High-Brightness and Color-Tunable FAPbBr(3) Perovskite Nanocrystals 2.0 Enable Ultrapure Green Luminescence for Achieving Recommendation 2020 Displays

To best catch human eyes in next-generation displays, the updated recommendation 2020 (Rec. 2020) standard has called for ultrapure green emitters to be qualified with a narrow emission of 525-535 nm with a full width at half-maximum (fwhm) below 25 nm. However, it is still challenging to find an emitter which can simultaneously cover these two criteria. Instead of traditional II-VI group semiconductor quantum dots, perovskite nanocrystals (NCs) can render versatile emitting tunability to allow them access to the Rec. 2020 standard. Herein, to realize the critical window of Rec. 2020, we have proposed a scalable, room temperature synthesis route of formamidinium lead bromide (FAPbBr3) NCs using a sole ligand of sulfobetaine-18 (SBE-18). The as-synthesized FAPbBr3 NCs exhibit an ideal emission at 534 nm with an ultranarrow fwhm of 20.5 nm and a high photoluminescence quantum yield of 90.6%, overwhelming the FAPbBr3 nanoplates capped with oleic acid/oleylamine (OA/OAM). Introducing these high quality NCs into backlight displays, an ultrapure green backlight which covers ≈85.7% of the Rec. 2020 standard in the CIE 1931 color space is achieved, signifying the "greenest" backlight till now. Thus, we can foresee perovskite NCs as the most potential candidates for next-generation displays

Impermeable inorganic “walls” sandwiching perovskite layer toward inverted and indoor photovoltaic devices

Interfaces between the perovskite active layer and the charge-transport layers (CTLs) play a critical role in both efficiency and stability of halide-perovskite photovoltaics. One of the major concerns is that surface defects of perovskite could cause detrimental nonradiative recombination and material degradation. In this work, we addressed this challenging problem by inserting ultrathin alkali-fluoride (AF) films between the tri-cation lead-iodide perovskite layer and both CTLs. This bilateral inorganic “walls” strategy makes use of both physical-blocking and chemical-anchoring functionalities of the continuous, uniform and compact AF framework: on the one hand, the uniformly distributed alkali-iodine coordination at the perovskite-AF interfaces effectively suppresses the formation of iodine-vacancy defects at the surfaces, thus reducing the trap-assisted recombination at the perovskite-CTL interfaces and therewith the open-voltage loss; on the other hand, the impermeable AF buffer layers effectively prevent the bidirectional ion migration at the perovskite-CTLs interfaces even under harsh working conditions. As a result, a power-conversion efficiency (PCE) of 22.02% (certified efficiency 20.4%) with low open-voltage deficit (<0.4 V) was achieved for the low-temperature processed inverted planar perovskite solar cells. Exceptional operational stability (500 h, ISOS-L-2) and thermal stability (1000 h, ISOS-D-2) were obtained. Meanwhile, a 35.7% PCE was obtained under dim-light source (1000 lux white LED light) with the optimized device, which is among the best records in perovskite indoor photovoltaics

Surface mediated ligands addressing bottleneck of room-temperature synthesized inorganic perovskite nanocrystals toward efficient light-emitting diodes

Cesium lead halide perovskites (CsPbX3) have become superior candidates for perspective optoelectronic applications. However, room temperature synthesized CsPbX3 nanocrystals (NCs) suffer from serious lattice/surface traps, mostly induced by nonequilibrium reactions and polar solvent systems. Thus, direct assembly of such poor crystals cannot be available toward high efficiency light emitting diodes (LEDs). To address this issue, differing from the general post-treatment works, here we propose a double-terminal diamine bromide salt to in situ passivate the surface traps of room temperature synthesized CsPbBr3 NCs. High-quality NC solutions with photoluminescence quantum yield (PLQY) beyond 90% are obtained owing to the renovated surface bromide vacancies. Meanwhile, instead of longer oleylamine (OLA) ligand, the abridged diamine bromine ligand could significantly enhance charge transport throughout the NC film. In addition, the NC based LED performance is found related to chain length of the ligand, where the optimal luminance of 14021 Cd m(-2) and current efficiency of 25.5 Cd A(-1) are achieved by 1, 4-butanediamine bromide passivated NC devices. This work provides a direct efficient approach to meet the device application of room temperature synthesized perovskite NCs, underlines the significance of selective ligands to address the challenges of NC emitters in future displays and solid-state lighting

