The concern over plastic contamination has led to bans on plastic shopping bags, often replaced by paper ones. However, logos painted or printed on paper bags may still contain plastics, as ...investigated herein. In some logos, for example, white pigment of titanium dioxide (TiO2) nanoparticles are bound with plastic binder onto the cellulose surface of the paper. This hybrid of plastic and nanoparticle is examined using scanning electron microscope (SEM) to characterise morphology physically, and Raman imaging to identify and visualise them chemically. Raman imaging scans the sample to separate images and identify not only plastic but also the co-formulated pigment. The scan generates a hyperspectral matrix containing hundreds to thousands of spectra, and subsequent analysis can enhance the signal-to-noise ratio. Decoding the hyperspectral matrix using chemometrics like principal component analysis (PCA) can effectively map plastic and pigment separately with increased certainty. The image can be further refined through 3-dimensional surface fitting for deconvolution, providing direct visualisation of the plastic-nanoparticle hybrid at a density of approximately 7.3 million particles per square millimetre. Overall, caution should be exercised when using paper bags, as they may not be entirely free of plastics. Raman imaging proves to be an effective method for identifying and visualising complex components, including plastics and nanoparticles.
The concern over plastic contamination has led to bans on plastic shopping bags, replaced by paper alternatives. However, some logos on paper bags may still contain plastics, which is investigated to confirm the presence of plastic-nanoparticle hybrid using SEM and Raman imaging. By employing decoding algorithms such as PCA to separately map plastic and pigment, and utilising 3D surface fitting to deconvolute the image, the hybrid plastic-nanoparticle is estimated at a density of approximately 7.3 million particles per square millimetre. It's important to exercise caution and not assume these items are plastic-free. This aspect of plastics may have been overlooked as another potential source of contamination.
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•paper bags might be not plastic-free at logo area.•hybrid structures of plastic-nanoparticle of TiO2 is estimated at ∼7.3 million/mm2.•Raman imaging via hyperspectral matrix can increase signal-to-noise ratio.•principal component analysis (PCA) can separately map the plastic and pigment.•Gaussian surface fitting towards deconvolution can further increase signal-to-noise ratio.
Total oxidisable precursor (TOP) assay can oxidise some per- and polyfluoroalkyl substances (PFASs) and their precursors, most of which cannot be quantitatively detected so far, and convert them to ...detectable PFASs, such as perfluoroalkyl acids (PFAAs). However, the conversion is constrained by the complexity of the target samples, including co-existent organics, unknown PFAS precursors, and background. In this study, the TOP assay is modified to increase the oxidation and conversion efficiency by changing the initial concentration of target sample, increasing oxidising doses, time, temperature, etc. The modified TOP assay is applied to test several aqueous film-forming foams (AFFF) and a PFAS-contaminated soil extract. The sum concentrations of the detectable PFASs are increased by up to ∼534× in the AFFF samples and ∼7× in the PFAS-contaminated soil extract. The detectable fluorotelomer sulfonate (FTS, such as 6:2/8:2 FTS) is accounted as an oxidation indicator to monitor the oxidation and conversion progress of the oxidisable PFASs precursors to the detectable PFASs. Overall, the modified TOP assay could be an appropriate method for identifying missing PFASs mass in complex matrices by detecting the PFASs precursors effectively.
Microplastics and nanoplastics have secretly entered our daily lives but the extent of the problem is still unclear, as the characterisation is still a challenge, particularly for nanoplastics. ...Herein we test a blender that we use in our kitchen to make juice and we find that a significant amount of microplastics and nanoplastics (∼0.36–0.78 × 109 within 30 s) are released from the plastic container. We advance the characterisation of microplastics and nanoplastics using Raman imaging to generate a scanning spectrum matrix, akin to a hyperspectral matrix, which contains 900 spectra (30 × 30). By mapping these hundreds of spectra as images, with help of algorithms, we can directly visualise the microplastics and nanoplastics with an increased sensitivity from statistical point of view. Raman imaging has a main disadvantage of the imaging resolution, originating from the diffraction of the laser spot, which is proposed to be improved by shrinking the scanning pixel size, zooming in the scanning area to capture details of nanoplastics. Using image re-construction towards deconvolution, the nanoplastics can be effective characterised and the bumpy image of microplastics stemming from the signal variation can be subsequently smoothened to further increase the signal-noise ratio. Overall, the advancements on Raman imaging can provide a suitable approach to characterise microplastics and nanoplastics released in our daily lives, for which we should be cautious.
