Dataset Information

Apparent Power Law Scaling of Variable Range Hopping Conduction in Carbonized Polymer Nanofibers.

ABSTRACT: We induce dramatic changes in the structure of conducting polymer nanofibers by carbonization at 800?°C and compare charge transport properties between carbonized and pristine nanofibers. Despite the profound structural differences, both types of systems display power law dependence of current with voltage and temperature, and all measurements can be scaled into a single universal curve. We analyze our experimental data in the framework of variable range hopping and argue that this mechanism can explain transport properties of pristine polymer nanofibers as well.

SUBMITTER: Kim KH

PROVIDER: S-EPMC5122886 | biostudies-literature | 2016 Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Apparent Power Law Scaling of Variable Range Hopping Conduction in Carbonized Polymer Nanofibers.

Kim Kyung Ho KH Lara-Avila Samuel S Kang Hojin H He Hans H Eklӧf Johnas J Hong Sung Ju SJ Park Min M Moth-Poulsen Kasper K Matsushita Satoshi S Akagi Kazuo K Kubatkin Sergey S Park Yung Woo YW

Scientific reports 20161125

We induce dramatic changes in the structure of conducting polymer nanofibers by carbonization at 800 °C and compare charge transport properties between carbonized and pristine nanofibers. Despite the profound structural differences, both types of systems display power law dependence of current with voltage and temperature, and all measurements can be scaled into a single universal curve. We analyze our experimental data in the framework of variable range hopping and argue that this mechanism can ...[more]

PMID: 27886233

Similar Datasets

Project description:Various types of neural-based signals, such as EEG, local field potentials and intracellular synaptic potentials, integrate multiple sources of activity distributed across large assemblies. They have in common a power-law frequency-scaling structure at high frequencies, but it is still unclear whether this scaling property is dominated by intrinsic neuronal properties or by network activity. The latter case is particularly interesting because if frequency-scaling reflects the network state it could be used to characterize the functional impact of the connectivity. In intracellularly recorded neurons of cat primary visual cortex in vivo, the power spectral density of V(m) activity displays a power-law structure at high frequencies with a fractional scaling exponent. We show that this exponent is not constant, but depends on the visual statistics used to drive the network. To investigate the determinants of this frequency-scaling, we considered a generic recurrent model of cortex receiving a retinotopically organized external input. Similarly to the in vivo case, our in computo simulations show that the scaling exponent reflects the correlation level imposed in the input. This systematic dependence was also replicated at the single cell level, by controlling independently, in a parametric way, the strength and the temporal decay of the pairwise correlation between presynaptic inputs. This last model was implemented in vitro by imposing the correlation control in artificial presynaptic spike trains through dynamic-clamp techniques. These in vitro manipulations induced a modulation of the scaling exponent, similar to that observed in vivo and predicted in computo. We conclude that the frequency-scaling exponent of the V(m) reflects stimulus-driven correlations in the cortical network activity. Therefore, we propose that the scaling exponent could be used to read-out the "effective" connectivity responsible for the dynamical signature of the population signals measured at different integration levels, from Vm to LFP, EEG and fMRI.

Dataset Information

Apparent Power Law Scaling of Variable Range Hopping Conduction in Carbonized Polymer Nanofibers.

Publications

Apparent Power Law Scaling of Variable Range Hopping Conduction in Carbonized Polymer Nanofibers.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets