Association between early-life mosquito repellents exposure and ADHD-like behaviours.

BACKGROUND: Limited research has explored the impact of mosquito repellents exposure during early life on ADHD symptoms. This study aimed to explore the associations of exposure to mosquito repellents from pregnancy to 3 years old and the prevalence of ADHD-like behaviours among children aged 3-9 years, and further identify the sensitive exposure period. METHODS: A cross-sectional study was conducted, including 12 275 children in Hefei City, China. Exposure was self-reported via primary caregivers. ADHD-like behaviours were measured by the Swanson, Nolan and Pelham, version IV scale (SNAP-IV), and Conners' Parent Rating Scale (CPRS). Cross-over analysis, binary logistic regression and linear regression were employed. RESULTS: After adjusting for confounding variables, early-life exposure to mosquito repellents was associated with a higher risk of ADHD-like behaviours (OR = 1.81, 95% CI = 1.49-2.19). By comparing the strength of the association for each subgroup, we found exposure during 1-3 years old was a sensitive period (OR = 1.89, 95% CI = 1.25-2.87) by the cross-over analysis. Furthermore, we found a dose-response relationship in which the likelihood of ADHD-like behaviours increased with children's early-life mosquito repellents exposure dose. CONCLUSIONS: Early-life exposure to mosquito repellents is linked with an elevated risk of ADHD-like behaviours in children, with a sensitive period identified during 1-3 years old.

Toxicological review of micro- and nano-plastics in aquatic environments: Risks to ecosystems, food web dynamics and human health.

The increase of micro- and nano-plastics (MNPs) in aquatic environments has become a significant concern due to their potential toxicological effects on ecosystems, food web dynamics, and human health. These plastic particles emerge from a range of sources, such as the breakdown of larger plastic waste, consumer products, and industrial outputs. This review provides a detailed report of the transmission and dangers of MNPs in aquatic ecosystems, environmental behavior, and interactions within aquatic food webs, emphasizing their toxic impact on marine life. It explores the relationship between particle size and toxicity, their distribution in different tissues, and the process of trophic transfer through the food web. MNPs, once consumed, can be found in various organs, including the digestive system, gills, and liver. Their consumption by lower trophic level organisms facilitates their progression up the food chain, potentially leading to bioaccumulation and biomagnification, thereby posing substantial risks to the health, reproduction, and behavior of aquatic species. This work also explores how MNPs, through their persistence and bioaccumulation, pose risks to aquatic biodiversity and disrupt trophic relationships. The review also addresses the implications of MNPs for human health, particularly through the consumption of contaminated seafood, highlighting the direct and indirect pathways through which humans are exposed to these pollutants. Furthermore, the review highlights the recommendations for future research directions, emphasizing the integration of ecological, toxicological, and human health studies to inform risk assessments and develop mitigation strategies to address the global challenge of plastic pollution in aquatic environments.

Pyridazine-Promoted Construction of Vinylene-Linked Covalent Organic Frameworks with Exceptional Capability of Stepwise Water Harvesting.

In this study, we successfully developed two novel vinylene-linked covalent organic frameworks (COFs) using 2-connected 3,6-dimethylpyridazine through Knoevenagel condensation. These COFs featured finely tailored micro-/nano-scale pore sizes, high surface areas and stable non-polar vinylene linkages. Finely resolved powder X-ray diffraction patterns demonstrated highly crystalline structures with a hexagonal lattice in the AA layer stacking. The resulting one-dimensional channels possess strong hydrogen-bond accepting sites arising from the decorated cis-azo/azine units with two pairs of fully exposed lone pair electrons, endowing the as-prepared COFs with exceptional water absorption properties. The g-DZPH-COF exhibited successive steep water uptake steps starting from low relative pressures (P/PSTA = 0.1), with the remarkable water uptake capacity of 0.26 g/g at P/PSTA = 0.2 (25°C), which is the optimal value recorded among the reported COFs. Dynamic vapour sorption measurements revealed the fast kinetics of these COFs, even in the cluster formation process. Water uptake and release cycling tests demonstrated their outstanding hydrolytic stability, durability, and adsorption-desorption retention ability.

