Showing papers by "Hong Kong Polytechnic University published in 2015"
TL;DR: A novel strategy to design HEAs using the eutectic alloy concept, i.e. to achieve a microstructure composed of alternating soft fcc and hard bcc phases is proposed, which can be readily adapted to large-scale industrial production of HEAs with simultaneous high fracture strength and high ductility.
Abstract: High-entropy alloys (HEAs) can have either high strength or high ductility, and a simultaneous achievement of both still constitutes a tough challenge. The inferior castability and compositional segregation of HEAs are also obstacles for their technological applications. To tackle these problems, here we proposed a novel strategy to design HEAs using the eutectic alloy concept, i.e. to achieve a microstructure composed of alternating soft fcc and hard bcc phases. As a manifestation of this concept, an AlCoCrFeNi 2.1 (atomic portion) eutectic high-entropy alloy (EHEA) was designed. The as-cast EHEA possessed a fine lamellar fcc/B2 microstructure, and showed an unprecedented combination of high tensile ductility and high fracture strength at room temperature. The excellent mechanical properties could be kept up to 700°C. This new alloy design strategy can be readily adapted to large-scale industrial production of HEAs with simultaneous high fracture strength and high ductility.
938 citations
TL;DR: A comprehensive overview of sparse representation is provided and an experimentally comparative study of these sparse representation algorithms was presented, which could sufficiently reveal the potential nature of the sparse representation theory.
Abstract: Sparse representation has attracted much attention from researchers in fields of signal processing, image processing, computer vision, and pattern recognition. Sparse representation also has a good reputation in both theoretical research and practical applications. Many different algorithms have been proposed for sparse representation. The main purpose of this paper is to provide a comprehensive study and an updated review on sparse representation and to supply guidance for researchers. The taxonomy of sparse representation methods can be studied from various viewpoints. For example, in terms of different norm minimizations used in sparsity constraints, the methods can be roughly categorized into five groups: 1) sparse representation with $l_{0}$ -norm minimization; 2) sparse representation with $l_{p}$ -norm ( $0 ) minimization; 3) sparse representation with $l_{1}$ -norm minimization; 4) sparse representation with $l_{2,1}$ -norm minimization; and 5) sparse representation with $l_{2}$ -norm minimization. In this paper, a comprehensive overview of sparse representation is provided. The available sparse representation algorithms can also be empirically categorized into four groups: 1) greedy strategy approximation; 2) constrained optimization; 3) proximity algorithm-based optimization; and 4) homotopy algorithm-based sparse representation. The rationales of different algorithms in each category are analyzed and a wide range of sparse representation applications are summarized, which could sufficiently reveal the potential nature of the sparse representation theory. In particular, an experimentally comparative study of these sparse representation algorithms was presented.
925 citations
TL;DR: The proposed opinion-unaware BIQA method does not need any distorted sample images nor subjective quality scores for training, yet extensive experiments demonstrate its superior quality-prediction performance to the state-of-the-art opinion-aware BIZA methods.
Abstract: Existing blind image quality assessment (BIQA) methods are mostly opinion-aware. They learn regression models from training images with associated human subjective scores to predict the perceptual quality of test images. Such opinion-aware methods, however, require a large amount of training samples with associated human subjective scores and of a variety of distortion types. The BIQA models learned by opinion-aware methods often have weak generalization capability, hereby limiting their usability in practice. By comparison, opinion-unaware methods do not need human subjective scores for training, and thus have greater potential for good generalization capability. Unfortunately, thus far no opinion-unaware BIQA method has shown consistently better quality prediction accuracy than the opinion-aware methods. Here, we aim to develop an opinion-unaware BIQA method that can compete with, and perhaps outperform, the existing opinion-aware methods. By integrating the features of natural image statistics derived from multiple cues, we learn a multivariate Gaussian model of image patches from a collection of pristine natural images. Using the learned multivariate Gaussian model, a Bhattacharyya-like distance is used to measure the quality of each image patch, and then an overall quality score is obtained by average pooling. The proposed BIQA method does not need any distorted sample images nor subjective quality scores for training, yet extensive experiments demonstrate its superior quality-prediction performance to the state-of-the-art opinion-aware BIQA methods. The MATLAB source code of our algorithm is publicly available at www.comp.polyu.edu.hk / $\sim $ cslzhang/IQA/ILNIQE/ILNIQE.htm.
