Search results

To the author’s knowledge there is few data in the open literature for the comparison of the friction and wear properties of PTFE coatings under vacuum conditions before and after gamma irradiation.
The experimental data are then compared to those of non-irradiated samples.
It is probably due to the reduction in molecular weight induced by gamma radiation leading to the enhancement of molecular mobility or migration on the interface, which leads to the damage of self-lubricating films more difficult.
The reduction in friction coefficient of irradiated PTFE coatings compared to the non-irradiated samples may be subject to scission reactions caused by unstable free radicals [14], which leads to the rupture of the backbones of main chains, the reduction in molecular weight of PTFE, and the improvement in chain mobility and migration at the surface.
The result in Fig. 6 indicates that the gamma irradiation (with a dose of 100KGy) induces a reduction in wear of PTFE coatings.

The objectives of rock mass evaluation are to confirm the physical properties of rock mass, to find abnormal rock mass, and finally to improve ground investigation data [1].
Analyzing data obtained from 212 tunnels at Scandinavia peninsular, Barton [2] suggested Q-system using the RQD, joint set number, joint roughness, joint alteration, ground water pressure, and stress reduction factor.
Finally, using Eq. 4 through Eq. 7, the resistivity of rock mass (ρRM) can be calculated as follows: [ ][ ]βσαασ π ρ IR xyxzJ RM ++ = 2 . (8) Correlation between Q-system and Electrical Resistivity The Q-system is defined as follows [2]: SRF Jw Ja Jr Jn RQD Q ⋅⋅= . (9) where Jn is the joint set number, Jn is the joint roughness number, Ja is the joint alteration number, Jw is the joint water reduction factor and SRF is the stress reduction factor.
The joint water reduction factor (Jw) is consider as Jw=1, indicating dry excavation or minor inflow and the SRF is consider as SRF =1 indicating a medium stress condition.

The reduction of dielectric level may cause reduction in condensation of contaminants and consequent increase in emission of aerosols.Therefore sensor for checking the dielectric level with an interlock system has to be included in the system.
Experimental investigation on the effect of different base metals on emissions is required since such data is not available.
Experimental investigation on the effect of different electrodes on the emission also required since there is no data available in the literature.
· Fire hazard · The reduction of dielectric level may cause reduction in condensation of contaminants and consequent increase in emission. · Sensor for checking the dielectric level with an interlock system. 10.

Study on Debenzylation of Pentabenzylpentaza[3,3,3]Propellane Yahan Xue1,a*, Jiarong Li1,b, Lizhu Zhang1,c, Guangwei Zhou1,d and Zhibin Xu1,e 1Beijing Institute of Technology, China axueyh666@163.com, bjrli@bit.edu.cn, czlzfx8@126.com, d2120161266@bit.edu.cn, ezbxu@bit.edu.cn Keyword: Catalyst, Aza-propellanes, reduction, debenzylation Abstract.
Based on the theoretical study of some polynitro-polyaza[3.3.3]propellane molecules, the debenzylation of pentabenzyl-pentaaza[3.3.3]propellane was studied by simple raw materials and convenient conversion. 2 Experimental 2.1 General Methods and Material Generally, the debenzylation method adopts the reduction method, and the reduction debenzylation is mostly carried out by catalytic hydrogenolysis. [40-41] Considering the experimental safety, the catalytic hydrogen transfer hydrogenolysis is used in this paper, and the hydrogen donor is replaced by hydrogen as ammonium formate, and Pd is used as the catalyst.
Reduction of all carbonyl groups on compound 4 with LAH as a reducing agent gave compound 5 in the yield of 63.33%.
The data from the high-resolution mass spectrometry detection revealed no signal of debenzylated derivative.
Based on the above data, we suspected that the debenzylation of compound 5 could first remove a benzyl group, and then lose another benzyl group to give the debenzylated product 6 (Scheme 2).

The data was collected from 10 to 100º 2θ with a step size of 0.02º and a count time of 12s on a Brüker D8 Advance Diffractometer (Brüker, UK) in flat plate geometry, using Ni filtered Cu Kα radiation.
The phases were identified using the Crystallographica Search-Match (CSM) software (Oxford Cryosystems, UK) and the International Centre for Diffraction Data (ICDD) database (vols. 1-42). 2.5.
Before running a sample, the machine was calibrated against a 4-point calibration curve, and the data was analysed using the Chromeleon ® software package.
This finding correlated well with the XRD data where the 0.25 and 0.3mol compositions revealed identical phases.
Considering the anion release, the P3O93 species showed the highest release rate which correlated well with the XRD data obtained, as this cyclic species was the dominant phase identified.

