Toxicity source apportionment of fugitive dust PM2.5-bound polycyclic aromatic hydrocarbons using multilayer perceptron neural network analysis in Guanzhong Plain urban agglomeration, China.

Polycyclic aromatic hydrocarbons (PAHs) in urban fugitive dust, known for their toxicity and ability to generate reactive oxygen species (ROS), are a major public health concern. This study assessed the spatial distribution and health risks of 15 PAHs in construction dust (CD) and road dust (RD) samples collected from June to November 2021 over the cities of Tongchuan (TC), Baoji (BJ), Xianyang (XY), and Xi'an (XA) in the Guanzhong Plain, China. The average concentration of ΣPAHs in RD was 39.5 ± 20.0 µg g-1, approximately twice as much as in CD. Four-ring PAHs from fossil fuels combustion accounted for the highest proportion of ΣPAHs in fugitive dust over all four cities. Health-related indicators including benzo(a)pyrene toxic equivalency factors (BAPTEQ), oxidative potential (OP), and incremental lifetime cancer risk (ILCR) all presented higher risk in RD than those in CD. The multilayer perceptron neural network algorithm quantified that vehicular and industrial emissions contributed 86 % and 61 % to RD and CD BAPTEQ, respectively. For OP, the sources of biomass and coal combustion were the key generator which accounted for 31-54 %. These findings provide scientific evidence for the direct efforts toward decreasing the health risks of fugitive dust in Guanzhong Plain urban agglomeration, China.

Hysteresis response of groundwater depth on the influencing factors using an explainable learning model framework with Shapley values.

Machine learning has been widely used for groundwater prediction. However, the hysteresis response of groundwater depth (GD) to input features has not been fully investigated. This study uses an interpretation method to reveal the interplay between climate, human activity, and GD while considering the response of groundwater to multiple factors. Six factors [precipitation (P), wind speed (WS), temperature (T), population (POP), gross domestic product (GDP), and effective irrigated area (EIA)] were selected to analyze the hysteresis response of GD in terms of the lag correlation coefficient and lag time. The correlation between climatic variables and GD was weaker than that of anthropogenic variables. The lag time between variables and different types of GD was less than four months at most sites, except for EIA and WS in deep groundwater. The SVM model achieved satisfactory performance in 89 % of the sites. If there were sharp changes in GD during the testing period or significant variations in its seasonal patterns at different times, the SVM model performed poorly. The model was interpreted using the Shapley additive explanation method. The impact of POP and GDP on deep groundwater in irrigated areas was higher than that of shallow groundwater. In urban areas with intensive human activities, anthropogenic variables were the main factors affecting shallow groundwater while the impact of climate was gradually increasing in the suburbs. The influence of precipitation on shallow groundwater was decreased after water transfer from the South-to-North Water Diversion project. Furthermore, this study proposed a multifactor-driven conceptual model that can provide recommendations for analyzing groundwater dynamics in similar areas.

Development and validation of a fully automated system using deep learning for opportunistic osteoporosis screening using low-dose computed tomography scans.

Background: Bone density measurement is an important examination for the diagnosis and screening of osteoporosis. The aim of this study was to develop a deep learning (DL) system for automatic measurement of bone mineral density (BMD) for osteoporosis screening using low-dose computed tomography (LDCT) images. Methods: This retrospective study included 500 individuals who underwent LDCT scanning from April 2018 to July 2021. All images were manually annotated by a radiologist for the cancellous bone of target vertebrae and post-processed using quantitative computed tomography (QCT) software to identify osteoporosis. Patients were divided into the training, validation, and testing sets in a ratio of 6:2:2 using a 4-fold cross validation method. A localization model using faster region-based convolutional neural network (R-CNN) was trained to identify and locate the target vertebrae (T12-L2), then a 3-dimensional (3D) AnatomyNet was trained to finely segment the cancellous bone of target vertebrae in the localized image. A 3D DenseNet was applied for calculating BMD. The Dice coefficient was used to evaluate segmentation performance. Linear regression and Bland-Altman (BA) analyses were performed to compare the calculated BMD values using the proposed system with QCT. The diagnostic performance of the system for osteoporosis and osteopenia was evaluated with receiver operating characteristic (ROC) curve analysis. Results: Our segmentation model achieved a mean Dice coefficient of 0.95, with Dice coefficients greater than 0.9 accounting for 96.6%. The correlation coefficient (R2) and mean errors between the proposed system and QCT in the testing set were 0.967 and 2.21 mg/cm3, respectively. The area under the curve (AUC) of the ROC was 0.984 for detecting osteoporosis and 0.993 for distinguishing abnormal BMD (osteopenia and osteoporosis). Conclusions: The fully automated DL-based system is able to perform automatic BMD calculation for opportunistic osteoporosis screening with high accuracy using LDCT scans.

