， and bentonite， validating the method’s applicability in complex particle backgrounds （Fig.5d~f）. However， NTA results are highly dependent on particle dispersion in the liquid phase， making them sensitive to sample pretreatment and dispersion conditions. Additionally， this method cannot directly distinguish between different polymer types and has a limited ability to identify non-plastic particles^[99]. Consequently， NTA is better suited as a tool for characterizing the size and quantity of nanoscale MPs rather than as an independent means of component identification， highlighting the necessity of integrating particle analysis techniques based on physical fields with chemical identification methods. It is typically combined with spectroscopic or mass spectrometry techniques to enhance the reliability and interpretive depth of detection results. For instance， Zhang et al.^[100] used NTA to determine the particle size distribution and concentration levels of PET MPs in commercially bottled water. They further integrated SERS to identify the chemical composition， effectively addressing NTA’s inherent limitations in distinguishing polymer types.

In contrast， Micro-CT is primarily employed as a high-resolution tool for structural characterization and result verification， rather than as a traditional sensing technology for microplastic detection. Micro-CT achieves non-destructive three-dimensional imaging by leveraging differences in X-ray absorption within various components of a sample. It enables the intuitive visualization of MPs’ spatial distribution， morphological characteristics， and internal structural information within complex environments or biological systems while preserving sample integrity. This provides a crucial complement to traditional two-dimensional microscopy techniques， which struggle to capture such three-dimensional structural details. For instance， Parobková et al.^[101] studied polyethylene MPs in zebrafish using Micro-CT for non-destructive 3D imaging. They successfully identified and localized microplastic particles with diameters ≥30 μm， clearly resolving their spatial distribution and morphological features within the gastrointestinal tract （Fig.5g~i）. This demonstrated the technique’s unique advantage for spatial visualization of MPs in biological systems. However， Micro-CT’s ability to distinguish materials primarily relies on density differences， and its identification capability remains limited for polymer types with similar densities. Additionally， high equipment costs， lengthy detection cycles， and complex data processing restrict its application in rapid testing and routine monitoring. Furthermore， Micro-CT cannot directly provide chemical composition information for MPs. Therefore， in microplastic research， it is more suitable as a high-resolution structural characterization and spatial validation tool rather than an independent method for component identification or quantitative sensing.

Overall， mass spectrometry and physical field-based characterization techniques can provide highly reliable chemical and physical information that is difficult to achieve using cutting-edge sensing methods alone. Mass spectrometry offers high accuracy in analyzing and quantitatively verifying polymer composition within complex matrices， while nano-physical field techniques provide complementary information such as particle size distribution， number concentration， and three-dimensional spatial structure. However， the destructive nature， instrument complexity， high cost， and limited portability of mass spectrometry， coupled with the lack of intrinsic chemical specificity in physical field methods， hinder their direct application as rapid or in-situ sensing technologies. From a particle-size discrimination perspective， mass spectrometry-based methods are typically size-independent after pyrolysis or chemical digestion， as polymer identification relies on characteristic molecular fragments rather than the dimensions of intact particles. However， recovery efficiency and representativeness of nanoscale particles may still be influenced by sample preparation procedures. In contrast， Micro-CT is primarily limited to detecting micrometer-scale microplastics due to spatial resolution constraints， while NTA is better suited for nanoscale particles because it determines their size by tracking the Brownian motion of particles in suspension. Consequently， these approaches are better suited as reference， validation， or auxiliary tools to support and complement rapid sensing platforms.

3 Challenges and future outlook

In recent years， novel sensing strategies have advanced rapidly in the field of microplastic rapid detection， providing robust technical support for environmental monitoring and risk assessment. However， translating laboratory achievements into practical field detection still faces multiple technical and engineering challenges. Systematically addressing these key bottlenecks and identifying future breakthrough directions is crucial for advancing the practical application and large-scale deployment of microplastic rapid detection technologies.

3.1 Key Challenges in Rapid Microplastic Detection

3.1.1 Inadequate sample processing and sensor interface compatibility

The accuracy of microplastic detection heavily relies on efficient， reproducible sample pretreatment protocols and corresponding sensor interface designs. However， in real-world environmental and human-related samples， complex matrices significantly interfere with microplastic separation， enrichment， and identification processes. Sample pretreatment often becomes a critical bottleneck limiting rapid and reliable microplastic detection.

In practical applications， samples from different sources exhibit substantial variations in matrix composition and interference characteristics. For instance， in natural water bodies， MPs frequently coexist with DOM， algae， microorganisms， and inorganic suspended particles， readily triggering non-specific adsorption and elevated background signals. In soil samples， abundant mineral particles， humic substances， and agglomerated structures substantially increase microplastic separation difficulty and compromise accurate characterization of particle size distribution^[102]. In human-related samples， particularly lung tissue or respiratory specimens， MPs are often embedded within complex biological matrices， tightly bound to proteins， lipids， and cellular debris. This demands higher efficiency in sample digestion and greater integrity of subsequent spectral and electrochemical signals^[103].