Input half electromagnet model into Maxwell2D, the results relative to air-gap of armature and change of ampere-turns through static calculation can be obtained; establish data document in the form of AMESim with data of electromagnetic force and inductance relative to air-gap and ampere-turns; and thus complete the data preparation in AMESim simulation of electromagnet.
The 3D curve of data document is as shown in Figure 2 and Figure 3.
Fig.2: Relationship between Electromagnetic Force and Ampere-turns & Air-gap Fig.3: Relationship between Inductance and Ampere-turns & Air-gap Obtain data table of relationship between electromagnetic output force/inductance and ampere-turns & air-gap through Ansoft parametric analysis; import relative data into the solenoid simulation model by adopting the EMLT40 sub-model in AMESim electromagnetic module; establish complete solenoid simulation model, and realize coupling among electromagnetic circuit, mechanical part and hydraulic system, with the model shown in Figure 4. 1: PWM modulation signal; 2: voltage source; 3: electromagnet; 4: medium parameter definition unit; 5: pressure source; 6: valve body; 7: flow sensor; 8: pressure sensor Fig.4: Coupling Simulation Model Analysis of Simulation Structure.
Figure 8 indicates that the reduction of opening and closing time of on-off valve can effectively increase the linear control zone and the largest response frequency, and thus improve the control features of pulse-width adjustment.
Reduction of the opening time and closing time can effectively enlarge the linear control zone of the valve, increase the response frequency, and improve the control performance of pulse-width adjustment.

The SEM image data confirmed the round and circular nature of Ag NPs.
The EDX data presented the elemental configuration with a solid peak at 65 KeV that matched the silver.
This method follows the bottom-up technique, where the reduction process is the main reaction.
The reduction of various metals happens using these functional groups into their respective metallic NPs [6].
The data are shown in Figure (6).

Bracket data unit Data Units.
This particular encapsulation of data conceals information that can be proprietary, and, therefore, guarantees privacy of information contained in the data unit, leveraging outsourcing effectively.
Data Exchange.
The values are encapsulated and collaborated via data units.
And, the last part is a group of auxiliary files derived from Data Units.

Chou and Hang [3] simulated and optimized the value of die gap and punch travel on springback reduction in ‘U’-channel bending using ABAQUS.
Geometry of tools and specimen of CAD data were imported to the simufact for further simulation by using either as Binary or ASCII STL (.stl) file, as Bulk Data (.bdf) file or as an Initial Graphics Exchange Specification (.igs) fail format.
Table 2 contains simulation experiment data, where 2 variable parameters for the parametric investigation on the springback amount.
The experiments data was obtained by the automatic signal acquisition system of UTM.
Dimensions and material parameters of simulation analysis Material data Workpiece DIN 1.0980 Geometry data: Punch corner radius, Rp (mm) 3 variable (2, 4, 6) Die opening, W2 (mm) 3 variable (30, 36, 48) Workpiece thickness, t (mm) 2.6 Workpiece width and length (mm) 50 x 150 Simulation data: Punch velocity, vp (mm/s) 45 Punch travel, Tp (mm) 52.6 Initial temperature, To (oC) 20 (workpiece, tools) Friction coefficient, μ 0.05 Press type Hydraulic Material Thickness [mm] YS 0.2 [MPa] UTS [MPa] E [Gpa] Elongation (%) SPFH 590 3 546 654 574 29 Table 3.

The specific steps of the trading regulatory risk evaluation are as follows: Step1: Collect the prime data of the secondary grade index Ik,j (the jth secondary grade index which is included by the kth first grade index), calculate the risk value Fk,j and determine the risk level based on the risk value.
Matrix Explanation Order 1 HG Comprehensive evaluation matrix 6×6 5 HD trading behavior 3×3 2 HA market coordination 3×3 6 HE transmission channels 2×2 3 HB transmission and distribution electricity quotation 3×3 7 HF energy conservation and emission reduction 3×3 4 HC trading scheme 3×3 A Numerical Example Simulated data are served for demonstrating the developed approach.
Secondly, based on the given data of all the secondary indices, the membership values of the secondary indices can be obtained depending on the fuzzy evaluation models.
The simulated data and the corresponding risk value are shown in Table 3.
Table 3 The simulated data and the corresponding risk value No. weights Risk value No. weights Simulated data Risk value No. weights Risk value No. weights Simulated data Risk value A 0.21 2.14 A1 0.40 0.68 2.18 B 0.18 1.31 B1 0.33 0.21 1.32 A2 0.30 0.94 1.69 B2 0.33 0.23 1.41 A3 0.30 0.25 2.45 B3 0.34 0.08 1.19 C 0.17 1.51 C1 0.40 0.83 1.66 D 0.16 1.65 D1 0.33 1.00 5.00 C2 0.40 0.05 1.74 D2 0.33 0 0 C3 0.20 0.1 0.74 D3 0.34 0 0 E 0.14 1.85 E1 0.50 0.69 1.93 F 0.14 2.34 F1 0.35 0.50 2.73 E2 0.50 0.34 1.76 F2 0.35 0.2 0.71 G 1.79 / / / F3 0.30 0.8 3.77 According to the maximum membership degree principle, the evaluation results for the 6 first grade regulatory risk indices of A~F and the comprehensive evaluation index G are “medium”, where the risk value of index F is maximum.