Simulation and visualization of self-assembled nanodevice networks synthesized via plasma–surface interaction

Using advanced visualization techniques, a comprehensive visualization of all the stages of the self-organized growth of internetworked nanostructures on plasma-exposed surface has been made. Atomistic kinetic Monte Carlo simulation for the initial stage of deposition, with 3-D visualization of the whole system and half-tone visualization of the density field of the adsorbed atoms, makes it possible to implement a multiscale predictive modeling of the development of the nanoscale system.

Distribution of ion-current density on substrate between carbon nanotubes grown from low-temperature plasma

Using Monte Carlo simulation technique, we have calculated the distribution of ion current extracted from low-temperature plasmas and deposited onto the substrate covered with a nanotube array. We have shown that a free-standing carbon nanotube is enclosed in a circular bead of the ion current, whereas in square and hexagonal nanotube patterns, the ion current is mainly concentrated along the lines connecting the nearest nanotubes. In a very dense array (with the distance between nanotubes/nanotube-height ratio less than 0.05), the ions do not penetrate to the substrate surface and deposit on side surfaces of the nanotubes.

Pulsed i-PVD of dielectric nanodot arrays using a nanoporous template

The results of multi-scale numerical simulations of pulsed i-PVD template-assisted nanofabrication of ZnO nanodot arrays on a silicon substrate are presented. The ratios and spatial distributions of the ion fluxes deposited on the lateral and bottom surfaces of the nanopores are computed as a function of the external bias and plasma parameters. The results show that the pulsed bias plays a significant role in the ion current distribution inside the nanopores. The results of numerical experiments of this work suggest that by finely adjusting the pulse voltage, the pulse duration and the duty cycle of the external pulsed bias, the nanopore clogging can be successfully avoided during the deposition and the shapes of the deposited ZnO nanodots can be effectively controlled. A figure is presented.

Magnetic-field-enhanced synthesis of single-wall carbon nanotubes in arc discharge

The ability to control the properties of single-wall nanotubes (SWNTs) produced in the arc discharge is important for many practical applications. Our experiments suggest that the length of SWNTs significantly increases (up to 4000 nm), along with the purity of the carbon deposit, when the magnetic field is applied to arc discharge. Scanning electron microscopy and transmission electron microscopy analyses have demonstrated that the carbon deposit produced in the magnetic-field-enhanced arc mainly consists of the isolated and bunched SWNTs. A model of a carbon nanotube interaction and growth in the thermal plasma was developed, which considers several important effects such as anode ablation that supplies the carbon plasma in an anodic arc discharge technique, and the momentum, charge, and energy transfer processes between nanotube and plasma. It is shown that the nanotube charge with respect to the plasma as well as nanotube length depend on plasma density and electric field in the interelectrode gap. For instance, nanotube charge changes from negative to positive value with an electron density decrease. The numerical simulations based on the Monte Carlo technique were performed, which explain an increase in the nanotubes produced in the magnetic-field-enhanced arc discharge. © 2008 American Institute of Physics.

Microscopic ion fluxes in plasma-aided nanofabrication of ordered carbon nanotip structures

Three-dimensional topography of microscopic ion fluxes in the reactive hydrocarbon-based plasma-aided nanofabrication of ordered arrays of vertically aligned single-crystalline carbon nanotip microemitter structures is simulated by using a Monte Carlo technique. The individual ion trajectories are computed by integrating the ion equations of motion in the electrostatic field created by a biased nanostructured substrate. It is shown that the ion flux focusing onto carbon nanotips is more efficient under the conditions of low potential drop Us across the near-substrate plasma sheath. Under low- Us conditions, the ion current density onto the surface of individual nanotips is higher for higher-aspect-ratio nanotips and can exceed the mean ion current density onto the entire nanopattern in up to approximately five times. This effect becomes less pronounced with increasing the substrate bias, with the mean relative enhancement of the ion current density ξi not exceeding ∼1.7. The value of ξi is higher in denser plasmas and behaves differently with the electron temperature Te depending on the substrate bias. When the substrate bias is low, ξi decreases with Te, with the opposite tendency under higher- Us conditions. The results are relevant to the plasma-enhanced chemical-vapor deposition of ordered large-area nanopatterns of vertically aligned carbon nanotips, nanofibers, and nanopyramidal microemitter structures for flat-panel display applications. © 2005 American Institute of Physics.

Plasma-deposited Ge nanoisland films on Si : is Stranski–Krastanow fragmentation unavoidable?

