Realistic molecular models for saccharose-based carbons

Pikunic, Jorge; Gubbins, Keith E.; Pellenq, Roland J.‐M.; Cohaut, N.; Rannou, I.; Guet, J.M.; Clinard, C.; Rouzaud, Jean-Noël

doi:10.1016/s0169-4332(02)00039-9

Cited by 18 publications

(11 citation statements)

References 17 publications

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“…Further applications of the CRMC algorithm by Walters et al [12], Thomson and Gubbins [13] and Pikunic et al [14,15] yielded some success, while still demonstrating the presence of unphysical configurations in constructed models of disordered carbon, which appeared to be an intrinsic byproduct of the RMC algorithm [8,9]. The major improvement in this regard was the development of the Hybrid Reverse Monte Carlo (HRMC) technique [9,[16][17][18].…”

Section: ୀଵmentioning

confidence: 99%

Hybrid Reverse Monte Carlo simulation of amorphous carbon: Distinguishing between competing structures obtained using different modeling protocols

Farmahini

Bhatia

2015

Carbon

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Please cite this article as: Farmahini, A.H., Bhatia, S.K., Hybrid reverse monte carlo simulation of amorphous carbon: Distinguishing between competing structures obtained using different modelling protocols, Carbon (2014), doi: http://dx.doi.org/10.1016/j.carbon. 2014.11.013 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. ABSTRACTWe explore different multi-stage and multi-constraint modelling strategies using the Hybrid Reverse Monte Carlo (HRMC) technique to develop realistic models for the amorphous structure of silicon carbide derived-carbon, and investigate the effect of modelling parameters on the development of nano-structural features of the constructed models. It is shown that application of long simulations with slow thermal quench rate is essential for modelling of amorphous structures. Nevertheless, very slow quenching rates are shown to lead to the formation of configurations with large fraction of sp 2 carbon, lacking the level of disorder required to match structure-related experimental data. The predicted gas adsorption isotherms are very sensitive to the pore size distribution (PSD), thus the final structure must reasonably reproduce the total pore volume and pore size distribution of the experimental sample. The frequently-observed strong first peak of the DFT-based PSD obtained from argon adsorption is shown to be an artifact of argon inaccessibility. Pore accessibility analysis of the constructed models, as well as MD simulations of gas transport demonstrate that the HRMC constructed structures contain short-range structural anisotropy, however the models are successful in capturing the long range internal energy barriers of amorphous carbon for methane.

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Section: ୀଵmentioning

confidence: 99%

Hybrid Reverse Monte Carlo simulation of amorphous carbon: Distinguishing between competing structures obtained using different modeling protocols

Farmahini

Bhatia

2015

Carbon

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“…The disorientation of the graphene-type units causes the porosity of the material. According to simulations [12,13], the graphene layers are not perfectly planar, and curvatures might be produced by defects such as pentagons and heptagons in their structure. On the basis of various observations, Harris et al [14][15][16] proposed a model for the structure/nanotexture of non-graphitizable carbons that consists of discrete fragments of curved graphene sheets, in which pentagons and heptagons are dispersed randomly throughout networks of hexagons, as illustrated in Figure 4.5.…”

Section: Activated Carbon Powdersmentioning

confidence: 99%

“…13 Galvanostatic cycling (20 mA cm −2 ) of the cell (black) based on the 0.8 nm CDC in ACN + 1 mol l −1 Et 4 NBF 4 electrolyte. The positive (blue) and the negative (red) electrode potentials were recorded during the cell cycling.…”

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confidence: 99%

Electrical Double‐Layer Capacitors and Carbons for EDLCs

2013

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IntroductionAccumulators, electrochemical capacitors (ECs) (or supercapacitors), and capacitors are the three main electrochemical systems that can be used to store energy. As described in the so-called Ragone plot shown in Figure 4.1, each of these systems offers different characteristics in terms of specific power and energy. From a general point of view, accumulators, and more precisely Li-ion batteries, can store high energy densities (up to 180 Wh kg −1 for commercial products) with low-power densities (up to about 2 kW kg −1 ). Electrical double-layer capacitors (EDLCs) can deliver very high-power density (15 kW kg −1 ) with lower stored energy (5 Wh kg −1 ) than that of batteries.EDLCs can be applied in the case of stationary and mobile systems requiring highpower pulses: car acceleration, tramways, cranes, forklifts, emergency systems, and so on. Moreover, owing to their low time constant, they can quickly harvest energy, for example, during deceleration or braking of vehicles.Although EDLCs are able to provide high power with a long cycle life compared to accumulators, they suffer from a relatively low energy density. Therefore, the main ongoing research direction concerns the optimization of the existing electrode materials and the development of new materials.Industrial EDLCs are essentially based on nanoporous carbon electrodes. The reasons for this choice lie in the high availability, low cost, chemical inertness, and good electrical conductivity of activated carbons (ACs), as well as a high versatility of texture and surface functionality. For these reasons, this chapter presents the capacitance properties of carbon-based electrodes, showing optimization strategies playing on the structure/nanotexture of the carbon and on the nature of the electrolyte.

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“…6 was integrated between 0 and V 3 . Knowing C S and the total charge Q, the average capacitance C 2S was determined with relation (11), as well as the f(V) function. The capacitance C 2 of the first complementary branch was finally obtained by using the relation (12).…”

Section: Complementary Branches R 2 C 2 and R 3 Cmentioning

confidence: 99%