Assessing the inconsistency of microplastic measurements in foods and beverages.

The widespread occurrence of microplastics (MPs) in the food chain has gained substantial recognition as a pressing concern, highlighting the inevitability of human exposure through ingestion of foodborne MPs, coupled with the release of MPs from plastic packaging. However, there are notable disparities in the reported numbers of MPs in foods and beverages, warranting a thorough investigation into the factors contributing to these discrepancies. Table salt is one of the major sources of MPs, and there was an approximately hundred-fold difference between the reviewed studies that reported the highest and lowest number of MPs. In addition, more noticeable discrepancies were discovered between studies on MPs released from teabags. One study reported that approximately 15 billion MPs were released into a cup of tea from a single teabag, whereas another research paper found only approximately 106.3 ± 14.6 MP/teabag after brewing. This comprehensive review focuses on the inconsistencies observed across studies examining MPs, shedding light on the plausible factors underlying these variations. Furthermore, the review outlines areas in analytical procedures that require enhancement and offers recommendations to promote accuracy and standardization in future research efforts, such as employing analytical methods capable of confirming the presence of MPs, using appropriate filter sizes, considering representative sample sizes when extrapolation is involved, and so on. By pinpointing the detection processes leading to the inconsistent results observed in MP studies, this comparative analysis will contribute to the development of reliable analytic methods for understanding the extent of microplastic contamination in the human food chain.

Real-Time Detection of Strawberry Ripeness Using Augmented Reality and Deep Learning.

Currently, strawberry harvesting relies heavily on human labour and subjective assessments of ripeness, resulting in inconsistent post-harvest quality. Therefore, the aim of this work is to automate this process and provide a more accurate and efficient way of assessing ripeness. We explored a unique combination of YOLOv7 object detection and augmented reality technology to detect and visualise the ripeness of strawberries. Our results showed that the proposed YOLOv7 object detection model, which employed transfer learning, fine-tuning and multi-scale training, accurately identified the level of ripeness of each strawberry with an mAP of 0.89 and an F1 score of 0.92. The tiny models have an average detection time of 18 ms per frame at a resolution of 1280 × 720 using a high-performance computer, thereby enabling real-time detection in the field. Our findings distinctly establish the superior performance of YOLOv7 when compared to other cutting-edge methodologies. We also suggest using Microsoft HoloLens 2 to overlay predicted ripeness labels onto each strawberry in the real world, providing a visual representation of the ripeness level. Despite some challenges, this work highlights the potential of augmented reality to assist farmers in harvesting support, which could have significant implications for current agricultural practices.

Robust Mesh Segmentation Using Feature-Aware Region Fusion.

This paper introduces a simple but powerful segmentation algorithm for 3D meshes. Our algorithm consists of two stages: over-segmentation and region fusion. In the first stage, adaptive space partition is applied to perform over-segmentation, which is very efficient. In the second stage, we define a new intra-region difference, inter-region difference, and fusion condition with the help of various shape features and propose an iterative region fusion method. As the region fusion process is feature aware, our algorithm can deal with complex 3D meshes robustly. Massive qualitative and quantitative experiments also validate the advantages of the proposed algorithm.

Trends of Augmented Reality for Agri-Food Applications.

Recent years have witnessed an increasing interest in deploying state-of-the-art augmented reality (AR) head-mounted displays (HMDs) for agri-food applications. The benefits of AR HMDs to agri-food industry stakeholders (e.g., food suppliers, retail/food service) have received growing attention and recognition. AR HMDs enable users to make healthier dietary choices, experience novel changes in their perception of taste, enhance the cooking and food shopping experience, improve productivity at work and enhance the implementation of precision farming. Therefore, although development costs are still high, the case for integration of AR in food chains appears to be compelling. This review will present the most recent developments of AR HMDs for agri-food relevant applications. The summarized applications can be clustered into different themes: (1) dietary and food nutrition assessment; (2) food sensory science; (3) changing the eating environment; (4) retail food chain applications; (5) enhancing the cooking experience; (6) food-related training and learning; and (7) food production and precision farming. Limitations of current practices will be highlighted, along with some proposed applications.

