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For over half a century, traditional silicon-based integrated circuits (ICs) have been the basis of computational electronics and are widely used in computers, cell phones, and other fields. With the rapid development of human society, silicon-based semiconductor technology is approaching its physical and engineering limits. Our increasing diversity of non-traditional computing needs, such as ultra-small, ultra-fast, ultra-low-power wearables, and space radiation protection, is driving the search for new electronic materials. Semiconducting single-walled carbon nanotubes (s-SWCNTs) have many excellent electrical properties, such as high carrier mobility and high ballistic transport, making them strong candidates for new semiconductor materials in the post-Moore era. Carbon-based electronic technology has been developed for over 20 years, and the fundamental issues such as the material purification of s-SWCNTs, preparation prospects of s-SWCNT-based field-effect transistors (CNT FETs), and device physics based on CNT FETs have been basically solved. However, the chiral diversity of s-SWCNTs may lead to problems such as fluctuations in the electrical performance of CNT FETs, limiting the application of s-SWCNTs in high-end ICs. Monochiral s-SWCNTs not only have excellent electrical properties but also have a controllable structure and uniformity, which are crucial for the high-end IC of CNTs. However, some problems exist in the purity and yield of monochiral s-SWCNT preparation and the optimization of monochiral CNT FETs. Therefore, the chiral sorting of CNTs is reviewed in this paper, and the progress of polymer reprocessing in chiral separation is highlighted. Then, the research progress of monochiral CNT FETs is introduced, and possible development directions are summarized and analyzed. Finally, the application prospects of chiral-enriched s-SWCNTs include challenges and future opportunities. [ABSTRACT FROM AUTHOR]

Thermal comfort standards have suggested a number of physical indices which can be calculated from either building simulations or in situ physical monitoring to assess the long-term thermal comfort of a space. However, the prohibitively high cost of sensor technologies has limited the applications of these physical indices, and their usefulness has never been established using data collected in real buildings. This paper is the first assessment of the six types of existing indices (23 total) found in standards and five types of new indices (36 total) and their correlation with the long-term thermal satisfaction of building occupants. Correlation analyses were based on continuous thermal comfort measurements and post-occupancy evaluation surveys from four air-conditioned office buildings in Sydney, Australia. We found that the majority of existing indices, especially those based on predicted mean vote (PMV) and predicted percentage dissatisfied (PPD) metrics, have a weak correlation with thermal satisfaction. The percentage of time outside a temperature range was the best-performing index from the standards (r=-0.63). A new index based on the percentage of time that daily temperature range is greater than a threshold reported the strongest correlation (r=-0.8) with thermal satisfaction for this dataset. The results suggest that occupants’ long-term thermal comfort is influenced more by pronounced excursions beyond some acceptable temperature range and large variations in daily temperature than the average experience over time. These findings support the use of continuous monitoring technologies for long-term thermal comfort evaluation and inform potential amendments of international thermal comfort standards.

The predicted mean vote (PMV) and predicted percentage of dissatisfied (PPD) are the most widely used thermal comfort indices. Yet, their performance remains a contested topic. The ASHRAE Global Thermal Comfort Database II, the largest of its kind, was used to evaluate the prediction accuracy of the PMV/PPD model. We focused on: (i) the accuracy of PMV in predicting both observed thermal sensation (OTS) or observed mean vote (OMV) and (ii) comparing the PMV-PPD relationship with binned OTS – observed percentage of unacceptability (OPU). The accuracy of PMV in predicting OTS was only 34%, meaning that the thermal sensation is incorrectly predicted two out of three times. PMV had a mean absolute error of one unit on the thermal sensation scale and its accuracy decreased towards the ends of the thermal sensation scale. The accuracy of PMV was similarly low for air-conditioned, naturally ventilated and mixed-mode buildings. In addition, the PPD was not able to predict the dissatisfaction rate. If the PMV model would perfectly predict thermal sensation, then PPD accuracy is higher close to neutrality but it would overestimate dissatisfaction by approximately 15-25% outside of it. Furthermore, PMV-PPD accuracy varied strongly between ventilation strategies, building types and climate groups. These findings demonstrate the low prediction accuracy of the PMV–PPD model, indicating the need to develop high prediction accuracy thermal comfort models. For demonstration, we developed a simple thermal prediction model just based on air temperature and its accuracy, for this database, was higher than PMV.

Anomalous patterns in subjective votes can bias thermal comfort models built using data-driven approaches. A stochastic-based two-step framework to detect outliers in subjective thermal comfort data is proposed to address this problem. The anomaly detection technique involves defining similar conditions using a k-Nearest Neighbor (KNN) method and then quantifying the dissimilarity of the occupants’ votes from their peers under similar thermal conditions through a Multivariate Gaussian approach. This framework is used to detect outliers in the ASHRAE Global Thermal Comfort Database I & II. The resulting anomaly-free dataset produced more robust comfort models avoiding dubious predictions. The proposed method has been proven to effectively distinguish outliers from inter-individual variabilities in thermal demand. The proposed anomaly detection framework could easily be applied to other applications with different variables or subjective metrics. Such a tool holds great promise for use in the development of occupancy responsive controls for automated building HVAC systems.

Current thermal comfort standards use Predicted Mean Vote (PMV) classes as the compliance criteria despite previous critiques. The implicit assumption is that a narrower PMV range ensures higher thermal acceptability among building occupants. However, our analysis of a global database of thermal comfort field studies demonstrates that PMV classes are not appropriate design compliance criteria, and reinforces the need for a new and robust approach to thermal comfort compliance assessment. We compared two statistical methods to derive acceptable temperature ranges from occupant responses applied one to the ASHRAE Global Thermal Comfort Database II. Derived acceptable temperature ranges in real buildings (7.4K-12.2K) using this new method are wider than the current standards mandate (2K-6K). Our findings support the call for a relaxation of suggested temperature ranges in thermal comfort standards so as to minimize unnecessary space conditioning. The proposed data-driven statistical methods to determine temperature design compliance criteria are viewed as an important step forward in the age of continuous and pervasive monitoring and the associated large databases of building comfort measurements.

In this study, we propose a polarized electron blocking layer (EBL) structure using AlxGa1−xN/AlxGa1−xN to enhance the internal quantum efficiency (IQE) of AlGaN-based ultraviolet light-emitting diodes (UV LEDs). Our findings indicate that this polarized EBL structure significantly improves IQE compared to conventional EBLs. Additionally, we introduce an electric-field reservoir (EFR) optimization method to maximize IQE. Specifically, optimizing the polarized EBL structure of AlxGa1−xN/AlxGa1−xN enhances the hole drift rate, resulting in an IQE improvement of 19% and an optical output power increase of 186 mW at a current of 210 mA. [ABSTRACT FROM AUTHOR]

With the increasing availability of satellite monitoring data, the demand for storage and computational resources for updating the results of monitoring the surface subsidence in a mining area continues to rise. Sequential adjustment (SA) models are considered effective for rapidly updating time series interferometry synthetic aperture radar (TS-InSAR) measurements. However, the accuracy of surface subsidence values estimated through traditional sequential adjustment is highly sensitive to abnormal observations or prior information on anomalies. Moreover, the surface subsidence associated with mining exhibits nonlinear and large gradient characteristics, making general InSAR methods challenging for obtaining reliable monitoring results. In this study, we employ the phase unwrapping network (PUNet) to obtain unwrapped values of differential interferograms. To mitigate the impact of abnormal errors in the near real-time small baseline subset InSAR (SBAS-InSAR) sequential updating process in mining areas, a robust sequential adjustment method based on M-estimation is proposed to estimate the temporal deformation parameters by using the equivalent weight model. Using a coal backfilling mining face in Shanxi, China, as the study area and the Sentinel-1 SAR dataset, we comprehensively evaluate the performance of unwrapping methods and subsidence time series estimation techniques and evaluate the effect of filling mining on surface subsidence control. The results are validated using leveling measurements within the study area. The relative error of the proposed method is less than 5%, which can meet the requirements of monitoring the surface subsidence in mining areas. The method proposed in this study not only enhances computational efficiency but also addresses the issue of underestimation encountered by InSAR methods in mining area applications. Furthermore, it also mitigates unwrapping phase anomalies on the monitoring results. [ABSTRACT FROM AUTHOR]

This work investigated the impact of periodic thickness and doping region on the doping efficiency of the P-type AlGaN superlattice. In this paper, the band structure of the simulated superlattice was analyzed. The superlattice structure of Al0.1Ga0.3N/Al0.4Ga0.6N, and the AlGaN buffer on the sapphire substrate, achieved a resistivity of ~3.3 Ω·cm. The results indicate that barrier doping and low periodic thickness offer significant advantages in introducing a reduction of the resistivity of P-type AlGaN superlattice structures. [ABSTRACT FROM AUTHOR]

Recognizing the value of open-source research databases in advancing the art and science of HVAC, in 2014 the ASHRAE Global Thermal Comfort Database II project was launched under the leadership of University of California at Berkeley’s Center for the Built Environment and The University of Sydney’s Indoor Environmental Quality (IEQ) Laboratory. The exercise began with a systematic collection and harmonization of raw data from the last two decades of thermal comfort field studies around the world. The ASHRAE Global Thermal Comfort Database II (Comfort Database), now an online, open-source database, includes approximately 81,846 complete sets of objective indoor climatic observations with accompanying “right-here-right-now” subjective evaluations by the building occupants who were exposed to them. The database is intended to support diverse inquiries about thermal comfort in field settings. A simple web-based interface to the database enables filtering on multiple criteria, including building typology, occupancy type, subjects’ demographic variables, subjective thermal comfort states, indoor thermal environmental criteria, calculated comfort indices, environmental control criteria and outdoor meteorological information. Furthermore, a web-based interactive thermal comfort visualization tool has been developed that allows end-users to quickly and interactively explore the data.

