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A photocatalyst-free, simple and green method for visible light driven photocatalytic synthesis of gem-dihaloenones from alkynes, tetrahalomethanes and water has been developed.

Insulator determines the insulation level and power supply reliability of transmission lines. The traditional operation and maintenance method of insulators has a large workload. This paper presents an insulator recognition and fault diagnosis system based on image recognition and machine learning. Firstly, the composite insulators in complex backgrounds have been identified by Faster RCNN algorithm, which helps to extract the image of insulators by drone shot. Then, the cracking of umbrella skirts has been carried out by means of image processing. Also, the contamination of composite insulator umbrella skirts is also identified. The appropriate feature quantity by Fisher’s discrimination is recommended, and the insulator contamination level has been identified by S component. Lastly, analyzing the infrared image of the insulator provides the basis for the normalization of insulator temperature rise detection results in different environments. Insulator image autonomous recognition and defect intelligent detection, which is based on deep reinforcement learning, is helpful for the operation, maintenance and repair of line insulators.

Magnetic resonance-guided focused ultrasound (MRgFUS) is a promising non-invasive surgical technique with spatial specificity and minimal off-target effects. Despite the expanding clinical applications, the major obstacles associated with MRgFUS still lie in low magnetic resonance imaging (MRI) sensitivity and safety issues. High ultrasound power is required to resist the energy attenuation during the delivery to the tumor site and may cause damage to the surrounding healthy tissues. Herein, a surface modification strategy is developed to simultaneously strengthen MRI and ultrasound ablation of MRgFUS by prolonging Fe

This letter introduces an attention-based unsupervised generative adversarial network, named Illu-GAN, for low- light camera image enhancement. Many images captured by camera sensors suffer from inadequate lighting conditions or over-/under- exposure. Previous low-light enhancement methods are mostly based on supervised learning-based and heavily rely on paired data. Existing unsupervised methods only utilize information in spatial domain and take no account of frequency information, which leads to results of poor quality. In this letter, we propose a new network, Illu- GAN, to incorporate the features from frequency and spatial domains to better guide the network through a Wavelet Transform module. Extensive experiments with Illu-GAN on various benchmarking datasets demonstrate qualitatively and quantitatively the improvement with respect to state-of-the-art methods. The qualitative and quantitative results demonstrate that the newly proposed Illu-GAN can generate more natural enhanced images, less noisy, and with better generalisation ability.

Christensen insightfully proposed disruptive innovation. The theory is growing stronger but is still haunted by vagueness and post hoc definition issues, which signifies the nature of disruptive innovation may remain unveiled. In order to eliminate the controversies and confusion, this study sorts out commonalities and key driving forces for market encroachment of disruptive innovation, and utilizes the interpretative structural model to reorganize the original theoretical framework based on the principle of “Occam’s razor”. The results show that (1) the value network is the theoretical outset of disruptive innovation and manifests as two different value dimensions at the product or technical level, which endows disruptive innovation with “duality”; (2) the duality makes disruptive innovation have asymmetric advantages and disadvantages compared to existing products or technologies in terms of price and performance; The asymmetry changes enterprises’ competition basis and consumers’ evaluation matrices, which essentially explains the key to market encroachment of disruptive innovation; (3) the duality is the essence that distinguishes disruptive innovation from other types of innovation.

The droplet-based biphasic reaction is an efficient strategy for the fabrication of surface-bound nanostructures. Here, we developed a process of fabricating ordered micro-ring arrays of silver (Ag) nanostructures from surface nanodroplet reaction on a micro-patterned hydrophobic substrate for reproducible detection by surface-enhanced Raman spectroscopy (SERS). Our process consisted of the generation of surface nanodroplet arrays, followed by a biphasic chemical reaction between droplets and the continuous flow of silver nitrate (AgNO3) precursor solution. The parameters in the formation and reaction of the droplet array were well controlled to maintain the uniformity of Ag nanostructures throughout the substrate. By scaling up the process parameters and the size of the microchamber, we were able to produce a SERS substrate with a surface area of > 60 cm^2 in a single run. Such a large area could be sufficient for analyzing more than a thousand samples. We demonstrated the repeatability of SERS measurements using Ag nanostructures by analyzing three environmental (rhodamine 6G, chlorpyrifos, triclosan), a biological (indoxyl sulfate), and a psychoactive drug (tetrahydrocannabinol) compounds. 2D mapping of SERS intensities was also performed for both small and large-scale substrates by collecting data from more than 100 locations on the substrate. Our work demonstrated droplet-based biphasic reaction as a simple approach for the fabrication of SERS substrate with a large area. The technique may help to eliminate the requirement for sophisticated equipment for the fabrication of SERS active substrate.