Activation of Toll-like receptor 2 enhances peripheral and tumor-infiltrating CD8+ T cell cytotoxicity in patients with gastric cancer.

BACKGROUND: Toll-like receptors (TLRs) play central roles in the initiation of innate immune response, and also control adaptive immunity activation. Thus, the aim of the study was to investigate the regulation of TLR activation to CD8+ T cells has not been fully elucidated in gastric cancer (GC). MATERIALS AND METHODS: Thirty-two GC patients and twenty-three healthy controls were enrolled. Expression profile of TLRs in peripheral and tumor-infiltrating CD8+ T cells was investigated. Purified CD8+ T cells were stimulated with Pam3Csk4, an agonist of TLR2, and cytotoxic and co-inhibitory molecules in CD8+ T cells was measured. Direct and indirect contact coculture system between CD8+ T cells and AGS cells was set up. Modulation of TLR2 activation to CD8+ T cells was assessed by measuring lactate dehydrogenase release and cytokine secretion. RESULTS: TLR2 mRNA and TLR2+ cell percentage was down-regulated in GC derived peripheral and tumor-infiltrating CD8+ T cells. CD8+ T cells from GC patients showed exhausted phenotype, which presented as decreased perforin/granzyme B, increased programmed death-1, and reduced cytotoxicity to AGS cells. TLR2 activation by Pam3Csk4 enhanced perforin and granzyme B expression in CD8+ T cells, however, did not affect either proinflammatory cytokine production or co-inhibitory molecules expression. Pam3Csk4 stimulation enhanced cytolytic activation of peripheral and tumor-infiltrating CD8+ T cells from GC, but not those from healthy individuals. CONCLUSION: The present data revealed an important immunomodulatory activity of TLR2 to CD8+ T cells in GC patients.

Synergistic adsorption and photocatalytic degradation of persist synthetic dyes by capsule-like porphyrin-based MOFs.

The synergistic effects involving surface adsorption and photocatalytic degradation commonly play significant roles in the removal of persistent synthetic organics from wastewater in the case of porous semiconductors. Inspired by the visible-light harvesting advantages of porphyrin-based MOFs, a capsule-like bimetallic porphyrin-based MOF (PCN-222(Ni/Hf)) has been successfully constructed through a facile hydrothermal method. In which, the Hf (IV) ions were exactly bonded to the carboxyl groups substituted on the porphyrin rings, meanwhile the Ni (II) ions were finely bonded to the -N inside the porphyrin rings. The adsorption/photocatalytic performances were assessed by using four persistent dyes including rhodamine B (RhB), basic violet 14 (BV14), crystal violet, and acid black 210 (AB210) as the target substances, and enhanced total removal efficiency was obtained by the bimetallic PCN-222(Ni/Hf) in comparison with that of single PCN-222(Hf). The electrochemical analyses and the sacrificial agent capture experiments were carried out to elucidate the photocatalytic mechanism, and the adsorption/photocatalytic stability of PCN-222(Ni/Hf) is also investigated. The work has broadened the applications of porphyrin-based MOFs in the removal of organics by combining their excellent surface adsorption capacity and photocatalytic activities.

Progress in the catalytic glycolysis of polyethylene terephthalate.

This paper briefly reviews the development history of polyethylene terephthalate (PET) and the recycling of PET. As one of the most promising way to degrade PET into oligomers and monomers that can be used for the production of high-quality PET, catalytic glycolysis is highlighted in this review. The developments on metal salt, metal oxide and ionic solvent catalysts for glycolysis of PET are systematically summarized, besides, the proposed catalytic mechanisms of ionic liquids (ILs) and deep eutectic solvents (DESs) are presented. The metallic catalysts show high catalytic performance but causing serious environmental pollution and high waste treatment costs, thereby it is proposed that metal-free catalysts, especially ILs and DESs can be the "greener" alternatives to address the PET waste problem. Additionally, the studies related to the glycolysis kinetics are discussed in this review, showing the results that PET glycolysis process consists of heterogeneous and homogeneous depolymerization, and different models should be used to investigate different depolymerization stages in order to obtain a more realistic picture.