Unprecedented Chiral Three-dimensional Hybrid Organic-Inorganic Perovskitoids.

Chiral three-dimensional hybrid organic-inorganic perovskites (3D HOIPs) would show unique chiroptoelectronic performance due to the combination of chirality and 3D structure. However, the synthesis of 3D chiral HOIPs remains a significant challenge. Herein, we constructed a pair of unprecedented 3D chiral halide perovskitoids (R/S-BPEA)EA6 Pb4 Cl15 (1-R/S) (R/S-BPEA=(R/S)-1-4-Bromophenylethylammonium, EA=ethylammonium), in which the large chiral cations can be contained in the big "hollow" inorganic frameworks induced by mixing cations. Notably, 3D 1-R/S shows natural chiroptical activity, as evidenced by its significant mirror circular dichroism spectra and the ability to distinguish circularly polarized light. Moreover, based on the unique 3D structure, 1-S presents sensitive X-ray detection performance with a low detection limit of 398 nGyair s-1 , which is 14 times lower than the regular medical diagnosis of 5.5 µGyair s-1 . In this work, 3D chiral halide perovskitoids provide a new route to develop chiral material in spintronics and optoelectronics.

Low Detection Limit Circularly Polarized Light Detection Realized by Constructing Chiral Perovskite/Si Heterostructures.

Chiral perovskites have been demonstrated as promising candidates for direct circularly polarized light (CPL) detection due to their intrinsic chirality and excellent charge transport ability. However, chiral perovskite-based CPL detectors with both high distinguishability of left- and right-handed optical signals and low detection limit remain unexplored. Here, a heterostructure, (R-MPA)2 MAPb2 I7 /Si (MPA = methylphenethylamine, MA = methylammonium) is constructed, to achieve high-sensitive and low-limit CPL detection. The heterostructures with high crystalline quality and sharp interface exhibit a strong built-in electric field and a suppressed dark current, not only improving the separation and transport of the photogenerated carriers but also laying a foundation for weak CPL signals detection. Consequently, the heterostructure-based CPL detector obtains a high anisotropy factor up to 0.34 with a remarkably low CPL detection limit of 890 nW cm-2 under the self-driven mode. As a pioneering study, this work paves the way for designing high-sensitive CPL detectors that simultaneously have great distinguishing capability and low detection limit of CPL.

Effects of indoor air purification intervention on blood pressure, blood­oxygen saturation, and heart rate variability: A double-blinded cross-over randomized controlled trial of healthy young adults.

The use of indoor air purifier (IAP) has received growing attention as a mitigation strategy for reducing indoor air pollution, but the evidence on their cardiovascular benefits is unclear. This study aims to evaluate whether the use of IAP can reduce the adverse effects of indoor particulate matter (PM) on cardiovascular health among young healthy population. A randomized, double-blind, cross-over, IAP intervention of 38 college students was conducted. The participants were assigned into two groups to receive the true and sham IAPs for 36 h in random order. Systolic and diastolic blood pressure (SBP; DBP), blood oxygen saturation (SpO2), heart rate variability (HRV) and indoor size-fractioned particulate matter (PM) were real-time monitored throughout the intervention. We found that IAP could reduce indoor PM by 41.7-50.5 %. Using IAP was significantly associated with a reduction of 2.96 mmHg (95 % CI: -5.71, -0.20) in SBP. Increased PM was significantly associated with increased SBP (e.g., 2.17 mmHg [0.53, 3.81], 1.73 mmHg [0.32, 3.14] and 1.51 mmHg [0.28, 2.75] for an IQR increment of PM1 [16.7 µg/m3], PM2.5 [20.6 µg/m3] and PM10 [37.9 µg/m3] at lag 0-2 h, respectively) and decreased SpO2 (-0.44 % [-0.57, -0.29], -0.41 % [-0.53, -0.30] and - 0.40 % [-0.51, -0.30] for PM1, PM2.5 and PM10 at lag 0-1 h, respectively), which could last for about 2 h. Using IAPs could halve indoor PM levels, even in relatively low air pollution settings. The exposure-response relationships suggested that the benefits of IAPs on BP may only be observed when indoor PM exposure is reduced to a certain level.

