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The attractive dielectric poly(vinylidene fluoride) (PVDF) and its copolymers are well confirmed possessing the highest electroactive response including dielectric constant, piezoelectric and ferroelectric effects, which have increasingly wide range of applications such as in energy transfer, energy generation and storage, monitoring and control, and include the development of capacitors, sensors, actuators and so on. In this study, by clarifying the reliability of dielectric performances on their crystal phase structure of various PVDF polymers, the different physical and chemical fabricating ways to achieve different forms of PVDF samples such as linear polymers, ferroelectrics, and relaxor ferroelectrics were identified and quantified. In addition, many recent advances in the PVDF-based polymer dielectrics and some developed applications of these polymers are presented, which gives a reference in academic and engineering area to select an appropriate PVDF series dielectric polymer.

The Permo-Carboniferous tectonic evolution in the Uliastai continental margin (UCM), north of the southeast Central Asian Orogenic Belt, remains controversial. This work examined the geochemical composition of the felsic volcanic rocks from the lower and upper part of the Baoligaomiao Formation in the UCM. Zircon U-Pb ages reveal that the Baoligaomiao Formation has a long-lived eruption duration, from ca. 285 to 328 Ma. The lower part (ca. 328-310 Ma) of the Baoligaomiao Formation is dominated by clastic and pyroclastic rocks with subordinate intermediate-felsic volcanic rocks, whereas the upper part (ca. 307-285 Ma) mainly consists of felsic volcanic rocks and pyroclastic rocks. Calculations reveal that the felsic volcanic rocks from the lower part have low zircon saturation temperatures (TZr = 747-795°C), whereas those from the upper part exhibit high TZr (ca. 793-930°C). Zircons from the lower part exhibit high εHf(t) values and 176Lu/177Hf ratios, in contrast to the low εHf(t) values and 176Lu/177Hf ratios of zircons from the upper part. Those petrogeological and geochemical shifts might support the tectonic switch model in the UCM at the end of the Carboniferous, providing new constraints on the Late Carboniferous closure of the Hegenshan Ocean.

Tea polyphenols (TPs) are the general compounds of natural polyhydroxyphenols extracted in tea. Although a large number of studies have shown that TPs have obvious neuroprotective and neuro repair effects, they are limited due to the low bioavailability in vivo. However, TPs can act indirectly on the central nervous system by affecting the “microflora–gut–brain axis”, in which the microbiota and its composition represent a factor that determines brain health. Bidirectional communication between the intestinal microflora and the brain (microbe–gut–brain axis) occurs through a variety of pathways, including the vagus nerve, immune system, neuroendocrine pathways, and bacteria-derived metabolites. This axis has been shown to influence neurotransmission and behavior, which is usually associated with neuropsychiatric disorders. In this review, we discuss that TPs and their metabolites may provide benefits by restoring the imbalance of intestinal microbiota and that TPs are metabolized by intestinal flora, to provide a new idea for TPs to play a neuroprotective role by regulating intestinal flora.

HighlightsThis review summarizes current developments in interface engineering for electrocatalytic CO2 reduction reaction (CO2RR).The interface engineering for electrocatalytic CO2RR involves the metal–metal interface, metal–metal oxide interface, metal–nonmetal interface, metal oxide–metal oxide interface, organic molecules–inorganic materials interface, electrode–electrolyte interface, and molecular catalysts–electrode interface.The opportunities and challenges of interface engineering for CO2RR are proposed.Electrocatalytic CO2 reduction reaction (CO2RR) can store and transform the intermittent renewable energy in the form of chemical energy for industrial production of chemicals and fuels, which can dramatically reduce CO2 emission and contribute to carbon-neutral cycle. Efficient electrocatalytic reduction of chemically inert CO2 is challenging from thermodynamic and kinetic points of view. Therefore, low-cost, highly efficient, and readily available electrocatalysts have been the focus for promoting the conversion of CO2. Very recently, interface engineering has been considered as a highly effective strategy to modulate the electrocatalytic performance through electronic and/or structural modulation, regulations of electron/proton/mass/intermediates, and the control of local reactant concentration, thereby achieving desirable reaction pathway, inhibiting competing hydrogen generation, breaking binding-energy scaling relations of intermediates, and promoting CO2 mass transfer. In this review, we aim to provide a comprehensive overview of current developments in interface engineering for CO2RR from both a theoretical and experimental standpoint, involving interfaces between metal and metal, metal and metal oxide, metal and nonmetal, metal oxide and metal oxide, organic molecules and inorganic materials, electrode and electrolyte, molecular catalysts and electrode, etc. Finally, the opportunities and challenges of interface engineering for CO2RR are proposed.