783 citations
TL;DR: A causal-chain framework was developed based on the input-moderator-mediator-output model to illustrate the causality between the research constructs used and the conceptualization of theoretical models/theories proposed by previous researchers.
627 citations
TL;DR: The Critical Success Factors for Public-Private Partnership is a major research interest worldwide therefore as discussed by the authors aims to methodically review studies on the CSFs for implementing PPP from some selected top tier academic journals from 1990 to 2013 (years inclusive).
563 citations
TL;DR: In this article, single-layer chromium trihalides (SLCTs) were shown to constitute a series of stable 2D intrinsic ferromagnetic (FM) semiconductors with indirect gaps and their valence and conduction bands are fully spin-polarized in the same spin direction.
Abstract: Two-dimensional (2D) intrinsic ferromagnetic (FM) semiconductors are crucial to develop low-dimensional spintronic devices. Using density functional theory, we show that single-layer chromium trihalides (SLCTs) (CrX3, X = F, Cl, Br and I) constitute a series of stable 2D intrinsic FM semiconductors. A free-standing SLCT can be easily exfoliated from the bulk crystal, due to a low cleavage energy and a high in-plane stiffness. Electronic structure calculations using the HSE06 functional indicate that both bulk and single-layer CrX3 are half semiconductors with indirect gaps and their valence and conduction bands are fully spin-polarized in the same spin direction. The energy gaps and absorption edges of CrBr3 and CrI3 are found to be in the visible frequency range, which implies possible opto-electronic applications. Furthermore, SLCTs are found to possess a large magnetic moment of 3 μB per formula unit and a sizable magnetic anisotropy energy. The magnetic exchange constants of SLCTs are then extracted using the Heisenberg spin Hamiltonian and the microscopic origins of the various exchange interactions are analyzed. A competition between a near 90° FM superexchange and a direct antiferromagnetic (AFM) exchange results in a FM nearest-neighbour exchange interaction. The next and third nearest-neighbour exchange interactions are found to be FM and AFM, respectively, and this can be understood by the angle-dependent extended Cr–X–X–Cr superexchange interaction. Moreover, the Curie temperatures of SLCTs are also predicted using Monte Carlo simulations and the values can be further increased by applying a biaxial tensile strain. The unique combination of robust intrinsic ferromagnetism, half semiconductivity and large magnetic anisotropy energies renders the SLCTs as promising candidates for next-generation semiconductor spintronic applications.
563 citations
TL;DR: A hierarchical graphene–metallic textile composite electrode concept is reported, which is lightweight, highly flexible, strong, durable in life cycle and bending fatigue tests, and integratable into various wearable electronic devices.
Abstract: One-dimensional flexible supercapacitor yarns are of considerable interest for future wearable electronics. The bottleneck in this field is how to develop devices of high energy and power density, by using economically viable materials and scalable fabrication technologies. Here we report a hierarchical graphene-metallic textile composite electrode concept to address this challenge. The hierarchical composite electrodes consist of low-cost graphene sheets immobilized on the surface of Ni-coated cotton yarns, which are fabricated by highly scalable electroless deposition of Ni and electrochemical deposition of graphene on commercial cotton yarns. Remarkably, the volumetric energy density and power density of the all solid-state supercapacitor yarn made of one pair of these composite electrodes are 6.1 mWh cm(-3) and 1,400 mW cm(-3), respectively. In addition, this SC yarn is lightweight, highly flexible, strong, durable in life cycle and bending fatigue tests, and integratable into various wearable electronic devices.