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•Raman imaging identifies acrylonitrile butadiene styrene micro- and nanoplastics.•The PCA/algebra-based algorithm minimises bias for plastic identification.•Image re-construction via Gaussian fitting helps to reduce the noise in Raman maps.•Billions of plastic fragments may be released during 30-second blending.
The ubiquitousness of per- and polyfluoroalkyl substances (PFAS) is a big concern and PFAS remediation is urgently needed such as via degradation. While previous studies have explored ultrasonic ...degradation of PFAS, work evaluating the operational parameters is rare, especially concerning real wastes such as aqueous film-forming foam (AFFF) and foam fractionate (FF). This study investigates the key operational parameters affecting the degradation efficiency of PFAS, encompassing ultrasonication frequency (580–1144 kHz), power intensity (125–187.5 W), initial concentration (0.08–40 ppm), treatment duration (0.5–3 h), sample volume (100–500 mL), and PFAS structure (perfluorooctanoic acid or PFOA; perfluorooctane sulfonate or PFOS; 6:2 fluorotelomer sulfonate or 6:2 FTS). The defluorination kinetics is different from the removal/degradation kinetics due to the generation of degradation intermediates, suggesting the complex degradation mechanism, which should be evaluated to close the mass balance effectively. Notably, the optimised ultrasonic system achieves ∼125%/∼115% defluorination in AFFF/FF example wastes (compared to ∼65%/∼97% removal) despite their complex composition and the involvement of total oxidizable precursor (TOP) assay. In the meantime, a few new PFAS are detected in the post-treatments, including perfluorohexane sulfonic acid (PFHxS) and 10:2 fluorotelomer sulfonate (10:2 FTS) in the AFFF, and perfluorooctane sulfonamide (FOSA) and 8:2 fluorotelomer sulfonate (8:2 FTS) in the FF, again suggesting the complex degradation mechanism. Overall, ultrasonication is effective to degrade PFAS real example wastes, advancing its potential for scale-up applications.
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•Ultrasonication can effectively degrade PFAS and AFFF/FF example wastes.•Ultrasonication of PFAS is balanced between degradation efficiency and absolute amount.•PFAS defluorination kinetics differs from degradation, mass balance needs to be closed.•PFAS example wastes can achieve defluorination exceeding 100% compared to TOP assay.
We are directly exposed to microplastic contamination via indoor air that we breathe daily, for which the characterisation of microplastics is still a challenge. Herein, two typical air filter ...samples were collected, one from an air-conditioner and another from a personal computer, both of which have been working for around half a year to collect and accumulate microplastics in the indoor air, like microplastic banks. After the sample preparation to remove the mineral dusts, Raman imaging was employed to directly and simultaneously identify and visualise microplastics of polyethylene terephthalate (PET) fibres, distinguish them from other fibres such as cellulose and cross-check them with a scanning electron microscope (SEM). To count the microplastics and to avoid the quantification bias, several areas were randomly scanned and imaged to statistically estimate the percentage of microplastic fibres in the analysed samples. The microplastics amount, which has been estimated at 73-88,000 fibers per filter per half a year, varies and depends on the indoor environment so that the air filter can work as a good indicator to monitor the quality of the indoor air from the microplastic perspective. Overall, human are directly exposed to this emerging contamination every day, raising environmental concerns. Raman imaging characterisation and its corresponding statistical information can help pursue further research on microplastics.