Characterisation and Classification of Foodborne Bacteria Using Reflectance FTIR Microscopic Imaging.

This work investigates the application of reflectance Fourier transform infrared (FTIR) microscopic imaging for rapid, and non-invasive detection and classification between Bacillus subtilis and Escherichia coli cell suspensions dried onto metallic substrates (stainless steel (STS) and aluminium (Al) slides) in the optical density (OD) concentration range of 0.001 to 10. Results showed that reflectance FTIR of samples with OD lower than 0.1 did not present an acceptable spectral signal to enable classification. Two modelling strategies were devised to evaluate model performance, transferability and consistency among concentration levels. Modelling strategy 1 involves training the model with half of the sample set, consisting of all concentrations, and applying it to the remaining half. Using this approach, for the STS substrate, the best model was achieved using support vector machine (SVM) classification, providing an accuracy of 96% and Matthews correlation coefficient (MCC) of 0.93 for the independent test set. For the Al substrate, the best SVM model produced an accuracy and MCC of 91% and 0.82, respectively. Furthermore, the aforementioned best model built from one substrate was transferred to predict the bacterial samples deposited on the other substrate. Results revealed an acceptable predictive ability when transferring the STS model to samples on Al (accuracy = 82%). However, the Al model could not be adapted to bacterial samples deposited on STS (accuracy = 57%). For modelling strategy 2, models were developed using one concentration level and tested on the other concentrations for each substrate. Results proved that models built from samples with moderate (1 OD) concentration can be adapted to other concentrations with good model generalization. Prediction maps revealed the heterogeneous distribution of biomolecules due to the coffee ring effect. This work demonstrated the feasibility of applying FTIR to characterise spectroscopic fingerprints of dry bacterial cells on substrates of relevance for food processing.

Unravelling the Promotional Effect of La2 O3 in Pt/La-TiO2 Catalysts for CO2 Hydrogenation.

This work reports the preparation of a La2 O3 -modified Pt/TiO2 (Pt/La-TiO2 ) hybrid through an excess-solution impregnation method and its application for CO2 hydrogenation catalysis. The Pt/La-TiO2 catalyst is characterized by XRD, H2 temperature-programmed reduction (TPR), TEM, X-ray photoelectron spectroscopy (XPS), Raman, EPR, and N2 sorption measurements. The Pt/La-TiO2 composite starts to catalyze the CO2 conversion reaction at 220 °C, which is 30 °C lower than the Pt/TiO2 catalyst. The generation of CH4 and CO of Pt/La-TiO2 is 1.6 and 1.4 times greater than that of Pt/TiO2 . The CO2 temperature-programmed desorption (TPD) analysis confirms the strengthened CO2 adsorption on Pt/La-TiO2 . Moreover, the in situ FTIR experiments demonstrate that the enhanced CO2 adsorption of Pt/La-TiO2 facilitates the formation of the active Pt-CO intermediate and subsequently boosts the evolution of CH4 and CO. The cycling tests reveal that Pt/La-TiO2 shows reinforced stability for the CO2 hydrogenation reaction because the La species can prevent Pt nanoparticles (NPs) from sintering. This work may provide some guidance on the development new rare-metal-modified hybrid catalysts for CO2 fixation.

Construction of an Aminated MIL-53(Al)-Functionalized Carbon Nanotube for the Efficient Removal of Bisphenol AF and Metribuzin.

