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Comprehensive Characterization of Structural, Electrical, and Mechanical Properties of Carbon Nanotube Yarns Produced by Various Spinning Methods

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A comprehensive characterization of various carbon nanotube (CNT) yarns provides insight for producing high-performance CNT yarns as well as a useful guide to select the proper yarn for a specific application. Herein we systematically investigate the correlations between the physical properties of six CNT yarns produced by three spinning methods, and their structures and the properties of the constituent CNTs. The electrical conductivity increases in all yarns regardless of the spinning method as the effective length of the constituent CNTs and the density of the yarns increase. On the other hand, the tensile strength shows a much stronger dependence on the packing density of the yarns than the CNT effective length, indicating the relative importance of the interfacial interaction. The contribution of each physical parameter to the yarn properties are quantitatively analyzed by partial least square regression.
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Citation: Watanabe, T.; Yamazaki, S.;
Yamashita, S.; Inaba, T.; Muroga, S.;
Morimoto, T.; Kobashi, K.; Okazaki, T.
Comprehensive Characterization of
Structural, Electrical, and Mechanical
Properties of Carbon Nanotube Yarns
Produced by Various Spinning
Methods. Nanomaterials 2022,12, 593.
https://doi.org/10.3390/nano12040593
Academic Editor: Simone Morais
Received: 12 January 2022
Accepted: 4 February 2022
Published: 10 February 2022
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4.0/).
nanomaterials
Article
Comprehensive Characterization of Structural, Electrical,
and Mechanical Properties of Carbon Nanotube Yarns Produced
by Various Spinning Methods
Takayuki Watanabe 1, Satoshi Yamazaki 2, Satoshi Yamashita 2, Takumi Inaba 1, Shun Muroga 1,
Takahiro Morimoto 1, Kazufumi Kobashi 1and Toshiya Okazaki 1,*
1CNT-Application Research Center, National Institute of Advanced Industrial Science and Technology,
Tsukuba 305-8565, Japan; takayuki.watanabe.r@gmail.com (T.W.); takumi.inaba@aist.go.jp (T.I.);
muroga-sh@aist.go.jp (S.M.); t-morimoto@aist.go.jp (T.M.); kobashi-kazufumi@aist.go.jp (K.K.)
2Research Association of High-Throughput Design and Development for Advanced Functional
Materials (ADMAT), Tsukuba 305-8565, Japan; satoshi.mayama.yamazaki@furukawaelectric.com (S.Y.);
satoshi.yamashita@furukawaelectric.com (S.Y.)
*Correspondence: toshi.okazaki@aist.go.jp
Abstract:
A comprehensive characterization of various carbon nanotube (CNT) yarns provides
insight for producing high-performance CNT yarns as well as a useful guide to select the proper
yarn for a specific application. Herein we systematically investigate the correlations between the
physical properties of six CNT yarns produced by three spinning methods, and their structures and
the properties of the constituent CNTs. The electrical conductivity increases in all yarns regardless
of the spinning method as the effective length of the constituent CNTs and the density of the yarns
increase. On the other hand, the tensile strength shows a much stronger dependence on the packing
density of the yarns than the CNT effective length, indicating the relative importance of the interfacial
interaction. The contribution of each physical parameter to the yarn properties are quantitatively
analyzed by partial least square regression.
Keywords: carbon nanotubes; yarn; electrical conductivity; tensile strength; nanotube length
1. Introduction
Carbon nanotubes (CNTs) display very high electrical conductivities, thermal conduc-
tivities, as well as mechanical strengths, and sorption abilities [
1
5
]. Because they also have
lower densities than metals such as copper and steel [
6
], CNTs have potential as alternating-
current power cables and wires. In fact, their estimated electrical conductivity is as high as
900,000 S/cm, which is much larger than that of copper (600,000 S/cm) [
7
]. Additionally,
the tensile strength of CNT bundles is ~80 GPa, whereas that of steel is ~1 GPa [2].
To preserve superior properties in macroscopic CNT-based structures, the production
of yarns composed of CNTs offers a potential for high-strength and lightweight materials
that are also thermally and electrically conductive [
8
12
]. Macroscopic CNT yarns show an
electrical conductivity and tensile strength of 10,900 S/cm and 9.6 GPa, respectively [
13
15
].
Their values are within a factor of those of aluminum and commercially available carbon
fibers, respectively. CNT yarns exhibit a strength similar to carbon fibers and an electrical
conductivity similar to metal.
