Power efficiency evaluation in Dickson and voltage doubler charge pump topologies

This paper presents a theoretical and experimental comparison between two charge pump architectures commonly used in CMOS integrated circuits, namely the Dickson scheme and the cascade of voltage doublers. The comparison is carried out considering power efficiency as the main feature of interest. To compare the two topologies, two charge pumps were integrated in 0.18-μm triple-well CMOS technology. The two charge pumps were designed with the same operating clock frequency, the same storage capacitance per stage, and the same number of stages (and, thus, approximately the same area). The theoretical and the experimental comparison showed that the power efficiency of the voltage doubler scheme is higher (by about 13% at ), mainly thanks to the lower parasitic capacitance associated to the boosted nodes.     

Ion beam sputter-deposition of LiCoO2 films

LiCoO₂ films, 200 nm in thickness, are produced by ion beam sputter-deposition. The structural, electrical and electrochemical properties of the films are studied by means of X-ray diffraction (XRD), electrical direct-current-measurements, and electron energy loss spectroscopy (EELS). The influence of preparation parameters like substrate temperature or oxygen partial pressure, are explored. While an oxygen deficiency was observed in films sputtered under pure argon atmosphere, the oxygen content of the thin films increases, significantly, in case of films sputtered under addition of O₂. At substrate temperatures below 300 °C, XRD measurements reveal a lattice structure similar to the low-temperature-phase, while the formation of the high-temperature-phase is clearly observed at temperatures above 500 °C and an Argon/Oxygen ratio of 3/2. Furthermore, the EELS technique is demonstrated to be a sensitive tool to characterize the lithiation state of the sputter-deposited thin films.     

Guided wave expansion in warped curvelet frames

Lamb wave testing for structural health monitoring (SHM) often relies on analysis of wavefields recorded through scanning laser Doppler vibrometers (SLDVs) or ultrasonic scanners. Damage detection and characterization with these techniques requires isolation of defect-induced reflections in the wavefield from the injected wave packet and from scattering events associated with structural features such as boundaries, rivets, joints, etc. This is a challenging task when dealing with complex structures and multimodal, dispersive propagation regimes, whereby various wave contributions in both the time/space and the frequency/wavenumber domain overlap. A new mathematical tool named warped curvelet frames (WCFs) is proposed to effectively decompose the recorded wavefields. The presented technique results from the combination of two operators, i.e., the curvelet transform (CT) and the warped frequency transform (WFT). The CT provides an optimally sparse representation of nondispersive wave propagators. Combining the CT with the WFT allows for a flexible analysis of multimodal wave propagation in dispersive media. Exploiting the spatial and temporal localization of curvelets, as well as the spectro-temporal adaptation of the analysis frame to the characteristics of each propagating mode, provided by frequency warping, a convenient decomposition of guided waves is achieved and relevant contributions can be effectively isolated. The proposed approach is validated through dedicated simulations and further tested experimentally to demonstrate the effectiveness of the method in separating guided wave modes corresponding to acoustic events in close spatial proximity.     

Lipid-based passivation in nanofluidics

Stretching DNA in nanochannels is a useful tool for direct, visual studies of genomic DNA at the single molecule level. To facilitate the study of the interaction of linear DNA with proteins in nanochannels, we have implemented a highly effective passivation scheme based on lipid bilayers. We demonstrate virtually complete long-term passivation of nanochannel surfaces to a range of relevant reagents, including streptavidin-coated quantum dots, RecA proteins, and RecA-DNA complexes. We show that the performance of the lipid bilayer is significantly better than that of standard bovine serum albumin-based passivation. Finally, we show how the passivated devices allow us to monitor single DNA cleavage events during enzymatic degradation by DNase I. We expect that our approach will open up for detailed, systematic studies of a wide range of protein-DNA interactions with high spatial and temporal resolution.     