Existing common microplastic pretreatment methods （such as chemical oxidation， enzymatic digestion， and density separation） can remove organic interferences in complex matrices to a certain extent. However， they often result in structural or surface damage to microplastic particles， significant fluctuations in recovery rates depending on particle size and polymer type， and residual oxidants or separation reagents. These methods also significantly prolong the overall analytical workflow. Taking Lee et al.^[104] systematic study in freshwater sediment samples as an example， their results indicate that conventional combined steps like chemical oxidation， density separation， and ultrasonication， while improving organic matter removal efficiency， inevitably introduce small-sized microplastic loss， size-dependent recovery bias， and process complexity， thereby increasing the uncertainty of detection results. Furthermore， in highly sensitive detection strategies such as electrochemical sensing and SERS， which rely heavily on interfacial processes， residual matrix impurities can cause electrode passivation， non-specific adsorption， or masking of plasmonic enhancement sites， significantly reducing signal response sensitivity and accuracy. Therefore， achieving synergistic design and high compatibility between sample pretreatment processes and sensing interfaces has become a critical scientific and engineering challenge for advancing rapid， reliable microplastic detection technologies toward real-world complex sample applications.

Future research urgently requires developing milder， highly selective， and integrable sample processing strategies. This involves creating integrated， microfluidic automated pretreatment chips or modules to achieve online， rapid， and trace-level microplastic enrichment and purification. Such approaches will minimize manual intervention and cross-contamination while enhancing detection throughput and reproducibility.

3.1.2 Limitations of detection technologies and the need for multidimensional analysis

Multiple factors， including polymer type， particle size distribution， morphological characteristics， degree of degradation， and spatial distribution， determine microplastic pollution. However， current microplastic detection technologies generally face the challenge that no single technique can achieve comprehensive analysis of all microplastic properties^[105].

Optical sensing strategies offer significant advantages in polymer chemical fingerprinting and morphological characterization； their detection limits are typically constrained by spatial resolution and background interference， making it challenging to achieve rapid， comprehensive analysis of submicron-sized or trace-level MPs^[106]. Electrochemical sensing strategies feature fast response times and ease of integration. Yet， their signals primarily stem from indirect perturbations of charge transfer or impedance characteristics at the electrode interface caused by MPs， failing to provide direct information on polymer chemical composition. Mass spectrometry offers highly reliable qualitative and quantitative capabilities， yet its complex instrumentation and destructive analysis make it unsuitable for rapid field screening^[17]. In contrast， physical field characterization techniques can provide physical characteristics such as particle size distribution， number concentration， or three-dimensional structure. However， they have a limited ability to distinguish polymer types and cannot independently accomplish the chemical identification of MPs.

Therefore， future development trends are more likely to shift from optimizing single technologies toward multimodal information fusion strategies： integrating optical， electrochemical， and physical field data with intelligent analysis algorithms to collaboratively acquire and interpret the full-dimensional characteristics of MPs^[107]. This approach enhances detection completeness， reliability， and field adaptability. Preliminary evidence supporting this concept has already emerged in areas including machine learning-assisted spectroscopy and sensor fusion.

3.1.3 Transformation and application for real-world environments

Current microplastic sensing research commonly exhibits systematic discrepancies between experimental models and environmental realities. According to a statistical analysis by Gouda et al.^[108]， 92% of toxicological studies rely on laboratory-simulated microplastics （47% being virgin microplastics and 45% being artificially aged microplastics）， while only 8% of studies focus on particles collected from the environment. Although this distribution data originates from toxicological research， it reflects a broader experimental trend： the vast majority of microplastics research remains laboratory-centered. Laboratory studies employ standardized microplastic particles with regular morphology， uniform particle size， and no degradation as model objects， which differ significantly from the complexly shaped， diverse in composition， and highly degraded MPs found in natural environments^[109]. Such differences may cause recognition mechanisms and signal response patterns established in laboratories to fail or deviate significantly when applied to real-world environmental samples. Furthermore， the actual abundance of MPs in environmental samples is typically extremely low， making analytical results highly susceptible to interference from non-plastic components. Under low signal-to-noise conditions， even trace amounts of organic matter introduced during experimental procedures may be misidentified as MPs， leading to significant overestimation. For instance， Witzig et al.^[110] discovered that stearates or surfactants released from disposable laboratory gloves could be misidentified as polyethylene in µ-FTIR， µ-Raman， and pyrolysis mass spectrometry analyses. This resulted in pronounced “false positive” signals in blank samples， highlighting the risk of sensor recognition rules failing in real， complex matrices.