Bioactive macromolecule mining is important for the functional chemome dissection of traditional Chinese vinegar. Here, saccharide-bearing macromolecules in Shanxi aged vinegar (SBMSAV) were isolated, characterized, and their immunomodulatory activity was evaluated. Four sub-fractions were obtained through ethanol precipitation followed by refining and DEAE-650M column chromatography. All sub-fractions had a maximum molecular weight of more than 2,667 kDa, with 10 types of monosaccharides identified. Comparative analysis of these sub-fractions with melanoidins from real food and model systems showed that all four sub-fractions had similarities with both melanoidins in elemental composition, spectral signature, and pyrolytic characteristics. Immunological assays showed that the four sub-fractions significantly improved macrophage cell viability and phagocytic activity, and promoted the secretion of NO, TNF-α, and IL-6. Immunomodulatory mechanism investigation indicated that SBMSAV enhanced aerobic glycolysis in macrophages and were recognized through TLR2, TLR4, MR, SR, and Dectin-1. Additionally, the MyD88/PI3K/Akt/NF-κB signaling pathway was found to be involved in the activation of macrophages.

Background and purpose: With deep-learning, gross tumour volume (GTV) auto-segmentation has substantially been improved, but still substantial manual corrections are needed. With interactive deep-learning (iDL), manual corrections can be used to update a deep-learning tool while delineating, minimising the input to achieve acceptable segmentations. We present an iDL tool for GTV segmentation that took annotated slices as input and simulated its performance on a head and neck cancer (HNC) dataset. Materials and methods: Multimodal image data of 204 HNC patients with clinical tumour and lymph node GTV delineations were used. A baseline convolutional neural network (CNN) was trained (n = 107 training, n = 22 validation) and tested (n = 24). Subsequently, user input was simulated on initial test set by replacing one or more of predicted slices with ground truth delineation, followed by re-training the CNN. The objective was to optimise re-training parameters and simulate slice selection scenarios while limiting annotations to maximally-five slices. The remaining 51 patients were used as an independent test set, where Dice similarity coefficient (DSC), mean surface distance (MSD), and 95% Hausdorff distance (HD95%) were assessed at baseline and after every update. Results: Median segmentation accuracy at baseline was DSC = 0.65, MSD = 4.3 mm, HD95% = 17.5 mm. Updating CNN using three slices equally sampled from the craniocaudal axis of the GTV in the first round, followed by two rounds of annotating one extra slice, gave the best results. The accuracy improved to DSC = 0.82, MSD = 1.6 mm, HD95% = 4.8 mm. Every CNN update took 30 s. Conclusions: The presented iDL tool achieved substantial segmentation improvement with only five annotated slices.

We report on a fluorescent-droplet-based acid-sensing scheme that allows limits of detection below 100 pM for weak acids. The concept is based on a strong partitioning of acid from an aqueous phase into octanol droplets. Using salicylic acid as a demonstration, we show that at a high concentration, the acid partitions into the organic phase by a factor of 260, which is approximately consistent with literature values. However, at lower concentrations, we obtain a partition coefficient as high as 10

Magnetic resonance imaging (MRI) is a non-invasive imaging technology to diagnose health conditions, showing the weakness of low sensitivity. Herein, we synthesize a contrast agent, SPIO@SiO2@MnO2, which shows decreased T1 and T2 contrast intensity in normal physiological conditions. In the acid environment of tumor or inflamed tissue, the manganese dioxide (MnO2) layer decomposes into magnetically active Mn2+ (T1-weighted), and the T1 and T2 signals are sequentially recovered. In addition, both constrast quenching-activation degrees of T1 and T2 images can be accurately regulated by the silicon dioxide (SiO2) intermediate layer between superparamagnetic iron oxide (SPIO) and MnO2. Through the “dual-contrast enhanced subtraction” imaging processing technique, the contrast sensitivity of this MRI contrast agent is enhanced to a 12.3-time difference between diseased and normal tissue. Consequently, SPIO@SiO2@MnO2 is successfully applied to trace the tiny liver metastases of approximately 0.5 mm and monitor tissue inflammation.

