Dual-Principal Component Analysis of the Raman Spectrum Matrix to Automatically Identify and Visualize Microplastics and Nanoplastics.

As emerging contaminants, microplastics are challenging to characterize, particularly when their size is at the nanoscale. While imaging technology has received increasing attention recently, such as Raman imaging, decoding the scanning spectrum matrix can be difficult to achieve result digitally and automatically via software and usually requires the involvement of personal experience and expertise. Herewith, we show a dual-principal component analysis (PCA) approach, where (i) the first round of PCA analysis focuses on the raw spectrum data from the Raman scanning matrix and generates two new matrices, with one containing the spectrum profile to yield the PCA spectrum and the other containing the PCA intensity to be mapped as an image; (ii) the second round of PCA analysis merges the spectrum from the first round of PCA with the standard spectra of eight common plastics, to generate a correlation matrix. From the correlation value, we can digitally assign the principal components from the first round of PCA analysis to the plastics toward imaging, akin to dataset indexing. We also demonstrate the effect of the data pretreatment and the wavenumber variations. Overall, this dual-PCA approach paves the way for machine learning to analyze microplastics and particularly nanoplastics.

Sequence-Controlled Polymers Through Entropy-Driven Ring-Opening Metathesis Polymerization: Theory, Molecular Weight Control, and Monomer Design.

The bulk properties of a copolymer are directly affected by monomer sequence, yet efficient, scalable, and controllable syntheses of sequenced copolymers remain a defining challenge in polymer science. We have previously demonstrated, using polymers prepared by a step-growth synthesis, that hydrolytic degradation of poly(lactic- co-glycolic acid)s is dramatically affected by sequence. While much was learned, the step-growth mechanism gave no molecular weight control, unpredictable yields, and meager scalability. Herein, we describe the synthesis of closely related sequenced polyesters prepared by entropy-driven ring-opening metathesis polymerization (ED-ROMP) of strainless macromonomers with imbedded monomer sequences of lactic, glycolic, 6-hydroxy hexanoic, and syringic acids. The incorporation of ethylene glycol and metathesis linkers facilitated synthesis and provided the olefin functionality needed for ED-ROMP. Ring-closing to prepare the cyclic macromonomers was demonstrated using both ring-closing metathesis and macrolactonization reactions. Polymerization produced macromolecules with controlled molecular weights on a multigram scale. To further enhance molecular weight control, the macromonomers were prepared with cis-olefins in the metathesis-active segment. Under these selectivity-enhanced (SEED-ROMP) conditions, first-order kinetics and narrow dispersities were observed and the effect of catalyst initiation rate on the polymerization was investigated. Enhanced living character was further demonstrated through the preparation of block copolymers. Computational analysis suggested that the enhanced polymerization kinetics were due to the cis-macrocyclic olefin being less flexible and having a larger population of metathesis-reactive conformers. Although used for polyesters in this investigation, SEED-ROMP represents a general method for incorporation of sequenced segments into molecular weight-controlled polymers.

A Gold Nanoclusters Film Supported on Polydopamine for Fluorescent Sensing of Free Bilirubin.

Serum bilirubin is an important biomarker for the diagnosis of various types of liver diseases and blood disorders. A polydopamine/gold nanoclusters composite film was fabricated for the fluorescent sensing of free bilirubin. Bovine serum albumin (BSA)-stabilized gold nanoclusters (AuNCs) were used as probes for biorecognition. The polydopamine film was utilized as an adhesion layer for immobilization of AuNCs. When the composite film was exposed to free bilirubin, due to the complex that was formed between BSA and free bilirubin, the fluorescence intensity of the composite film was gradually weakened as the bilirubin concentration increased. The fluorescence quenching ratio (F0/F) was linearly proportional to free bilirubin over the concentration range of 0.8~50 µmol/L with a limit of detection of 0.61 ± 0.12 µmol/L (S/N = 3). The response was quick, the film was recyclable, and common ingredients in human serum did not interfere with the detection of free bilirubin.

