Dataset Information

Exciton Diffusion in Organic Nanofibers: A Monte Carlo Study on the Effects of Temperature and Dimensionality.

ABSTRACT: Organic nanofibers have found various applications in optoelectronic devices. In such devices, exciton diffusion is a major aspect concerning their efficiency. In the case of singlet excitons, Förster transfer is the mechanism responsible for this process. Temperature and morphology are factors known to influence exciton diffusion but are not explicitly considered in the expressions for the Förster rate. In this work, we employ a Kinetic Monte Carlo (KMC) model to investigate singlet exciton diffusion in para-hexaphenyl (P6P) and α-sexithiophene (6T) nanofibers. Building from previous experimental and theoretical studies that managed to obtain temperature dependent values for Förster radii, exciton average lifetimes and intermolecular distances, our model is able to indicate how these parameters translate into diffusion coefficients and diffusion lengths. Our results indicate that these features strongly depend on the coordination number in the material. Furthermore, we show how all these features influence the emitted light color in systems composed of alternating layers of P6P and 6T. Finally, we present evidence that the distribution of exciton displacements may result in overestimation of diffusion lengths in experimental setups.

SUBMITTER: de Sousa LE

PROVIDER: S-EPMC6145872 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature

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Exciton Diffusion in Organic Nanofibers: A Monte Carlo Study on the Effects of Temperature and Dimensionality.

de Sousa Leonardo Evaristo LE da Silva Filho Demétrio Antônio DA de Sousa Rafael Timóteo RT de Oliveira Neto Pedro Henrique PH

Scientific reports 20180919 1

Organic nanofibers have found various applications in optoelectronic devices. In such devices, exciton diffusion is a major aspect concerning their efficiency. In the case of singlet excitons, Förster transfer is the mechanism responsible for this process. Temperature and morphology are factors known to influence exciton diffusion but are not explicitly considered in the expressions for the Förster rate. In this work, we employ a Kinetic Monte Carlo (KMC) model to investigate singlet exciton dif ...[more]

PMID: 30232380

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Project description:In diffusion MRI analysis, advances in biophysical multi-compartment modeling have gained popularity over the conventional Diffusion Tensor Imaging (DTI), because they can obtain a greater specificity in relating the dMRI signal to underlying cellular microstructure. Biophysical multi-compartment models require a parameter estimation, typically performed using either the Maximum Likelihood Estimation (MLE) or the Markov Chain Monte Carlo (MCMC) sampling. Whereas, the MLE provides only a point estimate of the fitted model parameters, the MCMC recovers the entire posterior distribution of the model parameters given in the data, providing additional information such as parameter uncertainty and correlations. MCMC sampling is currently not routinely applied in dMRI microstructure modeling, as it requires adjustment and tuning, specific to each model, particularly in the choice of proposal distributions, burn-in length, thinning, and the number of samples to store. In addition, sampling often takes at least an order of magnitude, more time than non-linear optimization. Here we investigate the performance of the MCMC algorithm variations over multiple popular diffusion microstructure models, to examine whether a single, well performing variation could be applied efficiently and robustly to many models. Using an efficient GPU-based implementation, we showed that run times can be removed as a prohibitive constraint for the sampling of diffusion multi-compartment models. Using this implementation, we investigated the effectiveness of different adaptive MCMC algorithms, burn-in, initialization, and thinning. Finally we applied the theory of the Effective Sample Size, to the diffusion multi-compartment models, as a way of determining a relatively general target for the number of samples needed to characterize parameter distributions for different models and data sets. We conclude that adaptive Metropolis methods increase MCMC performance and select the Adaptive Metropolis-Within-Gibbs (AMWG) algorithm as the primary method. We furthermore advise to initialize the sampling with an MLE point estimate, in which case 100 to 200 samples are sufficient as a burn-in. Finally, we advise against thinning in most use-cases and as a relatively general target for the number of samples, we recommend a multivariate Effective Sample Size of 2,200.

Dataset Information

Exciton Diffusion in Organic Nanofibers: A Monte Carlo Study on the Effects of Temperature and Dimensionality.

Publications

Exciton Diffusion in Organic Nanofibers: A Monte Carlo Study on the Effects of Temperature and Dimensionality.

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