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The rGO with a high number of oxygen functional groups produced in a short reduction time shows a faster response to analytes.
Fig. 4: EDAX of rGO thick film Table 1: Elemental distribution in the rGO thick film Element Wt. % Atomic % C 89.02 91.82 O 9.94 7.70 Al 1.04 0.48 Au 0.0 0.0 Table 2: Summary of structural parameters of rGO thick film Crystallite (grain) size, D (XRD) Average particle size, d (FESEM) Atomic % (EDAX) C O 28.42nm 232 nm 89.02 9.94 Table 2 shows summary of structural parameters of rGO thick film.
Thus, at the junctions between rGO grains, the depletion layer and potential barrier leads to the increasing of the electrical resistivity value [34, 35, 36].

It has been widely studied for a number of metal powders, including; Fe [5], Ti [6,7], Ti64 [8-10], Inconel 718 [11], 316L [12], 304 stainless steel [13], FeAl [14] and Cu composites [15].
Precipitation hardening [31], also called age hardening or dispersion hardening, is a heat treatment with temperature to produce fine particles of an impurity phase (out of the matrix and/or of existing metal impurities) that impede the movement of dislocations or defects in the crystalline grain structure of a material.
These plates are very fine due to the transformed martensite from which they evolved and limited time for grain growth to occur [37].

However, in a large number of hydraulic fracturing problems there is a strong interaction between deformation (stress) and fluid flow.
It is well known that rock is a heterogeneous geological material containing many natural weaknesses, such as pores, grain boundaries, and pre-existing cracks.
As the stress ratio decreases, the main fractures, while oriented in the maximum horizontal stress direction, are no longer straight and show a tendency to branch out along the grain boundaries.

In determining the cleanliness, the number, size and area fraction of the non-metallic inclusions were measured under the light-optical microscope.
Backscattered electron image (left) and EPMA measurement (right) of the 120 min annealed strip sample at 1150 °C Also, signs of internal oxidation at the grain boundaries in the peripheral area are recognizable in Fig. 7 on the left side.
Raabe, Grain size effect on strain hardening in twinning-induced plasticity steels, Scripta Materialia 66 (2012) 992-996

The power output of a single PV cell is about 1 – 2 Watts and are therefore connected in large numbers to form solar modules or panels which are also connected to form module arrays, translating into larger power outputs [7].
In ancient Iran and Afghanistan, windmills were utilized for grinding grains in the tenth century according to numerous historical sources.
In the 12th century, the Dutch also built a strong reputation for utilizing wind to crush grains and other activities that required power.

Third, he used the theoretical values of D0 to call in to question [17] much published data on diffusion coefficients, suggesting that short-circuit diffusion paths, such as grain boundaries, had distorted many reports.
Fischbach [23] devised a number of penetrating and clarifying experiments and sorted deftly through a number of possible explanations to reach the satisfying conclusions that the increase in conductivity on deformation (the Gyulai-Hartly effect) is attributable to the break-up of vacancy complexes by moving dislocations, while a small charge flow after deformation is traceable to the creep of charged dislocations.
As one might expect, it brought him into contact with urgent technological developments and he, and his group, responded with a number of innovations and patents.
Beyond this point, the number of papers becomes too great to deal with, the number of excellent students too large to mention individuals, and the distinctions too fine for this review.
Nowick, The production and detection of a nonequilibrium number of vacancies in a metal, Phys.

For example, if the aim is to detect high energy photons, dense materials composed of high Z (atomic number) are preferred.
If Lu2O3 is available for scintillator, the high effective atomic number (69) and the high density (9.4 g/cm3) are attractive.
Of course, grain boundaries may affect scintillation properties, and further investigations are required.
The horizontal axis shows the event number which means the number of detected ionizing radiation.
When the radiation energy becomes large, the number of generated scintillation photons also become large.

Presently known manufacturing methods of such items of equipment, for instance, stamping methods, have a number of technological problems.
Shear deformation and grain refinement in pure Al by asymmetric rolling.

”, a number of possible explanations have been offered by ecologists, such as “Ideal weeds characteristics” (Baker, 1965）[9], “Biodiversity resistance hypothesis(Elton, 1958)[10]”, “Enemies release hypothesis” (Darwin, 1859)）[11], “Evolution of increased competitive ability hypothesis” (Crawley et al., 1987）[12], “Niche opportunity hypothesis”(Shea and Chesson, 2002)[13] , “Novel weapon hypothesis”(Mallick, 2000) [14] and so on .
Triticum aestivum were purchased from Ningbo seed companies. the selected seeds of Setaria viridis grain weight 2 ±0.15g were used for the experiment.
Daily recording of the number of germinated seeds in each Petri dish was done and continued for 12 days.
Triticum aestivum and Setaria viridis seed germination were the following method to calculate the parameters: Germination rate = (Number of germinated seeds/100) ×100%; Germination vigour = (Number of germinated seeds in the first 7 days/100) ×100% Germination Index = ∑（Gt/Dt） Where, Gt: Number of germinated seeds after t: Days of cultivation and Dt: N umber of cultivation days.
Solidago canadensis seed germination were the following method to calculate the parameters: Germination rate = (Number of germinated seeds/weight) ×100%; Germination vigour = (Number of germinated seeds in the first 7 days/weight) ×100% Germination Index = ∑（Gt/Dt） Where, Gt: Number of germinated seeds after t: Days of cultivation and Dt: N umber of cultivation days.

Numbers of Neurons in Hidden Layer.
In order to optimize the network model, a number of topologies with different neurons numbers in hidden layer are tried in this paper in order to arrive at a topology which provides minimum effective error.
Comparing with errors, the optimum numbers of neurons are determined.
So the numbers of neurons in hidden layer are 11.
When the number of neurons hidden layer is 11, training error of network is 0.0171.