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REVIEW 4 major objections 5 minor 68 references

Mesoscopic Modeling of Structure-Transport Relationships in Dense CNT Films Containing Amorphous Carbon

T0 review · 4 major / 5 minor · reviewed 2026-08-04 · deepseek-v4-flash

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Pith's one-line read In dense carbon nanotube films, local bending and buckling boost electrical current while bundling suppresses it, a mesoscale simulation study finds.

desk verdict Useful mesoscale framework, but the headline morphology–current correlations are confounded by chirality-dependent junction conductances and need within-chirality checks before they can be trusted. read the letter →

arxiv 2510.22623 v3 pith:R3MHDC5S submitted 2025-10-26 physics.comp-ph cond-mat.mtrl-sci

Yvelin Giret , Filippo Federici Canova , Al-Moatasem El-Sayed , Thomas R. Durrant , Rahul Sen , Harry Luan , Gennadi Bersuker , Alexander L. Shluger
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David Z. Gao
This is my paper · ORCID
classification physics.comp-phcond-mat.mtrl-sci
keywords carbonnanotubefilmsmesoscalemodelingstructuraldescriptorselectricaltransportamorphousmemristivedevicescurvatureandbucklingbundling
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper tries to establish which structural features of high-density carbon nanotube (CNT) films actually control electrical current, using coarse-grained molecular dynamics to build dense mesoscale film models and a resistor-network nodal analysis to compute current through them. It claims that local mechanical distortion—measured by a curvature factor and a buckling factor—is strongly positively correlated with total current, while bundling of tubes into thick parallel groups is strongly negatively correlated. If true, film morphology, rather than just density or tube chirality, becomes a controllable lever for designing conductive CNT films for nanoelectronics and memory devices. The paper also argues that amorphous carbon inclusions alter morphology in a configuration-dependent way, sometimes enhancing curvature and connectivity and sometimes clustering into plaques that reduce conduction.

What carries the argument

The central object is a set of structural descriptors computed from coarse-grained CNT film models built with a mesoscopic tubular force field, where each CNT is a chain of 1 nm beads. The load-bearing descriptors are the curvature factor C (average geometric curvature of CNTs excluding kinks), the buckling factor B (fraction of beads where adjacent segments misalign by more than 2.08 degrees), and the bundling factor beta (average number of connected neighboring segments per segment, derived from a graph of local segment contacts with a six-neighbor cap). Current is computed by solving Kirchhoff's law on a sparse conductance matrix, with intratube conductance set to the ballistic 2G0 value

What would settle it

A direct experimental measurement comparing the conductivity of two dense CNT films with identical density, chirality distribution, and thickness—one deliberately compressed to induce buckling and curvature, the other processed to keep tubes straight and unbundled—would show, if the claim is wrong, that the more buckled film does not conduct better. Alternatively, a mesoscale simulation rerun with junction conductances drawn from a distribution spanning an order of magnitude around the fixed values, rather than fixed, would reveal whether the r = 0.93 correlation between buckling and current s

Watch

Extended reading notes

Core claim

The central discovery is a set of quantitative structure-transport correlations in dense CNT films. Across 32 simulated films varying chirality, tube length, density, layer count, and amorphous carbon content, the curvature factor and buckling factor correlate strongly positively with total current (Pearson r = 0.87 and 0.93, respectively, and even higher with current through the one-third metallic network), while the bundling factor correlates negatively with total current (r = -0.66). The authors interpret this as local mechanical distortion enhancing intertube contact and reducing junction resistance along conductive paths, whereas bundling reduces the number of effective intertube juncti

Load-bearing premise

The whole analysis assumes that real current through a dense CNT film is well approximated by an Ohmic resistor network in which tube interiors are ballistic conductors, tube-tube junctions have fixed conductances taken from a single quantum-transport calculation, and amorphous carbon carries no current.

Editorial extensions

If this is right

  • Film morphology can be treated as a design variable: inducing curvature and buckling, for example through compression protocols, should increase current output in dense CNT films.
  • Bundling should be minimized during fabrication, because thicker bundles correlate with reduced total current and weaker effective connectivity.
  • Amorphous carbon content can serve as a morphological tuning agent, but its effect is nonmonotonic: it can raise curvature and connectivity at some concentrations while forming plaques that suppress conduction at others.
  • Multi-layer stacking tends to reduce current in dense films, consistent with added junctions along percolation paths, so single-layer films may be preferable for high conductivity.
  • A limited set of descriptors—curvature, buckling, bundling, and effective connectivity—can capture most of the variance in transport behavior, enabling fast screening of candidate film morphologies.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If the morphology-current correlations hold experimentally, mechanical conditioning of CNT films, such as controlled compression or bending, could become a practical post-fabrication route to tune resistance states in CNT-based memory cells.
  • The strong curvature-current correlation suggests a possible feedback mechanism in memristive switching: local Joule heating or mechanical stress could deform the network, changing conductance, which the present static correlation analysis would not capture but which could matter under cycling.
  • The model treats amorphous carbon as non-conducting; if aC participates in tunneling or thermally activated conduction, the negative bundling trend might weaken, and the aC-content dependence could become even less predictable.
  • A testable extension would be to vary only the aC particle size and density while holding all other parameters fixed, to see whether the nonmonotonic current response is driven by morphology change or by the electrical properties of the inclusions themselves.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. This paper uses coarse-grained molecular dynamics to construct 32 dense (0.3 and 0.6 g·cm−3) CNT film models with controlled chirality, tube length, number of layers, and amorphous-carbon (aC) content, then computes structural descriptors—orientation, buckling B, curvature C, bundling β, effective connectivity C_eff, electrode contacts—and solves a linear resistor network with fixed intratube and intertube conductances to obtain the total current I_tot and the one-third-metallic current I_1/3. Pearson and Spearman correlations and PCA are used to link morphology to transport. The headline claim is that C and B correlate strongly positively with I_tot (r = 0.87 and r = 0.93) while β correlates negatively (r = −0.66), and that aC content acts in a nonmonotonic, configuration-dependent way.