This work demonstrates an original and ultrasensitive approach for surface-enhanced Raman spectroscopy (SERS) detection based on evaporation of self-lubricating drops containing silver supraparticles. The developed method detects an extremely low concentration of analyte that is enriched and concentrated on sensitive SERS sites of the compact supraparticles formed from drop evaporation. A low limit of detection of 10^-16 M is achieved for a model hydrophobic compound rhodamine 6G (R6G). The quantitative analysis of R6G concentration is obtained from 10^-5 to 10^-11 M. In addition, for a model micro-pollutant in water triclosan, the detection limit of 10^-6 M is achieved by using microliter sample solutions. The intensity of SERS detection in this approach is robust to the dispersity of the nanoparticles in the drop but became stronger after a longer drying time. The ultrasensitive detection mechanism is the sequential process of concentration, extraction, and absorption of the analyte during evaporation of self-lubrication drop and hot spot generation for intensification of SERS signals. This novel approach for sample preparation in ultrasensitive SERS detection can be applied to the detection of chemical and biological signatures in areas such as environment monitoring, food safety, and biomedical diagnostics., 25 Pages, 12 Figures

In order to explore the corrosion damage behavior of plasma-sprayed NiCoCrAlY/magnesia-stabilized zirconia (18~22 wt.% MgO-ZrO2, MSZ) thermal barrier and anti-ablation coatings in a salt spray environment, a series of neutral salt spray (NSS) tests for 96 h (N-2), 192 h (N-4) and 288 h (N-6) were carried out on the coatings. The microstructures, composition distributions, potentiodynamic polarization curves, bonding strengths and anti-ablation properties of the coatings before and after the NSS tests were characterized. The results show that, with the increase in salt spray time, the expansion and overflow of corrosion products (FexOy) led to more defects forming inside the coating; the porosity of the top coat (TC) of N-6 increased to 15.2% in comparison to that of the blank control sample (N-0), which was 8.5%. According to the potentiodynamic polarization curves, accelerated corrosion occurred in the coating samples after the salt spray time reached 192 h, and the corrosion current density rapidly surged from 2.2 × 10−8 A·cm−2 (N-4) to 2.2 × 10−5 A·cm−2 (N-6). Similar degradation tends could be found in the bonding strengths and mass ablation rates of the coatings due to the accumulation of loose corrosion products on the substrate, which affected the bonding states of the coatings and promoted their spalling under an oxyacetylene flame (OAF). As a result, the maximum service life of the NiCoCrAlY/MSZ coatings in the neutral salt spray environment was about 192 h. This work is instructive for studying the environmental adaptability of metal/ceramic double-layer coatings.

The high technology (high-tech) industry of China has gained a key strategic position in the Chinese economic goals. In this positioning, foreign direct investment (FDI) and technological innovation have emerged as strong pillars of the high-tech industry. However, there are growing concerns of carbon emission from this industry which is still debatable. In this context, this study measures the effect of FDI and technology innovation on carbon emissions in the high-tech industry from 28 provinces of China. The study uses the provincial data for China over the period 2000–2018. In addition to examining unit root properties, structural breaks, and cointegration, this study uses quantile regression for estimating long-run relationships among study variables. The findings reveal the negative impact of FDI on carbon emissions. Technology innovation positively impacts in the initial three quantiles, whereas negatively impacts in the next six quantiles. These results indicate that FDI and technology innovation have shaped the energy intensity in the high-tech industry, which causes fluctuation in carbon emissions over time. After controlling the effects of urbanization, energy intensity, and economic growth, this study recommends that policymakers should emphasize on the heterogeneous effects of FDI and technology-lead emissions at different quantiles during the process of CO2 emission reduction.

In this work, we investigate the crystallization of droplets formed on micropatterned surfaces. By solvent exchange in a microchamber, a ternary solution consisting of a model compound β-alanine, water, and isopropanol was displaced by a flow of isopropanol. In the process, oiling-out droplets formed and crystallized. Our results showed that the shape and size of the crystals on surfaces with chemical micropatterns could be simply mediated by the flow conditions of solvent exchange. More uniform crystals formed on hydrophilic microdomains compared to hydrophobic microdomains or homogeneous surfaces. Varying flow rates or channel heights led to the formation of thin films with microholes, connected networks of crystals, or small diamond-shaped crystals. Physical microstructures (represented by microlenses) on the surface allowed the easy detachment of crystals from the surface. Beyond oiling-out crystallization, we demonstrated that the crystal formation of another solute dissolved in the droplets could be triggered by solvent exchange. The length of crystal fibers after the solvent-exchange process was shorter at a faster flow rate. This study may provide further understanding to effectively obtain the crystallization of surface droplets through the solvent-exchange approach.

