Tuning the metal insulator transition (MIT) behavior of VO2 film through the interfacial strain is effective for practical applications. However, the mechanism for strain-modulated MIT is still under ...debate. Here we directly record the strain dynamics of ultrathin VO2 film on TiO2 substrate and reveal the intrinsic modulation process by means of synchrotron radiation and first-principles calculations. It is observed that the MIT process of the obtained VO2 films can be modulated continuously via the interfacial strain. The relationship between the phase transition temperature and the strain evolution is established from the initial film growth. From the interfacial strain dynamics and theoretical calculations, we claim that the electronic orbital occupancy is strongly affected by the interfacial strain, which changes also the electron–electron correlation and controls the phase transition temperature. These findings open the possibility of an active tuning of phase transition for the thin VO2 film through the interfacial lattice engineering.
The PL spectra and schematic illustration of the charge separation between the ZnO nanorods and TiO2 nanoparticles. Display omitted
ZnO nanorods/TiO2 nanoparticles composites were synthesized and the ...effects of TiO2 concentrations on the NO2 sensing properties were studied in detail. The as-prepared composites were characterized by XRD, SEM, TEM, PL, I-V and gas sensing measurements. The gas sensing results demonstrated that all the sensors based on ZnO/TiO2 nanocomposites exhibited much higher response than that of sensors based on pure ZnO nanorods. At the optimum operating temperature of 180°C, the response values of the sensors based on ZnO/TiO2 nanocomposites decorated with TiO2 concentrations of 0, 3, 5, 8 and 10wt% were 50, 140, 310, 350 and 258, respectively. The PL and I-V results indicated that the increased charge transfer between the ZnO nanorods mediated by TiO2 nanoparticles enhanced the conductivity of the ZnO/TiO2 nanocomposites. The gas sensing mechanism was also carefully analyzed. The attachment of TiO2 nanoparticles onto ZnO nanorods induced more active sites for the adsorption of oxygen molecules (O2) and O2 which can be more easily adsorbed on the surface of ZnO nanorods. Furthermore, the conduction channel of ZnO/TiO2 was much narrower as a result of the formation of heterojunction which may further contribute to the enhanced NO2 sensing properties.
•Cr doped diamond like carbon (Cr-DLC) nanocomposite coatings were deposited by using a combined system.•The stress of the Cr-DLC coatings dramatically decreases from 0.98GPa to 0.49GPa as the Cr ...contents increases from 0at% to 9.7at%.•The results showed that Cr-DLC coating with low Cr concentration was a effective protective coating.
Cr doped diamond like carbon (Cr-DLC) coatings were deposited by using a combined system consisting of middle frequency (MF) magnetron sputtering and ion plating. The structure and properties of the undoped and Cr-doped DLC coatings were analyzed by various testing, such as Raman, XPS, hardness and temperature-dependent frictional wear testing. The results showed that Cr-DLC coatings with low Cr concentration was a effective protective coating containing Cr–C nanometer grains, whose mechanical properties were obviously improved, such as, residual stress and cohesive strength, and still kept good wear resistance at the ambient temperature of 400°C.
Biochar is a promising agent for wastewater treatment, soil remediation, and gas storage and separation. This review summarizes recent research development on biochar production and applications with ...a focus on the application of biochar technology in wastewater treatment. Different technologies for biochar production, with an emphasis on pre-treatment of feedstock and post treatment, are succinctly summarized. Biochar has been extensively used as an adsorbent to remove toxic metals, organic pollutants, and nutrients from wastewater. Compared to pristine biochar, engineered/designer biochar generally has larger surface area, stronger adsorption capacity, or more abundant surface functional groups (SFG), which represents a new type of carbon material with great application prospects in various wastewater treatments. As the first of its kind, this critical review emphasizes the promising prospects of biochar technology in the treatment of various wastewater including industrial wastewater (dye, battery manufacture, and dairy wastewater), municipal wastewater, agricultural wastewater, and stormwater. Future research on engineered/designer biochar production and its field-scale application is discussed. Based on the review, it can be concluded that biochar technology represents a new, cost effective, and environmentally-friendly solution for the treatment of wastewater.