Significance. If the morphology–transport correlations are robust, the paper offers a practical design principle for CNT-based memristive films: introducing controlled mechanical distortion and reducing bundling can enhance conductivity, and aC may be used as a morphological tuning agent. A notable strength is that the complete descriptor and current table (Table S1) is provided, so each reported correlation can be independently recalculated; additionally, the authors use multiple independent statistical views (Pearson, Spearman, PCA) and are unusually explicit about the exploratory nature of the dataset. The main weakness is that the headline correlations are computed across structures that differ simultaneously in chirality, tube length, density, and aC content, while the junction conductances themselves are chirality-dependent inputs, so the causal reading is not yet adequately isolated.

major comments (4)
  1. [Section 2.5, Eq. (10); Fig. 4] The reported correlations of C and B with I_tot (r = 0.87 and 0.93) are computed across all 32 structures, but the current calculation uses chirality-dependent junction conductances: g_inter^{(16,0)} ≈ 4.6×10⁻⁶ S and g_inter^{(32,0)} ≈ 5.8×10⁻⁷ S, an ~8× difference. The high-C and high-B structures are predominantly (16,0) tubes, so the raw correlations may partly reflect the intrinsic junction-conductance advantage of (16,0) rather than a causal morphology effect. Please report within-chirality correlations, partial correlations controlling for chirality (or for the numeric value of g_inter), and/or a regression that includes a chirality dummy. A quick check of Table S1 suggests the C–I_tot correlation may persist within each chirality, but this needs to be shown explicitly because the central claim of the paper rests on it.
  2. [Section 3 (correlation analysis); Table S1] The correlations are computed from N = 32 structures with only one realization per condition, no error bars, and no significance or confidence intervals. Several of the 32 samples share systematic design parameters (e.g., all (16,0) high-density aC samples are single-layer), so the effective number of independent samples is much smaller than 32. Please report bootstrap or permutation-based confidence intervals for the key r values and state p-values that account for the multiple comparisons being made. At minimum, temper the conclusion that the paper 'demonstrates' the relationships; with the current data, 'suggests' is more appropriate.
  3. [Section 3 (structures #30/#31); SI Section S2] Structures #30 and #31 required a different compression protocol and the authors state they cannot explain the resulting morphology difference, yet these structures are retained in all correlation and PCA analyses. Because compression history is part of the protocol that produced the morphology, and because #30/#31 are high-aC (32,0) samples, their inclusion directly affects the aC-related correlations and the scatter in Fig. 5. Please provide a sensitivity analysis excluding #30 and #31, and either justify their inclusion on protocol-equivalence grounds or treat them as separate. This is load-bearing for the conclusions about aC's nonmonotonic role.
  4. [Section 2.5; SI Table S1] The absolute currents and, to a lesser extent, the correlation structure depend on the resistor-network assumptions: purely Ohmic junction conductances from a single prior DFTB/NEGF study, ballistic intratube conduction, non-conducting aC, and a hard connectivity cutoff. There is no experimental current or conductance validation for the dense-film geometry, and the paper itself acknowledges that real films may involve tunneling/hopping. This is not by itself an error, but the manuscript should state more explicitly that the reported 'strong correlations' are conditional on this transport model. A comparison with measured sheet/contact resistance for CNT films, even as an order-of-magnitude check, would considerably strengthen the claim that the descriptors predict experimentally relevant transport trends.
minor comments (5)
  1. [Title/header] The title in the arXiv metadata ('Mesoscopic Modeling of Structure-Transport Relationships in Dense CNT Films Containing Amorphous Carbon') differs from the title in the full text ('...for Memristive Device Applications'). Please ensure the final version uses one consistent title.
  2. [Eq. (8)] The effective connectivity C_eff is described as a 'weighted average', but Eq. (8) is a simple average of 1/n_junc². Either rename it 'average inverse-square junction count' or define the weights explicitly.
  3. [Fig. 4] The correlation matrices are difficult to read in the printed text because the row/column labels are compressed and some entries are duplicated (e.g., the Spearman matrix row for m and Cel appear identical). Please use a larger, clearly labeled layout and verify that the duplicated rows are not a typo.
  4. [Section 2.5 (Eq. 11–12)] In Eq. (12), I_ij = G_ij(V_i − V_j) uses the off-diagonal conductance matrix entries. Since G_ij enters Kirchhoff's law with a negative sign in the assembled matrix, the sign convention should be stated to avoid confusion.
  5. [SI S2] The compression protocol states that auxiliary forces are proportional to mass. It would be helpful to report the exact force values and protocol parameters for each structure, since morphology is protocol-dependent and structures #30/#31 differ.

