Search

Carbon nanotubes (CNTs) have received broad attention in the past decades due to their excellent physical and chemical properties and thus been regarded as a powerful candidate for future star-materials. Although various CNT products and their related applications have been demonstrated recently, their performance can hardly meet the researchers’ expectations compared with their theoretical properties. The current predicament is caused by the immature synthesis method, including the basic science and the producing technology. As the synthesis with controlled structures determines its future, this review summarizes the progress on the basic research and industrialization of CNTs in the past decades, including the fine structure control, aggregation status design and scale-up production, and further points out the way for the future development of CNTs combining with specific applications.

This paper presents a sequential Kriging modeling approach (SKM) for time-variant reliability-based design optimization (tRBDO) involving stochastic processes. To handle the temporal uncertainty, time-variant limit state functions are transformed into time-independent domain by converting the stochastic processes and time parameter to random variables. Kriging surrogate models are then built and enhanced by a design-driven adaptive sampling scheme to accurately identify potential instantaneous failure events. By generating random realizations of stochastic processes, the time-variant probability of failure is evaluated by the surrogate models in Monte Carlo simulation (MCS). In tRBDO, the first-order score function is employed to estimate the sensitivity of time-variant reliability with respect to design variables. Three case studies are introduced to demonstrate the efficiency and accuracy of the proposed approach.

Reliability measures the probability that engineered systems successfully perform the intended functionalities under various sources of uncertainties. A piecewise point classification (PPC) method is proposed in this work for effectively propagating uncertainty with nonlinear limit states and approximating the probability of failure accurately. The idea is to efficiently identify a set of points near the critical region, and thus enable capturing the nonlinearity of limit states by constructing piecewise linear approximations. In PPC, the first-order reliability method (FORM) is initially employed to search the most probable point. To handle the nonlinearity of failure surfaces, a sampling-based limit state learning algorithm is then developed to search critical points near the failure surface. With all the points evaluated during the search process, a distance-based piecewise point classification method is developed as a classifier to predict failure events. Monte Carlo simulation (MCS) is finally utilized to propagate uncertainties and approximate the probability of failure, in which a large size of sample points is generated randomly and classified by the developed piecewise point classifier. Three case studies are used to demonstrate the efficacy of the developed approach.

The semiconductor industry is increasingly of the view that Moore's law-which predicts the biennial doubling of the number of transistors per microprocessor chip-is nearing its end. Consequently, the pursuit of alternative semiconducting materials for nanoelectronic devices, including single-walled carbon nanotubes (SWNTs), continues. Arrays of horizontal nanotubes are particularly appealing for technological applications because they optimize current output. However, the direct growth of horizontal SWNT arrays with controlled chirality, that would enable the arrays to be adapted for a wider range of applications and ensure the uniformity of the fabricated devices, has not yet been achieved. Here we show that horizontal SWNT arrays with predicted chirality can be grown from the surfaces of solid carbide catalysts by controlling the symmetries of the active catalyst surface. We obtained horizontally aligned metallic SWNT arrays with an average density of more than 20 tubes per micrometre in which 90 per cent of the tubes had chiral indices of (12, 6), and semiconducting SWNT arrays with an average density of more than 10 tubes per micrometre in which 80 per cent of the nanotubes had chiral indices of (8, 4). The nanotubes were grown using uniform size Mo2C and WC solid catalysts. Thermodynamically, the SWNT was selectively nucleated by matching its structural symmetry and diameter with those of the catalyst. We grew nanotubes with chiral indices of (2m, m) (where m is a positive integer), the yield of which could be increased by raising the concentration of carbon to maximize the kinetic growth rate in the chemical vapour deposition process. Compared to previously reported methods, such as cloning, seeding and specific-structure-matching growth, our strategy of controlling the thermodynamics and kinetics offers more degrees of freedom, enabling the chirality of as-grown SWNTs in an array to be tuned, and can also be used to predict the growth conditions required to achieve the desired chiralities.

Critical engineering systems generally demand high reliability while considering uncertainties, which makes failure event identification and reliability analysis based on computer simulation codes computationally very expensive. Rare event can be defined as a failure event that has a small probability of failure value, normally less than 10ź5. This paper presents a new approach, referred to as Accelerated Failure Identification Sampling (AFIS), for probability analysis of rare events, enabling savings of vast computational efforts. To efficiently identify rare failure sample points in probability analysis, the proposed AFIS technique will first construct a Gaussian process (GP) model for system performance of interest and then utilize the developed model to predict unknown responses of Monte Carlo sample points. Second, a new quantitative measure, namely "failure potential", is developed to iteratively search sample points that have the best chance to be a failure sample point. Third, the identified sample points with highest failure potentials are evaluated for the true performance and then used to update the GP model. The failure identification process will be iteratively preceded and the Monte Carlo simulation will then be employed to estimate probabilities of rare events if the maximum failure potential of existing Monte Carlo samples falls below a given target value. Two case studies are used to demonstrate the effectiveness of the developed AFIS approach for rare events identification and probability analysis.

In order to study the impalpable effect of GFP in homozygous heart-specific GFP-positive zebrafish during the early stage, the researchers analyzed the heart function of morphology and physiology at the first 3 days after fertilization. This zebrafish line was produced by a large-scale Tol2 transposon mediated enhancer trap screen that generated a transgenic zebrafish with a heart-specific expression of green fluorescent protein (GFP)-tagged under control of the nppa enhancer. In situ hybridization experiments showed that the nppa:GFP line faithfully recapitulated both the spatial and temporal expressions of the endogenous nppa. Green fluorescence was intensively and specifically expressed in the myocardial cells located both in the heart chambers and in the atrioventricular canal. The embryonic heart of nppa:GFP line developed normally compared with those in the wild type. There was no difference between the nappa:GFP and wild type lines with respect to heart rate, overall size, ejection volume, and fractional shortening. Thus the excess expression of GFP in this transgenic line seemed to exert no detrimental effects on zebrafish hearts during the early stages.

Mitogen-activated protein kinases (MAPKs) are evolutionary conserved enzymes in cell signal transduction connecting cell-surface receptors to critical regulatory targets within cells and control cell survival, adaptation, and proliferation. Previous studies revealed that zinc finger proteins are involved in the regulation of the MAPK signaling pathways. Here we report the identification and characterization of a novel human zinc finger protein, ZNF436. The cDNA of ZNF436 is 3.8 kb, encoding 470 amino acids in the nucleus. The protein is highly conserved in evolution across different vertebrate species from rat to human. RT-PCR indicates that ZNF436 is expressed in all the human fetal tissues examined, with a high level in brain and heart. Overexpression of pCMV-tag2A-ZNF436 in the COS-7 cells represses the transcriptional activities of SRE and AP-1. These results suggest that ZNF436 is a member of the zinc finger transcription factor family and may act as a negative regulator in gene transcription mediated by the MAPK signaling pathways.

Common carp Cyprinus carpio genomic DNA repetitive sequence CR1 has been DIG-labeled and hybridized in situ against chromosomes of red common carp (Cyprinus carpio L. Xingguo red var.). It is found that the repetitive sequence CR1 is mainly localized at the centromeric regions of chromosomes of the red common carp, The application of the chromosomal in situ hybridization technique on fish and the relationship between CR1 repetitive sequence distribution and its function have been discussed.