Currently, CNT yarns are mainly produced by three methods: dry spinning from multi-
walled CNT (MWCNT) forests, direct spinning from a CVD furnace, and wet-spinning of
CNTs (Scheme 1). Elucidating their structures, properties, and relationships are critical to
realize practical applications of CNT yarns.
Nanomaterials 2022,12, 593. https://doi.org/10.3390/nano12040593 https://www.mdpi.com/journal/nanomaterials
Nanomaterials 2022,12, 593 2 of 11
Scheme 1.
Schematic representation of spinning methods for CNTs. (
a
) Dry spinning by draw twist
process from CNT forest, (b) dry spinning from CNT furnace, and (c) wet-spinning.
We previously investigated the electrical and mechanical properties of wet-spun CNT
yarns using far-infrared (FIR) spectroscopy. These properties are related to the effective
CNT length [
16
18
]. The observed FIR peak can be explained by the one-dimensional
plasmon model. Consequently, the estimated length can be ascribed to that of the clean
and straight CNT portion between defects or kinks (effective CNT length) [
19
22
]. FIR
spectroscopy is applicable to single-walled CNTs (SWCNTs) and MWCNTs because the
resonant frequency is insensitive to the CNT diameter [
19
,
21
]. On the other hand, the
G-band/D-band ratio in the Raman spectrum strongly depends on the CNT diameter and
CNT types.
Here, we systematically investigate the structure of various CNT yarns and the prop-
erties of the constituent CNTs. Our study evaluated yarns fabricated by three different
spinning methods using various CNTs. We observed the yarn structures and cross-sections
by scanning electron microscopy (SEM). We also estimated the average CNT diameters and
wall numbers by transmission electron microscopy (TEM), the CNT alignment by wide
angle X-ray diffraction (WAXD), the G/D ratios and the radial breathing modes by Raman
spectroscopy, and the effective CNT lengths by FIR spectroscopy.
Then we evaluated the electrical conductivities and the tensile strengths of the yarns,
and their correlation with the yarn and CNT properties. The electrical conductivity of
the yarns is significantly correlated with the effective CNT length, yarn density, and CNT
alignment in the yarns. In contrast, the tensile strength depends more on the yarn density
and the CNT alignment. To quantitatively evaluate the contribution of each physical
parameter to the properties of CNT yarn, the measurement data was analyzed using the
partial least square (PLS) regression [2325].
2. Materials and Methods
The six commercially available CNT yarns were studied here (Scheme 1). Two CNT
yarns by dry spinning from CNT forests were purchased from Hamamatsu Carbonics
Corporation (Shizuoka, Japan) and Taiyo Nippon Sanso Corporation (Tokyo, Japan). The
CNT yarn by direct spinning from a CVD furnace (Miralon CNT yarn) was obtained from
Nanocomp Technologies Inc. (Merrimack, NH, USA). The wet-spinning CNT yarns from
surfactant-assisted aqueous dispersion and strong acid dispersion were purchased from
Meijo Nano Carbon Co., Ltd. (Aichi, Japan) (EC-Y type I and II) and DexMat Inc. (Houston,
TX, USA), respectively.
The morphologies of the CNT yarns were observed by SEM microscopy (Hitachi
SU-8200, Tokyo, Japan). The diameter and wall number of the CNTs were estimated
by TEM microscopy (EM002B, Topcon, Tokyo, Japan), while WAXD (AichiSR, beam line
BL8S3, Aichi, Japan) assessed the CNT alignment in the yarns. The photon energy was
13.48 KeV (
λ
= 0.092 nm). The sample-to-detector distance was 0.208 m. The WAXD
Nanomaterials 2022,12, 593 3 of 11
spectra were measured using a two-dimensional detector (R-AXIS VII++). The scattering
wave vector q = 4
π
sin
θ
/
λ
, where
θ
is the scattering angle. The Raman spectra were
measured with inVia (Renishaw, Wotton-under-Edge, England, UK) with an excitation
wavelength of 532 nm. The effective length of each CNT was estimated from its FIR
optical absorption spectrum [
20
,
22
]. The FIR measurements were acquired using Vertex 80v
(Bruker Optics, Billerica, MA, USA) and TR-1000 (Otsuka Electronics, Osaka, Japan). The
average structural CNT lengths were estimated from the AFM measurements (Shimadzu
SFT-4500, Kyoto, Japan).
