Rapid single-particle chemical imaging of nanoplastics by SRS microscopy

SRS microscopy is well known to be orders of magnitude faster than regular Raman imaging (25, 26). The drastically higher imaging speed of SRS microscopy hence provides high throughput on particle imaging. However, whether high-speed SRS has a better detection limit than regular Raman and whether it can actually reach the single-particle sensitivity of nanoplastics are not obvious. A theoretical quantification is helpful to address the question in the first place. For a given major type of plastic polymer, we can estimate the mass of a 100-nm-diameter nanoplastics based on the plastic density and calculate the number of repeating units (i.e., constituting monomer) via its molecular weight. As shown in SI Appendix, Table S1, this number is around 10⁶ for most major plastic types, based on which we can further estimated the number of most abundant chemical bonds in a single plastic particle to be ~10⁷.

We can then theoretically explain why a 100 nm nanoplastic particle is difficult to be detected by conventional Raman microscopy. The spontaneous Raman cross-section of a typical C–H vibration is about 10⁻²⁹ cm². Hence, the spontaneous Raman cross-section of a 100-nm nanoparticle is 10⁻²² cm². The laser waist area can be shrunk to about 2 × 10⁻⁹ cm² under a high numerical aperture microscope objective. The probability of Raman scattering event per excitation photon is then (10⁻²² cm²)/(2 × 10⁻⁹ cm²) = 5 × 10⁻¹⁴. Assuming a moderately high laser power of 10 mW with a conventional 532 nm laser, which corresponds to an excitation flux of 3 × 10¹⁶ photons/s, and a rather long acquisition time of 100 ms (a small 128 × 128 image will take half an hour), only about 130 photons can be generated per particle in total via spontaneous Raman scattering. Considering the quantum yield of the entire instrument (including objective, filters, pinhole, spectrometer, and camera) typically is ~1%, roughly only 1.3 photons can be ultimately detected. Such a feeble signal can be easily overwhelmed by noise from other backgrounds such as autofluorescence.

By employing an additional coherent Stokes laser, SRS amplifies the feeble scattering crossing section of a specific spectral mode (defined by the energy difference between pump and Stokes lasers) via quantum stimulation. When a pulsed narrowband Stokes laser is used (24, 33), the stimulated Raman enhancement factor can be maximized to more than 10⁸ (32, 34). The probability of a stimulated Raman scattering event per pump excitation photon then becomes 5 × 10⁻⁶, which is measured as a stimulated Raman loss experienced by the pump beam targeting C–H vibration. The noise of the pump beam under high-speed SRS microscopy acquisition (18 µs/pixel) is measured to be 5 × 10⁻⁷ (Fig. 1), which is about 10× lower than the expected stimulated Raman loss signal from a single 100-nm plastic particle. Thus, we predict that narrowband SRS shall break the detectability barrier of spontaneous Raman and bring a single nanoplastic particle into detection in just tens of microseconds.

We then experimentally verify the detection sensitivity using standard plastic particles. Polystyrene is one of the most common plastics widely used in daily life. Polystyrene particles of specified sizes are commercially available as analytical standards and have been routinely used as a model material to study micro-nanoplastics (35, 36). The Raman spectrum of polystyrene suggests a prominent peak at 3,050 cm⁻¹ from aromatic C–H vibration on the phenyl ring (SI Appendix, Fig. S1), which can be selectively amplified for SRS imaging by tuning the difference of pump and Stokes beams to match this transition energy. Using commercial PS micro-nano spheres from 100 nm to 3 µm, we evaluated the detection sensitivity of our SRS microscope in imaging nanoplastics. To stabilize the particles during imaging, we embedded the diluted PS particles in agarose gel. As the particle size goes smaller, the residue of the water background around 3,000 cm⁻¹ starts to dominate (SI Appendix, Fig. S2a), overwhelming the authentic spectrum of individual PS nanoparticles. To resolve this background issue for better imaging contrast, we substituted regular H₂O with D₂O to prepare the agarose gel (SI Appendix, Fig. S2b). Compared to H₂O, the Raman spectrum of D₂O is red-shifted to the silent region (2,200 to 2,800 cm⁻¹, SI Appendix, Fig. S3), creating a background-free environment for probing C–H vibration.