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•Biochar technologies in various wastewater treatment are elucidated.•Feedstock pre-treatment and post-treatment effect on biochar production is reviewed.•Biochar as an innovative adsorbent to remove aqueous contaminants is discussed.•Future perspectives of biochar technology in wastewater treatment are summarized.
Mapping soil contamination enables the delineation of areas where protection measures are needed. Traditional soil sampling on a grid pattern followed by chemical analysis and geostatistical ...interpolation methods (GIMs), such as Kriging interpolation, can be costly, slow and not well-suited to highly heterogeneous soil environments. Here we propose a novel method to map soil contamination by combining high-resolution aerial imaging (HRAI) with machine learning algorithms. To support model establishment and validation, 1068 soil samples were collected from an arsenic (As) contaminated area in Zhongxiang, Hubei province, China. The average arsenic concentration was 39.88 mg/kg (SD = 213.70 mg/kg), with individual sample points determined as low risk (66.9%), medium risk (29.4%), or high risk (3.7%), respectively. Then, identified features were extracted from a HRAI image of the study area. Four machine learning algorithms were developed to predict As risk levels, including (i) support vector machine (SVM), (ii) multi-layer perceptron (MLP), (iii) random forest (RF), and (iii) extreme random forest (ERF). Among these, we found that the ERF algorithm performed best overall and that its prediction performance was generally better than that of traditional Kriging interpolation. The accuracy of ERF in test area 1 reached 0.87, performing better than RF (0.81), MLP (0.78) and SVM (0.77). The F1-score of ERF for discerning high-risk points in test area 1 was as high as 0.8. The complexity of the distribution of points with different risk levels was a decisive factor in model prediction ability. Identified features in the study area associated with fertilizer factories had the most important contribution to the ERF model. This study demonstrates that HRAI combined with machine learning has good potential to predict As soil risk levels.
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•Four machine learning algorithms were developed to predict As risk levels.•The ERF algorithm performed best overall.•Prediction performance was generally better than that of traditional Kriging.•Heterogeneity of soil pollution poses challenges to soil arsenic risk mapping.•The fertilizer factory was the primary pollution source in this area.
Use drone image recognition and machine learning to map soil pollution distribution at an arsenic-contaminated agricultural field.
External controlling the phase transition behavior of vanadium dioxide is important to realize its practical applications as energy-efficient electronic devices. Because of its relatively high phase ...transition temperature of 68 °C, the central challenge for VO2-based electronics, lies in finding an energy efficient way, to modulate the phase transition in a reversible and reproducible manner. In this work, we report an experimental realization of p–n heterojunctions by growing VO2 film on p-type GaN substrate. By adding the bias voltage on the p–n junction, the metal–insulator transition behavior of VO2 film can be changed continuously. It is demonstrated that the phase transition of VO2 film is closely associated with the carrier distribution within the space charge region, which can be directly controlled by the bias voltage. Our findings offer novel opportunities for modulating the phase transition of VO2 film in a reversible way as well as extending the concept of electric-field modulation on other phase transition materials.
•Random forest regression is proposed for on-line battery capacity estimation.•The estimation is developed from partial charging voltage-capacity data.•Two features indicative of battery capacity ...fade are extracted from charging curves.•An incremental capacity analysis is used for assisting battery feature selection.
Machine-learning based methods have been widely used for battery health state monitoring. However, the existing studies require sophisticated data processing for feature extraction, thereby complicating the implementation in battery management systems. This paper proposes a machine-learning technique, random forest regression, for battery capacity estimation. The proposed technique is able to learn the dependency of the battery capacity on the features that are extracted from the charging voltage and capacity measurements. The random forest regression is solely based on signals, such as the measured current, voltage and time, that are available onboard during typical battery operation. The collected raw data can be directly fed into the trained model without any pre-processing, leading to a low computational cost. The incremental capacity analysis is employed for the feature selection. The developed method is applied and validated on lithium nickel manganese cobalt oxide batteries with different ageing patterns. Experimental results show that the proposed technique is able to evaluate the health states of different batteries under varied cycling conditions with a root-mean-square error of less than 1.3% and a low computational requirement. Therefore, the proposed method is promising for online battery capacity estimation.