The electrical resistance of the CNT yarn was measured using the four-terminal
method with a probe system (Summit12000, Cascade Microtech, Beaverton, OR, USA) and
a semiconductor device analyzer (B1500A, Keysight, Santa Rosa, CA, USA). Three samples
were measured for each CNT yarn type. The distance between the two terminals of the
differential voltage measurement system was 30 mm. The obtained values were averaged
for, at least, three samples for each CNT yarn.
The breaking strengths of the CNT yarns were measured in tensile tests with a micro
strain tester (MST-I, Shimadzu, Kyoto, Japan). The test pieces and the test conditions
followed the Japanese standard, JIS R 7606, which is a standard protocol for carbon fiber
tensile testing. The gauge length was fixed to 25 mm, and the head speed was fixed to
1 mm/min. Three samples were measured for each CNT yarn type.
Thermogravimetric analysis (TGA) was used to estimate the carbonaceous purity of
the CNT yarns with TGAQ500 (TA instruments, New Castle, DE, USA).
PLS models were constructed from the covariance between the standardized response
variables (electrical conductivity or tensile strength) and the standardized explanatory
variables (physical parameters). Details for select measurement procedures are described
in the Supplementary Materials.
3. Results and Discussion
3.1. Structural Characterizations of Commercially Available CNT Yarns and Their Constituent CNTs
The structural and physical properties of CNT yarn strongly depend on the spinning
method [
8
12
,
26
]. Figures 1and 2show SEM images of the CNT yarns evaluated in this
study. Hamamatsu and Taiyo Nippon Sanso yarns were made by dry spinning from CNT
forests [
27
]. Their estimated diameters by laser micrometer are 59 and 31
µ
m, respectively
(Table 1, see the Supporting Information). This method spins CNTs from a CNT forest by
twisting. The twisted structure is visible from the side views (Figure 1). The roundness of the
cross-sections exceeds those of the other yarns, reflecting the high controllability of the dry
spinning methods (Figure 2a). The network structures in the high-magnification images show
the different diameters of the constituent CNTs (Figure S1 in the Supporting Information).
Figure 1. Side-view SEM images of CNT yarns.
Nanomaterials 2022,12, 593 4 of 11
Nanocomp Miralon yarn is produced by direct spinning from a CVD furnace [
28
]. It
has a unique structure among the CNT yarns, including conventional direct spun CNT
yarns [
29
31
]. The low-magnification image shows non-negligible voids in the yarn
(Figure 2a),
whereas the CNTs seem well packed and sub micrometer voids rarely ap-
pear in the high-magnification image (Figure 2b). The estimated yarn diameter is 230
µ
m,
which is the largest among the yarns in this study (Table 1).
Figure 2.
Cross-sectional SEM images of CNT yarns. (
a
) Low-magnification images. (
b
) High-
magnification images.
Table 1. Physical properties of the CNT yarns in this study.
CNT Yarns Hamamatsu
Carbonics
Taiyo Nippon
Sanso
Nanocomp
Miralon
Meijo EC-Y
Type I
Meijo EC-Y
Type II DexMat
Yarn diameter (µm) 59 31 230 90 76 20
Average diameter of CNTs (nm)
45 ±1 12 ±0.2 5.3 ±0.1 1.8 ±0.05 1.7 ±0.04 2.2 ±0.05
Average wall number 50 ±2 8.5 ±0.1 1.9 ±0.03 1.4 ±0.05 1.4 ±0.05 1.6 ±0.05
CNT type MWCNT MWCNT FWCNT SWCNT SWCNT FWCNT
WAXD (002) FWHM (degree) 42.8 23.7 9.4 21.4 38.4 7.7
Herman orientation factor 0.86 0.89 0.94 0.82 0.78 0.94
G/D ratio 2.2 ±0.03 1.2 ±0.03 3.3 ±0.3 50 ±5 98 ±9 31 ±2
CNT effective length (nm) 270 130 470 2300 2300 1800
CNT length by AFM (µm) 11 ±2(a) 1.1 ±0.07 (a) 3.9 ±0.4 (b) 3.2 ±0.2 (b) 1.7 ±0.1 (b) 1.3 ±0.1 (b)
Yarn Density (mg/cm3)450 1111 985 353 575 1670
Electrical conductivity (S/cm) 462 588 16,627 1725 3334 70,659
Tensile strength (MPa) 151 716 1193 69 111 1595
(a) Individual MWCNTs; (b) Bundles of SW or FWCNTs.