Circularity Check

1 steps flagged · score 2.0 of 10

No significant circularity in the central morphology–current correlations; the C/B/β descriptors are geometry-only and the currents come from a separately parameterized resistor network. One supporting connectivity descriptor is partly defined from the same current solution, and the chirality-dependent junction conductances are imported from the authors' prior non-fitting study (a confound, not a

  1. self definitional [Section 2.4 (Eq. 8) and Section 3 (Results, correlation discussion)]
    "Finally, to characterize the electrical connectivity of the network, we analyze the ensemble of current-carrying paths (see section 2.5). ... Ceff = 1/Npaths sum_paths 1/n_junc^2 ... The effective connectivity Ceff exhibits a moderate positive correlation with both currents (r=0.56 with I1/3 and r=0.39 with Itot), indicating that global network connectivity contributes to transport, though to a lesser extent than local geometric factors such as curvature and buckling."

    Ceff is not an independent structural descriptor: it is computed from the same solved current-carrying paths whose total current is Itot. In the linear Ohmic network, the current through a path with n_junc equal junction conductances scales approximately as 1/n_junc, so a path weighting 1/n_junc^2 makes Ceff roughly track Itot^2. The reported Ceff–current correlation is therefore partly fixed by the definitions rather than by an independent structure-transport relationship. This is a supporting descriptor only; the headline C/B correlations use geometry-only descriptors and are not defined from the current solution.

full rationale

The paper's central derivation is a two-stage pipeline. First, coarse-grained MD with a literature mesoscopic force field (Volkov/Zhigilei) and aC parameters fitted to AIREBO profiles produces independent geometries; the structural descriptors C, B, and β are defined purely from bead coordinates and segment connectivity, not from current. Second, a nodal-analysis resistor network computes Itot using Eq. (9)–(10), with intratube conductance from the ballistic 2G0 limit and intertube conductance from the authors' prior DFTB/NEGF junction study (Ref. 52). Neither stage fits C, B, or β to Itot, and the central correlations are emergent from the simulated geometries. The use of Ref. 52 is a parameter transfer from a separate atomistic calculation, not a fit to the present target result, so the self-citation does not by itself make the derivation circular. The main caveat is a chirality confound: the ~8× chirality-dependent g_inter input (Eq. 10) is correlated with chirality, and chirality also correlates with C and B, so the raw r=0.87/0.93 values may overstate the causal role of morphology. That is an inference/confound concern, not a self-referential reduction, and the paper itself notes the analysis is exploratory. Overall, aside from the minor by-construction element in Ceff, the central morphology–current claims are not circular.

Assumptions & free parameters 4 free parameters · 5 assumptions · 0 invented entities

No new physical particles or forces are postulated. Amorphous carbon is modeled as parameterized spherical beads representing experimentally known aC inclusions. The load-bearing inputs are the mesoscale force field, the hand-chosen connectivity thresholds, the aC LJ parameters, and the junction conductances from prior work.

free parameters (4)
  • aC bead Lennard-Jones parameters (ε, σ) = aC-aC: ε=0.180 eV, σ=1.327 nm; aC-CNT(16,0): 0.386 eV, 1.420 nm; aC-CNT(32,0): 0.440 eV, 2.051 nm
    Fitted to averaged AIREBO interaction energy profiles for aC spheres (Table 1); these parameters control how aC particles distribute within the film and thus directly influence the aC-related morphological trends.
  • CNT junction conductances g_inter = g(16,0)_inter ≈ 4.6083e-6 S; g(32,0)_inter ≈ 5.7803e-7 S
    Fixed inputs from the authors' prior DFTB/NEGF study (Eq. 10); every computed current and current–descriptor correlation depends on these values.
  • Connectivity and descriptor thresholds = r_c = 2r_CNT + vdW + 0.15 nm; electrode cutoff 1.0 nm; θ_B = 2.08°; bundling distance 1.5 d0; axial overlap 0.9 nm; max
    Hand-chosen cutoffs define which segments are 'connected', directly shaping the bundling metric β, effective connectivity C_eff, and the resistor-network paths.
  • aC particle geometry = sphere radius 0.6 nm, density 2.5 g/cm³
    Chosen to represent amorphous carbon inclusions; particle size/density affect packing and aC morphology, and hence the structural descriptors.
assumptions (5)
  • domain assumption The Volkov–Zhigilei mesoscopic tubular potential and 1 nm beads faithfully capture CNT bending, buckling, and nonbonded interactions in dense films.
    Section 2.2; all film morphologies and structural descriptors derive from this force field.
  • domain assumption Single-walled CNTs can represent experimentally multi-walled CNT films.
    Section 2.1 states simulated films are single-walled while experimental films are predominantly multi-walled; this discrepancy is acknowledged but not resolved.
  • domain assumption CNTs are Ohmic conductors with intratube conductance 2(Nnodes−1)G0 and mean free path exceeding 1 µm; semiconducting tubes are treated as non-conductive in the 1/3-metallic case.
    Section 2.5; this transport model neglects thermally activated hopping, tunneling, and semiconducting barrier effects that may be present in real dense films.
  • domain assumption Amorphous carbon particles do not contribute to electrical current.
    Section 2.5: 'The possible contributions of aC particles to the total film conductivity are neglected in the present analysis.'
  • domain assumption Experimental length and diameter distributions from Refs. 27/44 are representative of the target NRAM films.
    Section 2.1 and SI S1; these distributions motivate the chosen lengths (15/40/100 nm) and chiralities ((16,0),(32,0)) and anchor the simulation matrix.

