Dynamic Monte Carlo Model Research Articles

PETRA: A pencil beam trimming algorithm for analytical proton therapy dose calculations with the dynamic collimation system.

The Dynamic Collimation System (DCS) has been shown to produce superior treatment plans to uncollimated pencil beam scanning (PBS) proton therapy using an in-house treatment planning system (TPS) designed for research. Clinical implementation of the DCS requires the development and benchmarking of a rigorous dose calculation algorithm that accounts for pencil beam trimming, performs monitor unit calculations to produce deliverable plans at all beam energies, and is ideally implemented with a commercially available TPS. To present an analytical Pencil bEam TRimming Algorithm (PETRA) for the DCS, with and without its range shifter, implemented in the Astroid TPS (.decimal, Sanford, Florida, USA). PETRA was derived by generalizing an existing pencil beam dose calculation model to account for the DCS-specific effects of lateral penumbra blurring due to the nickel trimmers in two different planes, integral depth dose variation due to the trimming process, and the presence and absence of the range shifter. Tuning parameters were introduced to enable agreement between PETRA and a measurement-validated Dynamic Collimation Monte Carlo (DCMC) model of the Miami Cancer Institute's IBA Proteus Plus system equipped with the DCS. Trimmer position, spot position, beam energy, and the presence or absence of a range shifter were all used as variables for the characterization of the model. The model was calibrated for pencil beam monitor unit calculations using procedures specified by International Atomic Energy Agency Technical Report Series 398 (IAEA TRS-398). The integral depth dose curves (IDDs) for energies between 70 MeV and 160 MeV among all simulated trimmer combinations, with and without the ranger shifter, agreed between PETRA and DCMC at the 1%/1mm 1-D gamma criteria for 99.99% of points. For lateral dose profiles, the median 2-D gamma pass rate for all profiles at 1.5%/1.5mm was 99.99% at the water phantom surface, plateau, and Bragg peak depths without the range shifter and at the surface and Bragg peak depths with the range shifter. The minimum 1.5%/1.5mm gamma pass rates for the 2-D profiles at the water phantom surface without and with the range shifter were 98.02% and 97.91%, respectively, and, at the Bragg peak, the minimum pass rates were 97.80% and 97.5%, respectively. The PETRA model for DCS dose calculations was successfully defined and benchmarked for use in a commercially available TPS.

Monte Carlo simulation of photoinduced atom-transfer radical polymerization for dynamic microscopic properties

Atom-transfer radical polymerization (ATRP) is a powerful and versatile technique for the synthesis of well-defined polymers with precise control over the molecular weight and architecture. Photoinduced ATRP (photoATRP) is a variant of ATRP based on the transfer of radical polymers through a photochemical process, offering numerous benefits over traditional ATRP, including reduced environmental impact and improved polymer yields. Monte Carlo (MC) simulation is an attractive tool for simulating photoATRP, enabling one to predict microscopic properties of the polymers. In this work, a dynamic Monte Carlo (DMC) model is developed for the photoATRP processes at microscopic resolution. The dependency information of individual chains’ length on the radical, dormant, and dead polymers is captured. Furthermore, the polymerization process simulation of photoATRP systems based on the developed DMC method for temporal control over polymer growth is established. Several facets on the enhancement of temporal control are investigated under the condition of light intermittency.

Stochastic optimisation and economic analysis of combined high temperature superconducting magnet and hydrogen energy storage system for smart grid applications

High Temperature Superconducting (HTS) Magnetic Energy Storage (SMES) devices are promising high-power storage devices, although their widespread use is limited by their high capital and operating costs. This work investigates their inclusion in smart grids when used in tandem with hydrogen fuel cells and other energy storage devices using a novel two-stage model. The first stage presents a stochastic allocation algorithm to optimally size the smart grid assets with the maximum expected profit. The next stage models the time system evolution with these optimal capacities using a dynamic Monte Carlo model to investigate the system reliability, capacity credit and loss of load expectation. A novel energy management algorithm is also presented that maximises the time with which a fleet of energy storage devices can fulfil a stochastically unknown power request. The analysis shows that from a purely economic standpoint, HTS SMES and hydrogen energy do not achieve high penetration levels (up to 10 %) due to their high costs, although the penetration levels can be improved with higher power imbalance penalties in day ahead markets. The novel capacity credit and reliability investigation shows that they can improve the reliability and capacity credit of wind turbines by approximately 30 %, thereby improving potential wind penetration levels. The optimal energy management algorithm improves the fleet lifespan and reliability by up to 9 % depending on the connected capacity.