Therefore， future development of rapid microplastic detection technologies should prioritize application-oriented approaches and environmental adaptability assessments， shifting focus from identifying single polymer types to evaluating microplastic pollution status and trend dynamics. Integrating data-driven methods such as machine learning or deep learning for feature extraction and pattern recognition of complex signals will enhance field detection performance for low-concentration， multi-component MPs^[111].

3.2 Future development trends and technological outlook

Overall， the future advancement of rapid MPs detection technologies will no longer rely on isolated breakthroughs in single sensing principles or material systems. Instead， it is likely to evolve along a comprehensive path characterized by multi-technology synergy， material-system integration， and intelligent analysis. Integrating current research progress with application demands， future development directions can be summarized as follows.

3.2.1 Multimodal sensing synergy and information fusion

Future microplastic detection platforms are expected to evolve toward multimodal sensing architectures. Within a multimodal sensing framework， different detection technologies provide complementary information across multiple dimensions. Electrochemical sensing delivers rapid and sensitive responses to physicochemical changes induced by microplastics， making it suitable for preliminary screening. Optical techniques， including Raman spectroscopy and Fourier-transform infrared spectroscopy， provide polymer-specific chemical fingerprinting information， enabling accurate material identification. Thermal analysis methods like Py-GC-MS deliver highly reliable compositional verification through characteristic pyrolysis products， while physical field-based techniques （e.g.， Micro-CT and NTA） provide morphological and size-related information. Fusion of multi-source signals holds promise to overcome inherent limitations in sensitivity， selectivity， or information dimensions inherent to individual techniques， thereby enhancing the completeness and reliability of detection results^[112]. Future research efforts should focus on data-level and decision-level fusion strategies that enable complementary signal interpretation rather than merely parallel measurements.

Furthermore， microfluidic technology holds great promise for playing a pivotal role in future rapid microplastics detection systems. By integrating sample pretreatment， in-line enrichment， and signal detection within micro-scale channels， microfluidic platforms can optimize the overall analytical workflow and reduce detection time while maintaining high sensitivity and selectivity. They can integrate multiple recognition elements and detection modes within a single miniaturized device， enabling multi-parameter synergistic analysis of complex environmental samples^[113]. Leveraging its portability and compatibility with smart terminals， microfluidic-assisted platforms are recognized as a key technological pathway for achieving automated， continuous， and rapid point-of-care detection.

3.2.2 Rational design of functional materials and interface engineering optimization

For complex environmental samples， there is an urgent need to develop functionalized interface materials and nanostructures with resistance to non-specific adsorption， high selectivity， and structural stability. Future interface engineering should prioritize anti-fouling performance， chemical robustness， and long-term structural integrity under real-world sampling conditions. By modulating the surface chemistry， micro/nano-structural features， and energy level characteristics of materials， specific interactions between microplastics and sensing interfaces can be enhanced. This approach improves signal-to-noise ratios and detection stability while effectively mitigating interference from complex matrices^[114]. Integrating plasma， fluorescent， or electroactive nanostructures with customized surface functionalization represents a promising interface design strategy. This approach enables highly sensitive detection while maintaining the engineering feasibility required for rapid screening applications.

3.2.3 System integration and intelligent signal analysis

Combining automated sample pretreatment， sensor array integration， and artificial intelligence algorithms represents a key direction for achieving rapid， portable， and on-site microplastic detection. Data-driven approaches， including machine learning and pattern recognition algorithms， provide powerful tools for extracting meaningful features from complex multidimensional signals and compensating for sensor variability and environmental uncertainty^[115-116]. Future detection systems should emphasize modular design and algorithm-hardware co-optimization to achieve adaptive signal interpretation across different polymer types， particle sizes， and environmental matrices. This intelligent integration is crucial for transitioning microplastic sensing technology from laboratory prototypes to field-deployable analytical systems.

3.2.4 Standardization framework and database development

Establishing a standardized sample repository and a comprehensive database encompassing spectral， electrochemical， and physical field responses is a fundamental prerequisite for the long-term development of microplastic detection technologies. Standardizing data formats， preprocessing protocols， quality control standards， and performance evaluation metrics across different techniques is crucial for ensuring data comparability and reproducibility. Such a standardized framework will provide a reliable foundation for multimodal data fusion and machine learning model generalization， ultimately facilitating cross-platform integration and large-scale application of rapid microplastic detection systems^[18,117].