The phenomenon of micro-cracks on the sheds is due to aging of the composite insulator. With the development of the Unmanned Aerial Vehicle (UAV) technology, the online aging characterization based on micro-cracks turns out to be a reality. In this article, the micro-cracks identification and characterization on the sheds of composite insulators based on visible spectrum image was studied. At first, the shed area was segmented after graying and binarization operation. Then, the Laplace of Gaussian (LoG) operator and Otsu algorithm were used to extract the texture of micro-cracks. Box-counting method was used to calculate the fractal dimension of the micro-cracks on the composite insulator shed. The index of fractal dimension, a number between 1 and 2, can be used to describe the roughness and density of fractal dimension. The higher the fractal dimension was, the more severe the micro-cracks would be. The experimental results proved that the method proposed was feasible and effective on micro-cracks identification and characterization.

Spatial registration and track-to-track association (which are mutually coupled) are essential parts in the process of multi-sensor information fusion. The quality of the spatial registration and track association algorithm directly influences the subsequent fusion performance. Aiming to solve the spatial registration and track association problem in the case where incomplete measurements are provided by different sensors, this paper proposes a residual bias estimation registration (RBER) method based on maximum likelihood and the sequential m-best track association algorithm based on the new target density (SMBTANTD). The RBER method realizes the update of incomplete measurements by sequential filtering technology and eliminates the systematic bias of sensors by using information on the significant targets. The SMBTANTD method introduces a new target density in the correlation matrix, which effectively solves the association problem in the scenarios where the numbers of targets measured by multiple sensors are inconsistent. The reported simulation results demonstrate that the proposed algorithm can not only accurately estimate the systematic bias of the sensors, but also significantly improve the performance of track association.

Agglomeration of therapeutic nanoparticles in response to tumor microenvironments is a promising approach to enhance drug accumulation and improve therapeutic efficacy. Cytosine-rich DNA sequences ...

A combinative approach for chemical analysis makes it possible to distinguish a mixture of a large number of compounds from other mixtures in a single step. This work demonstrates a combinative analysis approach using surface nanodroplets for integrating nanoextraction and colorimetric reactions for the identification of multicomponent mixtures. The model analytes are acidic compounds dissolved in an oil that are extracted into aqueous droplets on a solid substrate. The proton from acid dissociation reacts with the halochromic chemical compounds inside the droplets, leading to the color change of the droplets. The rate of the colorimetric reaction exhibits certain specificity for the acid type, distinguishing acid mixtures with the same pH value. The underlying principle is that the acid transport rate is associated with the partition coefficient and the dissociation constant of the acid, as well as to the concentration in the oil. As a demonstration, we showed that droplet-based combinative analysis can be applied for anti-counterfeiting of various alcoholic spirits by comparing the decoloration time of organic acid mixtures in the spirits. The readout can be done by a common hand-hold mobile phone.

Chitosan is a polysaccharide widely used as a structuring agent in foods and other materials because of its positive charge (amino groups). At present, however, it is difficult to form and stabilize emulsions using chitosan due to its high hydrophilicity. In this study, oil-in-water emulsions were prepared using a one-pot green-chemistry method. The chitosan and aldehyde molecules were in situ interfacially conjugated during homogenization, which promoted the adsorption of chitosan onto the oil droplet surfaces where they created a protective coating. The universality of this method was verified by using chitosan with different molecular weights and four kinds of natural aldehydes [cinnamaldehyde (CA), citral (CT), citronella (CN), and vanillin (VL)]. Chitosan with higher molecular weight facilitated the formation of emulsions. By harnessing the dynamic covalent nature of imine bonds, chitosan emulsions with an imine link display dynamic behavior with acid-catalyzed hydrolysis. The aldehyde structure could control the pH point of trigger for breakdown of emulsions, which was 1.0, 3.0, 4.0, and 4.0 for CA emulsion, CT emulsion, CN emulsion, and VL emulsion, respectively. At pH 6.5, aldehyde helped to decrease the interfacial tension of chitosan to about 10 mN/m, while this value would increase if the pH decreased by adding acid during the measurement. Chemical kinetics studies indicated that the hydrophobicity and conjugation effect of the aldehyde together determined the trigger points and properties of the emulsion. Finally, we used the optimized emulsions to encapsulate and control the release of curcumin. The gastric release behavior of the curcumin depended on aldehyde structure: VL > CN > CT ≈ CA. Hence, a tailor-made trigger release emulsion system can be achieved by rational selection and design of aldehyde structure to control hydrophobicity and conjugation effect of aldehydes.