Background Nucleus pulposus cell (NPC) death and progressive reduction play important roles in intervertebral disc degeneration (IVDD). As part of a damage-associated molecular pattern, mitochondrial DNA (mtDNA) can be recognized by TLR9 and triggers the expression of NF-κB and NLRP3 inflammasomes, inducing pyroptosis and inflammatory response. However, whether mtDNA induces NPC pyroptosis via the TLR9-NF-κB-NLRP3 axis and promotes IVDD remains uncertain. Methods We constructed an in vitro NPC oxidative stress injury model to clarify the mechanism of mtDNA release, TLR9-NF-κB signaling pathway activation, and NPC injury. We further verified the mechanism of action underlying the inhibition of mtDNA release or TLR9 activation in NPC injury in vitro. We then constructed a rat punctured IVDD model to understand the mechanism inhibiting mtDNA release and TLR9 activation in IVDD. Results We used human NP specimen assays to show that the expression levels of TLR9, NF-κB, and NLRP3 inflammasomes correlated with the degree of IVDD. We demonstrated that mtDNA mediated TLR9-NF-κB-NLRP3 axis activation in oxidative stress-induced human NPC pyroptosis in vitro. Oxidative stress can damage the mitochondria of NPCs, causing the opening of the mitochondrial permeability transition pores (mPTP) and leading to the release of mtDNA into the cytosol. Furthermore, inhibition of mPTP opening or TLR9 activation blocked TLR9-NF-κB-NLRP3 axis activation and thereby mediated NPC pyroptosis and IVDD. Conclusion mtDNA plays a key role in mediating NPC pyroptosis and IVDD via the TLR9-NF-κB-NLRP3 axis. Our findings provide new potential targets for IVDD. Highlights NPC pyroptosis mechanism via mtDNA mediated TLR9-NF-κB-NLRP3 axis activation is unclear. We demonstrate that TLR9, NF-κB, and NLRP3 inflammasomes correlate with IVDD. mtDNA via the TLR9-NF-κB-NLRP3 axis mediates NPC pyroptosis and IVDD.

Beijing and Tianjin are two of the largest cities in northern China with high population densities and highly developed manufacturing industries. In the past decade, some authors have reported their PAH concentrations in surface soils, identified their sources and quantitatively reported their health risks. However, the contributions of different PAH sources to their toxicity have not been reported thus far. In this study, we reviewed the PAH concentrations, contributions of different sources to the toxicity, and cancer risks in soils from different land use types found within Beijing and Tianjin from data gathered by 41 studies. The total PAH concentration varied in the range of 175.7–1989.0 ng g -1 with a higher median PAH concentration detected in urban soils (789.7 ng g -1 ), followed by suburban soils (647.3 ng g -1 ) and rural soils (390.8 ng g -1 ). Source identification using diagnostic ratios and principal component analysis (PCA) suggested that the PAHs in all three land use types mainly originated from biomass and coal combustion, vehicular emissions, and petrogenic processes with contributions varying from 13% to 62%. Furthermore, results from a positive matrix factorization (PMF) model suggested that vehicular emissions and coal combustion in urban soils, and the vehicular emissions, coal combustion and biomass combustion in suburban and rural soils dominated the total PAH concentrations (>85%). These results were consistent with those of the PCA model. Results of the additional toxicity apportionment performed using the PMF model suggested that vehicular emissions and coal combustion contributed the most to the toxic equivalent quantity for Benzo(a)Pyrene (BaP TEQ ) and, by extension, to the carcinogenic potencies. The incremental lifetime cancer risk (ILCR) values suggested a low risk level for adults exposed to PAHs in the different land use types found within Beijing and Tianjin.

Vegetation plays a key role in regulating the material and energy exchanges among the biosphere, the atmosphere, and the pedosphere. Modeling and predicting vegetation key variables such as leaf area index (LAI) and gross primary productivity (GPP) are crucial to understand and project the processes of vegetation growth in response to climate change. While a number of studies developed models to simulate vegetation GPP using satellite‐derived LAI, the requirement of satellite‐based model inputs largely limits the predicting power of these developed models. This study developed a machine learning scheme, utilizing both support vector regression (SVR) and random forests (RF), which are capable of modeling LAI and GPP time series using only meteorological variables. We first simulated the LAI time series directly using meteorological variables as inputs and then buffered its unrealistic day‐to‐day fluctuation, and further modeled the GPP time series using meteorological variables and modeled LAI time series. This scheme enhanced the interpretability of machine learning models by considering the nonnegligible coupling between LAI and GPP. We tested our methods for four main plant functional types across North America and evaluated the models using both satellite‐based and flux tower data. The results demonstrated that the machine learning models perform well on simulating the time series of both LAI and GPP. We identified that there is a need to improve the phenology representation in the Biome‐BGCMuSo model. The machine learning models provide an alternative way to predict time series of LAI and GPP using only meteorological variables across large geographic regions, and also provide benchmarking accuracies for future developments of the process‐based models. Plain Language Summary Vegetation growth interacts external environment mainly through phenology (the periodic events in vegetation biological life cycles) and photosynthesis processes. Leaf area index (LAI, an index quantifies the vegetation leaf area during phenological cycles) and gross primary productivity (GPP, a metric quantifies carbon sequestration in photosynthesis) are important to monitor the processes of vegetation growth in response to climate change. While a number of models simulate vegetation GPP using satellite‐derived LAI (retrieved from remote sensing variables), accounting that satellite‐derived data is not able for predicting simulation, we developed a machine learning scheme to simulate LAI and GPP only via meteorological variables, without utilizing remote sensing data. The developed scheme considers the strong coupling between LAI and GPP and thus enhances the interpretability. We conducted the models in four main vegetation types across North America and evaluated the models in both continental and site scales. The results demonstrated that the machine learning models perform well on simulating both LAI and GPP. We identified that there is a need to improve the phenology representation in the Biome‐BGCMuSo model (a biogeochemical process‐based model). The machine learning models provide an alternative way to predict LAI and GPP using only meteorological variables. Key Points We developed an interpretable machine learning scheme to simulate leaf area index (LAI) and gross primary productivity (GPP) via only meteorological variables The results demonstrated that the machine learning models perform well on simulating the time series of both LAI and GPP The machine learning models provide benchmarking accuracies for future developments of the process‐based models