Mesoporous Vinylene-Linked Covalent Organic Frameworks with Heteroatom-Tuned Crystallinity and Photocatalytic Behaviors.

Owing to its prominent π-delocalization and stability, vinylene linkage holds great merits in the construction of covalent organic frameworks (COFs) with promising semiconducting properties. However, carbon-carbon double bond formation reaction always exhibits relatively low reversibility, unfavorable for the formation of high crystalline frameworks through self-error correction and assembling processes. In this work, we report a heteroatom-tuned strategy to build up a series of two-dimensional (2D) vinylene-linked COFs by Knoevenagel condensation of an electron-deficient methylthiazolyl-based monomer with different triformyl substituted (hetero-)aromatic derivatives. The resulting COFs show high-quality periodic mesoporous structures with high surface areas. Embedding heteroatoms into the backbones enables significantly improving their crystallinity, and finely tailoring their semiconducting structures. Upon visible light stimulation, one of the as-prepared COFs with donor-π-acceptor structure could deliver a nearly seven-fold increase in the catalytic activity of hydrogen generation as compared with the other two. Meanwhile, in combination with high crystallinity and the matched conduction band energy level, such kind of COFs can be able to selectively generate singlet oxygen and superoxide radicals in a high ratio of up to 30:1, allowing for catalyzing aerobic thioanisole oxidation in distinctly tunable activities through the substituent electronic effect of the substrates.

Hexatopic Vertex-Directed Approach to Vinylene-Linked Covalent Organic Frameworks with Heteroporous Topologies.

A D3h-symmetric hexatopic monomer was first prepared by attaching the three-fold ditopic moiety 2,6-dimethylpyridine to the meta-positions of a phenyl ring. It was further condensed at its six pyridylmethyl carbons with linear ditopic aromatic dialdehydes, resulting in two vinylene-linked COFs with heteroporous topologies, as revealed by powder X-ray diffraction (PXRD), nitrogen sorption, and pore-size distribution analyses, as well as transmission electron microscopy (TEM) image. The linear- and cross-conjugations, respectively, arising from the 2,6-linked pyridines and meta-linked phenylenes in the hexatopic nodes rendered the resultant COFs with well-patterned π-delocalization, allowing for efficiently catalyzing the bromination of aromatic derivatives with the pore-size-dependent conversion yields and regioselectivity under the irradiation of green light.

Diversifying Emotional Dialogue Generation via Selective Adversarial Training.

Emotional perception and expression are very important for building intelligent conversational systems that are human-like and attractive. Although deep neural approaches have made great progress in the field of conversation generation, there is still a lot of room for research on how to guide systems in generating responses with appropriate emotions. Meanwhile, the problem of systems' tendency to generate high-frequency universal responses remains largely unsolved. To solve this problem, we propose a method to generate diverse emotional responses through selective perturbation. Our model includes a selective word perturbation module and a global emotion control module. The former is used to introduce disturbance factors into the generated responses and enhance their expression diversity. The latter maintains the coherence of the response by limiting the emotional distribution of the response and preventing excessive deviation of emotion and meaning. Experiments are designed on two datasets, and corresponding results show that our model outperforms existing baselines in terms of emotional expression and response diversity.

ACG-EmoCluster: A Novel Framework to Capture Spatial and Temporal Information from Emotional Speech Enhanced by DeepCluster.

Speech emotion recognition (SER) is a task that tailors a matching function between the speech features and the emotion labels. Speech data have higher information saturation than images and stronger temporal coherence than text. This makes entirely and effectively learning speech features challenging when using feature extractors designed for images or texts. In this paper, we propose a novel semi-supervised framework for extracting spatial and temporal features from speech, called the ACG-EmoCluster. This framework is equipped with a feature extractor for simultaneously extracting the spatial and temporal features, as well as a clustering classifier for enhancing the speech representations through unsupervised learning. Specifically, the feature extractor combines an Attn-Convolution neural network and a Bidirectional Gated Recurrent Unit (BiGRU). The Attn-Convolution network enjoys a global spatial receptive field and can be generalized to the convolution block of any neural networks according to the data scale. The BiGRU is conducive to learning temporal information on a small-scale dataset, thereby alleviating data dependence. The experimental results on the MSP-Podcast demonstrate that our ACG-EmoCluster can capture effective speech representation and outperform all baselines in both supervised and semi-supervised SER tasks.