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Cite this review

Pith. "Pith review of Mesoscopic Modeling of Structure-Transport Relationships in Dense CNT Films Containing Amorphous Carbon." pith.science (2026). https://pith.science/paper/R3MHDC5S

@misc{pith2026251022623,
  author       = {Pith},
  title        = {Pith review of: Mesoscopic Modeling of Structure-Transport Relationships in Dense CNT Films Containing Amorphous Carbon},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/R3MHDC5S}},
  note         = {Machine review of arXiv:2510.22623}
}
read the original abstract

Carbon nanotube (CNT) films are widely considered as prospective building blocks for advanced electronic and nanostructured materials. In particular, electrical transport in high-density CNT films results from a complex interplay between network morphology and CNT connectivity, which remains challenging to characterize quantitatively. To identify the structural parameters that govern the electrical current in CNT films, we employed coarse-grained molecular dynamics to construct dense mesoscale CNT film models that include CNTs with different chiralities and lengths. The effects of CNT geometrical features on the film morphologies were quantified by devising a set of structural descriptors and analyzing their mutual correlations. The impact of varying the concentration of amorphous carbon (aC) inclusions on the film structure was assessed. Finally, we employed a nodal analysis framework to compute the electrical current across the networks and correlate the charge transport characteristics to the underlying structural descriptors. The current is found to be enhanced in films that exhibit high curvature and buckling, low bundling, and strong connectivity. We discuss how the presence of aC inclusions modifies these morphological and current characteristics. This work provides a mesoscale modeling framework for modeling structure-transport relationships in dense CNT films and highlights the role of morphological descriptors in guiding the interpretation of electrical transport in complex nanostructured networks.

Figures

Figures reproduced from arXiv: 2510.22623 by the authors.

Figure 1
Figure 1. Atomistic models of aC (a) and CNT (32,0) segment (b) used to compute the Lennard-Jones parameters describing their interactions. entity pair ϵ [eV] σ [nm] aC–aC 0.180 1.327 aC–CNT(16,0) 0.386 1.420 aC–CNT(16,0)† 0.193 1.420 aC–CNT(32,0) 0.440 2.051 aC–CNT(32,0)† 0.220 2.051 [PITH_FULL_IMAGE:figures/full_fig_p007_1.png] view at source ↗
Figure 2
Figure 2. Structures of single-layer films with a density of 0.6 g [PITH_FULL_IMAGE:figures/full_fig_p010_2.png] view at source ↗
Figure 3
Figure 3. Structures of single-layer films with a density of 0.6 g [PITH_FULL_IMAGE:figures/full_fig_p014_3.png] view at source ↗
Figures from the paper (2 more)
Figure 4
Figure 4. Figure 4: Correlation matrices between the different descriptors and the currents. [PITH_FULL_IMAGE:figures/full_fig_p018_4.png]
Figure 5
Figure 5. Figure 5: Principal Component Analysis of structural descriptors. Each point represents one [PITH_FULL_IMAGE:figures/full_fig_p021_5.png]

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Works this paper leans on

68 extracted references · 1 linked inside Pith

  1. [1]

    Carbon-based electronics

    Avouris, P.; Chen, Z.; Perebeinos, V. Carbon-based electronics. Nature Nanotechnology 2007, 2, 605--615

    2007

  2. [2]

    S.; Gr \"u ner, G

    Hu, L.; Hecht, D. S.; Gr \"u ner, G. Carbon Nanotube Thin Films: Fabrication, Properties, and Applications. Chemical Reviews 2010, 110, 5790--5844

    2010

  3. [3]

    H.; Lee, Y

    Chae, S. H.; Lee, Y. H. Carbon nanotubes and graphene towards soft electronics. Nano Convergence 2014, 1, 15

    2014

  4. [4]

    Rao, R. et al. Carbon nanotubes and related nanomaterials: critical advances and challenges for synthesis toward mainstream commercial applications. ACS Nano 2018, 12, 11756--11784

    2018

  5. [5]

    A.; Mariatti, M

    Khan, J.; Momin, S. A.; Mariatti, M. A review on advanced carbon-based thermal interface materials for electronic devices. Carbon 2020, 168, 65--112

    2020

  6. [6]