560 citations
TL;DR: In this article, a review on three streams of life cycle studies that have been frequently applied to evaluate the environmental impacts of building construction with a major focus on whether they can be used for decision making is provided.
547 citations
TL;DR: Zhang et al. as mentioned in this paper proposed a supervised learning framework to generate compact and bit-scalable hashing codes directly from raw images, where they pose hashing learning as a problem of regularized similarity learning.
Abstract: Extracting informative image features and learning effective approximate hashing functions are two crucial steps in image retrieval. Conventional methods often study these two steps separately, e.g., learning hash functions from a predefined hand-crafted feature space. Meanwhile, the bit lengths of output hashing codes are preset in the most previous methods, neglecting the significance level of different bits and restricting their practical flexibility. To address these issues, we propose a supervised learning framework to generate compact and bit-scalable hashing codes directly from raw images. We pose hashing learning as a problem of regularized similarity learning. In particular, we organize the training images into a batch of triplet samples, each sample containing two images with the same label and one with a different label. With these triplet samples, we maximize the margin between the matched pairs and the mismatched pairs in the Hamming space. In addition, a regularization term is introduced to enforce the adjacency consistency, i.e., images of similar appearances should have similar codes. The deep convolutional neural network is utilized to train the model in an end-to-end fashion, where discriminative image features and hash functions are simultaneously optimized. Furthermore, each bit of our hashing codes is unequally weighted, so that we can manipulate the code lengths by truncating the insignificant bits. Our framework outperforms state-of-the-arts on public benchmarks of similar image search and also achieves promising results in the application of person re-identification in surveillance. It is also shown that the generated bit-scalable hashing codes well preserve the discriminative powers with shorter code lengths.
457 citations
TL;DR: Semitransparent perovskite solar cells are prepared by laminating graphene transparent electrodes on the top for the first time and show high power conversion efficiencies when they are illuminated from both sides.
Abstract: Semitransparent perovskite solar cells are prepared by laminating graphene transparent electrodes on the top for the first time. The device performance is optimized by improving the conductivity of the graphene electrodes and the contact between the graphene and the perovskite active layers during the lamination process. The devices show high power conversion efficiencies when they are illuminated from both sides.
442 citations
TL;DR: Wang et al. as mentioned in this paper proposed an ensemble empirical mode decomposition (EEMD)-ARIMA model for forecasting annual runoff time series from Biuliuhe reservoir, Dahuofang reservoir and Mopanshan reservoir in China.
Abstract: Hydrological time series forecasting is one of the most important applications in modern hydrology, especially for effective reservoir management. In this research, the auto-regressive integrated moving average (ARIMA) model coupled with the ensemble empirical mode decomposition (EEMD) is presented for forecasting annual runoff time series. First, the original annual runoff time series is decomposed into a finite and often small number of intrinsic mode functions (IMFs) and one residual series using EEMD technique for a deep insight into the data characteristics. Then each IMF component and residue is forecasted, respectively, through an appropriate ARIMA model. Finally, the forecasted results of the modeled IMFs and residual series are summed to formulate an ensemble forecast for the original annual runoff series. Three annual runoff series from Biuliuhe reservoir, Dahuofang reservoir and Mopanshan reservoir, in China, are investigated using developed model based on the four standard statistical performance evaluation measures (RMSE, MAPE, R and NSEC). The results obtained in this work indicate that EEMD can effectively enhance forecasting accuracy and that the proposed EEMD-ARIMA model can significantly improve ARIMA time series approaches for annual runoff time series forecasting.
TL;DR: This paper provides a comprehensive review of the theoretical forecasting methodologies for both solar resource and PV power and applications of solar forecasting in energy management of smart grid are investigated in detail.