SRS intensity of individual particles can be thereby measured from single-channel narrow-band imaging with high-throughput (~1,000 particles in one 51 × 51 µm FOV within 2 s, SI Appendix, Fig. S4). This imaging speed is orders of magnitude faster than other nanoplastic imaging techniques, such as AFM-IR and STXM (21, 23, 37). With the optical diffraction limit, the optimal spatial resolution of SRS microscopy is measured to be 365 nm (Fig. 1 H and I). With a spatial sampling of 200 nm pixel size for high-throughput imaging, individual PS nanospheres of above 500 nm can be discerned with their shape from the images (Fig. 1 D–G). When the size of the particles goes smaller than the diffraction limit (Fig. 1 A–C), the finite optical resolution renders the particle image a diffraction-limited pattern. Yet, the SRS intensity of a single particle can still be readily recognized down to 100 nm based on the diffraction limit pattern and the intensity distribution (SI Appendix, Fig. S5). Thus experimentally, we have shown that compared to regular spontaneous Raman, SRS imaging can offer orders of magnitude higher imaging speed/throughput and a superior limit of detection for nanoplastics analysis.

A linear relationship was observed between the logarithm of SRS signal ( ${\mathrm{\Delta }I}_p/I_p$ ) and the logarithm of diameter for PS particles smaller than 0.7 µm (Fig. 1J and SI Appendix, Supplementary Note3). The trendline with a slope of 2.98 within the range indicates the SRS signal ( ${\mathrm{\Delta }I}_p/I_p$ ) increase linearly with the particles’ volume, which scales in cubic as the particles’ diameters increase. When the particles’ size is enlarged to overfill the effective focal volume sequentially in first x, y, and later z dimensions (SI Appendix, Fig. S14), the linear dependency disappears. This good linearity (R² = 0.998) is due to the fundamental linear dependency of the SRS signal on the concentration of the target analyte, providing powerful utilities in several aspects. First, the actual size of particles below the diffraction limit can be estimated based on the obtained calibration curve (SI Appendix, Fig. S16a), extending the size characterization limit. Second, with the known information on the plastic density, the same calibration curve can be transformed into a reference to deduce a particle mass out of a detected SRS nanoplastics image (SI Appendix, Supplementary Note3 and Fig. S16b). Finally, taking an SNR of one as the threshold, the detection limit of our narrowband SRS microscope can be determined (Fig. 1K) to reach PS nanospheres down to 60 nm.

Nano-sensitivity solves the first-order issue to ensure the plastic particles are detectable. The chemical specificity of a technique is also crucial to identify plastics from other co-existing substances and further distinguishing plastic polymers from each other. Harnessing vibrational spectroscopy as imaging contrast, SRS microscopy, in principle, holds the demanded specificity for chemical imaging. Instrumentally, we perform hyperspectral SRS imaging via the spectral-focusing technique (38, 39). To best cover the characteristic strong feature of the plastic Raman spectrum (SI Appendix, Fig. S1) within the tuning range of the instrument (790 to 910 nm), we carefully choose 793, 804, 886, and 897 nm as four central wavelengths to include the strong and characteristic spectral features of C–H (unsaturated and saturated carbons, 3,110 to 2,800 cm⁻¹), ester bonds (1,770 to 1,670 cm⁻¹), and double bond vibration (1,660 to 1,580 cm⁻¹) for better distinguishment between each plastic type. We constructed a small library by measuring the bulk SRS spectra of seven most common plastic polymers (Fig. 2A): polyamide 66 (PA), polypropylene (PP), polyethylene (PE), polymethyl methacrylate (PMMA), polyvinyl chloride (PVC), polystyrene (PS), and polyethylene terephthalate (PET) with fine spectral intervals (~3 cm⁻¹).