The Suitability of Different Nighttime Light Data for GDP Estimation at Different Spatial Scales and Regional Levels

Nighttime light data offer a unique view of the Earth’s surface and can be used to estimate the spatial distribution of gross domestic product (GDP). Historically, using a simple regression function, the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP/OLS) has been used to correlate regional and global GDP values. In early 2013, the first global Suomi National Polar-orbiting Partnership (NPP) visible infrared imaging radiometer suite (VIIRS) nighttime light data were released. Compared with DMSP/OLS, they have a higher spatial resolution and a wider radiometric detection range. This paper aims to study the suitability of the two nighttime light data sources for estimating the GDP relationship between the provincial and city levels in Mainland China, as well as of different regression functions. First, NPP/VIIRS nighttime light data for 2014 are corrected with DMSP/OLS data for 2013 to reduce the background noise in the original data. Subsequently, three regression functions are used to estimate the relationship between nighttime light data and GDP statistical data at the provincial and city levels in Mainland China. Then, through the comparison of the relative residual error (RE) and the relative root mean square error (RRMSE) parameters, a systematical assessment of the suitability of the GDP estimation is provided. The results show that the NPP/VIIRS nighttime light data are better than the DMSP/OLS data for GDP estimation, whether at the provincial or city level, and that the power function and polynomial models are better for GDP estimation than the linear regression model. This study reveals that the accuracy of GDP estimation based on nighttime light data is affected by the resolution of the data and the spatial scale of the study area, as well as by the land cover types and industrial structures of the study area

Analysis of Spatial-Temporal Characteristics of the PM2.5 Concentrations in Weifang City, China

Air pollution, which accompanies industrial progression and urbanization, has become an urgent issue to address in contemporary society. As a result, our understanding and continued study of the spatial-temporal characteristics of a major pollutant, defined as 2.5-micron or less particulate matter (PM2.5), as well as the development of related approaches to improve the environment, has become vital. This paper studies the characteristics of yearly, quarterly, monthly, daily, and hourly PM2.5 concentrations, and discusses the influencing factors based on the hourly data of nationally controlled and provincially controlled monitoring stations, from 2012 to 2016, in Weifang City. The main conclusion of this study is that the annual PM2.5 concentrations reached a peak in 2013. With efficient aid from the government, this value has decreased annually and has high spatial characteristics in the northwest and low spatial characteristics in the southeast. Second, the seasonal and monthly PM2.5 concentrations form a U-shaped trend, meaning that the concentration is high in the summer and low in the winter. These trends are highly relevant to the factors of plantation, humidity, temperature, and precipitation. Third, within a week, higher PM2.5 concentrations appear on Mondays and Saturdays, whereas the lowest concentration occurs on Wednesdays. It can be inferred that PM2.5 concentrations tend to be highly dependent on human activities and living habits. Lastly, there are hourly discrepancies within the peaks and troughs depending on the month, and the overall daytime PM2.5 concentrations and reductive rates are higher in the daytime than in the nighttime