Abstract: Due to the challenge of climate and energy crisis, renewable energy generation including solar generation has experienced significant growth. Increasingly high penetration level of photovoltaic (PV) generation arises in smart grid. Solar power is intermittent and variable, as the solar source at the ground level is highly dependent on cloud cover variability, atmospheric aerosol levels, and other atmosphere parameters. The inherent variability of large-scale solar generation introduces significant challenges to smart grid energy management. Accurate forecasting of solar power/irradiance is critical to secure economic operation of the smart grid. This paper provides a comprehensive review of the theoretical forecasting methodologies for both solar resource and PV power. Applications of solar forecasting in energy management of smart grid are also investigated in detail.
TL;DR: In this article, the authors analyzed the latest research development in this domain by reviewing selected articles published from 1997 to 2014 and identified four major research topics: stakeholder interests and influences, stakeholder management process, stake holder analysis methods, and stakeholder engagement.
TL;DR: In this paper, the authors present a review of the existing green BIM literature and outline the most important directions for future research, and suggest that a "one-stop-shop" BIM for environmental sustainability monitoring and management over a building's full life cycle should be considered.
TL;DR: Large energy storage textiles are fabricated by weaving flexible all-solid-state supercapacitor yarns to a 15 cm × 10 cm cloth on a loom and knitting in a woollen wrist band to form a pattern, enabling dual functionalities of energy storage capability and wearability.
Abstract: Wearable electronic textiles that store capacitive energy are a next frontier in personalized electronics. However, the lack of industrially weavable and knittable conductive yarns in conjunction with high capacitance, limits the wide-scale application of such textiles. Here pristine soft conductive yarns are continuously produced by a scalable method with the use of twist-bundle-drawing technique, and are mechanically robust enough to be knitted to a cloth by a commercial cloth knitting machine. Subsequently, the reduced-graphene-oxide-modified conductive yarns covered with a hierarchical structure of MnO2 nanosheets and a polypyrrole thin film were used to fabricate weavable, knittable and wearable yarn supercapacitors. The resultant modified yarns exhibit specific capacitances as high as 36.6 mF cm–1 and 486 mF cm–2 in aqueous electrolyte (three-electrode cell) or 31 mF cm–1 and 411 mF cm–2 in all solid-state two-electrode cell. The symmetric solid-state supercapacitor has high energy densities of 0.00...
TL;DR: An improved algorithm SVM-RFE + CBR is proposed by incorporating the correlation bias reduction (CBR) strategy into the feature elimination procedure, which outperforms the original SVM -RFE and other typical algorithms.
Abstract: Support vector machine recursive feature elimination (SVM-RFE) is a powerful feature selection algorithm. However, when the candidate feature set contains highly correlated features, the ranking criterion of SVM-RFE will be biased, which would hinder the application of SVM-RFE on gas sensor data. In this paper, the linear and nonlinear SVM-RFE algorithms are studied. After investigating the correlation bias, an improved algorithm SVM-RFE + CBR is proposed by incorporating the correlation bias reduction (CBR) strategy into the feature elimination procedure. Experiments are conducted on a synthetic dataset and two breath analysis datasets, one of which contains temperature modulated sensors. Large and comprehensive sets of transient features are extracted from the sensor responses. The classification accuracy with feature selection proves the efficacy of the proposed SVM-RFE + CBR. It outperforms the original SVM-RFE and other typical algorithms. An ensemble method is further studied to improve the stability of the proposed method. By statistically analyzing the features’ rankings, some knowledge is obtained, which can guide future design of e-noses and feature extraction algorithms.
07 Dec 2015
TL;DR: By working directly on the whole image, the proposed CSC-SR algorithm does not need to divide the image into overlapped patches, and can exploit the image global correlation to produce more robust reconstruction of image local structures.