A versatile organic-inorganic hybrid structure makes a metal-organic framework (MOF) an outstanding host for different kinds of guests; in addition, its easy pyrolysis nature has been proven to be useful as precursors in the construction of carbon-based materials with a special porous structure. Herein, a novel porous composite nanostructure of an aminated MIL-53(Al)@carbon nanotube (CNT) has been successfully constructed for the first time based on in situ synthesis combining the pyrolysis of ZIF-67. The resulting composite nanostructure was performed by the means of scanning electron microscopy, Brunauer-Emmett-Teller analysis, typical and high-resolution transmission electronic microscopy, X-ray photoelectron spectroscopy, etc. The results showed that a compact heterostructure has been formed between an aminated MIL-53(Al) and a CNT. The resulting composites, named N-MIL@CNT, represent distinct promoted activities in the removal of Bisphenol AF (BPAF) and Metribuzin from wastewater, and the maximum adsorption values were 274 mg/g (BPAF) and 213 mg/g (Metribuzin), which are larger than the results obtained by other MOF-based nanomaterials. The adsorption isotherm, kinetics, and thermodynamics were studied in detail, and the selective adsorption mechanism was also suggested. The excellent selectivity, reusability, and structure stability suggest the potential application of this composite nanostructure in the selective removal of BPAF or Metribuzin from the practical wastewater.

Deep Spectral-Spatial Features of Near Infrared Hyperspectral Images for Pixel-Wise Classification of Food Products.

Hyperspectral imaging (HSI) emerges as a non-destructive and rapid analytical tool for assessing food quality, safety, and authenticity. This work aims to investigate the potential of combining the spectral and spatial features of HSI data with the aid of deep learning approach for the pixel-wise classification of food products. We applied two strategies for extracting spatial-spectral features: (1) directly applying three-dimensional convolution neural network (3-D CNN) model; (2) first performing principal component analysis (PCA) and then developing 2-D CNN model from the first few PCs. These two methods were compared in terms of efficiency and accuracy, exemplified through two case studies, i.e., classification of four sweet products and differentiation between white stripe ("myocommata") and red muscle ("myotome") pixels on salmon fillets. Results showed that combining spectral-spatial features significantly enhanced the overall accuracy for sweet dataset, compared to partial least square discriminant analysis (PLSDA) and support vector machine (SVM). Results also demonstrated that spectral pre-processing techniques prior to CNN model development can enhance the classification performance. This work will open the door for more research in the area of practical applications in food industry.

Authentication and Provenance of Walnut Combining Fourier Transform Mid-Infrared Spectroscopy with Machine Learning Algorithms.

Different varieties and geographical origins of walnut usually lead to different nutritional values, contributing to a big difference in the final price. The conventional analytical techniques have some unavoidable limitations, e.g., chemical analysis is usually time-expensive and labor-intensive. Therefore, this work aims to apply Fourier transform mid-infrared spectroscopy coupled with machine learning algorithms for the rapid and accurate classification of walnut species that originated from ten varieties produced from four provinces. Three types of models were developed by using five machine learning classifiers to (1) differentiate four geographical origins; (2) identify varieties produced from the same origin; and (3) classify all 10 varieties from four origins. Prior to modeling, the wavelet transform algorithm was used to smooth and denoise the spectrum. The results showed that the identification of varieties under the same origin performed the best (i.e., accuracy = 100% for some origins), followed by the classification of four different origins (i.e., accuracy = 96.97%), while the discrimination of all 10 varieties is the least desirable (i.e., accuracy = 87.88%). Our results implicated that using the full spectral range of 700-4350 cm-1 is inferior to using the subsets of the optimal spectral variables for some classifiers. Additionally, it is demonstrated that back propagation neural network (BPNN) delivered the best model performance, while random forests (RF) produced the worst outcome. Hence, this work showed that the authentication and provenance of walnut can be realized effectively based on Fourier transform mid-infrared spectroscopy combined with machine learning algorithms.

Perovskite Oxide LaNiO3 Nanoparticles for Boosting H2 Evolution over Commercial CdS with Visible Light.

