The influence of compression molding techniques on thermal conductivity of UHMWPE/BN and UHMWPE/(BN + MWCNT) hybrid composites with segregated structure

Various ultra-high-molecular-weight polyethylene (UHMWPE)/boron nitride (BN) and UHMWPE/(BN + multi-wall carbon nanotube (MWCNT)) composites with segregated structure were prepared by using the compression molding process. The dispersion of fillers under different compression molding were observed by optical microscopy and scanning electron microscopy. The results showed that integrated thermal conductive networks were formed after cold-pressing sintering. However, these networks would be destroyed by middle-high pressure/high temperature treatment. Although the treatment of high pressure/high temperature can effectively improve the crystallinity and crystal size of UHMWPE, the thermal conductivity of composite dramatically decreased due to the replacement of filler-filler by filler-polymer-filler interface. The 1D-MWCNT is liable to entangle with 2D-BNs and formed MWCNT-BN networks even at high pressure/high temperature, leading to a nearly constant thermal conductivity (reached 1.794 W/m·K with the addition of 50% (BNs + MWCNT) hybrid fillers). Besides, the dispersion of the fillers have a great influence on thermal stability of the composites.     

Emotion space model for classifying opinions in stock message board

The online stock message is known to have impacts on the trend of the stock market. Understanding investor opinions in stock message boards is important, and the automatic classification of the investors’ opinions is one of the key methods for the issue. Traditional opinion classification methods mainly use terms and their frequency, part of speech, rule of opinions and sentiment shifters. But semantic information is ignored in term selection, and it is also hard to find the complete rules. In this paper, based on the classification of human emotions proposed by Ekman, we extend the traditional positive–negative analysis to the six important emotion states to build an extremely low dimensional emotion space model (ESM). It enables the prediction of investors’ emotions in public. Specifically, we use lexical semantic extension and correlation analysis methods to extend the scale of emotion words, which can capture more words with strong emotions for ad hoc domain, like network emotion symbols. We apply our ESM on messages of a famous stock message board TheLion. We also compare our model with traditional methods information gain and mutual information. The results show that ESM is not parameter sensitive. Besides, ESM is efficient for modeling sentiment classifying and can achieve higher classification accuracy than traditional ones.     

Rapid estimation of the probability of SRAM failure via adaptive multi-level sliding-window statistical method

Statistical analysis of SRAM has emerged as a challenging issue because the SRAM cell failure probability is extremely small. In this paper, we develop a novel efficient sampling, searching and estimating method to capture the probability of SRAM failure. Particularly, we propose an innovative Adaptive Multi-Level Sliding-Window (AMLSW) method to find the failure boundary in the parameter space with less computational cost. The proposed AMLSW method applies an integrated optimization engine to adaptively explore the failure boundary by sampling a sequence of parameter points and calculating the simulation result in sliding windows. Its key features include ellipsoid transformation, multi-level grid partition and sliding window algorithm to make our method efficient and accurate for finding the SRAM failure probability and failure boundary in parameter space. Furthermore, we provide theoretical analysis on our new AMLSW method. The experimental results of a commercial 65 nm SRAM cell demonstrate that the AMLSW method achieves 1.7–40× runtime speed-up over the existing methods without surrendering accuracy, and dramatically high speed-up over normal M-C sampling method. Moreover, this new method provides the failure boundary in the parameter space as shown in experiments.     

Iron-filled multiwalled carbon nanotubes surface-functionalized with paramagnetic Gd (III): A candidate dual-functioning MRI contrast agent and magnetic hyperthermia structure

A simple wet chemical method involving only sonication in aqueous GdCl₃ solution was used for surface functionalization of iron-filled multiwalled carbon nanotubes with gadolinium. Functional groups on the sidewalls produced by the sonication provide active nucleation sites for the loading of Gd³⁺ ions. Characterization by EPR, EELS, and HRTEM confirmed the presence of Gd³⁺ ions on the sidewall surface. The room temperature ferromagnetic properties of the encapsulated iron nanowire, saturation magnetization of 40 emu/g and coercivity 600 Oe, were maintained after surface functionalization. Heating functionality in an alternating applied magnetic field was quantified through the measurement of specific absorption rate: 50 W/g_Fe at magnetic field strength 8 kA/m and frequency of 696 kHz. These results point to candidacy for dual-functioning MRI imaging and magnetic hyperthermia structures for cancer therapy.     