Abstract: Most of the previous sparse coding (SC) based super resolution (SR) methods partition the image into overlapped patches, and process each patch separately. These methods, however, ignore the consistency of pixels in overlapped patches, which is a strong constraint for image reconstruction. In this paper, we propose a convolutional sparse coding (CSC) based SR (CSC-SR) method to address the consistency issue. Our CSC-SR involves three groups of parameters to be learned: (i) a set of filters to decompose the low resolution (LR) image into LR sparse feature maps, (ii) a mapping function to predict the high resolution (HR) feature maps from the LR ones, and (iii) a set of filters to reconstruct the HR images from the predicted HR feature maps via simple convolution operations. By working directly on the whole image, the proposed CSC-SR algorithm does not need to divide the image into overlapped patches, and can exploit the image global correlation to produce more robust reconstruction of image local structures. Experimental results clearly validate the advantages of CSC over patch based SC in SR application. Compared with state-of-the-art SR methods, the proposed CSC-SR method achieves highly competitive PSNR results, while demonstrating better edge and texture preservation performance.
TL;DR: In this article, the authors analyzed four basic IPT circuits with series-series (SS), series-parallel (SP), parallel series (PS), and parallel parallel (PP) compensations systematically to identify conditions for realizing load-independent output current or voltage, as well as resistive input impedance.
Abstract: The inductive power transfer (IPT) technique in battery charging applications has many advantages compared to conventional plug-in systems. Due to the dependencies on transformer characteristics, loading profile, and operating frequency of an IPT system, it is not a trivial design task to provide the battery the required constant charging current (CC) or constant battery charging voltage (CV) efficiently under the condition of a wide load range possibly defined by the charging profile. This paper analyzes four basic IPT circuits with series–series (SS), series–parallel (SP), parallel–series (PS), and parallel–parallel (PP) compensations systematically to identify conditions for realizing load-independent output current or voltage, as well as resistive input impedance. Specifically, one load-independent current output circuit and one load-independent voltage output circuit having the same transformer, compensating capacitors, and operating frequency can be readily combined into a hybrid topology with fewest additional switches to facilitate the transition from CC to CV. Finally, hybrid topologies using either SS and PS compensation or SP and PP compensation are proposed for battery charging. Fixed-frequency duty cycle control can be easily implemented for the converters.
TL;DR: This paper examined two types of splitting methods for solving this nonconvex optimization problem: alternating direction method of multipliers and proximal gradient algorithm and gives simple sufficient conditions to guarantee boundedness of the sequence generated.
Abstract: We consider the problem of minimizing the sum of a smooth function $h$ with a bounded Hessian and a nonsmooth function. We assume that the latter function is a composition of a proper closed function $P$ and a surjective linear map $\mathcal{M}$, with the proximal mappings of $\tau P$, $\tau > 0$, simple to compute. This problem is nonconvex in general and encompasses many important applications in engineering and machine learning. In this paper, we examined two types of splitting methods for solving this nonconvex optimization problem: the alternating direction method of multipliers and the proximal gradient algorithm. For the direct adaptation of the alternating direction method of multipliers, we show that if the penalty parameter is chosen sufficiently large and the sequence generated has a cluster point, then it gives a stationary point of the nonconvex problem. We also establish convergence of the whole sequence under an additional assumption that the functions $h$ and $P$ are semialgebraic. Further...
TL;DR: This review will firstly discuss the materials used in flexible organic bioelectronics, which is followed by an overview on various types of flexibleorganic bioelectronic devices.
Abstract: At the convergence of organic electronics and biology, organic bioelectronics attracts great scientific interest. The potential applications of organic semiconductors to reversibly transmit biological signals or stimulate biological tissues inspires many research groups to explore the use of organic electronics in biological systems. Considering the surfaces of movable living tissues being arbitrarily curved at physiological environments, the flexibility of organic bioelectronic devices is of paramount importance in enabling stable and reliable performances by improving the contact and interaction of the devices with biological systems. Significant advances in flexible organic bio-electronics have been achieved in the areas of flexible organic thin film transistors (OTFTs), polymer electrodes, smart textiles, organic electrochemical ion pumps (OEIPs), ion bipolar junction transistors (IBJTs) and chemiresistors. This review will firstly discuss the materials used in flexible organic bioelectronics, which is followed by an overview on various types of flexible organic bioelectronic devices. The versatility of flexible organic bioelectronics promises a bright future for this emerging area.