Flexible Threshold Switching Selectors with Ultrahigh Endurance Based on Halide Perovskites

AbstractFlexible electronics are essential for wearable setup. Due to the high nonlinear property, threshold switching (TS) selectors can effectively suppress interference currents and are compatible to large‐scale integration, enabling them to be promising for circuit applications. However, it is still a big challenge to develop flexible TS selectors with both high electrical performance and flexibility endurance due to the lack of suitable materials and processes. Organic–inorganic hybrid perovskites (OIHPs) show great potential in flexible TS selectors owing to fast ion migration, low‐temperature synthetic processes, and excellent mechanical flexibility. Here, the flexible TS selectors with an Ag/OIHP/indium tin oxide/polyethylene naphthalate structure are demonstrated by a low‐cost solution process. The devices exhibit ultrahigh endurance (1010 cycles), excellent mechanical bending stability (104 times), as well as fast switching speed (40 ns). One‐selector one‐memristor is assembled to perform the write and read operation, clearly indicating nearly free of interference current. The working mechanism of the selectors is also carefully studied by energy dispersive spectroscopy and dynamic Monte Carlo model, showing that the high electrical performance should be ascribed to the low migration barrier of Ag in OHIP films. This work should provide a new promising approach in developing large‐scale integration of flexible high‐performance electronic devices.

Parameter Estimation and Kinetic Monte Carlo Simulation of Styrene andn-Butyl Acrylate Copolymerization through ATRP

Controlled polymerization causes the final product to have predictable properties, allowing it to have a more diverse application, in turn, increasing its added value. In the present work, a dynamic Monte Carlo (MC) model is used to describe the copolymerization of styrene and n-butyl acrylate through atom transfer radical polymerization (ATRP). A further investigation was performed to estimate the values of kinetic constants with no explicit value reported in the literature using particle swarm optimization (PSO). The model had excellent performance when compared with the experimental data from the literature. The model also provided access to the molecular weight and chemical composition distributions and the polymer microstructures. The results show that the propagation and termination constants’ difference affected the polymer structure directly and significantly impacted the distribution curves’ broadening. The developed model might be used in the future for monitoring and controlling the average molecular weights and the molecular weight distributions in the chemical process industry.

Dynamic Monte Carlo Simulation for Chain‐Shuttling Polymerization of Olefin Block Copolymers in Continuous Stirred‐Tank Reactor

AbstractA dynamic Monte Carlo model coupling with residence time distribution is developed to simulate chain microstructures of olefin block copolymers (OBCs) produced with the chain‐shuttling polymerization in a continuous stirred‐tank reactor (CSTR). The simulated results provide information on how polymer chain microstructures (i.e., average molecular weight, average comonomer content, and microstructural distributions) evolve in a CSTR system and show a good agreement with previously reported theoretical and experimental results. The model is also used to investigate effect of reaction conditions (i.e., catalyst feed compositions, monomer feed compositions, and mean residence time) on time evolution of OBC chain microstructures.

Rotational and translational dynamics in dense fluids of patchy particles.