The value of using a deep learning image reconstruction algorithm of thinner slice thickness to balance the image noise and spatial resolution in low-dose abdominal CT.

Background: Traditional reconstruction techniques have certain limitations in balancing image quality and reducing radiation dose. The deep learning image reconstruction (DLIR) algorithm opens the door to a new era of medical image reconstruction. The purpose of the study was to evaluate the DLIR images at 1.25 mm thickness in balancing image noise and spatial resolution in low-dose abdominal computed tomography (CT) in comparison with the conventional adaptive statistical iterative reconstruction-V at 40% strength (ASIR-V40%) at 5 and 1.25 mm. Methods: This retrospective study included 89 patients who underwent low-dose abdominal CT. Five sets of images were generated using ASIR-V40% at a 5 mm slice thickness and 1.25 mm (high-resolution) with DLIR at 1.25 mm using 3 strengths: low (DLIR-L), medium (DLIR-M), and high (DLIR-H). Qualitative evaluation was performed for image noise, artifacts, and visualization of small structures, while quantitative evaluation was performed for standard deviation (SD), signal-to-noise ratio (SNR), and spatial resolution (defined as the edge rising slope). Results: At 1.25 mm, DLIR-M and DLIR-H images had significantly lower noise (SD in fat: 14.29±3.37 and 9.65±3.44 HU, respectively), higher SNR for liver (3.70±0.78 and 5.64±1.20, respectively), and higher overall image quality (4.30±0.44 and 4.67±0.40, respectively) than did the respective values in ASIR-V40% images (20.60±4.04 HU, 2.60±0.63, and 3.77±0.43; all P values <0.05). Compared with the 5 mm ASIR-V40% images, the 1.25 mm DLIR-H images had lower noise (SD: 9.65±3.44 vs. 13.63±10.03 HU), higher SNR (5.64±1.20 vs. 4.69±1.28), and higher overall image quality scores (4.67±0.40 vs. 3.94±0.46) (all P values <0.001). In addition, DLIR-L, DLIR-M, and DLIR-H images had a significantly higher spatial resolution in terms of edge rising slope (59.66±21.46, 58.52±17.48, and 59.26±13.33, respectively, vs. 33.79±9.23) and significantly higher image quality scores in the visualization of fine structures (4.43±0.50, 4.41±0.49, and 4.38±0.49, respectively vs. 2.62±0.49) than did the 5 mm ASIR-V40 images. Conclusions: The 1.25 mm DLIR-M and DLIR-H images had significantly reduced image noise and improved SNR and overall image quality compared to the 1.25 mm ASIR-V40% images, and they had significantly improved the spatial resolution and visualization of fine structures compared to the 5 mm ASIR-V40% images. DLIR-H images had further reduced image noise compared with the 5 mm ASIR-V40% images, and DLIR-H was the most effective technique at balancing the image noise and spatial resolution in low-dose abdominal CT.

Unanswered questions on the airborne transmission of COVID-19.