Meijo CNT yarns are wet-spun in a surfactant-assisted aqueous dispersion [
32
]. These
yarns have a ribbon shape, and the CNT bundles are sparsely entangled, rather than aligned
(Figure 2). DexMat yarn is produced by wet-spinning CNTs dispersed in a strong acid [
10
].
DexMat yarn has a rounded cross-section, and CNTs are well packed similar to Nanocomp
Miralon (Figure 2). Despite its high density indicated by the cross-section, the side view
clearly shows a bundled structure (Figure S1). DexMat has thicker bundles but its average
diameter is smaller (20
µ
m) than those of Meijo yarns (90
µ
m and 76
µ
m for type I and
type II, respectively) (Table 1).
Nanomaterials 2022,12, 593 5 of 11
The diameters and the wall numbers of the constituent CNTs were estimated from
TEM observations (Figure 3and Figure S2, Table 1). The average values of the CNT
diameters are widely distributed from 1.4 to 45 (Figure S2). Based on the observed wall
numbers, Hamamatsu Carbonics and Taiyo Nippon Sanso yarns consist of MWCNTs. The
other yarns, Nanocomp Miralon, Meijo, and DexMat, contain both FW and SWCNTs.
The CNT alignment along the long axis of the yarns was estimated by WAXD. The
WAXD spectra showed a broad peak between q= 11.41 and 25.27 nm
1
, which corresponds
to the planes perpendicular to the CNT axis. The CNT alignments were estimated based on
the full width at half maximum (FWHM) of the azimuthal scan of the peaks (Figure 4). The
Hamamatsu and Meijo yarns have Herman orientation factors of 0.78–0.86 (Table 1). On
the other hand, DexMat and Nanocomp yarns display higher alignments (=0.94). Note that
the broad small peak at an azimuth angle = 150–200
is X-ray scattering from the Kapton
tape, which is used as a window material for the 2D detector and x-ray guideline.
Figure 3. TEM images of constituent CNTs of each yarn.
Nanomaterials 2022,12, 593 6 of 11
Figure 4. WAXD azimuthal scan of CNT yarns.
3.2. Spectroscopic Characterizations
We performed two spectroscopic characterizations (Figure 5). Resonance Raman
spectroscopy is a powerful tool for evaluating CNT-based materials (Figure 5a). The
integrated G/D intensity ratio of each yarn ranges from 1.2 to 98. Meijo and DexMat
tubes show relatively larger G/D values, whereas the Taiyo Nippon Sanso, Hamamatsu
Carbonics, and Nanocomp Miralon samples show smaller values around two (Table 1).
Another parameter is the effective length (L
eff
), which is estimated from the optical ab-
sorption of CNTs in the FIR region (Figure 5b) [
19
22
]. Here, the length estimated based on
a one-dimensional plasmon resonance model [
22
] corresponds to the length (or the size) in
the high-crystallinity region of the CNT. Therefore, L
eff
directly correlates with the physical
properties of the CNT films and wet-spun yarns such as electrical
conductivity [16,19].
SW
and FWCNTs have a longer Leff, whereas MWCNTs show a shorter Leff (Table 1).
Figure 5.
(
a
) Resonance Raman spectra and (
b
) far-infrared spectra of the constituent CNTs in yarns.
Nanomaterials 2022,12, 593 7 of 11
3.3. Electrical Conductivity
Figure 6a plots the electrical conductivities of the yarns as functions of L
eff
of the con-
stituent CNTs. The obtained values are typical for the given spinning
method [14,1618,26,28].
The values range over almost two orders of magnitude. There is no apparent correlation
between electrical conductivity and the effective length. However, dividing the CNT yarns
into two groups by their density (i.e., high and low densities) reveals a positive dependence
on the effective length within the group.
Figure 6.
(
a
) Effective length, (
b
) density, and (
c
) Herman orientation factor dependences of the
electrical conductivity of yarns. (d) 2D contour plot of the yarn conductivity.
The density dependence of the electrical conductivity does not show a clear trend
(Figure 6b). Similar to above, classifying the CNT yarns into two groups depending
on the number of walls, (i.e., MWCNTs and FW/SWCNTs) highlights an obvious de-
pendence on the yarn density. Since the wall number of CNTs is usually related to the
crystallinity [
21
], the effective lengths of FW and SWCNTs are longer than those of MWC-
NTs. Consequently, the electrical conductivity depends on both L
eff
(crystallinity) and the
yarn density
(Figure 6d).
If one of these factors is low, the electrical conductivity will be low.