Processive Degradation of Crystalline Cellulose by a Multimodular Endoglucanase via a Wirewalking Mode.

Processive hydrolysis of crystalline cellulose by cellulases is a critical step for lignocellulose deconstruction. The classic Trichoderma reesei exoglucanase TrCel7A, which has a closed active-site tunnel, starts each processive run by threading the tunnel with a cellulose chain. Loop regions are necessary for tunnel conformation, resulting in weak thermostability of fungal exoglucanases. However, endoglucanase CcCel9A, from the thermophilic bacterium Clostridium cellulosi, comprises a glycoside hydrolase (GH) family 9 module with an open cleft and five carbohydrate-binding modules (CBMs) and hydrolyzes crystalline cellulose processively. How CcCel9A and other similar GH9 enzymes bind to the smooth surface of crystalline cellulose to achieve processivity is still unknown. Our results demonstrate that the C-terminal CBM3b and three CBMX2s enhance productive adsorption to cellulose, while the CBM3c adjacent to the GH9 is tightly bound to 11 glucosyl units, thereby extending the catalytic cleft to 17 subsites, which facilitates decrystallization by forming a supramodular binding surface. In the open cleft, the strong interaction forces between substrate-binding subsites and glucosyl rings enable cleavage of the hydrogen bonds and extraction of a single cellulose chain. In addition, subsite -4 is capable of drawing the chain to its favored location. Cellotetraose is released from the open cleft as the initial product to achieve high processivity, which is further hydrolyzed to cellotriose, cellobiose and glucose by the catalytic cleft of the endoglucanase. On this basis, we propose a wirewalking mode for processive degradation of crystalline cellulose by an endoglucanase, which provides insights for rational design of industrial cellulases.

RGD Peptides-Conjugated Pluronic Triblock Copolymers Encapsulated with AP-2α Expression Plasmid for Targeting Gastric Cancer Therapy in Vitro and in Vivo.

Gastric cancer, a high-risk malignancy, is a genetic disease developing from a cooperation of multiple gene mutations and a multistep process. Gene therapy is a novel treatment method for treating gastric cancer. Here, we developed a novel Arg-Gly-Asp (RGD) peptides conjugated copolymers nanoparticles-based gene delivery system in order to actively targeting inhibit the growth of gastric cancer cells. These transcription factor (AP-2α) expression plasmids were also encapsulated into pluronic triblock copolymers nanoparticles which was constituted of poly(ethylene glycol)-block-poly(propylene glycol)- block-poly(ethylene glycol) (PEO-block-PPO-block-PEO, P123). The size, morphology and composition of prepared nanocomposites were further characterized by nuclear magnetic resonance (NMR), transmission electron microscopy (TEM) and dynamic light scattering (DLS). In MTT (3-(4,5-dimethyl-2-thiazolyl)-2,5-diphenyltetrazolium bromide) analysis, these nanocomposites have minor effects on the proliferation of GES-1 cells but significantly decreased the viability of MGC-803, suggesting they own low cytotoxicity but good antitumor activity. The following in vivo evaluation experiments confirmed that these nanocomposites could prevent the growth of gastric cancer cells in the tumor xenograft mice model. In conclusion, these unique RGD peptides conjugated P123 encapsulated AP-2α nanocomposites could selectively and continually kill gastric cancer cells by over-expression of AP-2α in vitro and in vivo; this exhibits huge promising applications in clinical gastric cancer therapy.