    S.; Krupke, R.; Peng, L.-M

    Maruyama, S.; Arnold, M. S.; Krupke, R.; Peng, L.-M. Physics and applications of nanotubes. Journal of Applied Physics 2022, 131, 080401

    2022

  7. [7]

    T.; Chembattammal, R.; Mishra, S.; Sahoo, S.; Dave, H.; Karthik, S.; Jaisinghani, K

    Bharadwaj, S.; Thovelil, R. T.; Chembattammal, R.; Mishra, S.; Sahoo, S.; Dave, H.; Karthik, S.; Jaisinghani, K. Transitioning from Silicon to Carbon Nanotube based transistors. arXiv preprint arXiv:2509.00947 2025,

    arXiv 2025

  8. [8]

    Remarkable diversity of carbon--carbon bonds: structures and properties of fullerenes, carbon nanotubes, and graphene

    Dinadayalane, T.; Leszczynski, J. Remarkable diversity of carbon--carbon bonds: structures and properties of fullerenes, carbon nanotubes, and graphene. Structural Chemistry 2010, 21, 1155--1169

    2010

Show all 68 references
  1. [9]

    Carbon nanotube electronics--moving forward

    Wang, C.; Takei, K.; Takahashi, T.; Javey, A. Carbon nanotube electronics--moving forward. Chemical Society Reviews 2013, 42, 2592--2609

    2013

  2. [10]

    Kumar, A.; Sharma, K.; Dixit, A. R. A review on the mechanical properties of polymer composites reinforced by carbon nanotubes and graphene. Carbon Letters 2021, 31, 149--165

    2021

  3. [11]

    Strength of carbon nanotubes depends on their chemical structures

    Takakura, A.; Beppu, K.; Nishihara, T.; Fukui, A.; Kozeki, T.; Namazu, T.; Miyauchi, Y.; Itami, K. Strength of carbon nanotubes depends on their chemical structures. Nature Communications 2019, 10, 3040

    2019

  4. [12]

    J.; Shen, M.; Keblinski, P

    Evans, W. J.; Shen, M.; Keblinski, P. Inter-tube thermal conductance in carbon nanotubes arrays and bundles: Effects of contact area and pressure. Applied Physics Letters 2012, 100, 261908

    2012

  5. [13]

    Quantitative study of bundle size effect on thermal conductivity of single-walled carbon nanotubes

    Feng, Y.; Inoue, T.; An, H.; Xiang, R.; Chiashi, S.; Maruyama, S. Quantitative study of bundle size effect on thermal conductivity of single-walled carbon nanotubes. Applied Physics Letters 2018, 112, 191904

    2018

  6. [14]

    One-directional thermal transport in densely aligned single-wall carbon nanotube films

    Yamaguchi, S.; Tsunekawa, I.; Komatsu, N.; Gao, W.; Shiga, T.; Kodama, T.; Kono, J.; Shiomi, J. One-directional thermal transport in densely aligned single-wall carbon nanotube films. Applied Physics Letters 2019, 115, 223104

    2019

  7. [15]

    I.; Chiashi, S.; Maruyama, S

    Wang, P.; Feng, Y.; Xiang, R.; Inoue, T.; Anisimov, A.; Kauppinen, E. I.; Chiashi, S.; Maruyama, S. Phenomenological model of thermal transport in carbon nanotube and hetero-nanotube films. Nanotechnology 2021, 32, 205708

    2021

  8. [16]

    Nanoscale Mapping of Carrier Mobilities in the Ballistic Transports of Carbon Nanotube Networks

    Kim, J.; Oh, Y.; Shin, J.; Yang, M.; Shin, N.; Shekhar, S.; Hong, S. Nanoscale Mapping of Carrier Mobilities in the Ballistic Transports of Carbon Nanotube Networks. ACS Nano 2022, 16, 21626--21635

    2022

  9. [17]

    Controlled Assembly of Single-Walled Carbon Nanotubes into Thin Films and Their Applications

    Chen, Y.; Qiu, Z.; Yao, Y.; Liu, Y.; Zhang, Z.; Li, Y. Controlled Assembly of Single-Walled Carbon Nanotubes into Thin Films and Their Applications. Advanced Functional Materials 2025, 35, 2426084

    2025

  10. [18]

    Electrical properties of carbon nanotubes: From individual to assemblies

    Xiang, Y.; Zhang, L.; Liu, C. Electrical properties of carbon nanotubes: From individual to assemblies. Nanomaterials 2025, 15, 1165

    2025

  11. [19]

    Spark plasma sintering and thermal conductivity of carbon nanotube bulk materials

    Zhang, H.; Li, J.-F.; Yao, K.; Chen, L. Spark plasma sintering and thermal conductivity of carbon nanotube bulk materials. Journal of Applied Physics 2005, 97, 114310

    2005

  12. [20]

    E.; Guthy, C.; Zhang, M.; Fang, S.; Zakhidov, A

    Aliev, A. E.; Guthy, C.; Zhang, M.; Fang, S.; Zakhidov, A. A.; Fischer, J. E.; Baughman, R. H. Thermal transport in MWCNT sheets and yarns. Carbon 2007, 45, 2880--2888