LaNiO3 /CdS heterojunction photocatalysts are constructed by compositing LaNiO3 nanoparticles with commercially available CdS, and are used for efficient photocatalytic splitting of H2 O with visible light. The LaNiO3 /CdS hybrids are characterized systematically using a series of physicochemical techniques. The photocatalytic activity of the perovskite hybrids is examined by H2 evolution with Na2 S-NaSO3 as the hole scavenger. The optimized LaNiO3 /CdS sample without the assistance of any cocatalyst (e.g., Pt) delivers a high H2 production rate of 74 µmol h-1 (e.g., 3700 µmol h-1 g-1 ), which is substantially superior to the individual LaNiO3 and CdS. Besides, the composite photocatalyst also manifests high stability. The greatly improved H2 production performance of LaNiO3 /CdS is attributed to the facilitated separation and transport of photoinduced charge carriers, as evidenced by photoelectrochemical (PEC) analyses, such as photoluminscence spectroscopy, transient photocurrent responses, and electrochemical impedance spectroscopy. Moreover, a probable photocatalytic mechanism of the H2 evolution reaction is proposed on the basis of the results of the catalysis evaluation and PEC tests.

Developing an efficient NiCo2S4 cocatalyst for improving the visible light H2 evolution performance of CdS nanoparticles.

Developing efficient alternatives to the widely used Pt cocatalyst in photocatalytic H2O splitting is of great importance in view of large-scale production of clear H2 energy. Herein, we report the facile synthesis of NiCo2S4 and its first use as a highly active and cost-affordable cocatalyst to boost visible light H2 generation with the CdS semiconductor. The synthesized NiCo2S4/CdS composite materials are fully characterized by various techniques including X-ray diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM), energy-dispersive X-ray (EDX) spectroscopy, X-ray photoelectron spectroscopy (XPS), UV-Vis diffusion reflectance spectroscopy (DRS), and N2 adsorption measurements. With the optimized NiCo2S4/CdS composite sample, a high H2 generation rate of 137 µmol h-1 is obtained under visible light irradiation, which is more than 17 times higher than that of bare CdS material. The results of photoluminescence (PL) spectroscopy, transient photocurrent response and electrochemical impedance spectroscopy demonstrate the remarkably promoted migration and separation of photogenerated charge carriers over the heterostructured NiCo2S4/CdS material, thus leading to obviously enhanced photocatalytic performance. Moreover, a possible mechanism for the photocatalytic H2 evolution reaction is also proposed based on the observed results of activity evaluation and photoelectrochemical measurements.

Dissolving process of a cellulose bunch in ionic liquids: a molecular dynamics study.

In recent years, a variety of ionic liquids (ILs) were found to be capable of dissolving cellulose and mechanistic studies were also reported. However, there is still a lack of detailed information at the molecular level. Here, long time molecular dynamics simulations of cellulose bunch in 1-ethyl-3-methylimidazolium acetate (EmimAc), 1-ethyl-3-methylimidazolium chloride (EmimCl), 1-butyl-3-methylimidazolium chloride (BmimCl) and water were performed to analyze the inherent interaction and dissolving mechanism. Complete dissolution of the cellulose bunch was observed in EmimAc, while little change took place in EmimCl and BmimCl, and nothing significant happened in water. The deconstruction of the hydrogen bond (H-bond) network in cellulose was found and analyzed quantitatively. The synergistic effect of cations and anions was revealed by analyzing the whole dissolving process. Initially, cations bind to the side face of the cellulose bunch and anions insert into the cellulose strands to form H-bonds with hydroxyl groups. Then cations start to intercalate into cellulose chains due to their strong electrostatic interaction with the entered anions. The H-bonds formed by Cl(-) cannot effectively separate the cellulose chain and that is the reason why EmimCl and BmimCl dissolve cellulose more slowly. These findings deepen people's understanding on how ILs dissolve cellulose and would be helpful for designing new efficient ILs to dissolve cellulose.