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.

Heteroatom-Embedded Approach to Vinylene-Linked Covalent Organic Frameworks with Isoelectronic Structures for Photoredox Catalysis.

Embedding heteroatoms into the main backbones of polymeric materials has become an efficient tool for tailoring their structures and improving their properties. However, owing to comparatively harsh heteroatom-doping conditions, this has rarely been explored in covalent organic frameworks (COFs). Herein, upon aldol condensation of a trimethyl-substituted pyrylium salt with a tritopic aromatic aldehyde, a two-dimensional oxonium-embedded COF with vinylene linkages was achieved, which was further converted to a neutral pyridine-cored COF by in situ replacement of oxonium ions with nitrogen atoms under ammonia treatment. The two heteroatom-embedded COFs are conceptually isoelectronic with each other, featuring similar geometric structures but different electronic structures, rendering them capable of catalyzing the visible-light-promoted multi-component synthesis of tri-substituted pyridine derivatives with good recyclability.

MicroRNA-130a enhances the killing ability of natural killer cells against non-small cell lung cancer cells by targeting signal transducers and activators of transcription 3.

Non-small cell lung cancer (NSCLC) is a serious threat for human health and life. Natural killer (NK) cell-based immunotherapy is a promising anti-tumor strategy in various cancers including NSCLC. Emerging microRNA (miRNA) has been identified as vital regulators in NK cell-mediated immunosurveillance process. MicroRNA-130a (miR-130a) level and signal transducers and activators of transcription 3 (STAT3) mRNA level was measured by RT-qPCR assay. STAT3 protein level was determined by western blot assay. IFN-γ and TNF-α secretion was examined by corresponding ELISA kits. NK cell cytotoxicity was assessed by lactate dehydrogenase (LDH) assay. The interaction between miR-130a and STAT3 was explored by bioinformatics analysis, luciferase reporter assay and RNA immunoprecipitation (RIP) assay. We found that MiR-130a level was notably reduced and STAT3 expression was dramatically increased in primary NK cells isolated from NSCLC patients. But, miR-130a was highly expressed and STAT3 was low expressed in IL-2-activated NK-92 cells. Functional analysis revealed that miR-130a overexpression potentiated killing ability of NK cells against A549 cells. Further investigations unveiled that STAT3 was a target of miR-130a and STAT3 overexpression abrogated miR-130a-induced improvement in killing activity of NK cells against NSCLC cells. In conclusion, MiR-130a improved the killing capacity of NK cells against NSCLC cells by targeting STAT3, laying a foundation for future studies on the roles and molecular basis of miR-130a in NK cell-based immunotherapy against various cancers.

Visualization of the tissue distribution of fullerenols in zebrafish (Danio rerio) using imaging mass spectrometry.

With the wide application of fullerenols in biomedicine, their environmental exposure risks and toxicity to organisms have been extensively studied. However, there is still a lack of knowledge about the distribution of fullerenols in organisms as an important aspect of their mechanism of toxicity. High-resolution matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI-IMS) is an emerging technology for researching the distribution of molecules in biological tissue samples. Using this high-resolution technique, we map the distribution of fullerenols in zebrafish tissues, and the results suggest that fullerenols enter the gill, intestine, and muscle tissues and even permeate the blood-brain barrier, reaching the brain of zebrafish after aquatic exposure. Moreover, from the MS images of fullerenols, the distribution amount of fullerenols is highest in the gill, followed by that in the intestine and the small amount in muscle and brain tissues. As an emerging environmental pollutant, the establishment of this research method will provide a new method for the study of the environmental toxicity of carbon nanomaterials. Our results also indicated that this high-resolution imaging method could be applied to explore the mechanism of interaction between carbon nanomaterials and biological systems at the cellular level in the future.

Metformin Inhibit Lung Cancer Cell Growth and Invasion in Vitro as Well as Tumor Formation in Vivo Partially by Activating PP2A.