Policies and measures to control pandemics are often failing. While biological factors controlling transmission are usually well explored, little is known about the environmental drivers of transmission and infection. For instance, respiratory droplets and aerosol particles are crucial vectors for the airborne transmission of the severe acute respiratory syndrome coronavirus 2, the causation agent of the coronavirus 2019 pandemic (COVID-19). Once expectorated, respiratory droplets interact with atmospheric particulates that influence the viability and transmission of the novel coronavirus, yet there is little knowledge on this process or its consequences on virus transmission and infection. Here we review the effects of atmospheric particulate properties, vortex zones, and air pollution on virus survivability and transmission. We found that particle size, chemical constituents, electrostatic charges, and the moisture content of airborne particles can have notable effects on virus transmission, with higher survival generally associated with larger particles, yet some viruses are better preserved on small particles. Some chemical constituents and surface-adsorbed chemical species may damage peptide bonds in viral proteins and impair virus stability. Electrostatic charges and water content of atmospheric particulates may affect the adherence of virion particles and possibly their viability. In addition, vortex zones and human thermal plumes are major environmental factors altering the aerodynamics of buoyant particles in air, which can strongly influence the transport of airborne particles and the transmission of associated viruses. Insights into these factors may provide explanations for the widely observed positive correlations between COVID-19 infection and mortality with air pollution, of which particulate matter is a common constituent that may have a central role in the airborne transmission of the novel coronavirus. Supplementary Information: The online version contains supplementary material available at 10.1007/s10311-022-01557-z.

Near-infrared polarization-sensitive photodetection via interfacial symmetry engineering of an Si/MAPbI3 heterostructural single crystal.

Methylammonium lead iodide (MAPbI3) single crystals (SCs) have drawn particular attention in the optoelectronics field, due to their outstanding photoelectric performance. However, the structures of those MAPbI3 SCs are isotropic, which limits the further application of the materials for polarization-sensitive photodetection. Here, we propose a strategy of symmetry modulation by heterogeneously integrating large-sized MAPbI3 SCs with silicon (Si) wafers and we give the first demonstration of self-powered near-infrared (NIR) polarization-sensitive photodetection using MAPbI3 SCs. Created via a delicate solution method, the MAPbI3/Si heterostructures show a high crystalline quality and a solid interfacial connection. More importantly, the built-in electric field resulting from the band bending at the MAPbI3/Si heterostructure interface generates polar symmetry, which enables directional transport of photogenerated carriers, making the MAPbI3/Si heterostructures highly polarization-sensitive. Consequently, in the self-powered mode, NIR photodetectors of MAPbI3/Si heterostructures exhibit large polarization ratios of 3.3 at 785 nm and 2.8 at 940 nm. Moreover, a high detectivity of 7.35 × 1012 Jones of the present devices is also achieved. Our work gives the first demonstration of self-powered polarization-sensitive photodetection of MAPbI3 SCs and provides a strategy to design polarization-sensitive materials beyond the conventional limitations induced by isotropic structures.

Health benefits from substituting raw biomass fuels for charcoal and briquette fuels: In vitro toxicity analysis.

PM2.5 (particulate matters with diameter ≤ 2.5 µm) from biomass fuel combustion has been identified as a major cause of cardiopulmonary diseases. Briquette and charcoal are two representative processed fuels that exhibit different emission characteristics. This study compared three types of biomass fuels (maize straw, wheat straw, and wood branches) and their respective processed fuels in terms of their emission factors (EFs). The bioreactivity of human alveolar epithelial (A549) cells to exposure to various fuel-emitted PM2.5 was assessed. The EFs of lactic dehydrogenase (LDH) and interleukin-6 (IL-6) were calculated to compare actual cytotoxicity. The PM2.5 EFs of maize and wheat straw were higher than those of wood branches, and following the processes of briquetting and carbonization, the EFs of PM2.5 and chemical components were effectively reduced. Cell membrane damage and inflammatory responses were observed after A549 cell exposure to PM2.5 extracts. The expression of bioreactivity to briquettes and charcoals was lower than that to raw fuels. The EFs of LDH and IL-6 were also significantly reduced after briquetting and carbonization. This underscores the necessity of fuel treatment for reducing cytotoxicity. The crucial chemical components that contributed to cell oxidative and inflammatory responses were identified, including organic and elemental carbon, water-soluble ions (e.g., K+, Mg2+, and Ca2+), metals (e.g., Fe, Cr, and Ni), and high-molecular-weight PAHs. This study elucidated the similarities and differences of PM2.5 emissions and cytotoxicity of three types of biomass fuel and demonstrated the positive effects of fuel treatment on reducing adverse pulmonary effects.