Machine learning driven methodology for enhanced nylon microplastic detection and characterization.

In recent years, the field of microplastic (MP) research has evolved significantly; however, the lack of a standardized detection methodology has led to incomparability across studies. Addressing this gap, our current study innovates a reliable MP detection system that synergizes sample processing, machine learning, and optical photothermal infrared (O-PTIR) spectroscopy. This approach includes examining high-temperature filtration and alcohol treatment for reducing non-MP particles and utilizing a support vector machine (SVM) classifier focused on key wavenumbers that could discriminate between nylon MPs and non-nylon MPs (1077, 1541, 1635, 1711 cm-1 were selected based on the feature importance of SVM-Full wavenumber model) for enhanced MP identification. The SVM model built from key wavenumbers demonstrates a high accuracy rate of 91.33%. Results show that alcohol treatment is effective in minimizing non-MP particles, while filtration at 70 °C has limited impact. Additionally, this method was applied to assess MPs released from commercial nylon teabags, revealing an average release of 106 particles per teabag. This research integrates machine learning with O-PTIR spectroscopy, paving the way for potential standardization in MP detection methodologies and providing vital insights into their environmental and health implications.

Analysing micro- and nanoplastics with cutting-edge infrared spectroscopy techniques: a critical review.

The escalating prominence of micro- and nanoplastics (MNPs) as emerging anthropogenic pollutants has sparked widespread scientific and public interest. These minuscule particles pervade the global environment, permeating drinking water and food sources, prompting concerns regarding their environmental impacts and potential risks to human health. In recent years, the field of MNP research has witnessed the development and application of cutting-edge infrared (IR) spectroscopic instruments. This review focuses on the recent application of advanced IR spectroscopic techniques and relevant instrumentation to analyse MNPs. A comprehensive literature search was conducted, encompassing articles published within the past three years. The findings revealed that Fourier transform infrared (FTIR) spectroscopy stands as the most used technique, with focal plane array FTIR (FPA-FTIR) representing the cutting edge in FTIR spectroscopy. The second most popular technique is quantum cascade laser infrared (QCL-IR) spectroscopy, which has facilitated rapid analysis of plastic particles. Following closely is optical photothermal infrared (O-PTIR) spectroscopy, which can furnish submicron spatial resolution. Subsequently, there is atomic force microscopy-based infrared (AFM-IR) spectroscopy, which has made it feasible to analyse MNPs at the nanoscale level. The most advanced IR instruments identified in articles covered in this review were compared. Comparison metrics encompass substrates/filters, data quality, spatial resolution, data acquisition speed, data processing and cost. The limitations of these IR instruments were identified, and recommendations to address these limitations were proposed. The findings of this review offer valuable guidance to MNP researchers in selecting suitable instrumentation for their research experiments, thereby facilitating advancements in research aimed at enhancing our understanding of the environmental and human health risks associated with MNPs.

Baking releases microplastics from polyethylene terephthalate bakeware as detected by optical photothermal infrared and quantum cascade laser infrared.