In other words, both the CNT effective length and density must be controlled to improve
the conductivity of CNT yarn.
The Herman orientation factor dependence of the electrical conductivity (Figure 6c)
is similar to the density dependence (Figure 6b). This is reasonable since highly aligned
yarns have larger densities (Figures 1and 2). Indeed, the calculated correlation coefficient
between the Herman orientation factors and the yarn densities is 0.78.
Since L
eff
represents the length of the high-crystallinity region of CNTs, CNTs with a
longer L
eff
should exhibit a higher conductivity because there are fewer CNT junctions per
unit length. On the other hand, a common method to estimate the CNT length is performing
counting experiments with atomic force microscopy (AFM) [
19
,
33
,
34
]. Since the length
estimated by the AFM observation corresponds to the physically and structurally connected
one, we designated it as the ‘structural length’ (L
str
) in this paper. As shown in Figure
S4a in the Supporting Information, L
eff
and L
str
do not show a clear correlation, which is
consistent with the previous report [
19
]. Figure S4b shows the electrical conductivity of the
yarns as a function of L
str
estimated from the AFM observations. The electrical conductivity
of Hamamatsu Carbonics yarn is too low for the large L
str
. This means that although
the CNTs are physically connected, defects or kinks on the tube wall cause substantial
electrical resistance.
Nanomaterials 2022,12, 593 8 of 11
3.4. Tensile Strength
Figure 7a,b show the tensile strengths of CNT yarns as functions of L
eff
and the yarn
density. The obtained tensile strengths are typical values for CNT yarns produced by the
corresponding spinning method [
14
,
16
18
,
26
28
,
35
,
36
]. Similar to the conductivity, we
classified the results into two groups by the yarn density. Although high-density yarns
seem to depend on L
eff
, low-density yarns do not. This means that L
eff
is a good parameter
for the tensile strength only if the yarn is sufficiently dense.
Compared with the electrical conductivity, the tensile strength shows a stronger
dependence on the yarn density (Figure 7b) and Herman orientation factor (Figure 7c).
Intuitively, CNTs in a dense yarn should have a larger total contact area with neighboring
CNTs than those in a low-density yarn. Until the individual CNTs start to slip relative to
each other, a greater frictional force is applied to the CNT surface. Thus, dense yarn with
a high alignment exhibits a higher tensile strength. Apparently, the increase in friction
between CNTs should be higher for longer CNTs. This situation is consistent for CNT yarns
with a higher density (Figure 7a).
Figure 7.
(
a
) Effective length, (
b
) density, and (
c
) Herman orientation factor dependences of the
tensile strength of yarns.
Nanomaterials 2022,12, 593 9 of 11
3.5. PLS Regression Analysis
The contribution of each physical parameter to the electrical conductivity and the
tensile strength was quantitatively analyzed by PLS regression. PLS regression is com-
monplace in statistics or machine learning, especially for spectroscopic data with high
multi-dimensional collinearity [
23
25
]. PLS regression can efficiently extract information
from data even with the multi-dimensional collinearity of variables. Since the absolute
values of the regression coefficients of standardized variables (|
β
|) are related to the
weights/importance, they are good indicators for interpreting or refining the variables.
Based on the results (Figure 8), the contributions of the effective length, yarn density, and
degree of orientation are higher than the others for the electrical conductivity. This may
indicate that it is important to form a current path with the shortest distance without
CNT-CNT junctions in CNT yarns.
Figure 8. PLS analysis of the electrical conductivity and tensile strength of CNT yarns.
On the other hand, the tensile strength is less dependent on the effective length and the
G/D ratio, which are parameters of CNT quality. The density and the Herman orientation
factor contribute almost equally. The fracture mechanism of CNT yarn in tension should be
dominated by the withdrawal of CNT. Thus, how close a CNT is to other CNTs is more
important than the strength of an individual CNT.
4. Conclusions
We here conducted comprehensive characterizations of six CNT yarns produced by
the different spinning methods with various CNTs. The structural properties of the yarns
were investigated by SEM, laser microscope and WAXD. The properties of the constitute
CNTs such as diameter, wall number, and effective length were characterized by TEM,
resonance Raman, and FIR spectroscopy. The relationship between the physical properties
Nanomaterials 2022,12, 593 10 of 11
of the yarns and the obtained structural parameters were then examined. The CNT effective
length and the yarn density well characterize the electrical conductivity of all CNT yarns.