Effects of cutting angle,edge preparation,and nano-structured coating on milling performance of a gamma titanium aluminide

Gamma titanium aluminides are intermetallic alloys. Recently, they have been evaluated as important contenders for structural applications in the automotive and aerospace sectors. This is due to their excellent high-temperature performances and their significantly lower density compared to nickel-based superalloys. In this paper, an analysis of machinability of a gamma TiAl obtained via an electron beam melting (EBM) process is presented. The effects of tool geometry modifications, in terms of cutting tool angles and cutting edge preparation, were investigated. The reduction of radial rake angle and the drag finishing process for cutting edge preparation resulted in an increase of the tool life of the carbide end mills. Nanogradient tool coatings were also observed to affect tool wear during milling tests, and the results highlight that AlSiTiN coating performs better compared to CrAlSiN coating. A post-coating polishing treatment was also taken into account, and it allowed a further reduction of tool wear. The overall results indicate that the machinability of this difficult-to-cut material can be significantly improved by an adjustment of the cutting edge geometry, and by using an AlSiTiN coating system.     

释放阳光地带的光伏发电潜力

拥有非常丰富的太阳能辐射资源的阳光地带,人口占全球人口总数的75%,电力需求占全球总需求的40%,该地区经济发展受到能源和环境的制约,充分利用丰富的太阳能辐射资源开发光伏发电产业,是可持续发展的重要手段。预测结果表明,范式转型情景中,这些国家的光伏装机容量甚至可达1 100 GW,也就是该地域发电总量的12%;2020年阳光地带国家光伏系统的发电成本将比建设天然气和燃油电站更具竞争力;至2030年光伏发电可以与所有以煤和天然气为燃料的中负荷电厂竞争。阳光地带国家发展股份发电目前还面临着许多障碍,这些障碍涉及到政府对燃油价格的补贴、服务于市场的能力以及能源公司的知识有限等,这些障碍有待于在发展过程中不断克服。中国和印度是阳光地带中的大国,两国的光伏制造业对阳光地带的影响很大。新能源被锁定为中国十二五规划的七大战略性新兴产业之一,中国的十二.五规划的重心是重新平衡经济增长模式,包括逐步扩大内需;国内光伏市场一旦打开,光伏在中国的发展将会非常迅速。释放阳光地带光伏潜力要求众多利益相关者共同参与协作,包括政府、银行和金融机构以及各行各业的支持,才能充分释放阳光地带的光伏发电潜力,带动光伏发电产业链的发展,成为社会经济...     

Nickel versus cobalt catalysts for hydrogen production by ethanol steam reforming: Ni–Co–Zn–Al catalysts from hydrotalcite-like precursors

The urea hydrolysis method allowed to prepare well-crystallized Ni–Co–Zn–Al Layered Double Hydroxides to be used as precursors of mixed oxide catalysts for the Ethanol Steam Reforming (ESR) reaction. The calcination of the layered precursors gives rise to high surface area mixed oxides, being actually a mixture of a rock salt phase (NiO), a wurtzite phase (ZnO) and a spinel phase.     

Machine learning methods for short‐term bid forecasting in the renewable energy market: A case study in Italy

In liberalized markets, there usually exists a day‐ahead session where energy is sold and acquired for the following production day. Owing to the high uncertainty of its production, renewable energy (wind in particular) can significantly influence the network imbalance of the following day. In this work, we consider the problem of predicting the sum of the bid volumes for wind energy of all the producers inside the day‐ahead energy market. This is a valuable tool to be used by an energy provider in order to determine the imbalance of a market zone and, thus, properly size its bids. In particular, we focus on the estimation of the possible relationship between the meteorological forecasts and the wind power offered on the market by the companies for a market zone. We propose a machine learning model which is used to compute a 1‐day‐ahead forecast. The input‐output mapping is obtained by support vector regression. The input feature vector is defined by a suitable feature extraction technique since the meteorological forecasts are given on a lattice of thousands of geographical points. The computational experiments are performed considering the Italian market as a case study (years 2012‐2016). The results show that the proposed feature extraction technique, selecting only some geographical zones, manages to reduce the error attained using all the features. Moreover, classical statistical methods are shown to be outperformed by machine learning models. The analysis reveals also some weaknesses of the model, which may be due to other nonmeteorological factors at play.