We explore the effect of directionality on rotational and translational relaxation in glassy systems of patchy particles. Using molecular dynamics simulations, we analyze the impact of two distinct patch geometries, one that enhances the local icosahedral structure and the other one that does not strongly affect the local order. We find that in nearly all investigated cases, rotational relaxation takes place on a much faster time scale than translational relaxation. By comparing to a simplified dynamical Monte Carlo model, we illustrate that rotational diffusion can be qualitatively explained as purely local motion within a fixed environment, which is not coupled strongly to the cage-breaking dynamics required for translational relaxation. Nonetheless, icosahedral patch placement has a profound effect on the local structure of the system, resulting in a dramatic slowdown at low temperatures, which is strongest at an intermediate "optimal" patch size.

Dynamic Monte Carlo Simulation of Olefin Block Copolymers (OBCs) Produced via Chain‐Shuttling Polymerization: Effect of Kinetic Rate Constants on Chain Microstructure

AbstractOlefin block copolymers (OBCs) are thermoplastic elastomers with unique statistical multiblock structures produced by chain‐shuttling polymerization. In this study, a dynamic Monte Carlo model is used to simulate the effect of changing chain‐shuttling (kCS), catalyst deactivation (kd), and chain transfer (ktr) rate constants on the microstructure of OBCs made in a semi‐batch reactor. The simulations show how each rate constant governs different aspects of the OBC chains and provide guidelines on how to fine‐tune OBC microstructure to achieve precisely designed multiblock structures.

Systematic model for estimation of future inadvertent human intrusion into deep rad-waste repository by domestic groundwater well drilling

Future human intrusion into a geological repository for spent nuclear fuels and radioactive waste is a safety case that inherently involves a high uncertainty, especially in areas with a high population density. The uncertainty associated with inadvertent human intrusion needs to account for the complexity of various conceivable circumstances with the long-term evolution of society or technology. In this study, a new dynamic Monte Carlo model is developed to properly incorporate the potential future human intrusion frequency, which has never been performed in past studies. The developed model for inadvertent deep groundwater well drilling into a repository focuses on the assessment of the quantitative effects of factors mitigating or enhancing future human intrusion risk. Assuming the current groundwater well depth distribution in the Gyeongsangnam-do region of the Republic of Korea, the future human intrusion frequency is estimated to be 5.89×10-41/yr for a repository with a depth of 500 m and a hypothetical area of 5.27×10-1km2. The model predicts that the frequency may reach up to 2.18×10-31/yr as the depth of a groundwater well increases in the future, as expected from the data trending performed in this study. A sensitivity analysis shows that a proper design can suppress the frequency, which increases positively with the repository area and negatively with the depth of the repository. However, mitigation by a repository design tends to be weakened as the well depth distribution increases. Since the average well depth increases with time, the mitigation effects achieved by reducing the area or increasing the depth of a repository may not be effective against the current expectation. In this case, the most reliable approach for minimizing future human intrusion risk will be a reduction in the radiotoxicity concentration of the waste.

Understanding the Microstructure of Living Ethylene/1‐Octene Block Copolymers with Dynamic Monte Carlo Simulation

Living ethylene/1‐olefin copolymerization with multiple comonomer feeding stages allows the production of living block copolymers (LBCs) with well‐controlled microstructures. A dynamic Monte Carlo model is developed to simulate the production of LBCs in a semibatch reactor, and it is used to study how the polymer microstructure evolves during the polymerization. The model also describes how chain transfer reactions affect the microstructure of LBC blocks. These model predictions provide useful guidelines for producing LBCs with precisely designed microstructures. image

Understanding the Formation of Linear Olefin Block Copolymers with Dynamic Monte Carlo Simulation

Chain‐shuttling polymerization with dual catalysts has introduced a new class of polyolefins called olefin block copolymers (OBCs). A dynamic Monte Carlo model to describe the kinetics of chain‐shuttling copolymerization in a semi‐batch reactor is developed, and used it to study how the microstructure of OBCs with different numbers of blocks per chain evolves during polymerization. The model also describes how chain‐shuttling rate constants and concentration of chain‐shuttling agent affect populations of OBCs with different numbers of blocks per chain. These model predictions are useful to make OBCs with precisely designed microstructures.image

Dynamic Monte Carlo modeling of exciton dissociation in organic donor-acceptor solar cells

