Macroscopic Conductivity Research Articles

Unraveling impacts of polycrystalline microstructures on ionic conductivity of ceramic electrolytes by computational homogenization and machine learning

The ionic conductivity at the grain boundaries (GBs) in oxide ceramics is typically several orders of magnitude lower than that within the grain interior. This detrimental GB effect is the main bottleneck for designing high-performance ceramic electrolytes intended for use in solid-state lithium-ion batteries, fuel cells, and electrolyzer cells. The macroscopic ionic conductivity in oxide ceramics is essentially governed by the underlying polycrystalline microstructures where GBs and grain morphology go hand in hand. This provides the possibility to enhance the ion conductivity by microstructure engineering. To this end, a thorough understanding of microstructure–property correlation is highly desirable. In this work, we investigate numerous polycrystalline microstructure samples with varying grain and grain boundary features. Their macroscopic ionic conductivities are numerically evaluated by the finite element homogenization method, whereby the GB resistance is explicitly regarded. The influence of different microstructural features on the effective ionic conductivity is systematically studied. The microstructure–property relationships are revealed. Additionally, a graph neural network-based machine learning model is constructed and trained. It can accurately predict the effective ionic conductivity for a given polycrystalline microstructure. This work provides crucial quantitative guidelines for optimizing the ionic conducting performance of oxide ceramics by tailoring microstructures.

First-principles characterization of thermal conductivity in LaPO4 -based alloys

Alloys based on lanthanum phosphate (LaPO4) are often employed as thermal barrier coatings, due to their low thermal conductivity and structural stability over a wide temperature range. To enhance the thermal-insulation performance of these alloys, it is essential to comprehensively understand the fundamental physics governing their heat conduction. Here, we employ the Wigner formulation of thermal transport in conjunction with first-principles calculations to elucidate how the interplay between anharmonicity and compositional disorder determines the thermal properties of La1−xGdxPO4 alloys, and discuss the fundamental physics underlying the emergence and coexistence of particlelike and wavelike heat-transport mechanisms. We also show how the Wigner transport equation correctly describes the thermodynamic limit of a compositionally disordered crystal, while the Boltzmann transport equation does not. Our predictions for microscopic vibrational properties (temperature-dependent Raman spectrum) and for macroscopic thermal conductivity are validated against experiments. Finally, we leverage these findings to devise strategies to optimize the performance of thermal barrier coatings. Published by the American Physical Society 2024

Modeling heat conduction with dual-dissipative variables: A mechanism-data fusion method.

Many macroscopic non-Fourier heat conduction models have been developed in the past decades based on Chapman-Enskog, Hermite, or other small perturbation expansion methods. These macroscopic models have achieved great success in capturing non-Fourier thermal behaviors in solid materials, but most of them are limited by small Knudsen numbers and incapable of capturing highly nonequilibrium or ballistic thermal transport. In this paper, we provide a different strategy for constructing macroscopic non-Fourier heat conduction modeling, that is, using data-driven deep-learning methods combined with nonequilibrium thermodynamics instead of small perturbation expansion. We present the mechanism-data fusion method, an approach that seamlessly integrates the rigorous framework of conservation-dissipation formalism (CDF) with the flexibility of machine learning to model non-Fourier heat conduction. Leveraging the conservation-dissipation principle with dual-dissipative variables, we derive an interpretable series of partial differential equations, fine tuned through a training strategy informed by data from the phonon Boltzmann transport equation. Moreover, we also present the inner-step operation to narrow the gap from the discrete form to the continuous system. Through numerical tests, our model demonstrates excellent predictive capabilities across various heat conduction regimes, including diffusive, hydrodynamic, and ballistic regimes, and displays its robustness and precision even with discontinuous initial conditions.

Briefing: Selective laser sintering technology to print walnut shell/phenolic resin new biomass-based porous electrode

In this paper, porous carbon electrodes were prepared by using walnut shell and phenolic resin as matrix materials, combined with selective laser sintering (SLS) technology and carbonization process. The effects of different ratios and temperatures on the macroscopic morphology, graphitization degree, electrical conductivity and pore structure of the carbonized parts were investigated. The results show that the lower proportion of walnut shell and the higher carbonization temperature increase the degree of graphitization. High temperature has a significant effect on the conductivity. The conductivity at 1000 °C is 7.5197 S·cm−1, which is 752 % higher than that at 600 °C, while the conductivity at 30 % walnut shell content is only 75.4 % higher than that at 50 %. This study provides a new reference for the preparation of carbon electrodes with complex shape and structure by biomass consumables.