Despite their notable unidirectional water transport capabilities, Janus membranes are commonly challenged by the fragility of their chemical coatings and the clogging of open microchannels. Here, an on-demand mode-switching strategy is presented to consider the Janus functionality and mechanical durability separately and implement them by simply stretching and releasing the membrane. The stretching Janus mode facilitates unidirectional liquid flow through the hydrophilic micropores-microgrooves channels (PG channels) fabricated by femtosecond laser. The releasing protection mode is designed for the in-situ closure of the PG channels upon encountering external abrasion and impact. The protection mode imparts the Janus membrane robustness to reserve water unidirectional penetration under harsh conditions, such as 2000  cycles mechanical abrasion, 10 days exposure in air and other rigorous tests (sandpaper abrasion, finger rubbing, sand impact and tape peeling). The underlying mechanism of gridded grooves in protecting and enhancing water flow is unveiled. The Janus membrane serves as a fog collector to demonstrate its unwavering mechanical durability in harsh real-world conditions. The presented design strategy could open up new possibilities of Janus membrane in a multitude of applications ranging from multiphase separation devices to fog harvesting and wearable health-monitoring patches. Janus membranes are highly valued for their unique water unidirectional transportation capabilities. Here, authors report an on-demand mode-switching strategy that significantly enhances the durability of the Janus membrane.

The crystal phase-based heterostructures of noble metal nanomaterials are of great research interest for various applications, such as plasmonics and catalysis. However, the synthesis of unusual crystal phases of noble metals still remains a great challenge, making the construction of heterophase noble metal nanostructures difficult. Here, we report a one-pot wet-chemical synthesis of well-defined heterophase fcc-2H-fcc gold nanorods (fcc: face-centred cubic; 2H: hexagonal close-packed with stacking sequence of “AB”) at mild conditions. Single particle-level experiments and theoretical investigations reveal that the heterophase gold nanorods demonstrate a distinct optical property compared to that of the conventional fcc gold nanorods. Moreover, the heterophase gold nanorods possess superior electrocatalytic activity for the carbon dioxide reduction reaction over their fcc counterparts under ambient conditions. First-principles calculations suggest that the boosted catalytic performance stems from the energetically favourable adsorption of reaction intermediates, endowed by the unique heterophase characteristic of gold nanorods. The crystal phase-based heterostructures of noble metal nanomaterials are of interest for various applications. Here, the authors report the wet-chemical synthesis of gold nanorods with a well-defined fcc-2H-fcc heterophase, which possess unique optical and catalytic properties.

The morphological characteristics of a crack serve as crucial indicators for rating the condition of the concrete bridge components. Previous studies have predominantly employed deep learning techniques for pixel-level crack detection, while occasionally incorporating monocular devices to quantify the crack dimensions. However, the practical implementation of such methods with the assistance of robots or unmanned aerial vehicles (UAVs) is severely hindered due to their restrictions in frontal image acquisition at known distances. To explore a non-contact inspection approach with enhanced flexibility, efficiency and accuracy, a binocular stereo vision-based method incorporating full convolutional network (FCN) is proposed for detecting and measuring cracks. Firstly, our FCN leverages the benefits of the encoder–decoder architecture to enable precise crack segmentation while simultaneously emphasizing edge details at a rate of approximately four pictures per second in a database that is dominated by complex background cracks. The training results demonstrate a precision of 83.85%, a recall of 85.74% and an F1 score of 84.14%. Secondly, the utilization of binocular stereo vision improves the shooting flexibility and streamlines the image acquisition process. Furthermore, the introduction of a central projection scheme achieves reliable three-dimensional (3D) reconstruction of the crack morphology, effectively avoiding mismatches between the two views and providing more comprehensive dimensional depiction for cracks. An experimental test is also conducted on cracked concrete specimens, where the relative measurement error in crack width ranges from −3.9% to 36.0%, indicating the practical feasibility of our proposed method.