P. gingivalis in oral-prostate axis exacerbates benign prostatic hyperplasia via IL-6/IL-6R pathway.

BACKGROUND: Benign prostatic hyperplasia (BPH) is the most common disease in elderly men. There is increasing evidence that periodontitis increases the risk of BPH, but the specific mechanism remains unclear. This study aimed to explore the role and mechanism of the key periodontal pathogen Porphyromonas gingivalis (P. gingivalis) in the development of BPH. METHODS: The subgingival plaque (Sp) and prostatic fluid (Pf) of patients with BPH concurrent periodontitis were extracted and cultured for 16S rDNA sequencing. Ligature-induced periodontitis, testosterone-induced BPH and the composite models in rats were established. The P. gingivalis and its toxic factor P. gingivalis lipopolysaccharide (P.g-LPS) were injected into the ventral lobe of prostate in rats to simulate its colonization of prostate. P.g-LPS was used to construct the prostate cell infection model for mechanism exploration. RESULTS: P. gingivalis, Streptococcus oralis, Capnocytophaga ochracea and other oral pathogens were simultaneously detected in the Pf and Sp of patients with BPH concurrent periodontitis, and the average relative abundance of P. gingivalis was found to be the highest. P. gingivalis was detected in both Pf and Sp in 62.5% of patients. Simultaneous periodontitis and BPH synergistically aggravated prostate histological changes. P. gingivalis and P.g-LPS infection could induce obvious hyperplasia of the prostate epithelium and stroma (epithelial thickness was 2.97- and 3.08-fold that of control group, respectively), and increase of collagen fibrosis (3.81- and 5.02-fold that of control group, respectively). P. gingivalis infection promoted prostate cell proliferation, inhibited apoptosis, and upregulated the expression of inflammatory cytokines interleukin-6 (IL-6; 4.47-fold), interleukin-6 receptor-α (IL-6Rα; 5.74-fold) and glycoprotein 130 (gp130; 4.47-fold) in prostatic tissue. P.g-LPS could significantly inhibit cell apoptosis, promote mitosis and proliferation of cells. P.g-LPS activates the Akt pathway through IL-6/IL-6Rα/gp130 complex, which destroys the imbalance between proliferation and apoptosis of prostate cells, induces BPH. CONCLUSION: P. gingivalis was abundant in the Pf of patients with BPH concurrent periodontitis. P. gingivalis infection can promote BPH, which may affect the progression of BPH via inflammation and the Akt signaling pathway.

Accelerated transformation of plastic furniture into microplastics and nanoplastics by fire.

Numerous plastic items are known to gradually degrade and release microplastics and nanoplastics under certain conditions, which can be significantly accelerated by fire combustion. Unfortunately there is a limited knowledge about this burning process because the characterisation on microplastics and nanoplastics is still a challenge. In this study, an outdoor plastic chair is subjected to a combustion process, the change in the surface functional groups (due to different degree of burning) and the release of microplastics and nanoplastics are investigated. During the combustion process, the plastic is molten, burned and deposited on solid surfaces including concrete, stone and glass. Scanning electron microscopy (SEM) results show that the peeling off the deposited plastic generates a large number of fragments. Through Raman imaging, these fragments are characterised as polypropylene (PP) microplastics and nanoplastics due to appearance of characteristic peaks. To further increase the sensitivity, several algorithms are tested and optimised, including logic-based, non-supervised principal component analysis (PCA)-based, algebra-based and their hybrids (to intentionally correct the non-supervised PCA) to enable the effective extraction of the key information towards plastics characterisation, particularly by distinguishing the signal from the background noise towards the visualisation of the different degrees of burning. Based on the findings from Raman imaging and SEM, it is estimated that tens of microplastics and nanoplastics are created per µm2. Overall Raman imaging can be a suitable approach to characterise the microplastics and nanoplastics in a complex background, such as the fire-burned plastic items.

Unveiling microplastics from zippers: Characterisation and visualisation through Raman imaging analysis.