Unlike bulk spectra measurement, single-particle imaging of nanoplastics requires a much smaller pixel size, longer integration time, and higher power for optimal signal-to-noise ratio. Therefore, due to the fundamental trade-off between detection sensitivity and specificity, it is nearly impossible to measure nanoplastics with such fine spectral intervals (hours of imaging time per FOV with increasing possibility of sample drifting and burning during the time). Moreover, the spectral resolution of a hyperspectral SRS microscope based on spectral focusing is typically 10 to 25 cm⁻¹. For efficient hyperspectral imaging with a proper balance between throughput and spectral resolution, we further subsampled the spectra (SI Appendix, Fig. S6) with the spectral interval of ~15 cm⁻¹, which is only slightly above the spectral resolution and yielded acceptable imaging throughput (~0.5 h per 0.2 mm × 0.2 mm FOV) for single-particle chemical imaging of nanoplastics.

High-throughput plastic particle analysis also requires automated spectral analysis for plastic identification. Spectral matching algorithms for automated chemical identification are prevalently adopted in microplastic analysis based on FTIR or Raman spectroscopy (40, 41). With thousands of particle spectra in need of analysis in a typical environmental study, manual plastic identification and counting are not only impossibly labor-intensive but also subjected to human bias (14, 40–42). Automated particle analysis helps to speed up the measurement, analyze more particles, as well as ensure ubiquitous and unbiased plastic identification. Understanding the need for automation in environmental science, we started with applying the classic library matching algorithms in FTIR and Raman analysis but found them not so compatible with narrow-band SRS hyperspectral analysis. Take a detected spectrum from particle A prepared from grinding the PA standard as an example (Fig. 2B). After spectrum pre-processing on background subtraction and data normalization, the spectrum of particle A clearly matches the SRS signature of polyamide. However, when measuring the spectral similarities of particle A to bulk plastic standards from the library using common spectral matching algorithms (42), such as Pearson’s correlation coefficient (PC) or squared Euclidean cosine (SEC) measurement, the identification results appears elusive (Fig. 2 D and E). In a real-life sample analysis, there should be no premise to assume particle A should belong to any standard plastics in the library, which means a yes or no judgment has to be made independently for each plastic standard based on a given threshold. The common threshold employed in FTIR or spontaneous Raman analysis of microplastics is the similarity measurement above 0.7, which is clearly too low to identify Particle A. Since PS nanoparticles are available as model standards, we first try to study the similarity threshold of each algorithm for nanoplastics analysis under hyperspectral SRS imaging. The similarity threshold can then be determined based on the quartile of identifying at least 95% of the PS particles (similarity index above 0.75 for PC, and similarity index above 0.94 for SEC). However, the challenging part of making a binary identification judgment remains in the case of particle A as similarity measurements from three plastic polymers (PA, PP, and PVC) are very close in number and all above the threshold (Fig. 2 D and E). Note that one cannot simply pick the best score among all the standards because it is totally possible for A to be nonplastic materials in real sample analysis. In fact, if we simulate the possible nonplastic SRS spectra based on the model standard spectrum of biomass represented by E. coli, over 95% of them will have similarity measurements against PA standard over the given threshold for both two algorithms (SI Appendix, Fig. S12 a and b).

We reflect that the main reason underlying the above difficulty stems from the trade-off between detection sensitivity and specificity. Emphasizing the chemical specificity, spontaneous Raman spectroscopy, or other broadband coherent Raman microscopy can cover an extended spectral window (>1,000 cm⁻¹) by distributing the optical power among a large number of Raman vibrational modes. The rich spectral information can enable chemical identification with simple algorithms but comes with the cost of over thousand times compromised detection sensitivity under a limited pixel dwell time (43–45). However, in the context of nanoplastics analysis, detecting the particle signal is the premise before chemical identification from the vibrational spectrum. With the aim of measuring as small plastic particles as possible under practical throughput, eventually, only the strongest Raman features will be detectable with reasonable SNR. For most plastics, which are organic polymers by nature, the strongest Raman signatures reside within the limited C–H vibration window. In this case, specific chemical identification requires the algorithms to precisely capture the shape feature within the restricted spectral window, which is beyond the capacity of conventional spectral matching algorithms. Moreover, the inevitably compromised and circumscribed signal-to-noise ratio when imaging diminutive nanoparticles create further challenges in spectral interpretation for robust chemical identification. Therefore, new methods are demanded to address the specificity challenge imposed by the SRS instrumentation that enables unprecedented sensitivity in imaging nanoplastics.

