scholarly journals Low Energy Electron Attachment by Some Chlorosilanes

Molecules ◽  
2021 ◽  
Vol 26 (16) ◽  
pp. 4973
Author(s):  
Bartosz Michalczuk ◽  
Wiesława Barszczewska ◽  
Waldemar Wysocki ◽  
Štefan Matejčík
Keyword(s):  
Arrhenius Equation ◽  
Electron Attachment ◽  
Activation Energies ◽  
Negative Ion ◽  
Basis Sets ◽  
Rate Coefficients ◽  
Buffer Gas ◽  
Data Points ◽  
Low Energy Electron ◽  
The Relationship

In this paper, the rate coefficients (k) and activation energies (Ea) for SiCl4, SiHCl3, and Si(CH3)2(CH2Cl)Cl molecules in the gas phase were measured using the pulsed Townsend technique. The experiment was performed in the temperature range of 298–378 K, and carbon dioxide was used as a buffer gas. The obtained k depended on temperature in accordance with the Arrhenius equation. From the fit to the experimental data points with function described by the Arrhenius equation, the activation energies (Ea) were determined. The obtained k values at 298 K are equal to (5.18 ± 0.22) × 10−10 cm3·s−1, (3.98 ± 1.8) × 10−9 cm3·s−1 and (8.46 ± 0.23) × 10−11 cm3·s−1 and Ea values were equal to 0.25 ± 0.01 eV, 0.20 ± 0.01 eV, and 0.27 ± 0.01 eV for SiHCl3, SiCl4, and Si(CH3)2(CH2Cl)Cl, respectively. The linear relation between rate coefficients and activation energies for chlorosilanes was demonstrated. The DFT/B3LYP level coupled with the 6-31G(d) basis sets method was used for calculations of the geometry change associated with negative ion formation for simple chlorosilanes. The relationship between these changes and the polarizability of the attaching center (αcentre) was found. Additionally, the calculated adiabatic electron affinities (AEA) are related to the αcentre.

Negative ion formation by low energy electron attachment to gas-phase 5-nitrouracil

2008 ◽  
Vol 277 (1-3) ◽  
pp. 291-295 ◽  
Author(s):  
Sylwia Ptasińska ◽  
Elahe Alizadeh ◽  
Philipp Sulzer ◽  
Robert Abouaf ◽  
Nigel J. Mason ◽  
...  
Keyword(s):  
Gas Phase ◽  
Electron Attachment ◽  
Low Energy ◽  
Energy Electron ◽  
Negative Ion ◽  
Ion Formation ◽  
Low Energy Electron

Identifying Physico-Chemical Laws from the Robotically Collected Data

2019 ◽  
Author(s):  
Liwei Cao ◽  
Danilo Russo ◽  
Vassilios S. Vassiliadis ◽  
Alexei Lapkin
Keyword(s):  
Experimental Data ◽  
Numerical Models ◽  
Predictor Variable ◽  
Physical Models ◽  
Training Data ◽  
Mixed Integer ◽  
Physico Chemical ◽  
Data Points ◽  
Future Work ◽  
The Relationship

<p>A mixed-integer nonlinear programming (MINLP) formulation for symbolic regression was proposed to identify physical models from noisy experimental data. The formulation was tested using numerical models and was found to be more efficient than the previous literature example with respect to the number of predictor variables and training data points. The globally optimal search was extended to identify physical models and to cope with noise in the experimental data predictor variable. The methodology was coupled with the collection of experimental data in an automated fashion, and was proven to be successful in identifying the correct physical models describing the relationship between the shear stress and shear rate for both Newtonian and non-Newtonian fluids, and simple kinetic laws of reactions. Future work will focus on addressing the limitations of the formulation presented in this work, by extending it to be able to address larger complex physical models.</p><p><br></p>

scholarly journals Selective bond cleavage in RAFT agents promoted by low‐energy electron attachment

2021 ◽  
Author(s):  
Farhad Izadi ◽  
Eugene Arthur-Baidoo ◽  
Lisa T. Strover ◽  
Li-Juan Yu ◽  
Michelle L. Coote ◽  
...  
Keyword(s):  
Bond Cleavage ◽  
Electron Attachment ◽  
Low Energy ◽  
Energy Electron ◽  
Low Energy Electron

New experiments on the relationship between light-induced defects and photoconductivity degradation

2001 ◽  
Vol 664 ◽  
Author(s):  
Stephan Heck ◽  
Howard M. Branz
Keyword(s):  
Activation Energy ◽  
Defect Density ◽  
Activation Energies ◽  
Dangling Bond ◽  
Fast Recovery ◽  
Slow Recovery ◽  
Degradation Model ◽  
Induced Defects ◽  
The Relationship ◽  
Hydrogenated Amorphous