Simultaneous measurements of atmospheric organic carbon (OC), elemental carbon (EC) and water-soluble organic carbon (WSOC) were made at four sampling sites, namely Guangzhou (GZ), Zhaoqing (ZQ), ...PolyU Campus (PU) and Hok Tsui (HT), in the Pearl River Delta (PRD) region between 14 August 2006 and 28 August 2007. The highest concentrations of total carbon (TC) were found at the medium-scale roadside site (PU) and the lowest were found at the regional-scale site (HT). Among the four sampling sites, the average WSOC at ZQ showed the highest concentrations, while the lowest were seen at HT. OC and EC concentrations revealed spring/summer minima and autumn/winter maxima at all sites except PU, which had a consistently high EC concentration all over the year. The highest WSOC/OC ratio was found at ZQ with an average of 0.41, suggesting that the OC was more oxidized in the atmosphere of the semi-rural site. The lowest WSOC/OC was found at the roadside site of PU. Moreover, the WSOC/OC ratio increased in autumn, when the photochemical reactions are the most active in the PRD region. This can be attributed to aging and atmospheric processing of the organic compounds during their transportation, or to the formation of secondary organic aerosol (SOA). Average annual secondary organic carbon (SOC) concentrations in PM2.5 were estimated to be 2.2 and 3.5μgm−3 for GZ and ZQ, comprising 33.5% and 42.8% of the corresponding OC concentrations, respectively. The results indicate that SOC is significant in the PRD region, and its formation mostly occurs within the region.
► Seasonal and spatial characteristics of WSOC in the PRD region are investigated. ► There is lack of any long-term studies of carbonaceous aerosol in the PRD region. ► High concentrations of carbonaceous aerosol are observed in the downwind regions. ► Secondary organic carbon (SOC) is significant in the PRD region.
Although the sun is really far away from us, some solar activities could still influence the performance and reliability of space-borne and ground-based technological systems on Earth. Those ...time-varying conditions in space caused by the sun are also called solar storm or space weather. It is known that aviation activities can be affected during solar storms, but the exact effects of space weather on aviation are still unclear. Especially how the flight delays, the top topic concerned by most people, will be affected by space weather has never been thoroughly researched. By analyzing huge amount of flight data (~ 4 × 10
records), for the first time, we quantitatively investigate the flight delays during space weather events. It is found that compared to the quiet periods, the average arrival delay time and 30-min delay rate during space weather events are significantly increased by 81.34% and 21.45% respectively. The evident negative correlation between the yearly flight regularity rate and the yearly mean total sunspot number during 22 years also confirms such correlation. Further studies show that the flight delay time and delay rate will monotonically increase with the geomagnetic field fluctuations and ionospheric disturbances. These results indicate that the interferences in communication and navigation during space weather events may be the most probable reason accounting for the increased flight delays. The above analyses expand the traditional field of space weather research and could also provide us with brand new views for improving the flight delay predications.
Solar flares are one of the severest solar activities that have important effects on near-Earth space. Previous studies have shown that flight arrival delays increase as a result of solar flares, but ...the intrinsic mechanism behind this relationship is still unknown. In this study, we conducted a comprehensive analysis of flight departure delays during 57 solar X-ray events by using a huge amount of flight data (~ 5 × 10
records) gathered over a 5-year period. It is found that the average flight departure delay time during solar X-ray events increased by 20.68% (7.67 min) compared to quiet periods. Our analysis also revealed apparent time and latitude dependencies, with flight delays being more serious on the dayside than on the nightside and longer (shorter) delays tending to occur in lower (higher) latitude airports during solar X-ray events. Furthermore, our results suggest that the intensity of solar flares (soft X-ray flux) and the Solar Zenith Angle directly modulate flight departure delay time and delay rate. These results indicate that communication interferences caused by solar flares directly affect flight departure delays. This work expands our conventional understanding of the impacts of solar flares on human society and provides new insights for preventing or coping with flight delays.