Harnessing data science, we aim to develop algorithms to interpret the shape of detected SRS features and retrieve the chemical specificity for polymer identification. First, an SRS-tailored spectral matching coefficient (SMC_SRS) is developed as an indicator to quantify spectral similarity with minimized noise interference (Fig. 2, eq. 1). SMC_SRS uses an optimization algorithm that considers the detected SRS spectrum $\mathbf{x}$ originating from scaling (intensity factor $\mathrm{\alpha }$ ) the normalized bulk standard spectrum $\mathbf{s},$ plus a certain background contribution at the imaging condition ( $\mathrm{\beta }\mathbf{b}$ , $0\le \mathrm{\beta }$ < 1). The fitted spectrum ( $\mathrm{\alpha }\mathbf{s}+\mathrm{\beta }\mathbf{b}$ ) was compared with the detected particle spectrum $\mathbf{x}$ to find the minimum possible spectral distance as SMC_SRS. The smaller SMC_SRS value indicates a higher spectral similarity to the corresponding standards. This indicator SMC_SRS provides several advantages for the purpose of detecting nanoplastics. The optimization algorithm considers all spectral points simultaneously, which reduces the direct influences induced by the noise on each particular spectral point. The fitting process leverages the reliability of the similarity measurement. In addition, the outcome of the measurement is interpretable. The well-defined intensity factor α and background factor β can indicate the contribution from each spectral component (the particle and the surrounding backgrounds). Finally, the spectral distance measurement provides metric similarity evaluation.

With the spectral similarity quantified in this refined way, we returned to face the challenge of making a nonarbitrary binary judgment for polymer identification. We planned to develop a learning-based method to determine the previously elusive binary threshold for the identification of all plastic polymers. Our premise is that if we can measure the nanoparticle spectra for all types of plastics within the library, we shall be able to learn from the data and draw the correct boundary for identification based on the distribution of the particles with known identities. However, in reality, only PS nanospheres are commercially available to us with well-characterized chemical composition and nano sizes. Without reliable ground truth from other polymer nanoparticles, we have to seek alternative ways to gather the massive information needed for rigorous threshold determination.

Inspired by the increasing utilities of synthetic data in AI (46), and the growing involvement of data science in SRS microscopy (47–49), we realized that we could simulate the experimental SRS spectra of nanoplastics from the bulk standard spectra to serve as a training dataset (i.e., synthetic data). Based on our understanding of the SRS instrumentation, we proposed a model, where there are two main sources of noise in a typical hyperspectral SRS spectrum: one is fundamental noise on the SRS intensity as in a shot-noise-limited scenario, which can be easily read out from the same SRS image; the other is the frequency uncertainty imposed by the SRS instrumentation, where both the laser profile and the moving delay stage can result in fluctuation of the actual frequency excited in each measurement around the preset spectral points. Assuming the fluctuation follows a Gaussian distribution, we used PS nanospheres as the standard model to investigate the fluctuation range and found an impressive consistency in SMC_SRS calculation from the synthetic spectra and measured spectra of PS nanoparticles (SI Appendix, Supplementary Note 2 and Fig. S10). The combinatory nature of noise origins explains the dependency of the SMC_SRS value on the intensity of the spectrum ( $\mathrm{\alpha }$ ), as suggested in the simulation and validated by the experiment (Fig. 2F).

Applying the same model for all standards in the library, we generated a synthetic dataset containing the possible SRS spectra for nanoplastics of each polymer in the plastic library. A nice separation of the SMC_SRS value appears between the spectra of particle $\mathrm{X}$ ( $\mathrm{X}=\mathrm{R}$ , R is the correct identity of standard polymer) and spectra of particle $\mathrm{X}$ ( $\mathrm{X}\ne \mathrm{R}$ ) in all scatter plots (SI Appendix, Fig. S11). With the massively generated synthetic data points, a logarithmic function was fitted according to the trend of the scattered points as the threshold line for polymer identification (SI Appendix, Supplementary Note 2 and Table S2).

We first evaluate the identification performance by simulating another synthetic dataset from all standards in the library as testing data. Compared with conventional spectral matching algorithms, the SRS-tailored developed shows minimal false positives in plastic identification (SI Appendix, Fig. S12). No more than 0.5% of nonplastic spectra (simulated from E. coli) is misidentified as a hit for any plastic types in the library (SI Appendix, Fig. S12c), which is a drastic improvement from over 97% using conventional spectral matching algorithms (SI Appendix, Fig. S12 a and b). False positive between polymers of similar SRS spectrum is also much reduced with the maximum to be around 5% PA misidentified as PP (SI Appendix, Fig. S12c). The same number is also as high as over 97% if PC or SEC are used as similarity measurements with the determined thresholds (SI Appendix, Fig. S12 a and b).

To further address the possible rare cases where a particle is identified as hits for more than one polymer in the library, the chemical identity of the corresponding particle will be assigned to the polymer with the smallest SMC_SRS value. With the established spectral identification workflow, an over 96% identification rate can be achieved with a false positive rate below 1% for all polymers in the library (SI Appendix, Fig. S12d). Since PS nanosphere was the only available nanoplastic standard, the experimental validation of the workflow is based on the imaging of the corresponding microplastics prepared from grinding the polymer standards with the cryo-mill. Hoping to mimic a similar level of spectral variation to the best extent, the imaging condition is adjusted accordingly to match the signal-to-noise ratio of nanoplastic measurement. Finally, we confirmed the same identification rate of over 96% in the experimental particle measurement with no observed plastic particles misidentified as other polymers within the library (Fig. 2G).

Development of this data-driven algorithm allows for the identification of each plastic polymer with distinct vibrational features in a restricted spectral window, thus retrieving the required chemical specificity for automated spectral identification. Revisiting the identification of particle A and standard PS nanosphere B, we can correctly identify both particle A and particle B across the library to be PA and PS (Fig. 2 H–N), with SMC_SRS well captures the shape differences missed by conventional algorithms and threshold learned from the data-driven study. Coupling the mindset from data science with advanced measurement science, we finally overcome the fundamental sensitivity-specificity trade-off for high throughput hyperspectral SRS analysis. Superb nano-sensitivity from narrow-band SRS amplification and chemical specificity with robust chemical identification are simultaneously accomplished to fill the missing void in tools for nanoplastics analysis.

With the platform established, we moved on to apply the utility to study micro-nano plastics from real-life samples. Microplastics have been widely found in human foods (50), drinks (51), and product packaging (52–55), among which bottled water is of particular interest for being an important source of microplastics to be ingested in daily life (56–59). Limited by the sensitivity-specificity trade-off in analytical science (SI Appendix, Fig. S18b), the literature knowledge is constrained to microplastics in bottled water (SI Appendix, Table S4) (19, 60–62), leaving the nanoplastics mostly uncharted. So far, only ensemble characterizations using combinations of techniques are reported to analyze the aliquots of concentrated nanoparticles from bottled water. Information is demanded to address the intrinsic heterogeneity of nanoplastics contamination at a single-particle level (SI Appendix, Fig. S18a) (63, 64). Here, we report a concise workflow for comprehensive micro-nano plastics characterization enabled by rapid single-particle chemical imaging with nano-sensitivity by SRS microscopy. Rich information can be acquired from a single measurement to achieve simultaneous characterization of chemical composition and morphology, enabling multi-dimensional statistics through high-throughput single-particle analysis.

Filtration is one of the most common methods to collect particles above certain sizes onto a membrane surface. It would be highly preferable for analyzing real-world samples if the collected membrane is directly compatible for SRS imaging. Aluminum oxide membranes have minimal background in the target spectral window and have shown good compatibility with vibrational spectroscopy. The seemingly opaque aluminum oxide membrane can be easily transformed into a transparent imaging window by applying heavy water to reduce refractive index mismatch. This resulted in transmissive SRS imaging with acceptable signal retention (~70% of the original sensitivity, SI Appendix, Fig. S7 b and c). Embedding the particles on the membrane surface in situ with agarose gel prepared with D₂O further enabled stationary SRS imaging of individual particles with minimal imaging background. In this way, a concise sample preprocessing is enough for high-quality SRS imaging of the original filtration membrane (SI Appendix, Fig. S7a), avoiding undesirable sample loss or contamination in any complicated sample drying or transferring processes.

The established workflow for analyzing micro-nano plastics exposure from bottled water with hyperspectral SRS imaging is presented in Fig. 3. For each sample, five or more fields of views (FOVs) were randomly sampled within the collecting area for hyperspectral imaging under SRS microscopy (Fig. 3D). In each FOV, micro-nano plastics were detected by an integrated data analysis workflow that automatically performed the particle segmentation and plastic identification with the developed algorithms and validated threshold conditions. Morphological and chemical information of each individual plastic particle obtained from the hyperspectral SRS images was then combined to provide high-dimensional profiling (Fig. 3E). Following the procedure, we analyzed bottled water from three different brands acquired at the same time from a large retailer. With no access to plastic-free water in the lab (SI Appendix, Supplementary Note 6), the Anodisc filters are prepared and measured in the same way as blank control. In the results, we were able to detect individual particles for all seven plastic polymers in the library unambiguously by spectral matching with their corresponding bulk standards (Fig. 4), demonstrating the powerful plastic identification capability of our data-driven hyperspectral SRS imaging platform.

Quantification from single-particle images with identified plastic polymer composition provides multi-dimensional information to build the analytical panorama of underexplored nanoplastics in bottled water.

Number quantification through particle counting suggests that on average, 78 to 103 plastic particles were identified in each FOV (0.2 mm × 0.2 mm) for three different brands, which was significantly higher (P < 0.001) than the blank samples (Fig. 5A). Assuming a uniform distribution of micro-nano plastic particles on the surface of the membrane region (SI Appendix, Supplementary Note 5), we can make an estimation for the micro-nano plastic exposure from bottled water. We estimate that there are on average about 2.4 ± 1.3 $\times$ 10⁵ plastic particles ingested from every liter of bottled water measured from different brands(Fig. 5C). Individual particles of each type of polymer are analyzed separately to reveal chemical heterogeneity. Within the library, PA, PP, PET, PVC, and PS are found likely to play a significant role in micro-nano plastics exposure from bottled water (Fig. 5B). The exact chemical composition of the micro-nano plastics varied from brand to brand, but PA seem to be the common major contributors in number among all the three brands we analyzed.

Harnessing the linear relationship between SRS intensity and the amount of analytes within the focal volume, we are also able to provide an estimation of exposure in mass besides particle number. The mass calibration curve can be estimated for each polymer out of density and relative SRS intensity from the linear relationship obtained by standard PS nanospheres (SI Appendix, Fig. S16). Integrated intensity within the region of interest for each particle is thus converted to mass (Fig. 5 E and F). The estimated micro-nano plastic exposure in mass is calculated to be at the level of around 10 ng/L. Analyzing the chemical composition in mass, we find unneglectable differences between contribution quantified by mass and contribution by number. Take the results from Brand C as an example. The PS nanoplastics though dominated in particle number, only account for a minor portion of the mass. Instead, PET becomes the major contributor together in mass. Such seeming disparity highlights the potential misunderstanding of plastic composition from collective particle characterization, which originated from the heterogeneous nature of micro-nano plastics from real-world samples.

Morphological characterization of individual particles enabled by SRS microscopy directly reveals another dimension of particle heterogeneity. Statistical analysis of particle size and shape from the images of individual micro-nano particles with well-defined identities is reported. When measuring the size distribution, we are able to characterize particles below the diffraction limit by extrapolating the size from the intensity reading (assuming the particles as solid spheres) and by using the linear relationship between the volume of the particles and SRS signal as calibration (SI Appendix, Supplementary Note 3). As a result, we find that plastic particles of different chemical compositions actually have different size distribution patterns (Fig. 6 A–G). The direct observation of the particle heterogeneity here provides a natural explanation of chemical compositional differences observed from mass or number measurement. Take PS and PET as an example: the size distribution of PS particles centers around 100 to 200 nm, whereas PET particles tend to have a size distribution that nears 1 to 2 microns, which explains why PET is a more significant component when measuring in mass while PS clearly dominates when counting the number of particles (Fig. 5 D and F).