On the other hand, the tensile strength of the yarns exhibits much stronger dependence
on the yarn density. The stronger correlation of the tensile strength to the yarn density
indicates the importance of the interfacial interaction with adjacent CNTs (or CNT bundles)
in determining the mechanical properties. PLS analysis can explain the above observations
quantitatively. The present study offers the scientific perspectives on the neat CNT yarns.
One of the important findings is that DexMat yarn is composed of the longest class of
CNTs that we have ever measured [
16
20
], and the packing density is close to the highest
limit [
6
]. Therefore, to improve the properties of the CNT neat yarn further, post-treatment
processes such as doping [
10
] and cross-linking [
37
] will become crucial. The doping effects
on the electronic structures and the electrical conductivities of DexMat CNT yarn have
been investigated by the present research group, which will appear in near future.
Supplementary Materials:
The following supporting information can be downloaded at: https://
www.mdpi.com/article/10.3390/nano12040593/s1, Figure S1: Side-view SEM images of CNT yarn
at high magnification; Figure S2: (a) TEM images of CNTs used in each yarn. (b) Histograms of CNT
diameter and wall number; Figure S3: (a) AFM images of each CNT. (b) Histograms of CNT length;
Figure S4: (a) Relationship between structural length and effective length. (b) Structural length
dependences of electrical conductivity; Figure S5: (a) Photograph of custom-made fiber diameter
measurement system including high-speed optical micrometer (LS-9006MR series, Keyence).
(b) Schematic illustration of how to measure yarn diameter. (c) Cross-sectional SEM image of
Nanocomp Miralon yarn. (d) Voids extracted from the SEM image by using ImageJ software;
Table S1: Carbonaceous content of CNT yarns estimated by TGA.
Author Contributions:
T.W., S.Y. (Satoshi Yamazaki), S.Y. (Satoshi Yamashita), and T.I. performed
the experiments and analyzed the data. S.M. performed PLS analysis. T.O. and T.W. co-wrote the
original manuscript. T.W., S.Y. (Satoshi Yamazaki), S.Y. (Satoshi Yamashita), T.I., S.M., T.M., K.K. and
T.O. discussed the results, commented on the manuscript, and approved its submission. All authors
have read and agreed to the published version of the manuscript.
Funding:
This work was supported by a project (JPNP16010) commissioned by the New Energy and
Industrial Technology Development Organization (NEDO).
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: Not applicable.
Conflicts of Interest: The authors declare no conflict of interest.
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... Yarn production using carbon nanotubes (CNTs) holds promising prospects for manufacturing advanced composite materials that are both strong and lightweight [1][2][3]. Characterization [4] of the yarns produced by various spinning methods [1] revealed structurallyentangled CNTs, which are organized into close-packed, aligned CNT assemblies referred to as bundles [5]. Depending on their diameters and number of walls, the CNTs could remain cylindrical (as the~1.7 nm single-walled CNTs in Meijo EC-Y yarns and the~2.2 ...
... Depending on their diameters and number of walls, the CNTs could remain cylindrical (as the~1.7 nm single-walled CNTs in Meijo EC-Y yarns and the~2.2 nm doublewalled CNTs of DexMat yarns [4]) or ovalize and self-collapse into flat ribbons forming bundles of stacked CNTs [6] that maximize packing (as the~5 nm double-walled CNTs of the Nanocomp Miralon yarn [5]). The typical CNT length is on the order of a few μm. ...
... The typical CNT length is on the order of a few μm. The yarn tensile strength test shows a strong dependence on the CNT packing density [4,7] indicating that maximizing the CNT-CNT contact is an important way to enhance mechanical properties. ...
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Link to free text read or download (until January 14, 2024) https://authors.elsevier.com/a/1i91H8MuOhFrN6 While atomistic simulations can quantify the nano-mechanical properties of carbon nanotube (CNT), they become impractical when it comes to predicting their functions within CNT assemblies. Using the mesoscopic distinct element method (MDEM) for CNTs, we perform coarse-grained multiscale simulation to compare the impact of polymeric and cross-linking interfaces in highly discontinuous CNT bundle and network assemblies subjected to simple tension. Viscous friction and contact shear bonds are used in MDEM to capture the polymeric and cross-linking nano-mechanics computed separately with atomistic methods. Cross-linking is found to be the most effective at increasing elastic moduli and tensile strengths of the CNT bundles. These properties can be diminished by the nano-holes in the CNT wall associated with irradiation-generated cross-linking and captured here into the contacts representing CNT stretching. Simulations indicate the need to balance the benefits and drawbacks of the radiation-induced morphological changes in order to manufacture CNT yarns with superior tensile characteristics.