The use of plastic bakeware is a potential source of human exposure to microplastics (MPs). However, characterizing MPs remains a challenge. This study aims to employ optical photothermal infrared (O-PTIR) and quantum cascade laser infrared (QCL-IR) technology to characterise polyethylene terephthalate (PET) MPs shed from PET bakeware during the baking process. The bakeware, filled with ultrapure water, underwent baking cycles at 220 °C for 20 min, 60 min, and three consecutive cycles of 60 min each. Subsequently, particles present in the ultrapure water were collected using an Al2O3 filter. O-PTIR and QCL-IR were used to characterise PET MPs collected from the filtration. Analysis revealed that QCL-IR spectra exhibited broader absorption peaks, compared to O-PTIR. Notably, MP spectra obtained from both techniques displayed common absorption peaks around 1119, 1623, 1341 and 1725 cm-1. The dominant size of PET MPs detected by O-PTIR and QCL-IR was 1-3 µm and 5-20 µm, respectively. The quantity of identified PET MPs using O-PTIR was 18 times greater than that with QCL-IR, which was attributed to variations in spatial resolution, sampling methods for spectra collection, and data analysis employed by the two methods. Importantly, findings from both techniques highlighted a notably large quantity of MPs released from PET bakeware, particularly evident after 3 cycles of 60 min of baking, suggesting a substantial increase in the potential ingestion of MPs, especially in scenarios involving extended baking durations. The research outcomes will guide consumers on minimizing the intake of microplastics by using PET bakeware for shorter baking time. Additionally, the study will yield valuable insights into the application of O-PTIR and QCL-IR for MPs detection, potentially inspiring advancements in MPs detection methodologies through cutting-edge technologies.

Advanced optical photothermal infrared spectroscopy for comprehensive characterization of microplastics from intravenous fluid delivery systems.

Growing attention is being directed towards exploring the potential harmful effects of microplastic (MP) particles on human health. Previous reports on human exposure to MPs have primarily focused on inhalation, ingestion, transdermal routes, and, potentially, transplacental transfer. The intravenous transfer of MP particles in routine healthcare settings has received limited exploration in existing literature. Standard hospital IV system set up with 0.9 % NaCl in a laminar flow hood with MP contamination precautions. Various volumes of 0.9 % NaCl passed through the system, some with a volumetric pump. Fluid filtered with Anodisc filters washed with isopropyl alcohol. The IV cannula was immersed in Mili-Q water for 72 h to simulate vein conditions. Subsequently, the water was filtered and washed. Optical photothermal infrared (O-PTIR) microspectroscopy is used to examine filters for MP particles. All filters examined from the IV infusion system contained MP particles, including MPs from the polymer materials used in the manufacture of the IV delivery systems (polydimethylsiloxane, polypropylene, polystyrene, and polyvinyl chloride) and MP particles arising from plastic resin additives (epoxy resin, polyamide resin, and polysiloxane-containing MPs). The geometric mean from the extrapolated result data indicated that approximately 0.90 MP particles per mL of 0.9 % NaCl solution can be administered through a conventional IV infusion system in the absence of a volumetric pump. However, with the implementation of a pump, this value may increase to 1.57 particles per mL. Notably, over 72 h, a single cannula was found to release approximately 558 MP particles including polydimethylsiloxane, polysiloxane-containing MPs, polyamide resin, and epoxy resin. Routine IV infusion systems release microplastics. MP particles are also released around IV cannulas, suggesting transfer into the circulatory system during standard IV procedures.

The ionic liquids upon perchlorate to promote the C-C/C-O bonds cleavage in alkali lignin under photothermal synergism.

Transforming lignin into aromatic monomers is critically attractive to develop green and sustainable energy supplies. However, the usage of the additional catalysts like metal or base/acid is commonly limited by the caused repolymerized and environmental issues. The key step is to mediate electron transfer in lignin to trigger lignin C-C/C-O bonds cleavage without the catalysts mentioned above. Here, we report that the ionic liquids [BMim][ClO4] was found to trigger lignin electron transfer to cleave the C-C/C-O bonds for aromatic monomers without any additional catalyst. The proton transfer from [BMim]+ to [ClO4]- could polarize the anion and decrease its structure stability, upon which the active hydroxyl radical generated and induced lignin C-C/C-O bonds fragmentation via free radical-mediated routes with the assistance of photothermal synergism. About 4.4 wt% yields of aromatic monomers, mainly composed of vanillin and acetosyringone, are afforded in [BMim][ClO4] under UV-light irradiation in the air at 80 °C. This work opens the way to produce value-added aromatic monomers from lignin using an eco-friendly, energy-efficient, and simple route that may contribute to the sustainable utilization of renewable natural resources.