The formation of Ge quantum dot arrays by deposition from a low-temperature plasma environment is investigated by kinetic Monte Carlo numerical simulation. It is demonstrated that balancing of the Ge influx from the plasma against surface diffusion provides an effective control of the surface processes and can result in the formation of very small densely packed quantum dots. In the supply-controlled mode, a continuous layer is formed which is then followed by the usual Stranski-Krastanow fragmentation with a nanocluster size of 10 nm. In the diffusion-controlled mode, with the oversupply relative to the surface diffusion rate, nanoclusters with a characteristic size of 3 nm are formed. Higher temperatures change the mode to supply controlled and thus encourage formation of the continuous layer that then fragments into an array of large size. The use of a high rate of deposition, easily accessible using plasma techniques, changes the mode to diffusion controlled and thus encourages formation of a dense array of small nanoislands.

Ways to increase the length of single wall carbon nanotubes in a magnetically enhanced arc discharge

Ability to control the properties of single-wall nanotubes produced in the arc discharge is important for many practical applications. Our experiments suggest that the length and purity of single-wall nanotubes significantly increase when the magnetic field is applied to the arc discharge. A model of a single wall carbon nanotube interaction and growth in the thermal plasma was developed which considers several important effects such as anode ablation that supplies the carbon plasma in an anodic arc discharge technique, and the momentum, charge and energy transfer processes between nanotube and plasma. The numerical simulations based on Monte-Carlo technique were performed, which explain an increase of the nanotubes produced in the magnetic field - enhanced arc discharge.

Plasma-assembled carbon nanotubes : electric field–related effects

The paper presents results of comparative investigation of carbon nanotubes growth processes in dense low-temperature plasma and on substrate surface. Hybrid/Monte-Carlo numerical simulations were used to demonstrate the differences in the ion fluxes, growth rates and kinetics of adsorbed atoms re-distribution on substrate and nanotubes surfaces. We show that the plasma parameters significantly affect the nanotubes growth kinetics. We demonstrate that the growth rates of the nanotubes in plasma and on surface can differ by three orders, and the specific fluxes to the nanotube in the plasma can exceed the flux to surface-grown nanotube by six orders. We also show that the metal catalyst used for the nanotubes production on surface and in arc is a subject to very different conditions and this may be a key factor for the nanotube growth mode. The obtained dependencies for the ion fluxes to the nanotubes and nanotubes growth rates on the plasma parameters may be useful for selection of the production methods.

Growth of carbon nanocone arrays on a metal catalyst: The effect of carbon flux ionization

The growth of carbon nanocone arrays on metal catalyst particles by deposition from a low-temperature plasma is studied by multiscale Monte Carlo/surface diffusion numerical simulation. It is demonstrated that the variation in the degree of ionization of the carbon flux provides an effective control of the growth kinetics of the carbon nanocones, and leads to the formation of more uniform arrays of nanostructures. In the case of zero degree of ionization (neutral gas process), a width of the distribution of nanocone heights reaches 360 nm with the nanocone mean height of 150 nm. When the carbon flux of 75% ionization is used, the width of the distribution of nanocone heights decreases to 100 nm, i.e., by a factor of 3.6. A higher degree of ionization leads to a better uniformity of the metal catalyst saturation and the nanocone growth, thus contributing to the formation of more height-uniform arrays of carbon nanostructures.

Simulation of ion flux distribution in conductive and nonconductive nanotip patterns

The distribution of flux of carbon-bearing cations over nanopatterned surfaces with conductive nanotips and nonconductive nanoislands is simulated using the Monte-Carlo technique. It is shown that the ion current is focused to nanotip surfaces when the negative substrate bias is low and only slightly perturbed at higher substrate biases. In the low-bias case, the mean horizontal ion displacement caused by the nanotip electric field exceeds 10 nm. However, at higher substrate biases, this value reduces down to 2 nm. In the nonconductive nanopattern case, the ion current distribution is highly nonuniform, with distinctive zones of depleted current density around the nanoislands. The simulation results suggest the efficient means to control ion fluxes in plasma-aided nanofabrication of ordered nanopatterns, such as nanotip microemitter structures and quantum dot or nanoparticle arrays. © World Scientific Publishing Company.