... There are two fundamental types of nanotubes: single-wall carbon nanotubes (SWCNTs), made of a single graphite sheet, and multiwall carbon nanotubes (MWCNTs), made of multiple concentric graphene sheets [2]. Carbon nanotubes are considered among the more resistant and harder materials; these characteristics derive from the type of bonds that are present in their structure, in which the carbon is sp 2 hybridized [3]. Thanks to these peculiar properties, carbon nanotubes have been studied in various fields of application, such as electronics [4], medicine [5][6][7], and biology [8,9]. ...
... It is made up of a mixture of saturated hydrocarbons and aromatic hydrocarbons containing mainly from 14 to 20 carbon atoms ( Table 2). It has a density between 0.84 and 0.88 g/cm 3 . The organic molecules that constitute it are predominantly nonpolar, and therefore, it is insoluble in polar solvents such as water. ...
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... Carbon nanotubes possess remarkable chemical-physical properties, such as low density, high mechanical strength, and exceptional electronic and thermal characteristics [1]. These features make them valuable in various applications, including the production of composite materials, electronic devices, and biomedical applications [2]. ...
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The aim of this work was to obtain cobalt nanoparticles through a physical method, which could be formed simultaneously during the Catalytic Chemical Vapour Deposition (CCVD) synthesis of carbon nanotubes, under conditions suitable for both carbon nanotube synthesis and Co-nanoparticle formation. Co nanoparticles were prepared by Physical Vapour Deposition (PVD) using a 0.05 m3 magnetron on two different substrates, SiO2/Si and C, followed by a reduction treatment in an H2 atmosphere. Transmission Electron Microscopy (TEM) and Field Enhanced Scanning Electron Microscopy (FE-SEM) were used to characterize the Co nanoparticles. On the SiO2/Si substrate, cobalt silicate is formed, which stabilizes the Co nanoparticles, while the nanoparticles obtained on the C-substrate are sometimes surrounded by a layer of Co3O4, which deactivates the cobalt nanoparticles. To obtain suitable Co nanoparticles for carbon nanotube synthesis, the optimal Co-layer thickness is between 20 and 30 Å, and the optimal reduction temperature is 800 °C and 450 °C for SiO2/Si and C substrates, respectively.
... Among these materials, carbon nanotubes (CNTs) stand out due to their remarkable tensile strength, thermal conductivity, and electrical conductivity. CNTs boast a tensile strength 50 to 250 times greater than that of steel, along with 7.5 times higher thermal conductivity and 100 times higher electrical conductivity than copper [9][10][11][12][13][14]. Studies have explored enhancing the tensile strength, electrical conductivity, and heat performance of cement composites by incorporating CNT properties [15][16][17][18]. ...
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The self-heating temperature of the cement composite mixed with multi-walled carbon nanotubes (MWCNT–cement composite) is influenced by several factors, including the concentration of nano-material. However, conducting experiments to measure this temperature is time-consuming and expensive. Additionally, there are challenges in elucidating the correlations between the various influencing factors of the MWCNT–cement composite and its self-heating temperature. This study utilizes machine learning (ML) to predict the self-heating temperature of the MWCNT–cement composite and identify the correlation with influencing factors. ML techniques, including Random Forest (RF), eXtreme Gradient Boosting (XGB), and Gradient Boosting Machine (GBM), were employed. These ML models were optimized through hyperparameter tuning and k-fold cross-validation. The predictive performance of each model was evaluated using R2, mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE) metrics. All ML models exhibited high predictive performance, with the GBM model demonstrating the best thermal prediction capability, achieving an R2 value of 0.9795. Subsequently, the GBM model was used to analyze the major factors affecting the self-heating temperature of the MWCNT–cement composite. The analysis revealed that the concentration of MWCNTs, the amount of voltage, and the outdoor temperature are significant factors determining the self-heating temperature. Furthermore, it was found that the self-heating temperature of the MWCNT–cement composite increases as the concentration of MWCNTs and the amount of voltage increase and as the distance of the mesh decreases.
... For instance, the wet-spinning method has resulted in CNT fibers with electrical conductivity up to 109,000 S/cm [1], which is about 1/4 that of Al, while individual single-walled CNTs (SWCNTs) have been estimated to have a conductivity of 900,000 S/cm [2]. The electrical conductivity of CNT fibers is primarily determined by the density of the fibers and the length of their constituent CNTs [3,4]. Therefore, enhancing CNT alignment is crucial for producing CNT fibers to endow inherent CNT properties, as greater alignment leads to higher density and consequently improved electrical conductivity. ...