    2007

  13. [21]

    J.; Gaillard, J.; Tritt, T

    Yang, K.; He, J.; Puneet, P.; Su, Z.; Skove, M. J.; Gaillard, J.; Tritt, T. M.; Rao, A. M. Tuning electrical and thermal connectivity in multiwalled carbon nanotube buckypaper. Journal of Physics: Condensed Matter 2010, 22, 334215

    2010

  14. [22]

    B.; White, M

    P \"o hls, J.-H.; Johnson, M. B.; White, M. A.; Malik, R.; Ruff, B.; Jayasinghe, C.; Schulz, M. J.; Shanov, V. Physical properties of carbon nanotube sheets drawn from nanotube arrays. Carbon 2012, 50, 4175--4183

    2012

  15. [23]

    High-density carbon nanotube buckypapers with superior transport and mechanical properties

    Zhang, L.; Zhang, G.; Liu, C.; Fan, S. High-density carbon nanotube buckypapers with superior transport and mechanical properties. Nano Letters 2012, 12, 4848--4852

    2012

  16. [24]

    Thermal conductivity of carbon nanotube networks: A review

    Kumanek, B.; Janas, D. Thermal conductivity of carbon nanotube networks: A review. Journal of Materials Science 2019, 54, 7397--7427

    2019

  17. [25]

    H.; Das, A

    Rathanasamy, R.; Sahoo, S.; Lee, J. H.; Das, A. K.; Somasundaram, M.; Palaniappan, S. K.; Sivaraj, S. Carbon-based Multi-layered Films for Electronic Application: A Review. Journal of Electronic Materials 2021, 50, 1845--1892

    2021

  18. [26]

    Y.; Cheung, C.-L.; Lieber, C

    Rueckes, T.; Kim, K.; Joselevich, E.; Tseng, G. Y.; Cheung, C.-L.; Lieber, C. M. Carbon nanotube-based nonvolatile random access memory for molecular computing. Science 2000, 289, 94--97

    2000

  19. [27]

    C.; Rueckes, T.; Cleveland, L

    Gilmer, D. C.; Rueckes, T.; Cleveland, L. NRAM: a disruptive carbon-nanotube resistance-change memory. Nanotechnology 2018, 29, 134003

    2018

  20. [28]

    V.; Wei, C.; Srivastava, D

    Zhigilei, L. V.; Wei, C.; Srivastava, D. Mesoscopic model for dynamic simulations of carbon nanotubes. Physical Review B 2005, 71, 165417

    2005

  21. [29]

    N.; Zhigilei, L

    Volkov, A. N.; Zhigilei, L. V. Scaling laws and mesoscopic modeling of thermal conductivity in carbon nanotube materials. Physical Review Letters 2010, 104, 215902

    2010

  22. [30]

    N.; Zhigilei, L

    Volkov, A. N.; Zhigilei, L. V. Mesoscopic interaction potential for carbon nanotubes of arbitrary length and orientation . The Journal of Physical Chemistry C 2010, 114, 5513--5531

    2010

  23. [31]

    N.; Zhigilei, L

    Volkov, A. N.; Zhigilei, L. V. Heat conduction in carbon nanotube materials: Strong effect of intrinsic thermal conductivity of carbon nanotubes. Applied Physics Letters 2012, 101, 043113

    2012

  24. [32]

    Percolating conduction in finite nanotube networks

    Kumar, S.; Murthy, J.; Alam, M. Percolating conduction in finite nanotube networks. Physical Review Letters 2005, 95, 066802

    2005

  25. [33]

    Computational study of geometry-dependent resistivity scaling in single-walled carbon nanotube films

    Behnam, A.; Ural, A. Computational study of geometry-dependent resistivity scaling in single-walled carbon nanotube films. Physical Review B 2007, 75, 125432

    2007

  26. [34]

    N.; Zhigilei, L

    Volkov, A. N.; Zhigilei, L. V. Structural stability of carbon nanotube films: The role of bending buckling. ACS Nano 2010, 4, 6187--6195

    2010

  27. [35]

    N.; Shiga, T.; Nicholson, D.; Shiomi, J.; Zhigilei, L

    Volkov, A. N.; Shiga, T.; Nicholson, D.; Shiomi, J.; Zhigilei, L. V. Effect of bending buckling of carbon nanotubes on thermal conductivity of carbon nanotube materials. Journal of Applied Physics 2012, 111, 053501

    2012

  28. [36]

    A distinct element method for large scale simulations of carbon nanotube assemblies

    Ostanin, I.; Ballarini, R.; Potyondy, D.; Dumitricǎ, T. A distinct element method for large scale simulations of carbon nanotube assemblies. Journal of the Mechanics and Physics of Solids 2013, 61, 762--782

    2013

  29. [37]

    K.; Dumitrica, T

    Wang, Y.; Drozdov, G.; Hobbie, E. K.; Dumitrica, T. Excluded Volume Approach for Ultrathin Carbon Nanotube Network Stabilization: A Mesoscopic Distinct Element Method Study. ACS Applied Materials & Interfaces 2017, 9, 13611--13618

    2017

  30. [38]