Boosted photogenerated carriers separation in Z-scheme Cu3P/ZnIn2S4 heterojunction photocatalyst for highly efficient H2 evolution under visible light

Developing low-cost, highly efficient and robust photocatalystic hydrogen evolution system is a promising solution to environmental and energy crisis. Herein, a Z-scheme Cu₃P/ZnIn₂S₄ heterojunction photocatalyst was successfully constructed for the first time via a facile solution-phase hybridization method. The optimized Cu₃P/ZIS composite exhibited the highest H₂ production rate of 2561.1 μmol g⁻¹ h⁻¹ under visible light irradiation (>420 nm), which was 5.2 times greater than that of bare ZnIn₂S₄ and even exceeded the photocatalytic performance of Pt/ZIS composite. The apparent quantum yield of 10 wt% Cu₃P/ZnIn₂S₄ can reach 22.3% at 420 nm. The huge boost of photocatalytic hydrogen evolution activity is ascribed to the formation of heterojunction with the built in electric field within Cu₃P/ZnIn₂S₄ and Z-scheme charge carriers transfer pathway, which result in efficient separation and migration of charge carriers. In addition, both experimental and theoretical calculation confirmed that the charge-carriers transfer pathway of Cu₃P/ZnIn₂S₄ photocatalyst follows the Z-scheme mechanism instead of conventional type-Ⅱ heterojunction mechanism. This work is considered helpful for getting a great deal of insight into constructing high-activity and cost-effective transition metal phosphides (TMPs) based photcatalytic hydrogen production system and rationally designing Z-scheme heterojunction photocatalyst.     

Mathematical modeling of the anaerobic digestion in two-stage system with production of hydrogen and methane including three intermediate products

Anaerobic digestion is a multi-step biotechnological process, in which H2 is not detected as it is consumed immediately e.g. by hydrogenotrophic methanogens to produce CH₄ and CO₂. Recently a two-stage AD concept consisting of hydrogenic process followed by methanogenic process was suggested. However, only few models of this process are known. In this study a mathematical model of a continuous process of AD with production of hydrogen and methane in a cascade of two bioreactors, including some intermediate products in the first bioreactor was developed and investigated.     

A new image encryption algorithm based on non-adjacent coupled map lattices

We propose a new image encryption algorithm which is based on the spatiotemporal non-adjacent coupled map lattices. The system of non-adjacent coupled map lattices has more outstanding cryptography features in dynamics than the logistic map or coupled map lattices does. In the proposed image encryption, we employ a bit-level pixel permutation strategy which enables bit planes of pixels permute mutually without any extra storage space. Simulations have been carried out and the results demonstrate the superior security and high efficiency of the proposed algorithm.     

Selective oxidation of glycerol over nitrogen-doped carbon nanotubes supported platinum catalyst in base-free solution

In this communication, N-doped multiwall carbon nanotube (N-MWCNT) supported Pt NPs (1.8 nm) were prepared via a facile routine under microwave irradiation and tested in the selective oxidation of glycerol in an aqueous base-free solution. Characterizations confirmed that N-MWCNTs could improve the dispersion of Pt through strengthened metal-support interactions and donate its electron to metallic Pt. This electron-enriched Pt NPs on the surface of N-MWCNTs is active and stable for the selective oxidation of glycerol.     

用语图分析揭示语言系统中的隐性规律 ——赢家通吃和赢多输少算法

该文用“图”这一数学工具,通过定量分析来揭示语言系统中的隐性规律,设计了“赢家通吃”和“赢多输少”两种生成算法,将理想算法“步步竞争、择优而行”的博弈论思路贯彻到非理想状态。两种新算法都较前人有更好的概括能力。赢多输少算法更兼顾了充分概括和适度概括均衡。生成语图后,该设计着重准确率的最小简图和着重覆盖率的最大简图归纳算法,挖掘控制的主流规则、分析语言系统的语言学规律。在最小简图基础上提出控制度公式以评价语言系统。