The shape is another important morphological feature that matters as a critical aspect of nanotoxicity. Studies have shown that shape plays a role in determining the cellular uptake of micro-nano particles (65, 66). SRS images of plastic particles confirmed the existence of shape diversity for micro-nano plastics in bottled water. To account for the shape of plastic particles in a statistical manner, we measure the aspect ratio of individual particles above the diffraction limit (Fig. 6H). The aspect ratio is widely acknowledged in nanotoxicology studies (67, 68). The aspect ratio of the plastic particles detected ranges from 1 to 6, and the average aspect ratio for particles is around 1.7. Fig. 6 I–M provides a pictorial view of how the aspect ratio is related to the particle shape. Particles with an aspect ratio of above 3 are most likely to be fibrous in shape, while particles with an aspect ratio of below 1.4 will be largely spherical. Shape variation on plastic particles has been found in all polymers detected, confirming the widely recognized idea that real-world micro-nano plastics have diverse morphological prosperities. This dimension is hard to be resembled by engineered polymer nanoparticles commonly studied in research laboratories, and the toxicological consequences pertaining to real-life plastic particle exposures and their differing physicochemical properties (i.e., size, shape) have yet to be determined.

By developing the data-driven hyperspectral SRS imaging platform for micro-nano plastic analysis, we describe a methodology to improve nanoparticle detection sensitivity and polymer identification specificity, which has allowed us to start to address the long-lasting knowledge gap of nanoplastics. We estimate that the exposure to the micro-nano plastics from regular bottled water was at the level of 10⁵ particles per liter, which is two to three orders of magnitude more than the previously reported results merely focusing on large microplastics (SI Appendix, Table S4) (58, 59, 61, 69, 70). As it pertains to the estimation of human exposure, these values are substantially higher than those currently reported in the literature (56, 71), which is a result from the newly detected nanoplastic fraction of plastic particulate. The tiny particles previously invisible under conventional imaging actually dominate in number and account for ~90% of the entire population of plastic particles detected. The remaining 10% identified as microplastics have a concentration of around 3 × 10⁴ particles per liter (SI Appendix, Fig. S17), with the majority of them in the size below 2 µm. Larger particles (>2 µm), which are easier to identify under regular optical microscopy, are in the same order of magnitude as the reported microplastic analysis depending on the detection limited reported based on different technologies (SI Appendix, Fig. S17 and Table S4). Our results confirm the plastic fragmentation beyond the micron level by unambiguously detecting nanoplastics in real-life samples. Similar to many other particle size distributions in the natural world, there are substantially more nanoplastics, despite being invisible or unidentified under conventional particle imaging techniques, than previously counted large micron ones. This population of nanoplastics can be easily overlooked in mass quantification as well since nanoparticles with smaller sizes contain cubic-less substances. However, given the capability of these nanoplastic particles to cross the biological barrier, nanoparticles, despite the seemingly trivial contribution to the mass measurement, might play a predominant role in terms of toxicity evaluation (72, 73).

We also find many detected particles present SRS spectra that do not match any of the standards. In fact, our small library of seven plastic polymers can only account for roughly about 10% of the total particles/dots imaged under SRS microscopy. A similar level of identification rate is reported in the microplastic analysis in bottled water using vibrational microscopy, indicating the complicated particle composition inside the seemingly simple water sample (SI Appendix, Table S4). In this sense, if we assume all detected organic particles originate from plastics [the same assumption entailed by the quantitative result from SEM-EDX or Nile Red staining (19, 74)], the micro-nano plastic concentration could be as high as 10⁶ particles per liter. However, the common existence of natural organic matter certainly requires prudent distinction from spectroscopy with polymer specificity. Moreover, careful investigation of unidentified particles suggests other aspects that further increase the complexity of identifying chemical composition. For example, some particles exhibit identical features to the characteristic two peaks (C=O ester bond: 1,730 cm⁻¹; C=C double bond: 1,615 cm⁻¹) of the PET in the fingerprint region but present a great variety of vibrational peaks in the high-frequency C–H region (SI Appendix, Fig. S8 a–d). It is unlikely for a polymer material distinct from PET to display both the C=O and C=C vibrational signatures that perfectly match the standard PET spectrum. A more plausible explanation is that they are small heteroaggregates containing PET and other components, with their SRS spectrum being the superposition of the spectrum from each component. Indeed, for some larger ones, we can even capture the spatial chemical heterogeneity within the aggregates (SI Appendix, Fig. S8 a, e, and i). The possible formation of heteroaggregates between nanoplastics or other natural organic matter has long been recognized as a potential challenge in the analysis of nanoplastics and may influence toxicological outcomes within a biological exposure (11). Direct visualization of such heteroaggregates here in real-world samples supports such concerns. For other possible heteroaggregates formed without PET, rigorous identification will require expanding the spectral library and advancing analytical algorithms for SRS microscopy or other vibrational imaging techniques with extended spectral windows to address challenges imposed by massive particle heterogeneity (27, 75, 76).