    Toward large scale modeling of carbon nanotube systems with the mesoscopic distinct element method

    Ostanin, I.; Zhilyaev, P.; Petrov, V.; Dumitrica, T.; Eibl, S.; Ruede, U.; Kuzkin, V. Toward large scale modeling of carbon nanotube systems with the mesoscopic distinct element method. Letters on Materials 2018, 8, 240--245

    2018

  31. [39]

    Mesoscopic friction and network morphology control the mechanics and processing of carbon nanotube yarns

    Wang, Y.; Xu, H.; Drozdov, G.; Dumitrica, T. Mesoscopic friction and network morphology control the mechanics and processing of carbon nanotube yarns. Carbon 2018, 139, 94--104

    2018

  32. [40]

    S.; Yun, C

    Lee, H. S.; Yun, C. H.; Kim, S. K.; Choi, J. H.; Lee, C. J.; Jin, H. J.; Lee, H.; Park, S. J.; Park, M. Percolation of two-dimensional multiwall carbon nanotube networks . Applied Physics Letters 2009, 95, 134104

    2009

  33. [41]

    K.; Banna, A

    Wittmaack, B. K.; Banna, A. H.; Volkov, A. N.; Zhigilei, L. V. Mesoscopic modeling of structural self-organization of carbon nanotubes into vertically aligned networks of nanotube bundles. Carbon 2018, 130, 69--86

    2018

  34. [42]

    K.; Volkov, A

    Wittmaack, B. K.; Volkov, A. N.; Zhigilei, L. V. Mesoscopic modeling of the uniaxial compression and recovery of vertically aligned carbon nanotube forests. Composites Science and Technology 2018, 166, 66--85

    2018

  35. [43]

    K.; Volkov, A

    Wittmaack, B. K.; Volkov, A. N.; Zhigilei, L. V. Phase transformation as the mechanism of mechanical deformation of vertically aligned carbon nanotube arrays: Insights from mesoscopic modeling. Carbon 2019, 143, 587--597

    2019

  36. [44]

    K.; Buffat, S.; Gilmer, D

    Sen, R.; Kocab, T.; Black, J.; McDermott, J.; Pal, S. K.; Buffat, S.; Gilmer, D. C.; Rueckes, T. Engineering nanomaterials and nanostructures for electronic applications: A case study of carbon nanotubes for memory devices. 2020 4th IEEE Electron Devices Technology & Manufactu...

    2020

  37. [45]

    A.; Boies, A

    Kateris, N.; Kloza, P.; Qiao, R.; Elliott, J. A.; Boies, A. M. From collisions to bundles: An adaptive coarse-grained model for the aggregation of high-aspect-ratio carbon nanotubes. The Journal of Physical Chemistry C 2020, 124, 8359--8370

    2020

  38. [46]

    Carbon Nanotube Alignment within Macrostructures

    Kloza, P. Carbon Nanotube Alignment within Macrostructures. Doctoral dissertation 2024,

    2024

  39. [47]

    P.; Aktulga, H

    Thompson, A. P.; Aktulga, H. M.; Berger, R.; Bolintineanu, D. S.; Brown, W. M.; Crozier, P. S.; in 't Veld, P. J.; Kohlmeyer, A.; Moore, S. G.; Nguyen, T. D.; Shan, R.; Stevens, M. J.; Tranchida, J.; Trott, C.; Plimpton, S. J. LAMMPS - a flexible simulation tool for particle-b...

    2022

  40. [48]

    J.; Tutein, A

    Stuart, S. J.; Tutein, A. B.; Harrison, J. A. A reactive potential for hydrocarbons withintermolecular interactions. The Journal of Chemical Physics 2000, 112, 6472

    2000

  41. [49]

    Microstructural origin of resistance--strain hysteresis in carbon nanotube thin film conductors

    Jin, L.; Chortos, A.; Lian, F.; Pop, E.; Linder, C.; Bao, Z.; Cai, W. Microstructural origin of resistance--strain hysteresis in carbon nanotube thin film conductors. Proceedings of the National Academy of Sciences 2018, 115, 1986--1991

    2018

  42. [50]

    W.; McEuen, P

    Park, J.-Y.; Rosenblatt, S.; Yaish, Y.; Sazonova, V.; \"U st \"u nel, H.; Braig, S.; Arias, T.; Brouwer, P. W.; McEuen, P. L. Electron-phonon scattering in metallic single-walled carbon nanotubes. Nano Letters 2004, 4, 517--520

    2004

  43. [51]

    Frank, S.; Poncharal, P.; Wang, Z.; Heer, W. A. d. Carbon nanotube quantum resistors. Science 1998, 280, 1744--1746

    1998

  44. [52]

    R.; El-Sayed, A.-M.; Gao, D

    Durrant, T. R.; El-Sayed, A.-M.; Gao, D. Z.; Rueckes, T.; Bersuker, G.; Shluger, A. L. Atomistic Modeling of the Electrical Conductivity of Single-Walled Carbon Nanotube Junctions. Physica Status Solidi (RRL)--Rapid Research Letters 2022, 16, 2200118

    2022

  45. [53]