Effect of sintering temperature on microstructure and electrical properties of Na0.5Bi0.47Sr0.02TiO3-δ ceramics

In this investigation, a series of Na0.5Bi0.47Sr0.02TiO3-δ samples, sintered from 1000 to 1100 °C, were synthesized utilizing the solid-state method. The XRD results indicate that all samples exhibit a perovskite phase, with the exception of those sintered at 1100 °C, which display no discernible impurity peaks. The SEM results indicate that the average grain size exhibits an increase proportionate to the sintering temperature, which attributes to the variations in the actual Na/Bi ratio. From the study of grain conductivity we can obtain that the dominant factor affecting grain conductivity is the activation energy. At 1100 °C, the oxygen vacancy concentration emerges as the primary determinant of grain conductivity. It should be noticed that the phase transition temperature of the sample increases (350 °C→400 °C) with the increase of sintering temperature, which is due to the local A-site disorder aggravated by high temperature sintering. Therefore, reducing the sintering temperature is beneficial to improve the grain conductivity. The grain boundary conductivity initially rises, then diminishes with increasing sintering temperature. It is evident that the enhanced macroscopic grain boundary conductivity stems from a marked reduction in space charge potential within the 1000–1025 °C temperature range. Within the 1050 °C−1100 °C temperature range, the space charge potential barely changed, the significant decrease of intrinsic grain boundary conductivity can be attributed to the surge in the coverage ratio of grain boundary impurities and the number of grain boundary impurities increased caused by the rapid reduction of grain boundary area and thermal evaporation. The research on electrical performance of NBT-based oxygen-ion conductors will promote the application of NBT-based oxygen ion conductors in solid oxide fuel cells, oxygen sensors and other fields.

A phase-resolvedFermi-LAT analysis of the mode-changing pulsar PSR J2021+4026 shows hints of a multipolar magnetosphere

Context.The radio-quietγ-ray pulsar PSR J2021+4026 is a peculiarFermi-LAT pulsar showing repeated and quasi-periodic mode changes. Itsγ-ray flux shows repeated variations between two states at intervals of ∼3.5 years. These events occur over timescales < 100 days and are correlated with sudden changes in the spin-down rate. Multiwavelength observations also revealed an X-ray phase shift relative to theγ-ray profile for one of the events. PSR J2021+4026 is currently the only known isolatedγ-ray pulsar showing significant variability, and thus it has been the object of thorough investigations.Aims.The goal of our work is to study the mode changes of PSR J2021+4026 with improved detail. By accurately characterizing variations in theγ-ray spectrum and pulse profile, we aim to relate theFermi-LAT observations to theoretical models. We also aim to interpret the mode changes in terms of variations in the structure of a multipolar dissipative magnetosphere.Methods.We continually monitored the rotational evolution and theγ-ray flux of PSR J2021+4026 using more than 13 years ofFermi-LAT data with a binned likelihood approach. We investigated the features of the phase-resolved spectrum and pulse profile, and from these we inferred the macroscopic conductivity, the electric field parallel to the magnetic field, and the curvature radiation cutoff energy. These physical quantities are related to the spin-down rate and theγ-ray flux and therefore are relevant to the theoretical interpretation of the mode changes. We introduced a simple magnetosphere model that combines a dipole field with a strong quadrupole component. We simulated magnetic field configurations to determine the positions of the polar caps for different sets of parameters.Results.We clearly detect the previous mode changes and confirm a more recent mode change that occurred around June 2020. We provide a full set of best-fit parameters for the phase-resolvedγ-ray spectrum and the pulse profile obtained in five distinct time intervals. We computed the relative variations in the best-fit parameters, finding typical flux changes between 13% and 20%. Correlations appear between theγ-ray flux and the spectral parameters, as the peak of the spectrum shifts by ∼10% toward lower energies when the flux decreases. The analysis of the pulse profile reveals that the pulsed fraction of the light curve is larger when the flux is low. Finally, the magnetosphere simulations show that some configurations could explain the observed multiwavelength variability. However, self-consistent models are required to reproduce the observed magnitudes of the mode changes.