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We successfully prepared a surfactant-assisted carbon nanotube (CNT) liquid crystal (LC) dispersion with double-walled CNTs (DWCNTs) having a high aspect ratio (≈1378). Compared to dispersions of single-walled CNTs (SWCNTs) with lower aspect ratio, the transition concentrations from isotropic phase to biphasic state, and from biphasic state to nematic phase are lowered, which is consistent with the predictions of the Onsager theory. An aligned DWCNT film was prepared from the DWCNT dispersion by a simple bar-coating method. Regardless of the higher aspect ratio, the order parameter obtained from the film is comparable to that from SWCNTs with lower aspect ratios. This finding implies that precise control of the film formation process, including a proper selection of substrate and deposition/drying steps, is crucial to maximize the CNT-LC utilization.
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We report on the latest properties of solution spun carbon nanotube fiber (CNTF) and discuss these results in the context of the field of CNTF, as well as the broader field of high-performance fibers. Using high aspect ratio, high purity CNTs, we have produced neat CNTF with an electrical conductivity of 10.9 MS/m and a tensile strength of 4.2 GPa. We find that properties for solution spun CNTF have doubled every three years since the first reports in the mid-2000s. Companies are driving up the scale and lowering the costs of CNT and CNTF production. If the recent improvement trends in properties, cost, and scale can be sustained for the next several years, CNTF will be uniquely poised for large-scale market adoption.
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The hydrolysis of polylactide (PLA) occurs during melt processing, leading to product defects in, for example, appearance and mechanical properties. Hydrolyzed PLA products, whose mechanical properties were deteriorated, must be detected during processing to ensure the quality control of PLA products. In this study, near-infrared (NIR) hyperspectral imaging was applied for evaluating the extent of hydrolysis. NIR spectra in the wavelength range from 1200 to 1600 nm were changed by (1) hydrolysis induced by molding and (2) crystallization induced by annealing. The changes in the NIR spectra mediated by these two factors were distinctly different but overlapping, leading to difficulty in evaluating PLA at only a single wavelength. Partial least squares regression was introduced for detecting the hydrolyzed PLA. NIR imaging combined with the constructed partial least square models clearly visualized the differences in the extent of hydrolysis in hydrolyzed PLA under varying degrees of crystallization. © 2017 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2017, 135, 45898.
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A collection of interlocked carbon nanotubes forming a long and continuous fiber is a carbon nanotube fiber. The production of neat carbon nanotube fibers has paved the way for realization of superior mechanical and physical properties of individual carbon nanotubes at macroscopic scale. In this review paper, we elucidate the state of the art advances in fabrication methods, characterization, modeling of mechanical properties, and applications of carbon nanotube fibers as next generation high performance fibers for composites. Recommendations have been made on the aspects of design and scale-up for large scale manufacturing of CNT fibers using (a) a step wise protocol for the determination rate controlling step/s and the estimation of overall rate of reaction. (b) computational fluid dynamics. Finally, the challenges and opportunities in carbon nanotube fiber research have been clearly brought out and suggestions have been made for future work.
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We study how intrinsic parameters of CNT samples affect the properties of macroscopic CNT fibers with optimized structure. We measure CNT diameter, number of walls, aspect ratio, graphitic character, and purity (residual catalyst and non-CNT carbon) in samples from 19 suppliers; we process the highest quality CNT samples into aligned, densely packed fibers, by using an established wet-spinning solution process. We find that fiber properties are mainly controlled by CNT aspect ratio and that sample purity is important for effective spinning. Properties appear largely unaffected by CNT diameter, number of walls, and graphitic character (determined by Raman G/D ratio) as long as the fibers comprise thin few-walled CNTs with high G/D ratio (above ~20). We show that both strength and conductivity can be improved simultaneously by assembling high aspect ratio CNTs, producing continuous CNT fibers with an average tensile strength of 2.4 GPa and a room temperature electrical conductivity of 8.5 MS/m, ~2-times higher than the highest reported literature value (~15% of copper’s value), obtained without post-spinning doping. This understanding of the relationship of intrinsic CNT parameters to macroscopic fiber properties is key to guiding CNT synthesis and continued improvement of fiber properties, paving the way for CNT fiber introduction in large-scale aerospace, consumer electronics, and textile applications.