Microplastics have emerged as a global concern due to the increased plastic contamination found in a variety of sources. Herein we unveil microplastics released from plastic zippers that can generally be found in our clothes and textiles. We first employ a scanning electron microscope (SEM) to visualise the scratches developed on the zipper teeth and the derived particles. We then use Raman imaging to identify and simultaneously visualise the plastics from the chemical or molecular spectrum window. Based on hundreds to thousands of spectra, rather than a single spectrum or even a single peak that works as just a pixel in the image, imaging analysis can significantly increase the signal-to-noise ratio. Furthermore, the non-uniform distribution of components or multi-components can also be effectively imaged to avoid the possible bias from the single-spectrum analysis. The challenge to convert the hundreds to thousands of spectra of a hyperspectral matrix to an image is also discussed, and chemometrics is adopted and recommended to further improve the signal-to-noise ratio. The co-ingredient of titanium oxide in the zipper teeth/sewing lines is also effectively identified by Raman imaging. Based on the effective characterisation, we estimate that up to ~410 microplastics could be potentially released during each time of on-off zipping, although the variation can be expected and depends on several other factors. This study reminds us to be aware of the potential contamination derived from similar types of microplastic sources in our daily lives.

Raman imaging to identify microplastics released from toothbrushes: algorithms and particle analysis.

Microplastics are small plastic fragments that are of increasing concern due to their potential impacts on the environment and human health. The source of microplastics is not completely clear and might originate in daily lives such as from toothbrushes. When toothbrushes are used to clean teeth, small plastic debris and fragments can be potentially released into mouths directly or environment indirectly. This study aims to examine the release of microplastics from toothbrushes, using Raman imaging to identify and visualise the plastic debris with an increased signal-noise ratio via hyper-spectrum analysis. Using algorithms to convert the hyper-spectrum to an image, the plastic can be distinguished from the co-formulated titanium oxide particles that are not uniformly distributed along the plastics. The non-uniform distribution can lead to the bias results if a single spectrum analysis is conducted at one position rather than imaging analysis to scan an area. The potential false image originating from the off-focal position for the confocal Raman is overcome using the terrain map to guide the Raman imaging. The imaging analysis balancing between the low magnification to capture the overview and the high magnification to test the details is also discussed. While the release amount of microplastics from the toothbrush is estimated at thousands daily with the expected variation, the results of this study have confirmed the release of microplastics in daily lives. The imaging analysis approach along with algorithm can help to identify the chemical elements of microplastics from the complex background, which can benefit the further research on microplastics towards risk assessment and remediation.

Uptake, bioaccumulation, biodistribution and depuration of polystyrene nanoplastics in zebrafish (Danio rerio).

Plastic nanoparticles formed from both daily use of plastics and their wastes have emerged as a potential health and environmental hazard. It is necessary to study the biological process of nanoplastics in ecological risk assessment. To address this concern, we quantitatively investigated the accumulation and depuration of polystyrene nanoplastics (PSNs) in the tissues of zebrafish after the aquatic exposure using a quantitative method based on matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS). Via the PSNs-spiked freshwater, zebrafish were exposed to three different concentrations of PSNs for 30 days, followed by 16 days of depuration. The results showed that the amounts of PSNs accumulated in zebrafish tissues were in the following order: intestine > liver > gill > muscle > brain. The uptake and depuration of PSNs in zebrafish both followed pseudo-first-order kinetics. It was revealed that the bioaccumulation was concentration, tissue and time dependent. When the PSNs concentration is low, the steady state might take longer time (or not occur) than that of a high concentration. After 16 days of depuration, there were still some PSNs present in the tissues particularly in the brain, where it might take 70 days or more to remove 75 % of PSNs. Overall, this work offers important knowledge on the bioaccumulation of PSNs, which may be useful for future studies into the health hazards of PSNs in aquatic environments.

Ultrasound-enhanced Magnéli phase Ti4O7 anodic oxidation of per- and polyfluoroalkyl substances (PFAS) towards remediation of aqueous film forming foams (AFFF).