Green Travel Mode: Trajectory Data Cleansing Method for Shared Electric Bicycles

Location-based service (LBS) technologies provide a new perspective for the analysis of the spatiotemporal dynamics of urban systems. Previous studies have been performed using data from mobile communications, public transport vehicles (taxis and buses), wireless hotspots and shared bicycles. However, corresponding analyses based on shared electric bicycle (e-bike) have not yet been reported in the literature. Data cleaning and extraction of the origin-destination (O-D) are prerequisites for the study of the spatiotemporal patterns of urban systems. In this study, based on a dataset of a week of shared e-bike GPS data in the city of Tengzhou (Shandong Province), sparse characteristics of discontinuities and nonuniformities of the GPS trajectory and a lack of riding status are observed. Based on the characteristics and the actual road, we proposed a method for the extraction of O-D pairs for every trajectory segment from continuous and stateless trajectory GPS data. This method cleans the incomplete and invalid trajectory records, which is suitable for sparse trajectory data. A week of shared e-bike GPS data in Tengzhou is scrubbed and, by the sampling method, the extraction accuracy of 91% is verified. We provide preliminary cleaning rules for sparse trajectory shared e-bike data for the first time, which are highly reliable and suitable for data mining from other forms of sparse GPS trajectory data

Different Sourcing Point of Interest Matching Method Considering Multiple Constraints

Point of interest (POI) matching is critical but is the most technically difficult part of multi-source POI fusion. The accurate matching of POIs from different sources is important for the effective reuse of POI data. However, the existing research on POI matching usually adopts weak constraints, which leads to a low POI matching accuracy. To address the shortcomings of previous studies, this paper proposes a POI matching method with multiple determination constraints. First, according to various attributes (name, class, and spatial location), a new calculation model considering spatial topology, name role labeling, and bottom-up class constraints is established. In addition, the optimal threshold values corresponding to the different attribute constraints are determined. Second, according to the multiattribute constraint values and optimal thresholds, a constraint model with multiple strict determination constraints is proposed. Finally, actual POI data from Baidu Map and Gaode Map in Dongying city is used to validate the method. Comparing to the existing method, the accuracy and recall of the proposed method increase 0.3% and 7.1%, respectively. The experimental results demonstrate that the proposed POI matching method attains a high matching accuracy and high feasibility

Transport Pathways and Potential Source Region Contributions of PM2.5 in Weifang: Seasonal Variations

As air pollution becomes progressively more serious, accurate identification of urban air pollution characteristics and associated pollutant transport mechanisms helps to effectively control and alleviate air pollution. This paper investigates the pollution characteristics, transport pathways, and potential sources of PM2.5 in Weifang based on PM2.5 monitoring data from 2015 to 2016 using three methods: Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT), the potential source contribution function (PSCF), and concentration weighted trajectory (CWT). The results show the following: (1) Air pollution in Weifang was severe from 2015 to 2016, and the annual average PM2.5 concentration was more than twice the national air quality second-level standard (35 &mu;g/m3). (2) Seasonal transport pathways of PM2.5 vary significantly: in winter, spring and autumn, airflow from the northwest and north directions accounts for a large proportion; in contrast, in summer, warm-humid airflows from the ocean in the southeastern direction dominate with scattered characteristics. (3) The PSCF and CWT results share generally similar characteristics in the seasonal distributions of source areas, which demonstrate the credibility and accuracy of the analysis results. (4) More attention should be paid to short-distance transport from the surrounding areas of Weifang, and a joint pollution prevention and control mechanism is critical for controlling regional pollution

Evaluation System: Evaluation of Smart City Shareable Framework and Its Applications in China

Smart city evaluation is a critical component in smart city construction and plays an important role in guiding and promoting smart development of cities. Currently, existing research and applications of smart city evaluation are still in the exploration stage. They mainly focus on evaluation of one single aspect, use indicators with distinct regional characteristics and poor extensibility, and cannot be well-integrated with common and shareable smart city frameworks; these limitations have led to biased evaluation results. Based on a common and shareable smart city framework, this paper proposes a well-integrated, universal, strongly practical, and highly extensible evaluation system. Then, using the above-mentioned evaluation system, 17 smart cities in China are assessed. This application demonstrates that the evaluation system plays an important guiding role for better understanding the overall smart city platform construction situation in China, performing horizontal comparisons and establishing benchmarks among smart cities. Comparative analyses of indicators demonstrate that future smart city construction in China should pay more attention to novel innovations, the construction of dynamic information resources and spatiotemporal big data