DNA nanotechnology plays an increasingly important role in the biomedical field; however, its application in the design of organic nanomaterials is underexplored. Herein, we report the use of DNA nanotechnology to transport a NIR-II-emitting nanofluorophore across the blood-brain barrier (BBB), facilitating non-invasive imaging of brain tumors. Specifically, the DNA block copolymer, PS-b-DNA, is synthesized through a solid-phase click reaction. We demonstrate that its self-assembled structure shows exceptional cluster effects, among which BBB-crossing is the most notable. Therefore, PS-b-DNA is utilized as an amphiphilic matrix to fabricate a NIR-II nanofluorephore, which is applied in in vivo bioimaging. Accordingly, the NIR-II fluorescence signal of the DNA-based nanofluorophore localized at a glioblastoma is 3.8-fold higher than the NIR-II fluorescence signal of the PEG-based counterpart. The notably increased imaging resolution will significantly benefit the further diagnosis and therapy of brain tumors.

The bird's nest on the transmission line tower has a bad impact on the transmission equipment, and even threaten the safe and stable operation of the power grid. In recent years, the number of bird pest in transmission line is increasing year by year, resulting in increasing economic losses. The traditional bird's nest identification method of transmission line is time-consuming and labor-intensive, and its security level is low. Therefore, this paper proposes an automatic detection method of bird's nest on transmission line tower based on Faster_RCNN convolution neural network. This method can automatically identify the location of the bird's nest on the transmission line tower by using the image collected by unmanned aerial vehicle (UAV). The problem of insufficient training samples and overfitting of neural network classifier is solved by enlarging the bird's nest image. The experimental results show that this method can effectively detect bird's nest targets in complex environment, and the highest recall rate can reach 95.38%, the highest F1 score can reach 96.87%, and the detection time of each image can reach 0.154s. Compared with the traditional nest detection method, this method has stronger applicability and generalization ability. It provides technical support for analyzing bird activities and taking effective preventive measures.

The bird's nest on the transmission line tower has a bad impact on the transmission equipment, and even threaten the safe and stable operation of the power grid. In recent years, the number of bird pest in transmission line is increasing year by year, resulting in increasing economic losses. The traditional bird's nest identification method of transmission line is time-consuming and labor-intensive, and its security level is low. Therefore, this paper proposes an automatic detection method of bird's nest on transmission line tower based on Faster_RCNN convolution neural network. This method can automatically identify the location of the bird's nest on the transmission line tower by using the image collected by unmanned aerial vehicle (UAV). The problem of insufficient training samples and overfitting of neural network classifier is solved by enlarging the bird's nest image. The experimental results show that this method can effectively detect bird's nest targets in complex environment, and the highest recall rate can reach 95.38%, the highest F1 score can reach 96.87%, and the detection time of each image can reach 0.154s. Compared with the traditional nest detection method, this method has stronger applicability and generalization ability. It provides technical support for analyzing bird activities and taking effective preventive measures.

Effective detection of biomolecules is important for biological research and medical diagnosis. We here propose a ligation-triggered and protein-assisted fluorescence anisotropy amplification platform for sensitive and selective detection of small biomolecules in a complex biological matrix. In the proposed method, in the presence of target small molecules, FAM-labeled DNA 1 and biotin-labeled DNA2 were ligated to produce an integrated DNA. As a result, taking advantage of the extraordinary strong interaction between biotin and streptavidin, we employed a novel mass amplification strategy for sensitive detection of small molecules through fluorescence anisotropy. The method could detect ATP from 0.05 to 1 μM, with a detection limit of 41 nM, and detect NAD+ from 0.01 to 1 μM, with a detection limit of 6.7 nM. Furthermore, ligase-specific dependence of different cofactors provides good selectivity for the detection platform. As a result, the new platform has a broad spectrum of applications both in bioanalysis and biomedical fields.