    Buldum, A.; Lu, J. P. Contact resistance between carbon nanotubes. Physical Review B 2001, 63, 161403

    2001

  46. [54]

    Transport mechanisms in metallic and semiconducting single-wall carbon nanotube networks

    Yanagi, K.; Udoguchi, H.; Sagitani, S.; Oshima, Y.; Takenobu, T.; Kataura, H.; Ishida, T.; Matsuda, K.; Maniwa, Y. Transport mechanisms in metallic and semiconducting single-wall carbon nanotube networks. ACS Nano 2010, 4, 4027--4032

    2010

  47. [55]

    Ultrahigh-performance transparent conductive films of carbon-welded isolated single-wall carbon nanotubes

    Jiang, S.; Hou, P.-X.; Chen, M.-L.; Wang, B.-W.; Sun, D.-M.; Tang, D.-M.; Jin, Q.; Guo, Q.-X.; Zhang, D.-D.; Du, J.-H., et al. Ultrahigh-performance transparent conductive films of carbon-welded isolated single-wall carbon nanotubes. Science Advances 2018, 4, eaap9264

    2018

  48. [56]

    Noise reduction in speech processing; Springer Science & Business Media, 2009; Vol

    Benesty, J.; Chen, J.; Huang, Y.; Cohen, I. Noise reduction in speech processing; Springer Science & Business Media, 2009; Vol. 2; pp 37--40

    2009

  49. [57]

    Conductivity scaling with bundle length and diameter in single walled carbon nanotube networks

    Hecht, D.; Hu, L.; Gr \"u ner, G. Conductivity scaling with bundle length and diameter in single walled carbon nanotube networks. Applied Physics Letters 2006, 89, 133112

    2006

  50. [58]

    E.; De, S.; Blighe, F.; Nicolosi, V.; Pereira, L

    Lyons, P. E.; De, S.; Blighe, F.; Nicolosi, V.; Pereira, L. F. C.; Ferreira, M. S.; Coleman, J. N. The relationship between network morphology and conductivity in nanotube films. Journal of Applied Physics 2008, 104, 044302

    2008

  51. [59]

    A.; Luo, W.; Douglas, J

    Simien, D.; Fagan, J. A.; Luo, W.; Douglas, J. F.; Migler, K.; Obrzut, J. Influence of nanotube length on the optical and conductivity properties of thin single-wall carbon nanotube networks. ACS Nano 2008, 2, 1879--1884

    2008

  52. [60]

    N.; Lyons, P

    Nirmalraj, P. N.; Lyons, P. E.; De, S.; Coleman, J. N.; Boland, J. J. Electrical connectivity in single-walled carbon nanotube networks. Nano Letters 2009, 9, 3890--3895

    2009

  53. [61]

    H.; Shim, H

    Shin, D. H.; Shim, H. C.; Song, J.-W.; Kim, S.; Han, C.-S. Conductivity of films made from single-walled carbon nanotubes in terms of bundle diameter. Scripta Materialia 2009, 60, 607--610

    2009

  54. [62]

    K.; Lee, K.; Kim, G

    Geng, H.-Z.; Kim, K. K.; Lee, K.; Kim, G. Y.; Choi, H. K.; Lee, D. S.; An, K. H.; Lee, Y. H.; Chang, Y.; Lee, Y. S.; Kim, B.; Lee, Y. J. Dependence of material quality on performance of flexible transparent conducting films with single-walled carbon nanotubes. Nano 2007, 2, 157--167

    2007

  55. [63]

    Comparison of values of Pearson's and Spearman's correlation coefficients on the same sets of data

    Hauke, J.; Kossowski, T. Comparison of values of Pearson's and Spearman's correlation coefficients on the same sets of data. Quaestiones Geographicae 2011, 30, 87--93

    2011

  56. [64]

    Principal component analysis

    Wold, S.; Esbensen, K.; Geladi, P. Principal component analysis. Chemometrics and Intelligent Laboratory Systems 1987, 2, 37--52

    1987

  57. [65]

    Abdi, H.; Williams, L. J. Principal component analysis. Wiley Interdisciplinary Reviews: Computational Statistics 2010, 2, 433--459

    2010

  58. [66]

    International Encyclopedia of Statistical Science; Springer, 2011; pp 1094--1096

    Jolliffe, I. International Encyclopedia of Statistical Science; Springer, 2011; pp 1094--1096

    2011

  59. [67]

    Principal component analysis-a tutorial

    Tharwat, A. Principal component analysis-a tutorial. International Journal of Applied Pattern Recognition 2016, 3, 197--240

    2016

  60. [68]

    Mesoscopic Modeling of High–Density Carbon Nanotube Films for Memristive Device Applications

    Greenacre, M.; Groenen, P. J.; Hastie, T.; d’Enza, A. I.; Markos, A.; Tuzhilina, E. Principal component analysis. Nature Reviews Methods Primers 2022, 2, 100 mcitethebibliography main.tex0000664000000000000000000021111615077363625011244 0ustar rootroot [journal=aaembp,manuscri...

    2022

Pith tools

Reviewed August 4, 2026 · model on record in the stance chip above.