Application of the 2D diamond scheme to predict the macroscopic thermal conductivity of a three-phase composite with local anisotropy directions

In this contribution, we are interested in the prediction of the macroscopic thermal conductivity of three-phasic composite materials such as wood fibers. The main idea is to make use of up-scaling such as homogenization to link the pore scale and the sample scale. The studied wood species incorporates three phases: the air, the lumen or matrix and the fibers. Image processing is preformed on a tomographic acquisition with high resolution of some micrometers. It serves to identify the present phases and generates the morphology mesh. The 2D diamond scheme is employed to discretize Fourier's model posed on the segmented morphology. The resulting algorithm is validated using an exact solution. Optimal numerical convergence rates of second order are recorded. The thermal conductivity is therefore estimated following the 2D orthotropic directions. An emphasis is set on the impact of the orientation of fibers on this thermal property.

Conductive Glassy Nonconjugated Open-Shell Radical Polymer with Organosulfur Backbone for Macroscopic Conductivity.

Nonconjugated organic radicals with an open-shell radical active group exhibit unique functionality due to their radical pendant site. Compared with the previously studied doped conjugated polymers, radical polymers reveal superior processability, stability, and optical properties. Despite the success of organic radical polymer conductors based on the TEMPO radicals, it still requires potential design substitutions to meet the fundamental limits of charge transport in the radical polymer. To do so, we demonstrate that the amorphous, nonconjugated radical polymer with backbone-pendant group interaction and low glass transition temperature enables the macromolecules to have rapid charge transport in the solid state, resulting in conductivity higher than 32 S m-1. This charge transport is due to the formation of the local ordered regime with an energetically favored orientation caused by the strong coupling between the backbone and pendant group, which can significantly modulate the polymer packing with active electronic communications. The nonconjugate nature of the radical polymer maintains an optical transparency up to 98% at a 1.5 μm thick film. Thus, this effort will be a dramatically advanced model in the organic radical community for the creation of next-generation polymer conductors.

Understanding macroscopic thermal conduction in composites reinforced with 2D nanosheets

Two-dimensional (2D) nanosheets, such as graphene and hexagonal boron nitride, are considered as the most promising fillers for enhancing thermal conductivity of polymers and phase-change materials. Nevertheless, the effect of various 2D nanosheets on the effective thermal conductivity of composites is not fully understood, and the corresponding prediction model is still lacking, since numerous influence factors and complex thermal transfer networks are involved. This paper aims to study the macroscopically effective thermal conductivity of the nanosheets-reinforced composites in a systematical way, and develop a robust machine learning based prediction model. To this end, a series of representative volume elements are reconstructed based on the SEM observations of experimental samples, and high-throughput simulations are performed via the updated lattice Boltzmann scheme proposed in our recent work. The effects of shape, size, orientation, intrinsic thermal conductivity, interface thermal resistance, surface coating, and hybrid filling of the 2D nanosheets are clarified. This work could provide a deep insight into the effective thermal conductivity of the nanosheets-reinforced composites, and may offer important guidelines for the custom-design of polymer and phase-change composites with targeted thermal performances.

Understanding the role of aliovalent cation substitution on the li-ion diffusion mechanism in Li6+xP1-xSixS5Br argyrodites.

Due to their high ionic conductivity, lithium-ion conducting argyrodites show promise as solid electrolytes for solid-state batteries. Aliovalent substitution is an effective technique to enhance the transport properties of Li6PS5Br, where aliovalent Si substitution triples ionic conductivity. However, the origin of this experimentally observed increase is not fully understood. Our density functional theory (DFT) study reveals that Si4+ substitution increases Li diffusion by activating Li occupancy in the T4 sites. Redistribution of Li-ions within the lattice results in a more uniform distribution of Li around the T4 and neighboring T5 sites, flattening the energy landscape for diffusion. Since the T4 site is positioned in the intercage jump pathway, an increase in the intercage jump rate is found, which is directly related to the macroscopic diffusion and bulk conductivity. Analysis of neutron diffraction experiments confirms partial T4 site occupancy, in agreement with the computational findings. Understanding the aliovalent substitution effect on interstitials is crucial for improving solid electrolyte ionic conductivity and advancing solid-state battery performance.