A general dynamic Monte Carlo model for exciton dissociation at a donor-acceptor interface that includes exciton delocalization and hot charge separation is developed to model the experimental behavior observed for the poly(3-hexylthiophene):fullerene system and predict the theoretical performance of future materials systems. The presence of delocalized excitons and the direct formation of separated charge pairs has been recently measured by transient photo-induced absorption experiments and has been proposed to facilitate charge separation. The excess energy of the exciton dissociation process has also been observed to have a strong correlation with the charge separation yield for a series of thiophene based polymer:fullerene systems, suggesting that a hot charge separation process is also occurring. Hot charge separation has been previously theorized as a cause for highly efficient charge separation. However, a detailed model for this process has not been implemented and tested. Here, both conceptual models are implemented into a dynamic Monte Carlo simulation and tested using a simple bilayer donor-acceptor system. We find that exciton delocalization can account for a significant reduction in geminate recombination when compared to the traditional, bound polaron pair model. In addition, the hot charge separation process could further reduce the geminate recombination, but only if the hot charge mobility is several orders of magnitude larger than the standard charge mobility.

Dynamic Monte Carlo simulation of film–substrate interface mixing in the deposition of Co on Cu (001)

Dynamic Monte Carlo simulations are performed to investigate the interface mixing of Co atoms deposited on a Cu (001) substrate. A tight-binding potential was used to determine the input parameters (jump probabilities and energy barriers) for the Dynamic Monte Carlo model. The results show that more Co adatoms penetrate into the substrate as the temperature rises and/or as the deposition rate decreases, and that the intermixing between the layers becomes concomitantly more pronounced. Cu atoms migrating into the Co layer via exchange processes during the growth of consecutive Co layers are proposed to be responsible for the intermixing. Furthermore, an initial Co clustering followed by a layer-by-layer growth mode was observed in the simulations, with the surface concentration of Cu atoms depending on the fraction of migrating Cu atoms and decaying into the Co film following a power law. The fraction of Cu atoms migrating into the Co layer can be adjusted by varying the deposition rate and the substrate temperature.

Computational characterization of organic photovoltaic devices

We present recent progresses on applying the theoretical models and computational tools in assessing the performance of organic solar cells, especially the bulk heterojunction solar cells. Both the continuum device model and the dynamic Monte Carlo model are developed to investigate the photocurrent-voltage characteristics based on the exciton and charge carrier dynamics. Insights into key factors influencing the organic photovoltaic performances have been gained from these studies.

An improved dynamic Monte Carlo model coupled with Poisson equation to simulate the performance of organic photovoltaic devices

We describe a new dynamic Monte Carlo model to simulate the operation of a polymer-blend solar cell; this model provides major improvements with respect to the one we developed earlier [J. Phys. Chem. B 114, 36 (2010)] by incorporating the Poisson equation and a charge thermoactivation mechanism. The advantage of the present approach is its capacity to deal with a nonuniform electrostatic potential that dynamically depends on the charge distribution. In this way, the unbalance in electron and hole mobilities and the space-charge induced potential distribution can be treated explicitly. Simulations reproduce well the experimental I-V curve in the dark and the open-circuit voltage under illumination of a polymer-blend solar cell. The dependence of the photovoltaic performance on the difference in electron and hole mobilities is discussed.

Dynamic Monte-Carlo modeling of thermal desorption of H 2 from H + irradiated graphite

Thermal desorption of hydrogen molecules from H + irradiated graphite is studied using dynamic Monte Carlo simulation. The purpose of this study is to understand the experimentally observed phenomena that the thermal desorption of H 2 from the graphite exhibits sometimes single desorption peak, sometimes double peaks, and even three desorption peaks under certain circumstances. The study result reveals that the fluence of pre-implanted H +, the concentration of trap sites, porosity, and mean crystallite volume are important parameters in determining the number of desorption peaks. It is found that low implantation fluence and high concentration of trap sites easily lead to the occurrence of single desorption peak at around 1000 K, and high implantation fluence and low concentration of trap sites favor the occurrence of double desorption peaks, with a new desorption peak at around 820 K. It is also found that small porosity of graphite and large crystallite volume benefit the occurrence of single desorption peak at around 1000 K while large porosity of graphite and small crystallite volume facilitate the occurrence of double desorption peaks, respectively, at around 820 and 1000 K. In addition, experimentally observed third desorption peak at lower temperature is reproduced by simulation with assuming the graphite containing a small concentration of solute hydrogen atoms.

