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However, the synthesis of pure graphene oxide and its subsequent reduction to restore conductivity remain a focus for research.
These were then transformed into conductive graphene fibers through a wet-spinning and hydroiodic acid reduction process.
A continuous and interconnected fibrous network with a rough surface is seen, indicating successful reduction of graphene oxide.
Conversely, rGO displayed a reduced D-band intensity, suggesting partial restoration of the sp2 carbon network upon reduction [11].
The Nyquist plot in Fig. 3e illustrates the EIS data of the supercapacitor, measured across a frequency range of 10 MHz to 0.1 MHz using a voltage amplitude of 10 mV.

Growth kinetic parameters of E.coli and S.aureus affected by silver nanocomposites were calculated by modeling of absorbance data according to Gomperz equation.
The results indicated that a modified Gompertz equation (Eq. 3) was fitted well to all experimental absorbance data.
Parameter values obtained by fitting Equation (3) to the experimental data of LBL deposited nanocomposites for E. coli.
Parameter values obtained by fitting Equation (4.4) to the experimental data of LBL deposited nanocomposites for S. aureus.
The growth curves result from fitting the Equation (3) to the experimental data for E. coli.

Characterization of the catalysts Powder XRD data were collected on a Rigaku D/max-2500 X-ray diffractometer (40 mV and 100mA) with monochromatized CuKα radiation.
The resulting composites were analyzed by ICP-AES with resultant data listed in Table 1.
Table 1 Effects on the loading by different alkali sources Molecular sieves Alkali source CuO Cont. (%) MOR ammonia urea 14.8 16.8 zeolite β ammonia urea 9.95 12.2 Table 2 ICP-AES analytical data of the samples with and without the ultrasonic wash Molecular sieves CuO Cont.
Fig. 9 XRD patterns of samples treated with ammonia (a) CuO/β prepared without ultrasonic washing (b) CuO/β prepared with ultrasonic washing c) CuO Fig. 10 The TPR profile of CuO/MOR with different CuO amount (a) 8.26% (b) 11.7% (c) 16.8% The ICP-AES analytical data of the samples with and without ultrasonic wash treatment are given in Table 2.
The peak at 843K reflects the reduction of the Cu+ situated in the framework.

FIR Digital Filter Design Based on Virtual Instrument Yu-jian Du1,a, Zu-bin Chen1,b ,Teng Yu1,c,Yang yang1,d 1College of Instrumentation & Electrical Engineering,Jilin University China 1344107728@qq.com Keywords:FIR Digital Filter,Virtual Instrument,MATLAB Abstract.With the information era and the advent of the digital world, digital signal processing has become extremely important in today's one of the disciplines and technical fields.Digital signal processing in seismic signal ,communications, voice, image, automatic control radar, and other fields has been widely used.In this paper,I design several kind of FIR digital filters based on virtual instrument to solve the problem that signal noise reduction.
FIR Filter implement in MATLAB In this paper, I design four kinds of filter to reach the goal of signal reduction through calling the function in MATLAB.
in MATLAB workplace.LabVIEW supply control interface to MATLAB, we can call the function in MATLAB directly.So combine Labview and MATLAB,we can use massive reliable algorithm in MATLAB and Graphics Programming in virtual instrument to develop powerful application software.In the mixed programming,we usually use LabVIEW to design graphical interface for collecting data and network communication.And use MATLAB to supply processing and analysis for Large algorithm.The result is called by LabVIEW.
the back panel,through Functions-MATLAB ScriptNods to open MATLAB script control.Second step:add MATLAB script program.There are two methods.1.Have debugged a MATLAB program in MATLAB environment.Right-click the mouse at the frame of the MATLAB script control,select "import" in the option.Introduce the MATLAB program into MATLAB script node.2.Write MATLAB program in the MATLAB script node directly.The third step:Define transfer function and data type of LabVIEW and MATLAB,connect and simulate.We must notice:The data communication between the LabVIEW8.2 and MATLAB only supports six kinds of formats of the data including Real, Complex 1-D Array of Real,1-D Array of complex,2-D the Array of Real,2-D Array of Complex,And must be selected according to the specific circumstances.MATLAB script node and its external LabVIEW block diagram program rely on the input and output of the script node to connect to, you can add the input and output in the script node shortcut menu, select and add the Input
Experimental Data We can input sine wave into the FIR Filter.And we can observe whether the wave is filtered in particular range.