A review of modern computational algorithms for Bayesian optimal design

Bayesian experimental design is a fast growing area of research with many real-world applications. As computational power has increased over the years, so has the development of simulation-based design methods, which involve a number of algorithms, such as Markov chain Monte Carlo, sequential Monte Carlo and approximate Bayes methods, facilitating more complex design problems to be solved. The Bayesian framework provides a unified approach for incorporating prior information and/or uncertainties regarding the statistical model with a utility function which describes the experimental aims. In this paper, we provide a general overview on the concepts involved in Bayesian experimental design, and focus on describing some of the more commonly used Bayesian utility functions and methods for their estimation, as well as a number of algorithms that are used to search over the design space to find the Bayesian optimal design. We also discuss other computational strategies for further research in Bayesian optimal design.

Internal calibration of gel dosimeters : a feasibility study

In this work we test the feasibility of a new calibration method for gel dosimetry. We examine, through Monte Carlo modelling, whether the inclusion of an organic plastic scintillator system at key points within the gel phantom would perturb the dose map. Such a system would remove the requirement for a separate calibration gel, removing many sources of uncertainty.

Sensitivity of an Elekta iView GT a-Si EPID model to delivery errors for pre-treatment verification of IMRT fields

A Monte Carlo model of an Elekta iViewGT amorphous silicon electronic portal imaging device (a-Si EPID) has been validated for pre-treatment verification of clinical IMRT treatment plans. The simulations involved the use of the BEAMnrc and DOSXYZnrc Monte Carlo codes to predict the response of the iViewGT a-Si EPID model. The predicted EPID images were compared to the measured images obtained from the experiment. The measured EPID images were obtained by delivering a photon beam from an Elekta Synergy linac to the Elekta iViewGT a-Si EPID. The a-Si EPID was used with no additional build-up material. Frame averaged EPID images were acquired and processed using in-house software. The agreement between the predicted and measured images was analyzed using the gamma analysis technique with acceptance criteria of 3% / 3 mm. The results show that the predicted EPID images for four clinical IMRT treatment plans have a good agreement with the measured EPID signal. Three prostate IMRT plans were found to have an average gamma pass rate of more than 95.0 % and a spinal IMRT plan has the average gamma pass rate of 94.3 %. During the period of performing this work a routine MLC calibration was performed and one of the IMRT treatments re-measured with the EPID. A change in the gamma pass rate for one field was observed. This was the motivation for a series of experiments to investigate the sensitivity of the method by introducing delivery errors, MLC position and dosimetric overshoot, into the simulated EPID images. The method was found to be sensitive to 1 mm leaf position errors and 10% overshoot errors.

Bayesian hierarchical models for analysing spatial point-based data at a grid level: A comparison of approaches

Spatial data are now prevalent in a wide range of fields including environmental and health science. This has led to the development of a range of approaches for analysing patterns in these data. In this paper, we compare several Bayesian hierarchical models for analysing point-based data based on the discretization of the study region, resulting in grid-based spatial data. The approaches considered include two parametric models and a semiparametric model. We highlight the methodology and computation for each approach. Two simulation studies are undertaken to compare the performance of these models for various structures of simulated point-based data which resemble environmental data. A case study of a real dataset is also conducted to demonstrate a practical application of the modelling approaches. Goodness-of-fit statistics are computed to compare estimates of the intensity functions. The deviance information criterion is also considered as an alternative model evaluation criterion. The results suggest that the adaptive Gaussian Markov random field model performs well for highly sparse point-based data where there are large variations or clustering across the space; whereas the discretized log Gaussian Cox process produces good fit in dense and clustered point-based data. One should generally consider the nature and structure of the point-based data in order to choose the appropriate method in modelling a discretized spatial point-based data.

Assessing uncertainty in pollutant wash-off modelling via model validation

Stormwater pollution is linked to stream ecosystem degradation. In predicting stormwater pollution, various types of modelling techniques are adopted. The accuracy of predictions provided by these models depends on the data quality, appropriate estimation of model parameters, and the validation undertaken. It is well understood that available water quality datasets in urban areas span only relatively short time scales unlike water quantity data, which limits the applicability of the developed models in engineering and ecological assessment of urban waterways. This paper presents the application of leave-one-out (LOO) and Monte Carlo cross validation (MCCV) procedures in a Monte Carlo framework for the validation and estimation of uncertainty associated with pollutant wash-off when models are developed using a limited dataset. It was found that the application of MCCV is likely to result in a more realistic measure of model coefficients than LOO. Most importantly, MCCV and LOO were found to be effective in model validation when dealing with a small sample size which hinders detailed model validation and can undermine the effectiveness of stormwater quality management strategies.