With the continuous maturation of sensor networks， self-powered systems， and intelligent data processing technologies， rapid microplastics detection platforms are poised to transition from laboratory validation to deployment in real-world environments. This systemic advancement will provide a more robust technological foundation for refined environmental monitoring， large-scale data collection， and microplastics pollution risk assessment.

4 Conclusions

This review systematically summarizes recent advances in emerging sensing strategies and material designs for rapid microplastic detection， encompassing optical sensing， electrochemical sensing， mass spectrometry-based techniques， and physically driven methods. These approaches， alongside the development of corresponding functional materials， collectively enrich analytical methods for characterizing MPs in terms of chemical composition， particle size， morphology， and spatial distribution. However， given the complexity of real-world environmental samples and the multifaceted nature of microplastic pollution， no single technology currently enables comprehensive， rapid， and precise field detection.

Optical sensing strategies remain indispensable for polymer fingerprinting and morphological characterization， yet their detection performance is inherently constrained by spatial resolution limitations， spectral interference， and time-consuming scanning processes. Electrochemical sensing platforms demonstrate significant advantages in portability， rapid response， and system integration， highlighting their immense potential for field monitoring. However， their indirect signal mechanisms and sensitivity to matrix interference fundamentally limit their selectivity and robustness when used alone. While mass spectrometry and physical field characterization techniques offer unparalleled chemical resolution or structural analysis capabilities， they are better positioned as verification or auxiliary tools rather than frontline rapid sensing solutions.

Future advancements in rapid microplastic detection will increasingly rely on synergistic multimodal sensing strategies， rational design of novel functional materials， automated sample pretreatment， and data-driven intelligent analysis. By integrating multiple sensing mechanisms and combining machine learning methods to synthesize chemical， physical， and spatial information， we can potentially overcome the inherent limitations of single-detection modes. Concurrently， establishing standardized reference materials， unified analytical workflows， and interoperable data platforms is crucial for ensuring the reliability and comparability of results across different technical pathways.

In summary， advancing microplastic detection technology from laboratory validation to reliable field-deployable systems requires synergistic development spanning single-method innovation to system integration， and from material design to sensing platform construction. This transformation holds significant importance for conducting precise environmental monitoring， human exposure assessment， and evidence-based governance within the global context of microplastic pollution.

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基金资助

国家自然科学基金项目(21705115)

江苏省研究生科研与实践创新计划项目(SJCX25_1843)

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Received	Published	Revised
2026-01-07	2026-07-24	2026-03-09
Online Date
2026-05-23

摘要

Abstract

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引用本文

1 Introduction

2 Novel sensing strategies for rapid Detection of MPs

2.1 Optical sensing strategies

2.1.1 μ-FTIR

2.1.1.1 Advancements in high-throughput imaging techniques

2.1.1.2 Development of intelligent spectral analysis algorithms

2.1.1.3 Optimization of sample pretreatment methods

2.1.2 SERS

2.1.2.1 Development of functionalized and enriched SERS substrates

2.1.2.2 Plasmonic nanoarrays and flexible SERS platforms for in-situ analysis

2.1.2.3 Machine learning-assisted spectral processing and classification

2.2 Electrochemical sensing strategies

2.2.1 Core mechanisms and strategic framework for electrochemical microplastic detection

2.2.1.1 Direct detection mechanisms based on intrinsic electrochemical responses and interface perturbations

2.2.1.2 Signal enhancement strategies driven by electrode modification and interface engineering

2.2.1.3 Novel device structures and intelligent signal reading modes

2.2.1.4 Specific detection mechanism driven by biological/bionic recognition elements

2.2.2 Key technical bottlenecks and application challenges in electrochemical MPs detection

2.2.2.1 The absence of standardized protocols compromises signal comparability and model universality

2.2.2.2 Interference and misidentification caused by complex environmental matrices

2.2.2.3 Practical application orientation： insufficient sensor engineering and long-term stability

2.2.2.4 Design of highly specific recognition elements and environmental adaptability challenges

2.3 Mass spectrometry and other physical field sensing strategies

2.3.1 Methodological advances in MS-based microplastic analysis

2.3.2 Other physical field sensing strategy

3 Challenges and future outlook

3.1 Key Challenges in Rapid Microplastic Detection

3.1.1 Inadequate sample processing and sensor interface compatibility

3.1.2 Limitations of detection technologies and the need for multidimensional analysis

3.1.3 Transformation and application for real-world environments

3.2 Future development trends and technological outlook

3.2.1 Multimodal sensing synergy and information fusion

3.2.2 Rational design of functional materials and interface engineering optimization

3.2.3 System integration and intelligent signal analysis

3.2.4 Standardization framework and database development

4 Conclusions

参考文献

基金资助

**2.1.2.2 Plasmonic nanoarrays and flexible SERS platforms for in-situ analysis**