Introduction In many practical applications, such as pattern classification, information retrieval, data mining and microarray analysis, the observed data often lies in high dimensional space.
However, the supervised methods cannot work well when there is a lack of sufficient labeled data.
Contrarily, abundant unlabeled data can be obtained more easily and cheaply.
Thus, how to utilize both the labeled data and unlabeled data to improve the performance of classification become an important problem and a hot research direction in such situation.
(1) Inputs: The sample data set denotes as the matrix

Among them, carbo-thermal reduction nitridation (CRN) method was widely used [4, 5].
It was due to the existence of residual carbon, which could not be totally removed during the fabrication process and caused the reduction of the bending strength.
The reason of weight loss could be mainly attributed to the carbo-thermal reduction process and the exclusion of liquid phase.
This indicated that iron silicon formed during sintering was excluded out of porous body and caused the weight loss, which was in accordance with the analysis of weight loss data.
SiAlON was formed by carbo-thermal reduction nitridation process.

The results showed that the lightweight aggregate concrete with Brazilian expanded clay are extremely suitable for the production of prefabricated elements slender, mainly due to the reduction in density and excellent performance in mechanical properties, especially modulus of elasticity, despite the low toughness of lightweight aggregate.
Some properties, such as density reduction and maintenance of mechanical strength, provide weight reduction itself and loads of foundation, which reflects the reduction of the final cost.
Compared to conventional concrete, the concrete showed a slight reduction in density between 20% and 36%, a fact confirmed by Rossignolo (2009) [2].
From the test of modulus of elasticity, it could be seen that the lightweight concrete produced in this study showed a reduction between 40% and 50%, when compared to values achieved with conventional aggregates for concrete, meeting the expectations according to the CEB/FIP (1977) [4].
These results show that the aggregate is the main factor limiting the performance of the mechanical strength of the concrete, better said, the strongest part of the lightweight concrete is the mortar, thus confirming the data obtained by Rossignolo (2009) [2] and Angelin et al. (2013) [5].

The intensity data were collected over a 2θ range from 10º to 80º.
With the help of the International Center for Diffraction Data (ICDD) files, crystalline phases were identified.
The strong peak was assigned to the reduction of Mn3O4 to MnO, while the weak reduction peak was attributed to the reduction of MnO2 and Mn2O3 to Mn3O4 [3,9].
The Co-Mn/TiO2 catalyst showed three reduction peaks.
The third one was related to the reduction of the CoMn2O4.

First, the reduction of Mg2+ ions with NaBH4 in ethylene glycol solution and reflux at 350 - 400 °C for 3 hr and Si nanoparticles, which were prepared by magnesiothermic reduction, was conducted.
The amount of Mg and Si substances also can be calculated from XRD data as show in Table 1 compared with EDS analysit.
Regarding the magnesium that can be found, The EDS method is consistent with the amount and method used for calculation from XRD data using obtained volume fraction calculation by direct comparison method as show in Table 1.
Reduction of particle size to nanoscale has resulted in many changes.
Si was obtained by magnesiothermic reduction and Mg was prepared from NaBH4 reduction and reflux.

In conclusion, it is demonstrated that the cathodic reduction of such organic compounds involves formation of stable and unstable intermediates.
We define the collection efficiency on the ring R iN for oxidation or reduction of the product by the following equation %100 .
If, by increasing the electrode rotation speed, we can reach the limiting value of the studied component yield, i.e. not affected by the further increase of the rotation speed, and if, on the basis of independent data, we can estimate disc current density I D for this substance, then knowing the value of nD we can easily find n R from equation (2).
When camphor is added to the solution, the total cathodic wave of nitroferrocene reduction splits into two waves with the first one corresponding to the one-electron process of the initial substance reduction (Fig. 4).
However, in all cases, several independent methods should be used to obtain additional data to assure reliable identification of the new kinds of intermediate products.