It is generally considered that photoacoustic imaging (PAI) and fluorescence imaging (FLI) cannot be enhanced concurrently, as they are dependent on competitive photophysical processes at the single-molecule level. Herein, we reveal that BDTR9-OC8 and BDTR9-C8, which have identical π-conjugated backbones but are substituted by side chains of different rigidity, show distinct phototheranostic properties in the aggregated state. The NIR-II FLI and PAI brightness of BDTR9-C8 nanoparticles are enhanced by 4.6 and 1.4 times compared with BDTR9-OC8 nanoparticles. Theoretical calculations and GIWAXS analysis revealed that BDTR9-C8 with rigid side chains shows a relative amorphous condensed state, which will benefit the efficient transportation of photo-generated excitons and phonons, subsequently enhancing the FLI and PAI signals. Besides, both nanoparticles exhibit excellent photothermal conversion efficiency due to their strong light-harvesting capability and are considered effective photothermal therapy materials. This work provides an illuminating strategy for material design in the future.

The ultralow concentration of nucleic acids in complex biological samples requires fluorescence probes with high specificity and sensitivity. Herein, a new kind of spherical nucleic acids (SNAs) is developed by using fluorescent π-conjugated polymers (FCPs) as a light-harvesting antenna to enhance the signal transduction of nucleic acid detection. Specifically, amphiphilic DNA-grafted FCPs are synthesized and self-assemble into FCP-SNA structures. Tuning the hydrophobicity of the graft copolymer can adjust the size and light-harvesting capability of the FCP-SNAs. We observe that more efficient signal amplification occurs in larger FCP-SNAs, as more chromophores are involved, and the energy transfer can go beyond the Förster radius. Accordingly, the optimized FCP-SNA shows an antenna effect of up to 37-fold signal amplification and the limit of detection down to 1.7 pM in microRNA detection. Consequently, the FCP-SNA is applied to amplified in situ nucleic acid detecting and imaging at the single-cell level.

In order to explore the quality variation and flavor formation of dry-cured donkey leg, the changes in physical–chemical composition, lipolytic, free amino acids content and volatile flavor compounds were investigated in this study. Six fresh, trimmed hind legs with average weight of 8.12 ± 0.8 kg were taken from male Dezhou donkeys slaughtered at the age of 24 months with the average live weight of 240 kg. The entire processing time was eight months long including six stages, specifically: cooling, salting, air-drying, fermenting and aging. Samples were collected at 0 d, 10 d, 20 d, 30 d, 65 d, 105 d and 165 d of processing. The results showed that the pH value remained stable in the range of 6.2~6.6. The moisture and water activity significantly decreased (p < 0.05) during processing. The chloride content, ash, total volatile basic nitrogen (TVB-N) and peroxide value (POV) significantly increased (p < 0.05), from 0.45% to 12.39%, from 3% to 17%, from 1.43 mg/kg to 8.98 mg/kg and from 1.39 g/100 g to 5.26 g/100 g, respectively. The thiobarbituric acid (TBARS) value reached its highest value of 0.39 mg MDA/kg at the end of the salting stage and then decreased to 0.34 mg MDA/kg. Eighteen free amino acids and fifteen free fatty acids were detected, and their contents were significantly increased during processing (p < 0.05). Volatile compounds were analyzed using solid-phase microextraction (SPME) and gas chromatography–mass spectrometry (GC–MS). Among 114 volatile compounds detected in dry-cured donkey leg, aldehydes, esters, alkane and alcohols were more abundant in the final products, with relative concentrations of 41.88%, 5.72%, 5.35% and 5.25%, respectively. Processing significantly affected the physical–chemical properties, which could contribute to the formation of flavor substances of dry-cured donkey leg.

Surface-bound nanomaterials are widely used in clean energy techniques from lithium batteries, solar-driven evaporation in desalination to hydrogen production by photocatalytic electrolysis. Reactive surface nanodroplets may potentially streamline the process of fabrication of a range of surface-bound nanomaterials invoking biphasic reactions at interfaces. In this work, we demonstrate the feasibility of reactive surface nanodroplets for in-situ synthesis and anchoring of nanocaps of metal oxides with tailored porous structures. Spatial arrangement and surface coverage of nanocaps are predetermined during the formation of reactive nanodroplets, while the crystalline structures of metal oxides can be controlled by thermal treatment of organometallic nanodroplets produced from the biphasic reactions. Notably, tuning the ratio of reactive and non-reactive components in surface nanodroplets enables the formation of porous nanocaps that can double photocatalytic efficiency in the degradation of organic contaminants in water, compared to smooth nanocaps. In total, we demonstrate in-situ fabrication of four types of metal oxides in the shape of nanocaps. Our work shows that reactive surface nanodroplets may open a door to a general, fast and tuneable route for preparing surface-bound metal oxides. This fabrication approach may help develop new nanomaterials needed for photocatalytic reactions, wastewater treatment, optical focusing, solar energy conversion and other clean energy techniques.