Another important insight is that the particle size distribution varies with the different chemical compositions, suggesting an interconnection between particle morphology and chemical composition. The observed nonorthogonality between plastic composition and particle morphologies challenges the conventional assumption for micro-nano plastics characterization from ensemble measurement. Take the result from brand C analysis as an example, ensemble measurement of micro-nano plastics might suggest that the major substance is PET from compositional analysis and most of the plastic particles have sizes below 500 nm from the morphological analysis. Assuming the two dimensions as being independent properties, people might have an impression that most of the plastic particles in the bottled water from brand C should be PET particles with a size below 500 nm. However, our result from single-particle analysis presents a clear disparity: the sample turns out to contain a small number of PET particles of about micron size and a large number of PS particles with size below 500 nm.

Such nonorthogonality might provide valuable information to understand, trace, and eventually prevent possible sources of micro-nano plastic contamination. Specifically in drinking water production, plastic contamination is confirmed in every step from the well to the bottle (77). The discovered size differences among different plastic polymers might indicate precious information about contamination sources during water production. For example, PET and PE, which are used as the packaging material for bottled water for all three brands we analyzed, have similar size distribution patterns, with a major population of micron sizes compared to other polymers. A possible explanation is that some particles of this kind are newly released from the bottle package during transportation or storage, which are retained faithfully in the water sample. Other polymers such as PA, PP, PS, and PVC, which are not the packaging material but also identified with significant numbers, are most likely introduced before or during water production. PP and PA, which share the same broad distribution of sizes, are widely used as equipment components or coagulant aids in water treatment (78). Particularly, PA is the most popular membrane material used in reverse osmosis (79), which is a common water purification method shared by all three brands. PVC and PS, which have a unique size distribution favoring small nanoplastics, might indicate a contamination source even earlier. PVC is identified to be the most abundant polymer type in raw water from microplastic analysis (77). PS is known to be used as backbone material for ion exchange resins in water purification (80). It is possible large particles of PVC or PS get removed by the RO membranes in the later step of the water treatment, leaving mostly nano populations.

Lastly, the interconnection between particle morphology and chemical composition has profound implications for toxicological concerns. As studies with engineered nanoparticles have suggested and investigations of plastic particles are starting to indicate, toxicity induced by micro-nano particles is not only dose-dependent but also related to particle physicochemical characteristics and their effect on cellular interactions and uptake (81, 82). In the case of bottled water from brand C, the cytotoxicity induced by PS nanoplastics plus a small number of PET microplastics would be presumably different from the effect assumed from PET nanoparticles. True comprehensive toxicity evaluation for micro-nano plastics would require multidimensional characterization of plastic particles and the integration of each individual plastic particle regarding their divergent properties on chemical composition and particle morphologies. Single-particle imaging with nanoparticle sensitivity and plastic specificity provides indispensable information to address the rising toxicity concern. Not only it enables plastic particle profiling with accurate exposure quantification, but also it has a unique potential to directly visualize the particle-biology interactions. Therefore, we envision that the data-driven hyperspectral SRS imaging platform will continue bridging the gap of knowledge on plastic pollution at the nano level with an expanded spectral library to study more complicated biological and environmental samples.

We thank data scientists Tingran Wang and Mariam Avagyan for the discussion on the algorithms. We thank the support from Research Initiatives in Science and Engineering (RISE) of Columbia University, Hudson River Foundation, NIEHS Center for Environmental Health and Justice in Northern Manhattan (NIEHS P-30-ES009089), and Rutgers Center for Environmental Exposure and Disease (NIEHS P30-ES005022).