An implicit thermo-mechanical coupling model for heat conduction and thermal cracking in brittle materials

In this paper, an implicit thermo-mechanical coupling model is developed to simulate heat conduction and thermal cracking in brittle materials. In the thermal analysis, an implicit particle heat conduction model (PHCM) is proposed. A linear algebraic system of heat conduction equations is derived based on the temperatures of the particle system and the heat flux among adjacent particles. An analytical expression is introduced for the involved correction coefficient, with which the macroscopic thermal conductivity coefficient can be directly adopted without calibration. Furthermore, a thermal conductivity reduction coefficient is introduced to account for the thermal resistance of the cracks. In the mechanical analysis, particle discontinuous deformation analysis (PDDA), which is an implicit variant of the discrete element method, is employed. The PHCM is first verified against analytical and finite element solutions for transient heat conduction problems. The results show that the PHCM is an effective and accurate heat conduction model that readily reflects the thermal resistance of cracks. Then, the thermo-mechanical coupling model is verified by the expansion of heated specimens under different conditions. Finally, the successful application of the coupling model in the simulation of thermal shock tests demonstrates its effectiveness for thermal cracking problems.

Enhanced optical conductivity and many-body effects in strongly-driven photo-excited semi-metallic graphite

The excitation of quasi-particles near the extrema of the electronic band structure is a gateway to electronic phase transitions in condensed matter. In a many-body system, quasi-particle dynamics are strongly influenced by the electronic single-particle structure and have been extensively studied in the weak optical excitation regime. Yet, under strong optical excitation, where light fields coherently drive carriers, the dynamics of many-body interactions that can lead to new quantum phases remain largely unresolved. Here, we induce such a highly non-equilibrium many-body state through strong optical excitation of charge carriers near the van Hove singularity in graphite. We investigate the system’s evolution into a strongly-driven photo-excited state with attosecond soft X-ray core-level spectroscopy. We find an enhancement of the optical conductivity of nearly ten times the quantum conductivity and pinpoint it to carrier excitations in flat bands. This interaction regime is robust against carrier-carrier interaction with coherent optical phonons acting as an attractive force reminiscent of superconductivity. The strongly-driven non-equilibrium state is markedly different from the single-particle structure and macroscopic conductivity and is a consequence of the non-adiabatic many-body state.

Self-optimization wavelet-learning method for predicting nonlinear thermal conductivity of highly heterogeneous materials with randomly hierarchical configurations

In the present work, we propose a self-optimization wavelet-learning method (SO-W-LM) with high accuracy and efficiency to compute the equivalent nonlinear thermal conductivity of highly heterogeneous materials with randomly hierarchical configurations. The randomly structural heterogeneity, temperature-dependent nonlinearity and material property uncertainty of heterogeneous materials are considered within the proposed self-optimization wavelet-learning framework. Firstly, meso- and micro-structural modeling of random heterogeneous materials are achieved by the proposed computer representation method, whose simulated hierarchical configurations have relatively high volume ratio of material inclusions. Moreover, temperature-dependent nonlinearity and material property uncertainties of random heterogeneous materials are modeled by a polynomial nonlinear model and Weibull probabilistic model, which can closely resemble actual material properties of heterogeneous materials. Secondly, an innovative stochastic three-scale homogenized method (STSHM) is developed to compute the macroscopic nonlinear thermal conductivity of random heterogeneous materials. Background meshing and filling techniques are devised to extract geometry and material features of random heterogeneous materials for establishing material databases. Thirdly, high-dimensional and highly nonlinear material features of material databases are preprocessed and reduced by wavelet decomposition technique. The neural networks are further employed to excavate the predictive models from dimension-reduced low-dimensional data. At the same time, advanced intelligent optimization algorithms are utilized to self-search the optimal network structure and learning rate for obtaining the optimal predictive models. Finally, the computational accuracy and efficiency of the presented approach are validated via various numerical experiments on realistic random composites.

Stochastic interpretable machine learning based multiscale modeling in thermal conductivity of Polymeric graphene-enhanced composites

We introduce an interpretable stochastic integrated machine learning based multiscale approach for the prediction of the macroscopic thermal conductivity in Polymeric graphene-enhanced composites (PGECs). This method encompasses the propagation of uncertain input parameters from the meso to macro scale, implemented through a foundational bottom-up multi-scale framework. In this context, Representative Volume Elements in Finite Element Modeling (RVE-FEM) are employed to derive the homogenized thermal conductivity. Besides, we employ two sets of techniques: Regression-tree-based methods (Random Forest and Gradient Boosting Machine) and Neural networks-based approaches (Artificial Neural Networks and Deep Neural Networks). To ascertain the relative influence of factors on output estimations, the SHapley Additive exPlanations (SHAP) algorithm is integrated. This interpretable machine learning methodology demonstrates strong alignment with published experimental data. It holds promise as an efficient and versatile tool for designing new composite materials tailored to applications involving thermal management.