BACKGROUND The aim of this study was to investigate whether PP2A activation is involved in the anti-cancer activity of metformin. MATERIAL AND METHODS A549 and H1651 human lung cancer cells were constructed with stable a4 overexpression (O/E α4) or knockdown of PP2A catalytic subunit A/B(sh-PP2Ac). Influences of okadaic acid (OA) treatment, O/E α4 or sh-PP2Ac on metformin treated cells were investigated by cell viability, proliferation, apoptosis, and Transwell invasion assay in vitro. Protein expression levels of Bax, Bcl-2, Myc, and Akt as well as serine phosphorylation level of Bax, Myc, and Akt were examined by western blot. For in vivo assays, wild type (WT) or modified A549 cells were subcutaneously injected in nude mice, and metformin treatment on these xenografted tumors were assayed by tumor formation assay and western blot detecting cell proliferation marker PCNA (proliferating cell nuclear antigen) as well as protein expression level and serine phosphorylation level of Akt and Myc. RESULTS Metformin treatment significantly reduced A549 or H1651 cell growth and invasive capacity in vitro as well as Ser184 phosphorylation of Bax, Ser62 phosphorylation of Myc, and Ser473 phosphorylation of Akt, all of which could be partially attenuated by OA treatment, O/E α4 or sh-PP2Ac. Metformin treatment also significantly reduced tumor formation in vivo as well as protein expression of PCNA, Akt, Myc, and serine phosphorylation of the latter 2, which can be partially blocked by O/E α4 or sh-PP2Ac. CONCLUSIONS Metformin reduced lung cancer cell growth and invasion in vitro as well as tumor formation in vivo partially by activating PP2A.

Active learning for bird sound classification via a kernel-based extreme learning machine.

In recent years, research fields, including ecology, bioacoustics, signal processing, and machine learning, have made bird sound recognition a part of their focus. This has led to significant advancements within the field of ornithology, such as improved understanding of evolution, local biodiversity, mating rituals, and even the implications and realities associated to climate change. The volume of unlabeled bird sound data is now overwhelming, and comparatively little exploration is being made into methods for how best to handle them. In this study, two active learning (AL) methods are proposed, sparse-instance-based active learning (SI-AL), and least-confidence-score-based active learning (LCS-AL), both effectively reducing the need for expert human annotation. To both of these AL paradigms, a kernel-based extreme learning machine (KELM) is then integrated, and a comparison is made to the conventional support vector machine (SVM). Experimental results demonstrate that, when the classifier capacity is improved from an unweighted average recall of 60%-80%, KELM can outperform SVM even when a limited proportion of human annotations are used from the pool of data in both cases of SI-AL (minimum 34.5% vs minimum 59.0%) and LCS-AL (minimum 17.3% vs minimum 28.4%).

Capillary electrophoresis strategy to monitor the released and remaining nitric oxide from the same single cell using a specially designed water-soluble fluorescent probe.

Gasotransmitters including nitric oxide (NO), carbon monoxide (CO), and hydrogen sulfide (H2S) have attracted more and more attention in the past decades due to their unique signaling and functions. However, as a fundamental issue in the investigations of gasotransmitters, the cell membrane permeability and release behavior of them is controversial in reports because of the lack of an efficient approach to determine gasotransmitters released out of and remaining in the same cells simultaneously. To solve such problem, taking NO as representative, a robust and facile strategy has been reported based on a completely water-soluble fluorescent probe and a commercially available capillary electrophoresis system. A specially designed boron-dipyrromethene (BODIPY)-based fluorescent probe with two sulfonate groups, disodium 2,6-disulfonate-1,3,5,7-tetramethyl-8-(3',4'-diaminophenyl) difluoroboradiaza-s-indance (TMDSDAB), has been developed. As a turn-on fluorescent probe, TMDSDAB can react with NO promptly in aqueous media, and 540-fold enhancement of fluorescence is obtained. Using TMDSDAB, the trapping and quantification of NO released out of and remaining in the same single cell was achieved by capillary electrophoresis with laser-induced fluorescence detection. The limit of detection is 0.5 nM for NO. The proposed method has been applied to estimate the release behavior of single macrophages, and the results indicated that the cell membrane should be a barrier to NO diffusion.

Microplastics or micro-bioplastics released by wrinkling paper cup.

Paper cups have been widely used such as in the fast-food industry for drinking and are generally made of disposable material. To make the paper cup waterproof and prevent leakage, a thin layer of plastic such as polylactic acid (PLA) is commonly coated onto the inner wall surface. This plastic layer can potentially release debris as microplastics, particularly when the cup is wrinkled/crumpled to break and peel off the coating layer, which is tested herein. Using scanning electron microscope (SEM), the broken coating layer can be clearly observed. We then identify the coating material as plastic using mass and Raman spectra. We further employ Raman imaging to identify the released and fallen down debris as microplastics. We cross-check Raman image with SEM image to benefit each other and increase the analysis certainty, because Raman imaging can identify plastic via hyper spectrum to increase the signal-to-noise ratio, while SEM can visualise plastic with a high resolution down to micro-/nano- size. We then employ particle analysis algorithm to estimate the release amount, at approximate 180 microplastic/wrinkle, or micro-bioplastic if considering the main material of PLA as a bioplastic. Overall, we should not wrinkle the paper cup to avoid the potential release of microplastics or micro-bioplastics particularly before and during the drinking process, and the characterisation in this report might be helpful for further research on microplastics.

Association between caregiver-child interaction and autistic-like behaviors at around three years of age.

BACKGROUND: The prevalence of autistic-like behaviors is increasing worldwide, both in developed and developing countries. With a high disease burden and complex developmental causes, there has been much interest in the etiology of the disease, and there is a lack of evidence on the relationship between caregiver-child interaction and autistic-like behaviors. AIM/OBJECTIVE: This study investigated the association between caregiver-child interaction and children's autistic-like behaviors during early childhood. METHOD: The subjects of this study were 171 kindergartens selected from the Longhua Child Cohort Study (LCCS), and a total of 40,237 children around the age of three were included. Sociodemographic characteristics, family income, and frequency of interaction between caregivers and children were all filled in by the child's primary caregiver, and the adapted Chinese Autism Behavior Checklist was used to assess children's autism-like behaviors. Tobit Regression and ancovariance analysis (ANCOVA) were used to measure the relationship between caregiver-child interactions (family and social activities) and autism-like behaviors, with a two-tailed p value of <0.05 being significant. RESULTS: Tobit regression analyses found that in the 0-1 year age group, different frequencies of singing activities by caregivers with children (<3 times per week, 3-6 times per week, 6 times or more per week) were significantly negatively associated with autistic-like behaviors in a dose-response manner (B values of -0.323, -0.381, -0.544, all p < 0.0001); in the 1-3 year age group, different frequencies of reading interactions by caregivers with children (<3 times per week, 3-6 times per week, 6 times or more per week) were also significantly negatively associated with autistic-like behaviors in a dose-response manner (B values of -0.388, -0.632, -0.956, all p < 0.0001), and similar associations were found in singing and chatting interactions. CONCLUSIONS: Our findings suggest that higher frequencies of early caregiver-child interactions are associated with lower levers of autistic-like behaviors in children around the age of three years.

Nanoplastic contamination: Impact on zebrafish liver metabolism and implications for aquatic environmental health.

Nanoplastics (NPs) are increasingly pervasive in the environment, raising concerns about their potential health implications, particularly within aquatic ecosystems. This study investigated the impact of polystyrene nanoparticles (PSN) on zebrafish liver metabolism using liquid chromatography hybrid quadrupole time of flight mass spectrometry (LC-QTOF-MS) based non-targeted metabolomics. Zebrafish were exposed to 50 nm PSN for 28 days at low (L-PSN) and high (H-PSN) concentrations (0.1 and 10 mg/L, respectively) via water. The results revealed significant alterations in key metabolic pathways in low and high exposure groups. The liver metabolites showed different metabolic responses with L-PSN and H-PSN. A total of 2078 metabolite features were identified from the raw data obtained in both positive and negative ion modes, with 190 metabolites deemed statistically significant in both L-PSN and H-PSN groups. Disruptions in lipid metabolism, inflammation, oxidative stress, DNA damage, and amino acid synthesis were identified. Notably, L-PSN exposure induced changes in DNA building blocks, membrane-associated biomarkers, and immune-related metabolites, while H-PSN exposure was associated with oxidative stress, altered antioxidant metabolites, and liver injury. For the first time, L-PSN was found depolymerized in the liver by cytochrome P450 enzymes. Utilizing an analytical approach to the adverse outcome pathway (AOP), impaired lipid metabolism and oxidative stress have been identified as potentially conserved key events (KEs) associated with PSN exposure. These KEs further induced liver inflammation, steatosis, and fibrosis at the tissue and organ level. Ultimately, this could significantly impact biological health. The study highlights the PSN-induced effects on zebrafish liver metabolism, emphasizing the need for a better understanding of the risks associated with NPs contamination in aquatic ecosystems.