Improving spatial resolution and diagnostic confidence with thinner slice and deep learning image reconstruction in contrast-enhanced abdominal CT.

OBJECTIVE: To evaluate image quality and diagnostic confidence improvement using a thin slice and a deep learning image reconstruction (DLIR) in contrast-enhanced abdominal CT. METHODS: Forty patients with hepatic lesions in enhanced abdominal CT were retrospectively analyzed. Images in the portal phase were reconstructed at 5 mm and 1.25 mm slice thickness using the 50% adaptive statistical iterative reconstruction (ASIR-V) (ASIR-V50%) and at 1.25 mm using DLIR at medium (DLIR-M) and high (DLIR-H) settings. CT number and standard deviation of the hepatic parenchyma, spleen, portal vein, and subcutaneous fat were measured, and contrast-to-noise ratio (CNR) was calculated. Edge-rise-slope (ERS) was measured on the portal vein to reflect spatial resolution and the CT number skewness on liver parenchyma was calculated to reflect image texture. Two radiologists blindly assessed the overall image quality including subjective noise, image contrast, visibility of small structures using a 5-point scale, and object sharpness and lesion contour using a 4-point scale. RESULTS: For the 1.25-mm images, DLIR significantly reduced image noise, improved CNR and overall subjective image quality compared to ASIR-V50%. Compared to the 5-mm ASIR-V50% images, DLIR images had significantly higher scores in the visibility and contour for small structures and lesions; as well as significantly higher ERS and lower CT number skewness. At a quarter of the signal strength, the 1.25-mm DLIR-H images had a similar subjective noise score as the 5-mm ASIR-V50% images. CONCLUSION: DLIR significantly reduces image noise and maintains a more natural image texture; image spatial resolution and diagnostic confidence can be improved using thin slice images and DLIR in abdominal CT. KEY POINTS: • DLIR further reduces image noise compared with ASIR-V while maintaining favorable image texture. • In abdominal CT, thinner slice images improve image spatial resolution and small object visualization but suffer from higher image noise. • Thinner slice images combined with DLIR in abdominal CT significantly suppress image noise for detecting low-density lesions while significantly improving image spatial resolution and overall image quality.

Solid fuel derived PM2.5 induced oxidative stress and according cytotoxicity in A549 cells: The evidence and potential neutralization by green tea.

PM2.5 (particulate matter with aerodynamic diameter ≤ 2.5 µm) is a well-known cytotoxic pollutant that capable to induce severe intracellular oxidative stress while the underlying mechanisms remain unclear. Herein, 4 types of PM2.5 derived from solid fuel burning were selected as stimuli in A549 cells exposure model to evaluate their effects on oxidative stress and inflammatory responses. Although resulting in different responses in cell viability, all PM2.5 exhibited over 50 % higher oxidative stress than control group, expression as intracellular reactive oxygen species, malondialdehyde and superoxide dismutase levels. The Pearson's correlation results indicated that cations (e.g., Ca2+), heavy metals (e.g., Cr and Pb), nPAHs (nitro-polycyclic aromatic hydrocarbons, e.g., 6-nitrochrysene) and oPAHs (oxygenated PAHs, e.g., 9-fluorenone) were the main functioning toxics (r > 0.6). A key finding was the dual-directional regulation function of ECG (epicatechin gallate), that is, it could either increase the low A549 cell viabilities in coal combustion PM2.5 group or reduce them in charcoal PM2.5 group (P < 0.05). The dual-directional effects were likely because ECG can activate Nrf2 oxidation signaling pathway then inhibit the inflammatory signaling pathway NF-κB accordingly. Therefore, evidences indicated cytotoxicity of solid fuel derived PM2.5 were mainly caused by oxidative stress, which was proved to be reversed by green tea, providing a potential therapy method to PM2.5 and other hazards.

Assessing the Fit of N95 Filtering Facepiece Respirators Fitted with an Ear Loop Strap System: A Pilot Study.

Throughout the COVID-19 pandemic, hundreds of millions of people worldwide have become new users of respiratory protective devices. Facemasks and KN95 respirators utilizing an ear loop straps system (ELSS) have recently become popular among occupational and non-occupational populations. Part of this popularity is due to the ease of wearability as compared with traditional devices utilizing two headbands, one worn over the head and the other behind the neck-a universal strap system used in NIOSH-certified N95 filtering facepiece respirators (FFRs). Some users convert the two-strap configuration to an adjustable ELSS. The first objective of this pilot study was to quantitatively characterize how such a conversion impacts the respirator fit. Additionally, a novel faceseal (NFS) technology, which has been previously demonstrated to enhance the fit of N95 FFRs, was deployed to modify the ELSS-converted N95 FFRs. The second objective of this study was to quantify the fit improvement that results from adding the NFS to the ELSS. The study was conducted by performing the Occupational Safety and Health Administration (OSHA)-approved quantitative fit testing (QNFT) on 16 human subjects featuring different facial shapes and dimensions. Three models of cup-shaped N95 FFRs were tested in three versions: the standard version with manufacturer's strap system, the ELSS-converted, and the ELSS-converted version modified by adding the NFS. QNFT demonstrated that the fit of an N95 FFR featuring the traditional/standard headbands strap system is negatively impacted when this system is converted to an ELSS. The fit of an ELSS-converted respirator can be significantly improved by the addition of the NFS. We found that the FFR model and the strap system version are significant factors affecting the QNFT-determined respirator fit factor (FF), as well as the OSHA QNFT pass rate (FF ≥100). The findings suggest that the current NFS, if further improved, has a potential for developing a 'universally fitting' ELSS-equipped N95 FFR that can be used by the general public, the vast majority of whom do not have access to OSHA fit requirements.

Image quality improvement in low-dose chest CT with deep learning image reconstruction.

OBJECTIVES: To investigate the clinical utility of deep learning image reconstruction (DLIR) for improving image quality in low-dose chest CT in comparison with 40% adaptive statistical iterative reconstruction-Veo (ASiR-V40%) algorithm. METHODS: This retrospective study included 86 patients who underwent low-dose CT for lung cancer screening. Images were reconstructed with ASiR-V40% and DLIR at low (DLIR-L), medium (DLIR-M), and high (DLIR-H) levels. CT value and standard deviation of lung tissue, erector spinae muscles, aorta, and fat were measured and compared across the four reconstructions. Subjective image quality was evaluated by two blind readers from three aspects: image noise, artifact, and visualization of small structures. RESULTS: The effective dose was 1.03 ± 0.36 mSv. There was no significant difference in CT values of erector spinae muscles and aorta, whereas the maximum difference for lung tissue and fat was less than 5 HU among the four reconstructions. Compared with ASiR-V40%, the DLIR-L, DLIR-M, and DLIR-H reconstructions reduced the noise in aorta by 11.44%, 33.03%, and 56.1%, respectively, and had significantly higher subjective quality scores in image artifacts (all p < 0.001). ASiR-V40%, DLIR-L, and DLIR-M had equivalent score in visualizing small structures (all p > 0.05), whereas DLIR-H had slightly lower score. CONCLUSIONS: Compared with ASiR-V40%, DLIR significantly reduces image noise in low-dose chest CT. DLIR strength is important and should be adjusted for different diagnostic needs in clinical application.

Clarifying winter clean heating importance: Insight chemical compositions and cytotoxicity exposure to primary and aged pollution emissions in China rural areas.

Residential solid fuel combustion (RSFC) is an important source of PM2.5. Here we investigate the cytotoxicity of primarily emitted and photochemically aged PM2.5 to A549 cells. Owing to the formation of water-soluble ions and organics (e.g., oPAHs and nPAHs), emission factors of PM2.5 were increased by 44.4% on average after 7-day equivalent photochemical aging, which greatly altered chemical profiles of freshly emitted PM2.5. Consequently, the cytotoxicity varied with aging duration that 2-day and 7-day aged PM2.5 induced 22.5% and 35.1%, respectively, higher levels of reactive oxygen species than primary emissions. Similar increases were also observed for multi-cytotoxicity. Correlation analysis and western blot results collectively confirmed HO-1/Nrf-2 signaling pathway dominated the cytotoxicity of aged PM2.5 from RSFC, which was regulated by the enhanced o-PAHs and n-PAHs during photochemical aging. Thus, aged and secondary aerosol exposure needs to be paid more attention due to the enhanced cytotoxicity and the vast crowd involved.