Thermal conductivity of the cell wall of wood predicted by inverse analysis of 3D homogenization

This work proposes an accurate prediction of the directional thermal conductivity of wood at the cell wall scale. To that purpose, the strategy combines the physical characterization at the macroscopic scale, the description of the cellular morphology, homogenization calculations and an inverse analysis. The first step consisted in measuring the macroscopic thermal conductivity using the Transient Plane Source (TPS) method in the three material directions of spruce and poplar. The values range from 0.10 to 0.31 Wm−1K−1 depending on direction and species. The 3D morphology was acquired by micro-tomography and subsequently processed to generate a digital representation of the cellular structure. This representation is the input geometry in an homogenization procedure. Based on the results of this algorithm, the measurements of the solid fraction and the macroscopic values, we are able to determine the microscopic thermal conductivity of the cell wall λ0s, which is impossible to measure directly. λ0s was also found to be anisotropic with respective values of 0.6 and 1.0 Wm−1K−1 in the transverse plane and the longitudinal direction.

Mesoscopic trap and elastic properties of polyetherimide nanocomposites with improved energy storage performance

Polymer nanocomposites (PNCs) are important energy storage dielectrics for capacitors. However, the lack of quantitative research on the properties of mesoscopic scale conductivity, traps, and Young's modulus in interfacial regions between polyetherimide and nanofillers results in an unclear understanding of the relation between the structure and properties. Polyetherimide/Al2O3 PNCs are prepared by in-situ polymerization, and the dielectric, thermal, and mechanical properties are measured. The experiments show that the film doped with 3 wt% nanofillers has the highest resistivity, Young's modulus, breakdown strength, and energy storage density. Then, the mesoscale distributions of the conductivity and Young's modulus in interfacial regions are solved as the inversion problem of electric and stress fields, and the macroscopic conductivity and Young's modulus of PNCs are obtained by high-throughput simulations. The conductivity, trap, and Young's modulus distributions are obtained by comparing the simulation and experimental results. It shows that the molecular chains in interfacial regions are bounded by nanofillers to form a tighter aggregated structure, hindering the molecular motion. Accordingly, the charge transport between molecular chains slows down, the trap energy increases and the conductivity decreases. The tighter aggregated structure makes PNCs have higher breakdown strength, energy storage density, and lower energy loss.

Study on the Properties of Iron-based Alloys 17-4PH Powder Manufactured by Laser Additive Manufacturing

Background: Laser additive manufacturing has been used for surface repair and remanufacturing due to fast laser processing speed, high energy density, and dense microstructure. However, the properties of coating samples produced by laser additive manufacturing of ironbased alloys vary considerably, resulting in a large amount of data that needs to be accumulated and analyzed. Methods: The coating properties of iron-based alloy powders manufactured by laser cladding are studied. The optimal process parameters of the laser cladding are determined by exploring and comparing the macroscopic appearance, hardness, and conductivity of the junction of the cladding. Results: From the macroscopic appearance, when the ratio of the height to the width of the cladding layer is 3.615, the surface of the cladding layer has a smooth surface and is closely combined with the substrate. Conclusions: The hardness of the cladding layer is found to increase significantly, with an average hardness of 663 HV. Besides, it is found that the blackhead's hole causes the conductivity change. The ratio of the largest hole area to the smallest hole area is 8.29 times, and the depth ratio is 1.91 times, but the average resistance ratio is about 1.6 times.

Nanoelectrical Mapping of Carbon Nanofibers By Means of Peakforce Tunneling Atomic Force Microscopy