Per-and polyfluoroalkyl substances (PFAS) remediation is still a challenge. In this study, we propose a hybrid system that combines electrochemical treatment with ultrasound irradiation, aiming for an enhanced degradation of PFAS. Equipped with a titanium suboxide (Ti4O7) anode, the electrochemical cell is able to remove perfluorooctanoic acid (PFOA) effectively. Under the optimal conditions (50 mA/cm2 current density, 0.15 M Na2SO4 supporting electrolyte, and stainless steel/Ti4O7/stainless steel electrode configuration with a gap of ∼10 mm), the electrochemical process achieves ∼100 % PFOA removal and 43 % defluorination after 6 h. Applying ultrasound irradiation (130 kHz) alone offers a limited PFOA removal, with 33 % PFOA removal and 5.5 % defluorination. When the electrochemical process is combined with ultrasound irradiation, we observe a significant improvement in the remediation performance, with ∼100 % PFOA removal and 63.5 % defluorination, higher than the sum of 48.5 % (43 % achieved by the electrochemical process, plus 5.5 % by the ultrasound irradiation), implying synergistic removal/oxidation effects. The hybrid system also consistently shows the synergistic defluorination during degradation of other PFAS and the PFAS constituents in aqueous film forming foam (AFFF). We attribute the synergistic effect to an activated/cleaned electrode surface, improved mass transfer, and enhanced production of radicals.

Microplastics and nanoplastics released from a PPE mask under a simulated bushfire condition.

Due to COVID-19, large amounts of personal protective equipment (PPE) have been used, and many PPE units are made of plastics, such as face masks. The masks can be burned naturally in a bushfire or artificially at the incineration plants, and release microplastics and nanoplastics from the mask plastic fibres. A fire can cause the plastic, such as polypropylene (PP) fibres, to be molten and stick to the solid surface, such as glass, soil, concrete or plant, as films or islands, due to the binding property of the molten plastic material. Once the films or islands are peeled off in the processes such as weathering, ageing, or treatment and clean-up, there are residuals leftover, which are identified as nanoplastics and microplastics via Raman imaging, with the significant release amount of ~1100 nanoplastics / 10 µm2 or ~11 billion / cm2, and ~50 microplastics / 420 µm2 or ~12 million / cm2. Moreover, surface group is deviated on the plastic surface, which can also be distinguished and visualised as well via Raman imaging, down to nano size. This test validates the Raman imaging approach to capture microplastics and nanoplastics, and also provides important information about the fate and transportation of PPE mask in the environment, particularly when subjected to a fire. Overall, Raman imaging can be an effective option to characterise the microplastics and nanoplastics, along with the deviated surface group.

Investigating kitchen sponge-derived microplastics and nanoplastics with Raman imaging and multivariate analysis.

Microplastics can be found almost everywhere, including in our kitchens. The challenge is how to characterise them, particularly for the small ones (<1 µm), referred to as nanoplastics, when they are mixed with larger particles and other components. Herewith we advance Raman imaging to characterise microplastics and nanoplastics released from a dish sponge that we use every day to clean our cookware and eating utensils. The scanning electron microscopy result shows significantly different structures of the soft and hard layers of the sponge, with the hard layer being more likely to shed particles. By scanning the sample surface to generate a spectrum matrix, Raman imaging can significantly improve signal-noise-ratio, compared with individual Raman spectra. Through mapping the characteristic peaks from the matrix that contains hundreds, even thousands of Raman spectra, it is confirmed that the particles released from the soft and hard layers of the sponge are mainly Nylon PA6 and polyethylene terephthalate, respectively. Using principal component analysis (PCA) to decode the spectrum matrix further enhances the signal-noise ratio, which enables mapping the whole set of the spectrum, rather than the selected peaks. By optimising the Raman scanning parameters, the PCA-Raman imaging is able to reliably capture and visualise microplastics and nanoplastics released from both sides of the dish sponge, including a plastic-surrounding-sand composite structure. Overall, PCA-Raman imaging is a holistic and effective approach to characterising miniature plastic particles.

Raman imaging of microplastics and nanoplastics released from the printed toner powders burned by a mimicked bushfire.