Direct Detection of Near-Infrared Circularly Polarized Light via Precisely Designed Chiral Perovskite Heterostructures.

Chiral metal halide perovskites (CMHPs) have recently shown great potential for direct circularly polarized light (CPL) detection. However, owing to the limited cutoff wavelength edge of these CMHPs, most of the detectors presented thus far are characterized only in the ultraviolet and visible range; CMHPs that target at the near-infrared (NIR) region are still greatly desired. Here, we design a novel CMHP heterostructure, synthesized via solution-processed epitaxial growth of crystalline 3D MAPbI3 on a 2D chiral (R-BPEA)2PbI4 (R-BPEA = (R)-1-(4-bromophenyl)ethylammonium) crystal, and provide the first demonstration of self-powered direct NIR-CPL detection. Compared with individual chiral (R-BPEA)2PbI4, the heterostructure not only retains the spin selectivity but also allows much broader absorbance, especially beyond 780 nm, where the (R-BPEA)2PbI4 cannot absorb. Furthermore, the built-in electric potential in the heterojunction forces spontaneous separation/transport of photogenerated carriers, enabling the fabrication of devices operating without external energy supply. By making use of the abovementioned advantages, the self-powered CPL detectors of the (R-BPEA)2PbI4/MAPbI3 heterostructures hence show competitive circular polarization sensitivity at 785 nm with a high anisotropy factor of up to 0.25. In addition, a large on/off switching ratio of ∼105 and an impressive detectivity of ∼1010 Jones are also achieved. As a pioneer study, our results may broaden the material scope for future chiroptical devices based on CMHPs.

Self-Assembly of 2D Hybrid Double Perovskites on 3D Cs2 AgBiBr6 Crystals towards Ultrasensitive Detection of Weak Polarized Light.

We report the self-assembly of 2D double perovskite (BLA)2 CsAgBiBr7 (BLA=benzylammonium) on 3D Cs2 AgBiBr6 crystals, providing the first demonstration of polarization-sensitive photodetection using lead-free double perovskite heterocrystals (HCs). The (BLA)2 CsAgBiBr7 /Cs2 AgBiBr6 HC successfully combines the anisotropy of 2D double perovskites with the well-defined interface provided by heterogeneous integration. Driven by the built-in electric field in junction, photodetectors of HCs exhibit unique polarization dependence of zero-bias photocurrent with a large anisotropy ratio up to 9, which is 6 times amplified as compared to the pristine 2D (BLA)2 CsAgBiBr7 . More importantly, the present devices can remain polarization-sensitive with incident light intensity down to the nW cm-2 level. Our study on lead-free hybrid perovskite HCs marks a step toward establishing robust material foundations for fundamental scientific investigations and the development of optoelectronic devices.

A comprehensive evaluation of PM2.5-bound PAHs and their derivative in winter from six megacities in China: Insight the source-dependent health risk and secondary reactions.

Atmospheric PAHs (polycyclic aromatic hydrocarbons) and their derivatives are a global concern that influences environments and threatens human health. Concentrations of 52 PAHs and the main derivatives in six Chinese megacities were measured in the winter of 2019. The concentrations of ∑PAHs (sum of 52 PAHs) ranged from 19.42 ± 7.68 to 65.40 ± 29.84 ng m-3, with significantly higher levels in northern cities (Harbin [HB], Beijing [BJ], and Xi'an [XA]) than southern ones (Wuhan [WH], Chengdu [CD] and Guangzhou [GZ]). Source apportionment of ∑PAHs was conducted by the PMF model and results showed coal combustion and traffic emissions were the two dominant sources, which dominated ∑PAHs in northern and southern cities, respectively. Biomass burning was also characterized as a crucial source of ∑PAHs and showed extremely high contributions in XA (42.5%). Assisted by the individual PAH source apportionment results, the source-depend TEQ (total BaP equivalent) and incremental lifetime cancer risk (ILCR) were firstly reported in these cities. The results highlighted the contributions of coal combustion and biomass burning to both TEQ and ILCR, which were underestimated by ∑PAHs source apportionment. Secondary organic aerosol-derived PAHs were demonstrated to increase the TEQ compared with the fresh PAHs and three parameters, namely temperature, relative humidity, and O3 concentrations were characterized by multiple linear regression as the principal factors influencing secondary reactions of PAHs in winter. This study provides accurate human health-orientated results and potential control measures to mitigate the toxicity of secondary formed PAHs, and significantly decrease the uncertainty level of traditional methods. The results also revealed great progress in air pollution control by the Chinese government in the past 20 years, but still a long way to go to formulate strict emission control strategies from both environmental and human health-protective perspectives.