ABSTRACTWe report experimental results that help settle apparent inconsistencies in earlier work on photoconductivity and light-induced defects in hydrogenated amorphous silicon (a-Si:H) and point toward a new understanding of this subject. After observing that light-induced photoconductivity degradation anneals out at much lower T than the light-induced increase in deep defect density, Han and Fritzsche[1] suggested that two kinds of defects are created during illumination of a-Si:H. In this view, one kind of defect degrades the photoconductivity and the other increases defect sub-bandgap optical absorption. However, the light-induced degradation model of Stutzmann et al.[2] assumes that photoconductivity is inversely proportional to the dangling-bond defect density. We observe two kinds of defects that are distinguished by their annealing activation energies, but because their densities remain in strict linear proportion during their creation, the two kinds of defects cannot be completely independent.In our measurements of photoconductivity and defect absorption (constant photocurrent method) during 25°C light soaking and during a series of isochronal anneals between 25 < T < 190°C, we find that the absorption measured with E ≤1.1 eV, first increases during annealing, then exhibits the usual absorption decrease found for deeper defects. The maximum in this absorption at E ≤1.1eV occurs simultaneously with a transition from fast to slow recovery of photoconductivity. The absorption for E ≤1.1eV shows two distinct annealing activation energies: the signal rises with about 0.87 eV and falls with about 1.15 eV. The 0.87 eV activation energy roughly equals the activation energy for the dominant, fast, recovery of photoconductivity. The 1.15 eV activation energy roughly equals the single activation energy for annealing of the light-induced dangling bond absorption.

Field Assisted Sintering Technology (FAST) Model for Prediction of Final Properties

2010 ◽  
Author(s):  
Rajiv Paul ◽  
Anil K. Kulkarni ◽  
Jogender Singh
Keyword(s):  
Electrical Breakdown ◽  
Extensive Literature ◽  
Heating Rates ◽  
Predictive Tool ◽  
Simultaneous Application ◽  
Plasma Sintering ◽  
Spark Plasma ◽  
Data Points ◽  
Field Assisted Sintering ◽  
The Relationship

Sintering is the process of making materials from powder form by heating the powder below its melting point until the particles fuse to each other. Field assisted sintering technology (FAST), also sometimes known as spark plasma sintering (SPS), uses a pulsed and/or continuous electric current along with the simultaneous application of compressive pressure which leads to extremely high heating rates and short processing durations. A high relative density and small grain size promote superior properties such as greater hardness and electrical breakdown. Hence, selection of the proper sintering parameters is of paramount importance and a predictive model would be extremely useful in narrowing the range of experimental parameters. This will drastically reduce the number of extra attempts at obtaining certain properties in a material and save experimentation time, effort and material to name a few. Four of the most important FAST parameters: target temperature, holding time, heating rate and initial particle size, have been reviewed to assess their effect on the densification, hardening and grain growth of Alumina, Copper, Silicon Carbide, Tungsten and Tungsten Carbide through extensive literature survey. The relationship between each has been incorporated in a Microsoft Excel program which acts as a predictive tool to determine an estimate of the final properties based on the initial parameters chosen. This is done by curve fitting a polynomial onto the existing data points as closely as possible and using the polynomial to obtain final properties as a function of the initial parameters. The model was verified against an existing paper which sought to obtain the optimum sintering parameters for Copper. While the actual experimentation range was 400°C to 800°C, the program would have suggested a much narrower range from 650°C to 800°C and hence saved unnecessary additional efforts.

scholarly journals Exploring the relation between aerosol optical depth and PM<sub>2.5</sub> at Cabauw, the Netherlands

2008 ◽  
Vol 8 (5) ◽  
pp. 17939-17986 ◽  
Author(s):  
M. Schaap ◽  
A. Apituley ◽  
R. M. A. Timmermans ◽  
R. B. A. Koelemeijer ◽  
G. de Leeuw
Keyword(s):  
The Netherlands ◽  
Ground Level ◽  
Weather Conditions ◽  
Fair Weather ◽  
Modis Aod ◽  
Data Points ◽  
Set Up ◽  
The Relationship ◽  
Lidar Observations ◽  
Pm2.5 Concentration

Abstract. To acquire daily estimates of PM2.5 distributions based on satellite data one depends critically on an established relation between AOD and ground level PM2.5. In this study we aimed to experimentally establish the AOD-PM2.5 relationship for the Netherlands. For that purpose an experiment was set-up at the AERONET site Cabauw. The average PM2.5 concentration during this ten month study was 18 μg/m3, which confirms that the Netherlands are characterised by a high PM burden. A first inspection of the AERONET level 1.5 (L1.5) AOD and PM2.5 data at Cabauw showed a low correlation between the two properties. However, after screening for cloud contamination in the AERONET L1.5 data, the correlation improved substantially. When also constraining the dataset to data points acquired around noon, the correlation between AOD and PM2.5 amounted to R2=0.6 for situations with fair weather. This indicates that AOD data contain information about the temporal evolution of PM2.5. We had used LIDAR observations to detect residual cloud contamination in the AERONET L1.5 data. Comparison of our cloud-screed L1.5 with AERONET L2 data that became available near the end of the study showed favorable agreement. The final relation found for Cabauw is PM2.5=124.5*AOD–0.34 (with PM2.5 in μg/m3) and is valid for fair weather conditions. The relationship determined between MODIS AOD and ground level PM2.5 at Cabauw is very similar to that based on the much larger dataset from the sun photometer data, after correcting for a systematic overestimation of the MODIS data of 0.05. We applied the relationship to a MODIS composite map to assess the PM2.5 distribution over the Netherlands. Spatial dependent systematic errors in the MODIS AOD, probably related to variability in surface reflectance, hamper a meaningful analysis of the spatial distribution of PM2.5 using AOD data at the scale of the Netherlands.

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