Magnetic resonance imaging (MRI) is a non-invasive imaging technology to diagnose health conditions, showing the weakness of low sensitivity. Herein, we synthesize a contrast agent, SPIO@SiO

On the path to industrialization, the energy and environmental problems caused by the rapid growth of the manufacturing industry have attracted widespread attention. At the top scale of technological innovation, high-tech manufacturing is considered to have achieved a balance between the “Made in China 2025” strategy and energy conservation. Therefore, in this paper, we explore inter-factor substitution and the influence of different sources of technological progress on China's high-tech industries using an estimation model that incorporates a translog cost function and the ‘panel seemingly unrelated regression’ method. Changes in energy intensity are further decomposed into different causes including budget, output, factor substitution, and technological progress at various points throughout the period under study. The empirical findings show Morishima substitutions among three different input factors. The substitution elasticity between labor and energy is the highest, and further most sources of technological progress are biased toward saving energy. Technological progress and budget are the critical driving forces throughout the period. However, the effect of factor substitution has gradually increased in recent years. From a regional perspective, energy intensity in the Beijing-Tianjin-Hebei region shows the most apparent decline, while the smallest decline occurs in China's western region.

The household sector of China is responsible for 11.7% of national energy consumption and 12.6% of carbon dioxide (CO2) emissions in 2015. Thus, revealing the determinants of household CO2 emissions (HCE) is extremely important for national efforts towards emissions mitigation. In this context, this paper investigates the driving forces behind national and regional HCE changes employing a temporal Logarithmic Mean Divisia Index (LMDI) model. It also analyses interregional discrepancies in HCE using a spatial LMDI model. This study decomposes the changes or discrepancies of national and regional HCE from 2000 to 2015 into seven key factors. The temporal analysis indicates that household income is the predominant determinant explaining the increase of HCE, whereas energy intensity is the crucial determinant contributing to the mitigation of HCE. The spatial analysis reveals that the number of households and energy intensity are primary driving forces accountable for growing discrepancies in HCE, followed by household income, energy structure and carbon emission coefficient. The analysis also suggests that resourceful and economically developed regions generally have larger potential for mitigation of HCE. The study concludes with some policy recommendations based on the results.

This study introduces a special converted measurement Kalman filter (CMKF) algorithm which apply to satellite for tracking target. Typical CMKF applies to radar which measures targets in spherical coordinate system. The new algorithm can be used by satellite which measures target's latitude and longitude. Improved CMKF algorithm tracks target in simple situation but is not able to deal with multi-target and information missing situation. Multiple hypothesis tracking (MHT) can track multi-target accurately and has the ability to initial track and end track at all times. For satellite, MHT use improved CMKF to calculate cost matrix and update the state estimate and estimate covariance. In the simulation, tracking process is simulated by MATLAB. Single target measured by satellite can be tracked accurately by CMKF algorithm. MHT can track multi-target accurately and distinguish the information missing rightly.

This study is concerned with the “up-cycling” postconsumer wastes into the value-added products of multiwall carbon nanotubes (CNTs). It investigated combinations of methods for pretreating different types of stainless-steel catalysts that increase the yield of CNTs form common waste plastics. The polymers were pyrolyzed at 800 °C, in nitrogen, and their gaseous pyrolyzates were passed over fixed catalyst substrates preheated to 800 °C. The substrates consisted of SS-304, SS-316 and SS-316L fine stainless-steel wire-cloths. Therein synthesis of CNTs took place by chemical vapor deposition (CVD). The wire-cloths were used either as-received or chemically etched by acid wash and/or heat-treated in air, nitrogen or helium at 800 °C, and then rapidly air-quenched. Results showed that the catalyst type, composition and pretreatment method, as well as the type of feedstock, are all influential on the yields and physical characteristics of the synthesized CNTs.