Explorations of tire and road wear microplastics in road dust PM2.5 at eight megacities in China.

Tire and road wear microplastics (TRWMPs) in road dust are a key source of atmospheric particulate matter and have an adverse impact on human health and the environment. In this study, samples of particulate matter with a diameter of 2.5 µm or less (PM2.5) in road dust were collected from eight megacities in China to determine the TRWMP content, including that of natural rubber (NR), styrene-butadiene rubber (SBR), and butadiene rubber (BR). The total abundance of TRWMPs was the highest in Lanzhou (174.7 ± 17.0 µg g-1), followed by Xi'an (169.3 ± 23.8 µg g-1), Beijing (107.5 ± 7.5 µg g-1), Changchun (102.2 ± 8.4 µg g-1), Chengdu (101.6 ± 12.9 µg g-1), Guangzhou (98.8 ± 6.5 µg g-1), Wuhan (96.0 ± 5.3 µg g-1), and Shanghai (86.1 ± 30.1 µg g-1). A considerably higher TRWMP fraction in road dust PM2.5 was observed in the northern cities than in the southern cities and is attributable to the higher frictional resistance of roads subjected to less precipitation. The abundance of TRWMPs in the southern cities was dependent on road type, but this dependence was not observed in the northern cities. In the south, road dust PM2.5 on main roads contained more TRWMPs than that on branch roads. Correlation analysis indicated that TRWMPs were associated with tire, road, and break wear. In relation to intracellular oxidative stress factors, higher correlations were observed between TRWMPs and lactate dehydrogenase (r = 0.83) than between TRWMPs and reactive oxygen species (r = 0.59), possibly because TRWMPs destroy the integrity of the cell membrane, with NR exhibiting a higher cytotoxicity than SBR or BR. This study provides evidence that TRWMPs have an adverse impact on human health by inducing cellular oxidative stress. Therefore, further research on TRWMPs in respirable road dust is required.

Evaluating the Effects of Built Environment on Street Vitality at the City Level: An Empirical Research Based on Spatial Panel Durbin Model.

There is evidence that the built environment has an influence on street vitality. However, previous studies seldom assess the direct, indirect, and total effect of multiple environmental elements at the city level. In this study, the features of the street vitality on Xiamen Island are described based on the location-based service Big Data. Xiamen Island is the central urban area of Xiamen, one of the national central cities in China. With the help of multi-source data such as street view images, the condition of design that is difficult to effectively measure with traditional data can be better explored in detail on a macro scale. The built environment is measured through a 5D system at the city level, including Density, Diversity, Design, Destination accessibility, and Distance to transit. Spatial panel Durbin models are constructed to analyze the influence of the built environment on the street vitality on weekdays and weekends, and the direct, indirect, and total effects are evaluated. Results indicate that at the city level, the built environment plays a significant role in promoting street vitality. Functional density is not statistically significant. Most of the elements have spatial effects, except for several indicators in the condition of the design. Compared with the conclusions of previous studies, some indicators have different effects on different spatial scales. For instance, on the micro scale, greening can enhance the attractiveness of streets. However, on the macro scale, too much greening brings fewer functions along the street, which inhibits the street vitality. The condition of design has the greatest effect, followed by destination accessibility. The differences in the influences of weekdays and weekends are mainly caused by commuting behaviors. Most of the built environment elements have stronger effects on weekends, indicating that people interact with the environment more easily during this period.