Nickel-iron layered double hydroxide (NiFe-LDH) nanosheets have shown optimal oxygen evolution reaction (OER) performance; however, the role of the intercalated ions in the OER activity remains unclear. In this work, we show that the activity of the NiFe-LDHs can be tailored by the intercalated anions with different redox potentials. The intercalation of anions with low redox potential (high reducing ability), such as hypophosphites, leads to NiFe-LDHs with low OER overpotential of 240 mV and a small Tafel slope of 36.9 mV/dec, whereas NiFe-LDHs intercalated with anions of high redox potential (low reducing ability), such as fluorion, show a high overpotential of 370 mV and a Tafel slope of 80.8 mV/dec. The OER activity shows a surprising linear correlation with the standard redox potential. Density functional theory calculations and X-ray photoelectron spectroscopy analysis indicate that the intercalated anions alter the electronic structure of metal atoms which exposed at the surface. Anions with low standard redox potential and strong reducing ability transfer more electrons to the hydroxide layers. This increases the electron density of the surface metal sites and stabilizes their high-valence states, whose formation is known as the critical step prior to the OER process.
Atomic composition tuning and defect engineering are effective strategies toenhance the catalytic performance of multicomponent catalysts by improvingthe synergetic effect; however, it remains challenging to dramatically tune the active sites on multicomponent materials through simultaneous defect engineeringat the atomic scale because of the similarities of the local environment. Herein,using the oxygen evolution reaction (OER) as a probe reaction, we deliberatelyintroduced base-soluble Zn(II) or Al(III) sites into NiFe layered double hydroxides(LDHs), which are one of the best OER catalysts. Then, the Zn(II) or Al(III) siteswere selectively etched to create atomic M(II)/M(III) defects, which dramaticallyenhanced the OER activity. At a current density of 20 mA·cm?2, only 200 mV overpotential was required to generate M(II) defect-rich NiFe LDHs, which is the best NiFe-based OER catalyst reported to date. Density functional theory(DFT) calculations revealed that the creation of dangling Ni–Fe sites (i.e., unsaturated coordinated Ni–Fe sites) by defect engineering of a Ni–O–Fe site at the atomic scale efficiently lowers the Gibbs free energy of the oxygen evolutionprocess. This defect engineering strategy provides new insights into catalysts atthe atomic scale and should be beneficial for the design of a variety of catalysts.
We present a non-intrusive molecular dye based method, i.e., laser-induced fluorescence photobleaching anemometer (LIFPA),
to significantly increase temporal resolution (TR) for velocity measurement of fast transient electrokinetic flows. To our
knowledge, the TR has been for the first time achieved to 5–10 μs, about 100 times better than that published from state-of-the-art
micro particle image velocimetry (μPIV), which is currently the most widely used velocimetry in the microfluidics community.
The new method provides us with new opportunities to study experimentally the fundamental phenomena of unsteady electrokinetics
(EK) and to validate relevant theoretical models. One application of the new method is demonstrated by measuring the rise
time of DC electroosmotic flows (EOFs) in a microcapillary of 10 μm in diameter. 相似文献
This paper presents an automatic stock portfolio selection system. In the proposed approach, 53 financial indices are collected
for each stock item and are consolidated into six financial ratios [Grey relational grades (GRGs)] using a Grey relational
analysis model. The GRGs are processed using a modified form of the PBMF index method (designated as the Huang index function)
to determine the optimal number of clusters per GRG. The resulting cluster indices are then processed using rough set theory
to identify the stocks within the lower approximate sets. Finally, the GRGs of each stock item in the lower approximate sets
are consolidated into a single GRG, indicating the ability of the stock item to maximize the rate of return. It is demonstrated
that the proposed stock selection mechanism yields a higher rate of return than several existing portfolio selection systems. 相似文献
This study proposes a technique based upon Fuzzy C-Means (FCM) classification theory and related fuzzy theories for choosing an appropriate value of the Variable Precision Rough Set (VPRS) threshold parameter (β) when applied to the classification of continuous information systems. The VPRS model is then combined with a moving Average Autoregressive Exogenous (ARX) prediction model and Grey Systems theory to create an automatic stock market forecasting and portfolio selection mechanism. In the proposed mechanism, financial data are collected automatically every quarter and are input to an ARX prediction model to forecast the future trends of the collected data over the next quarter or half-year period. The forecast data are then reduced using a GM(1, N) model, classified using a FCM clustering algorithm, and then supplied to a VPRS classification module which selects appropriate investment stocks in accordance with a pre-determined set of decision-making rules. Finally, a grey relational analysis technique is employed to weight the selected stocks in such a way as to maximize the rate of return of the stock portfolio. The validity of the proposed approach is demonstrated using electronic stock data extracted from the financial database maintained by the Taiwan Economic Journal (TEJ). The portfolio results obtained using the proposed hybrid model are compared with those obtained using a Rough Set (RS) selection model. The effects of the number of attributes of the RS lower approximation set and VPRS β-lower approximation set on the classification are systematically examined and compared. Overall, the results show that the proposed stock forecasting and stock selection mechanism not only yields a greater number of selected stocks in the β-lower approximation set than in the RS approximation set, but also yields a greater rate of return. 相似文献
In this paper, we present an algorithm for performing permutations of messages on multistage interconnection networks. Permutations of messages are needed in many parallel algorithms. The proposed algorithm is feasible for any networks that can connect each input to each output using a set of N nonblocking connections, where N is the number of ports on the network. Messages are segmented into N submessages that are sent independently in each time step. For any permutation, the settings of switches are changed with fixed patterns. Partitioning of the network into independent subnetworks is also supported, each capable of simultaneously routing a different permutation 相似文献