Kalman filtering is used to filter GNSS mining subsidence monitoring data in order to solve the problems of long period and high frequency GNSS mining subsidence monitoring data that are greatly affected by the external environment, high data interference noise and low data reliability. Firstly, the regression analysis method is adopted to automatically divide the monitoring data into three parts: the initial stage, the active stage and the decline stage. The subsidence in the initial stage and the decline stage is relatively stable, and the subsidence data is filtered by the standard Kalman filtering model. The add correction kalman filter model was constructed to deal with the data in the active stage of rapid subsidence change. The filter program was established by Python language, and the monitoring data of five-year and hourly interval sampling rate in a mining area in Ningxia were calculated and analyzed. The results showed that the process curve of different kalman filtering results in different stages was consistent with the measured results, and the filtering effect was good. The add correction kalman filter model can effectively process the monitoring data of mining area with large subsidence variation. The method constructed in this paper can effectively reduce the impact of data fluctuation on the subsidence result and improve the reliability of monitoring data. The research results provide a scientific basis for long-term and high-frequency settlement monitoring data processing.

The atomically thin two-dimensional (2D) transition-metal dichalcogenide (e.g., MoS2) material can withstand large strains up to 11% to change its energy band structure, thereby further tuning its optical, electrical, and other physical properties. However, the slippage of 2D materials on substrate hammers the further strain tuning of the properties of 2D materials. Hereby, a facile three points approach combined with a dry transfer method that can apply uniaxial strain to two-dimensional materials is provided. The slippage of WSe2 on polycarbonate (PC) substrate can be suppressed by thermally annealing WSe2/PC in low pressure Ar atmosphere above 100 °C for 3 h. Straining cycle evolution experiments revealed that the thermal annealing of (1L) WSe2 could suppress slippage from the surface of the PC. The spectral gauge factor of 1L WSe2 is found to be around -60 meV/%. After thermal treatment, WSe2/PC stacking can survive in DI water for at least 24 h without the degradation of the spectral gauge factor. Dome structures are formed after thermal treatments with the interplay of the viscoelasticity and surface tension of the PC and the 0.4% tensile strain on WSe2, and the RMS roughness of WSe2/PC increased from 820 to 1292 pm, indicating that there could be larger lateral friction force to suppress slippage following thermal annealing. Our findings enrich the strain engineering of 2D materials and their device applications. [ABSTRACT FROM AUTHOR]

Open-pit mining activities inevitably affect the surrounding ecological environment. Therefore, it is crucial to clarify the disturbance characteristics of open-pit mining activities on the surrounding vegetation and scientifically implement ecological restoration projects. This study investigates the impact of open-pit coal mining in arid and semi-arid regions on surrounding vegetation from a vegetation phenology perspective. Initially, we construct a high-frequency time series of vegetation indices by Harmonized Landsat 8 and Sentinel-2 surface reflectance dataset (HLS). These time series are then fitted using the Double Logistic and Asymmetric Gaussian methods. Subsequently, we quantify three pivotal phenological phases: Start of Season (SOS), End of Season (EOS), and Length of Season (LOS) from the fitted time series. Finally, utilizing mine boundaries as spatial units, we create a buffer zone of 100 m increments to statistically analyze changes in phenological phases. The results reveal an exponential variation in vegetation phenological metrics with increasing distance from the mining areas of Heidaigou-Haerwusu (HDG-HEWS), Mengxiang (MX), and Xingda (XD) in northwest China. Then, we propose a method to identify the disturbance range. HDG-HEWS, MX, and XD mining areas exhibit disturbance ranges of 1485.39 m, 1571.47 m, and 671.92 m for SOS, and 816.72 m, 824.73 m, and 468.92 m for EOS, respectively. Mineral dust is one of the primary factors for the difference in the disturbance range. The HDG-HEWS mining area exhibits the most significant disruption to vegetation phenological metrics, resulting in a delay of 6.4 ± 3.4 days in SOS, an advancement of 4.3 ± 3.9 days in the EOS, and a shortening of 6.7 ± 3.5 days in the LOS. Furthermore, the overlapping disturbance zones of the two mining areas exacerbate the impact on phenological metrics, with disturbance intensities for SOS, EOS, and LOS being 1.38, 1.20, and 1.33 times those caused by a single mining area. These research results are expected to provide a reference for the formulation of dust suppression measures and ecological restoration plans for open-pit mining areas. [ABSTRACT FROM AUTHOR]

Molybdenum disulfide (MoS2) has been deemed as one of the promising noble-metal-free electrocatalysts for hydrogen evolution reaction (HER), but it suffers from the inert basal plane and low electronic conductivity. Regulating the morphology of MoS2 during the synthesis on conductive substrates is a synergistic strategy for enhancing the HER performance. In this work, vertical MoS2 nanosheets were fabricated on carbon cloth (CC) using an atmospheric pressure chemical vapor deposition method. The growth process could be effectively tuned through introducing hydrogen gas during vapor deposition process, resulting in nanosheets with increased edge density. The mechanism for edge-enriching through controlling the growth atmosphere is systematically studied. The as-prepared MoS2 exhibits excellent HER activity due to the combination of optimized microstructures and coupling with CC. Our findings provide new insights to design advanced MoS2-based electrocatalysts for HER. [ABSTRACT FROM AUTHOR]