Plastic contamination is a growing global concern, but the characterisation approaches for microplastics are limited so far, and even more lacking for nanoplastics. As another public concern, bushfire has the potential to exacerbate the negative ecological effects of plastic waste. We thus study the release of microplastics and nanoplastics from toner powers printed on a paper sheet following a mimicked bushfire. The results show that, along the fire frontier, there is a charred area first, then a cindered area towards mineralisation via a full combustion. We find that, depending on the extent of burning, the printed toner powers containing microplastics can melt to aggregate, or crack to break down to nanoplastics, which are well characterised by mass spectrometry and Raman imaging combined with algorithms. Overall, the results shed new light on the microplastics and nanoplastics once affected by bushfire.

Characterising microplastics in shower wastewater with Raman imaging.

Microplastics can potentially be released in our daily activities, such as via our showers, as our clothes are made of plastic fibres, and/or cotton fibres. The challenge is how to characterise these microplastics in shower debris. Herewith we employ Raman imaging to directly visualise the microplastics collected from shower wastewater. Raman can map an image from the scanning array that contains a matrix of thousands of spectra, featuring a considerably higher signal-noise ratio than that from a single spectrum. The increased signal-noise ratio reduces the complexity of sample preparation. Consequently, after the shower debris was sampled and washed, Raman imaging allowed us to distinguish the microplastic fibres from the background including cotton fibres and dirt aggregates. Interestingly, by adjusting the laser power intensity, the scanning process enabled simultaneous in-situ bleaching of the colorants formulated in the textile fibres and collection of signals. The disadvantage of Raman imaging such as the short focusing/working distance is also presented and discussed. Overall, the Raman imaging can extract meaningful information from the complex shower debris samples to enable analysis of microplastics.

Fire releases micro- and nanoplastics: Raman imaging on burned disposable gloves.

Raman imaging can effectively characterise microplastics and nanoplastics, which is validated here to capture the items released from the plastic gloves when subjected to a mimicked fire. During the COVID-19 pandemic, large quantities of personal protective equipment (PPE) units have been used, such as the disposable gloves. If discarded and poorly managed, plastics gloves might break down to release secondary contaminants. The breakdown process can be accelerated by burning in a bushfire or at the incineration plants. During the burning process, the functional groups on the surface can be burned differently due to their different thermal stabilities. The different degrees of burning can be distinguished and visualised via Raman imaging. In the meantime, at the bottom of the burned plastics, microplastics and nanoplastics can be generated at a significant amount. The possible false Raman imaging on microplastics and nanoplastics is also discussed, by effectively extracting and distinguishing the weak signal from the background or noise. Overall, these findings confirm the importance of effectively working waste incineration plants and litter prevention, and suggest that Raman imaging is a suitable approach to characterise microplastics and nanoplastics.

Raman imaging for the identification of Teflon microplastics and nanoplastics released from non-stick cookware.

The characterisation of microplastics is still difficult, and the challenge is even greater for nanoplastics. A possible source of these particles is the scratched surface of a non-stick cooking pot that is mainly coated with Teflon. Herein we employ Raman imaging to scan the surfaces of different non-stick pots and collect spectra as spectrum matrices, akin to a hyperspectral imaging process. We adjust and optimise different algorithms and create a new hybrid algorithm to extract the extremely weak signal of Teflon microplastics and particularly nanoplastics. We use multiple characteristic peaks of Teflon to create several images, and merge them to one, using a logic-based algorithm (i), in order to cross-check them and to increase the signal-noise ratio. To differentiate the varied peak heights towards image merging, an algebra-based algorithm (ii) is developed to process different images with weighting factors. To map the images via the whole set of the spectrum (not just from the individual characteristic peaks), a principal component analysis (PCA)-based algorithm (iii) is employed to orthogonally decode the spectrum matrix to the PCA spectrum and PCA intensity image. To effectively extract the Teflon spectrum information, a new hybrid algorithm is developed to justify the PCA spectra and merge the PCA intensity images with the algebra-based algorithm (PCA/algebra-based algorithm) (iv). Based on these developments and with the help of SEM, we estimate that thousands to millions of Teflon microplastics and nanoplastics might be released during a mimic cooking process. Overall, it is recommended that Raman imaging, along with the signal recognition algorithms, be combined with SEM to characterise and quantify microplastics and nanoplastics.