Gradient Copolymers by ATRP in Semibatch Reactors: Dynamic Monte Carlo Simulation

AbstractWe developed a dynamic Monte Carlo model for ATRP in semibatch reactors. Semibatch reactors can be used to produce gradient copolymers even if the difference between the reactivity ratios of the comonomers is not significant by using different comonomer feed policies. The model was used to predict average molecular weights, polydispersity index, copolymer composition and complete distributions of molecular weight, chemical composition, and comonomer sequence length at any polymerization time. Two case studies, poly[styrene‐co‐(methyl methacrylate)] and poly[acrylonitrile‐co‐(methyl methacrylate)], were chosen to demonstrate the effect of comonomer feed compositions on the final chemical composition distribution of the copolymer.magnified image

Luminescence simulations of ensembles of silicon nanocrystals

AbstractThe luminescence of silicon nanocrystals (NCs) has attracted a great deal of interest due to the numerous potential photonic applications of light‐emitting silicon. However, the excitation mechanisms and cluster–cluster interactions in densely‐packed ensembles, as well as the recombination processes that influence the emission spectrum and lifetime are not yet well understood. In order to generate a more complete picture of the controlling parameters in the luminescence, a dynamic Monte Carlo model that incorporates several key physical processes for luminescent nanocrystal ensembles is developed. The model simulates Forster‐type multipole energy transfer, tunnelling interactions, radiative decay and non‐radiative trapping in physically realistic (lognormal) distributions of silicon NCs. The results of the simulation illustrate the effects of the NC size distribution, homogeneous and inhomogeneous broadening, NC packing density, and non‐radiative trapping on the ensemble luminescence spectrum. (© 2009 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)

High performance computations using dynamical nucleation theory

Chemists continue to explore the use of very large computations to perform simulations that describe the molecular level physics of critical challenges in science. In this paper, we describe the Dynamical Nucleation Theory Monte Carlo (DNTMC) model - a model for determining molecular scale nucleation rate constants - and its parallel capabilities. The potential for bottlenecks and the challenges to running on future petascale or larger resources are delineated. A ‘master-slave’ solution is proposed to scale to the petascale and will be developed in the NWChem software. In addition, mathematical and data analysis challenges are described.

Photocurrent Generation in Nanostructured Organic Solar Cells

Photocurrent generation in nanostructured organic solar cells is simulated using a dynamical Monte Carlo model that includes the generation and transport properties of both excitons and free charges. Incorporating both optical and electrical properties, we study the influence of the heterojunction nanostructure (e.g., planar vs bulk junctions) on donor-acceptor organic solar cell efficiencies based on the archetype materials copper phthalocyanine (CuPc) and C(60). Structures considered are planar and planar-mixed heterojunctions, homogeneous and phase-separated donor-acceptor (DA) mixtures, idealized structures composed of DA pillars, and nanocrystalline DA networks. The thickness dependence of absorption, exciton diffusion, and carrier collection efficiencies is studied for different morphologies, yielding results similar to those experimentally observed. The influences of charge mobility and exciton diffusion length are studied, and optimal device thicknesses are proposed for various structures. Simulations show that, with currently available materials, nanocrystalline network solar cells optimize both exciton diffusion and carrier collection, thus providing for highly efficient solar energy conversion. Estimations of achievable energy conversion efficiencies are made for the various nanostructures based on current simulations used in conjunction with experimentally obtained fill factors and open-circuit voltages for conventional small molecular weight materials combinations.