Electrospun polyacrylonitril (PAN) based carbon nanofibers (CNFs) are promising candidates for applications in energy conversion and storage. This originates from their electrical properties,[1] and their relatively easy manufacturing from abundant and cheap material.[2] However, the utilization of PAN CNF mats in devices such as electrolyzers is currently limited by their low conductivities. Advanced nanoelectrical characterization methods, such as Conductive Atomic Force Microscopy (C-AFM),[3] give insights into nanoscale limitations of the CNF’s conductivity. Revealing the limitations will help tailoring CNF’s conductivity. Thereby, a rational CNF design for applications in energy devices will be enabled.In this work, morphology, structure and nanoelectrical properties of electrospun PAN CNF mats, which were carbonized at different temperatures, are investigated by means of PeakForce Tunneling Atomic Force Microscopy (PF TUNA, Bruker). Next to the fundamentals of PF TUNA, topography and current maps of PAN CNF networks are presented and critically discussed. Topography maps reveal relatively homogeneous CNFs, which are in line with the homogeneous currents detected across the CNF network. The detected current signals indicate electrically well-interconnected fibers within the mats. Consequently, poor fiber interconnections or heterogeneities are not the limiting factor for an ideal macroscopic conductivity. High resolution maps of CNFs show that a large fraction of the surface area is non-conductive and that the fraction of conductive domains depends critically on the carbonization temperature. The nanoelectrical currents detected by PF TUNA on CNFs carbonized at different temperatures correlate strongly to the respective macroscopic conductivities measured by the four point method.The obtained results show that PF Tuna is a powerful tool to correlate nano- and macroscale conductivities. Future investigations, especially with CNFs containing integrated additives, will provide significant insights into nano- and macroscale relations and pave the way towards CNFs with desired electrical properties.Literature:[1] Gehring et al., RSC Adv. 2019, 9 (47), 27231–27241.[2] Kretzschmar et al., ChemSusChem 2020, 13 (12), 3180–3191.[3] Butnoi et al., JMR&T 2021, 12, 2153–2167.

Pristine SWCNT Based Cathode Materials for Solid-State Li-Sulfur Batteries

Lithium-Sulfur is a long-studied, but yet to be practically implemented battery chemistry which carries a promise of creating high energy density batteries. It uses cheap and abundant materials, but faces major technological challenges. One of the key challenges is that sulfur as the cathode material has negligible electronic conductivity (5x10-28 S/m), preventing electron exchange in the cathode during battery charge/discharge cycling. The other challenge is the significant (up to 80%) volume changes of sulfur during battery cycling that leads to cracking, delamination, voids formation and loss of electrical contacts, therefore limiting battery durability. To overcome these problems, we have developed a solution-free technology of manufacturing composite self-standing electrodes by in-situ mixing of the aerosolized phases and co-deposition [1]. The electrodes consist of sulfur and catholyte particles imbedded in a 3D network of as grown, pristine single-walled carbon nanotubes. The electrodes are flexible and do not include current collector metal foils or binders. They can be produced up to 1 mm thick, up to 200 mgsulfur/cm2 loading, or 330 mAh/cm2 capacity without the loss of the SWCNT network 3D structure. The macroscopic electrical conductivity of the material increases 30 orders of magnitude compared to that of sulfur, and reaches 102-104 S/m, depending on the SWCNT concentration. The presence of SWCNT network not only solves the electrical conductivity challenge but also durability issue, since flexible/stretchable SWCNT network accommodates the volume change of the sulfur active particles without disruption of the cathode integrity. The electrodes fabricated by this method are being studied as perspective cathodes for solid-state LiS batteries. A. Kuznetsov, S. Mohanty, E. Pigos, G. Chen, W. Cai, A.R. Harutyunyan, High energy density flexible and ecofriendly lithium-ion smart battery. Energy Storage Materials, 2023 (54) 266-275, ISSN 2405-8297, doi.org/10.1016/j.ensm.2022.10.023

Carbonization-Temperature-Dependent Electrical Properties of Carbon Nanofibers-From Nanoscale to Macroscale.

An exact understanding of the conductivity of individual fibers and their networks is crucial to tailor the overall macroscopic properties of polyacrylonitrile (PAN)-based carbon nanofibers (CNFs). Therefore, microelectrical properties of CNF networks and nanoelectrical properties of individual CNFs, carbonized at temperatures from 600 to 1000 °C, are studied by means of conductive atomic force microscopy (C-AFM). At the microscale, the CNF networks show good electrical interconnections enabling a homogeneously distributed current flow. The network's homogeneity is underlined by the strong correlation of macroscopic conductivities, determined by the four-point-method, and microscopic results. Both, microscopic and macroscopic electrical properties, solely depend on the carbonization temperature and the exact resulting fiber structure. Strikingly, nanoscale high-resolution current maps of individual CNFs reveal a large highly resistive surface fraction, representing a clear limitation. Highly resistive surface domains are either attributed to disordered highly resistive carbon structures at the surface or the absence of electron percolation paths in the bulk volume. With increased carbonization temperature, the conductive surface domains grow in size resulting in a higher conductivity. This work contributes to existing microstructural models of CNFs by extending them by electrical properties, especially electron percolation paths.