TL;DR: The successful synthesis of large single-crystal h-BN grains on rational designed Cu-Ni alloy foils is reported, found that the nucleation density can be greatly reduced to 60 per mm(2) by optimizing Ni ratio in substrates.
Abstract: High nucleation density has thus far limited the quality and grain size of CVD-grown hexagonal boron nitride. Here, by optimizing the Ni ratio in Cu–Ni substrates, the authors successfully reduce nucleation density and report single-crystal hexagonal boron nitride grains up to 7500 μm2.
TL;DR: In this article, the growth of the graphene-Bi2Te3 heterostructure is reported, where Bi2Te is a small bandgap material from topological insulator family with a similar hexagonal symmetry to graphene.
Abstract: Recently, research on graphene based photodetectors has drawn substantial attention due to ultrafast and broadband photoresponse of graphene. However, they usually have low responsivity and low photoconductive gain induced by the gapless nature of graphene, which greatly limit their applications. The synergetic integration of graphene with other two-dimensional (2D) materials to form van der Waals heterostructure is a very promising approach to overcome these shortcomings. Here we report the growth of graphene–Bi2Te3 heterostructure where Bi2Te3 is a small bandgap material from topological insulator family with a similar hexagonal symmetry to graphene. Because of the effective photocarrier generation and transfer at the interface between graphene and Bi2Te3, the device photocurrent can be effectively enhanced without sacrificing the detecting spectral width. Our results show that the graphene–Bi2Te3 photodetector has much higher photoresponsivity (35 AW–1 at a wavelength of 532 nm) and higher sensitivity ...
TL;DR: This article will primarily focus on the stimuli responsive properties of UCL materials beyond light-matter interaction, which can aid both fundamental research and widespread applications of the materials.
Abstract: Upconversion luminescence (UCL) refers to nonlinear optical processes, which can convert near-infrared photons to short-wavelength emission. Recent advances in nanotechnology have contributed to the development of photon upconversion materials as promising new generation candidates of fluorescent bioprobes and spectral converters for biomedical and optoelectronic applications. Apart from the remarkable photoluminescence of the materials under photon excitation, some UCL materials may exhibit intrinsic magnetic, ferroelectric, X-ray absorption properties, and so on. These interesting characteristics provide an opportunity for us to couple a single stimulus or multiple stimuli (electric field, magnetic field, X-ray, electron beam, temperature and pH, etc.) to various types of UCL materials. In this review, we will primarily focus on the stimuli responsive properties of UCL materials beyond light–matter interaction, which can aid both fundamental research and widespread applications of the materials. The mechanisms of the response to various stimuli in the UCL materials are discussed. This article will also highlight recent advances in the development of these materials in response to various stimuli and their applications in multimodal bioimaging, drug delivery and release, electro-optical devices, magnetic, temperature and pH sensors and multiple anti-counterfeiting inks. Lastly, we will present potential directions of future research and challenging issues which arise in expanding the applications of stimuli responsive UCL materials.
TL;DR: A review of the vibration isolation theory and/or methods which were developed, mainly over the last decade, specifically for or potentially could be used for, micro-vibration control can be found in this paper.
TL;DR: In this article, the authors established a link between CEO hubris and corporate social responsibility (CSR), and explored the boundary conditions of hubris effects and how these relationships are moderated by resource dependence mechanisms.
Abstract: Grounded in the upper echelons perspective and stakeholder theory, this study establishes a link between CEO hubris and corporate social responsibility (CSR). We first develop the theoretical argument that CEO hubris is negatively related to a firm's socially responsible activities but positively related to its socially irresponsible activities. We then explore the boundary conditions of hubris effects and how these relationships are moderated by resource dependence mechanisms. With a longitudinal dataset of S&P 1500 index firms for the period 2001–2010, we find that the relationship between CEO hubris and CSR is weakened when the firm depends more on stakeholders for resources, such as when its internal resource endowments are diminished as indicated by firm size and slack, and when the external market becomes more uncertain and competitive. The implications of our findings for upper echelons theory and the CSR research are discussed
TL;DR: In this paper, a pumped hydro storage system is proposed, which is considered as a promising technology for solar energy penetration and particularly for small autonomous systems in remote areas The mathematical models for the major components are developed, and system reliability and economic criteria are discussed as a benchmark for optimization The genetic algorithm (GA), along with Pareto optimality concept, is used for the system technoeconomic optimization: to maximize power supply reliability and minimize system lifecycle cost simultaneously.
TL;DR: In this paper, a detailed review of the literature focused on the use of phase change materials (PCM) for photovoltaic (PV) module thermal regulation and electrical efficiency improvement is presented.
Abstract: The study presented in this paper is based on a detailed review of the literature focused on the use of phase change materials (PCM) for photovoltaic (PV) module thermal regulation and electrical efficiency improvement. The influence of high temperature on PV power generation has been examined and the findings have highlighted the importance of effective thermal regulation for PV models. Various PV cooling methods employed to maintain better PV performance are discussed and the recently emerging PV–PCM system concept for thermal regulation is introduced. A comprehensive literature review of the state-of-the-art aspects of this technology, such as system development, performance evaluation, materials selection, heat transfer improvement, numerical models, simulation, and application in practice is given. The PV–PCM system, however, might not be economically feasible if the enhancement of PV efficiency only is sought. The PV–ST–PCM system, i.e. integrated with a solar thermal (ST) system, has therefore been investigated as the stored heat can be extracted for other thermal applications. The dual PCM roles demonstrate significant application prospects for the combined technology. However, both PV–PCM and PV–ST–PCM systems are still mainly in the research and laboratory test stages, with obvious scope for practical applications but with attendant challenges. Suggestions for the future work are presented.
TL;DR: Using this concept of seamless stitching, synthesis of 6 cm × 3 cm monocrystalline graphene without grain boundaries on polished copper (111) foil is possible, which is only limited by the chamber size.
Abstract: Seamless stitching of graphene domains on polished copper (111) is proved clearly not only at atomic scale by scanning tunnelling microscopy (STM) and transmission electron micoscopy (TEM), but also at the macroscale by optical microscopy after UV-treatment. Using this concept of seamless stitching, synthesis of 6 cm × 3 cm monocrystalline graphene without grain boundaries on polished copper (111) foil is possible, which is only limited by the chamber size.
TL;DR: In this work, a novel fluorescence resonance energy transfer (FRET) biosensor based on graphene quantum dots (GQDs) and gold nanoparticles (AuNPs) pairs was developed for Staphylococcus aureus specific gene sequence detection.
TL;DR: In this article, the authors investigated the major factors inhibiting the adoption of off-site construction with reference to the Chinese construction market and identified 30 factors influencing the use of OSC through a literature review, questionnaire survey, and face-to-face interview with professionals in the construction industry.
Abstract: Off-site construction (OSC) has been regarded as an effective means of improving construction quality and efficiency. The adoption of OSC has been slower in developing countries such as China than in developed countries, and research on the barriers inhibiting the sector’s growth is inadequate. This paper aims to investigate the major factors inhibiting the adoption of OSC with reference to the Chinese construction market. Thirty factors influencing the use of OSC were identified through a literature review, questionnaire survey, and face-to-face interview with professionals in the construction industry. A questionnaire was sent out to developers in China, and 83 completed questionnaires were retrieved. Ranking analysis was used to identify 18 critical factors. The top three barriers are “absence of government regulations and incentives,” “high initial cost,” and “dependence on traditional construction methods.” Factor analysis enables grouping of the 18 critical factors into five categories, name...