Applying Raman imaging to capture and identify microplastics and nanoplastics in the garden.

The characterisation of microplastics is still a challenge, and the challenge is even greater for nanoplastics, of which we only have a limited knowledge so far. Herewith we employ Raman imaging to directly visualise microplastics and nanoplastics which are released from the trimmer lines during lawn mowing. The signal-noise ratio of Raman imaging is significantly increased by generating an image from hundreds or thousands of Raman spectra, rather than from a single spectrum, and is further increased by combining with the logic-based and PCA-based algorithms. The increased signal-noise ratio enables us to capture and identify microplastics and particularly nanoplastics, including plastic fragments or shreds (with diameters / widths of 80 nm - 3 µm) and nanoparticles (with diameters of < 1000 nm) that are released during the mimicked mowing process. Using Raman imaging, we estimate that thousands of microplastics (0.1-5 mm), and billions of nanoplastics (< 1000 nm), are released per minute when a line trimmer is used to mow lawn. Overall, Raman imaging provides effective characterisation of the microplastics and is particularly suitable for nanoplastics.

Raman imaging and MALDI-MS towards identification of microplastics generated when using stationery markers.

The characterisation of microplastics is still a challenge, particularly when the sample is a mixture with a complex background, such as an ink mark on paper. To address this challenge, we developed and compared two approaches, (i) Raman imaging, combined with logic-based and principal component analysis (PCA)-based algorithms, and (ii) matrix-assisted laser desorption/ionisation-mass spectrometry (MALDI-MS). We found that, accordingly, (i) if the Raman signal of plastics is identifiable and not completely shielded by the background, Raman imaging can extract the plastic signals and visualise their distribution directly, with the help of a logic-based or PCA-based algorithm, via the "fingerprint" spectrum; (ii) when the Raman signal is shielded and masked by the background, MALDI-MS can effectively capture and identify the plastic polymer, via the "barcode" of the mass spectrum linked with the monomer. Overall, both Raman imaging and MALDI-MS have benefits and limitations for microplastic analysis; if accessible, the combined use of these two techniques is generally recommended, especially when assessing samples with strong background interference.

Identification and visualisation of microplastics via PCA to decode Raman spectrum matrix towards imaging.

To visualise microplastics and nanoplastics via Raman imaging, we need to scan the sample surface over a pixel array to collect Raman spectra as a matrix. The challenge is how to decode this spectrum matrix to map accurate and meaningful Raman images. This study compares two decoding approaches. The first approach is used when the sample contains several known types of microplastics whose standard spectra are available. We can map the Raman intensity at selected characteristic peaks as images. In order to increase the image certainty, we employ a logic-based algorithm to merge several images that are simultaneously mapped at several characteristic peaks to one image. However, the rest of the signals other than the selected peaks are ignored, meaning a low signal-noise ratio. The second approach for decoding is used when samples are complicated and standard spectra are not available. We employ principal component analysis (PCA) to decode the spectrum matrix. By selecting principal components (PC) and generating PC score curves to mimic the Raman spectrum, we can justify and assign the suspected items to microplastics and other materials. By mapping the PC loadings as images, microplastics and other materials can be simultaneously visualised. We analyse a sample containing two known microplastics to validate the effectiveness of the PCA-based algorithm. We then apply this method to analyse "unknown" microplastics printed on paper to extract Raman spectra from the complicated background and individually assign the images to paper fabric/additive, black carbon and microplastics, etc. Overall, the PCA-based algorithm shows some advantages and suggests a further step to decode Raman spectrum matrices towards machine learning.