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.

Separation and flow cytometry analysis of microplastics and nanoplastics.

In recent years, the utilization of flow cytometry for quantitative microplastic analysis has gained prominence. However, the current methods have some drawbacks that need to be improved. The present study aims to enhance the flow cytometry detection protocols for Nile red (NR) stained microplastics, facilitating distinct microplastic and nanoplastic enumeration. By elevating dimethyl sulfoxide (DMSO) concentration to 20%-30% within the solution, NR solubility improved and agglomeration reduced. The analysis of 26 replicates of polystyrene (PS) liquid samples through four distinct dot plots highlighted the superior accuracy of dot plots integrating yellow fluorescence. Through systematic staining of varying NR concentrations across three microplastic liquid samples (polyethylene terephthalate, polyethylene, and polypropylene), the optimal staining concentration was determined to be 15-20 µg/mL. The distributions of agglomerated NR and NR stained PS under two scenarios-dissolved NR and partially agglomerated NR-were compared. Results showed their distinct distributions within the side scatter versus yellow fluorescence dot plot. Counting results from gradient-diluted PS liquid samples revealed a microplastic detection lower limit of 104 particles/mL, with an optimal concentration range of 105-106 particles/mL. Flow cytometric assessment of PS microspheres spanning 150 nm to 40 µm indicated a 150 nm particle size detection minimum. Our investigation validated the efficacy of NR staining and subsequent flow cytometry analysis across eleven types of microplastics. Separation and concentration of microplastics (1.0-50.0 µm) and nanoplastics (0.2-1.0 µm) were achieved via sequential sieving through 50, 1.0, and 0.2 µm filter membranes. We used a combination of multiple filtration steps and flow cytometry to analyze microplastics and nanoplastics in nine simulated water samples. Our results showed that the combined amount of microplastics (1.0-50.0 µm) and nanoplastics (0.2-1.0 µm) after filtration had a ratio of 0.80-1.19 compared to the total microplastic concentration before filtration. This result confirms the practicality of our approach. By enhancing flow cytometry-based microplastic and nanoplastic detection protocols, our study provides pivotal technical support for research concerning quantitative toxicity assessment of microplastic and nanoplastic pollution.

Preparation of Cross-Sectional Membrane Samples for Scanning Electron Microscopy Characterizations Using a New Frozen Section Technique.

Characterization of the cross-sectional morphologies of polymeric membranes are critical in understanding the relationship of structure and membrane separation performances. However, preparation of cross-sectional samples with flat surfaces for scanning electron microscopy (SEM) characterizations is challenging due to the toughness of the non-woven fabric support. In this work, a new frozen section technique was developed to prepare the cross-sectional membrane samples. A special mold was self-designed to embed membranes orientationally. The frozen section parameters, including the embedding medium, cryostat working temperature, and sectioning thickness were optimized. The SEM characterizations demonstrated that the frozen section technique, using ultrapure water as the embedding medium at a working temperature of -30 °C and a sectioning thickness of 0.5 µm, was efficient for the preparation of the membrane samples. Three methods of preparation for the cross-sectional polymeric membranes, including the conventional liquid nitrogen cryogenic fracture, the broad ion beam (BIB) polishing, and the frozen section technique were compared, which showed that the modified frozen section method was efficient and low cost. This developed method could not only accelerate the development of membrane technology but also has great potential for applications in preparation of other solid samples.