1. An Experimental Design Approach in Formulating
a Ceramifiable EVA/PDMS Composite Coating
for Electric Cable Insulation
E.E. Ferg, S.P. Hlangothi, S. Bambalaza
Department of Chemistry, Nelson Mandela Metropolitan University, P.O. Box 77000, Port Elizabeth 6031,
South Africa
The study used D-optimal mixture design of experi-
ments to formulate a ceramifiable EVA/PDMS compos-
ite with optimized ceramified flexural strength
properties after being exposed to elevated tempera-
tures. The ideal amounts of inorganic fillers and their
interaction within the polymer composite were studied.
It was found that good polymer and ceramic properties
were achieved when using 59% EVA/PDMS polymer
blend with inorganic fillers of 11% calcium carbonate,
10% aluminium hydroxide, 11% muscovite mica, and
9% calcined kaolinite, respectively. TGA, SEM, and
PXRD were employed to study the behavioral changes
of the EVA/PDMS composite during and postceramifi-
cation process. Although all inorganic fillers used were
important, muscovite mica played a special role not
only in the ceramification process, but also in keeping
the ceramic product physically stable. Microstructural
analysis of the cross-sectional area of the ceramic
product showed that it was multilayered with an inho-
mogeneous distribution of the chemical composition
across its layers. POLYM. COMPOS., 00:000–000, 2015.
VC 2015 Society of Plastics Engineers
INTRODUCTION
Polymer-based products are commonly employed as
insulation material in electrical cabling. Over time, it has
become very important that these materials are tailored to
be as close to being inflammable as possible in order to
reduce the spread of flames in a fire situation. There are
a number of publications and specifications that govern
the use of flame retarding mechanisms to reduce the
flammability of polymer-based materials used in cable
insulations [1–11]. However, recent studies have proposed
the concept of ceramic-forming polymer composites as
suitable coatings to curb flammability of electric cable
insulations [12–15]. In principle, this refers to the forma-
tion of a solid ceramic-like product on the polymer com-
posite surface upon exposing it to temperatures of up to
1,0008C. It has been reported that the ceramic layer does
not only provide insulation properties but it also provides
some form of mechanical support to the underlying metal
wire [15].
Silicone-based polymers have been extensively used in
the development of ceramifiable polymer composites for
electric cable applications [16–20]. Studies on the thermal
decomposition behavior of silicones in air have shown
that the decomposition of most silicones, such as polydi-
methylsiloxane (PDMS), is customarily accompanied by
the formation of a solid ceramic-like silica (SiO2) residue.
This residue usually begins to form on the outer surface
of the polymer matrix and consequently helps to keep the
underlying polymer intact while being exposed to the
increasing heat of the flame [18, 19]. However, prolonged
exposure to temperatures of around 1,0008C resulted in
the residue comprising of mainly SiO2 to remain as the
end product. This residue is typically in a powdery form
and thus readily disintegrates and is unable to form the
envisaged continuous and compact solid layer that can
protect the underlying electrical cables. Therefore, the use
of silicone alone was found not to be ideal for electric
cable insulation applications.
Hanu et al. [12] reported that the strength of the
ceramic formed upon heating of a silicone polymer can
be improved by the addition of muscovite mica. They
suggested that this is as a result of a eutectic reaction
which occurs between the SiO2 particles and the potas-
sium and aluminum oxides from the muscovite mica. In a
similar study, Mansouri et al. [20] exposed a silicone/
mica composite to elevated temperature and also reported
a generally improved ceramic strength with the ceramifi-
cation process preceded by the formation of a skin-like
ceramic product on the outer surface of the composite.
Similar findings were reported by Hamdani et al. [14],
who further proposed that when silicone/CaCO3 compos-
ite samples are exposed to temperatures above 8008C,
CaSiO3 (Wollasonite) and Ca2SiO4 (Larnite) forms and
Correspondence to: E.E. Ferg; e-mail: ernst.ferg@nmmu.ac.za
Contract grant sponsors: Powertech Aberdare Cables South Africa and
South African National Research Foundation (NRF).
DOI 10.1002/pc.23595
Published online in Wiley Online Library (wileyonlinelibrary.com).
VC 2015 Society of Plastics Engineers
POLYMER COMPOSITES—2015

2. consequently result in a continuous and compact ceramic
skin layer residue with relatively good mechanical proper-
ties, such as flexural strength.
The generally accepted ceramic-formation mechanism
comprises three general stages; namely: (i) formation of a
liquid phase; (ii) gas-phase entrapment; and (iii) solidifi-
cation to form the final ceramic product [15, 21]. Stage
(i) is the most crucial as it facilitates the thermal reactions
that can occur between the specific fillers present in the
formulation and the polymer. These are the reactions that
lead to the formation of crystalline products which even-
tually forms part of the final ceramic product.
Nonetheless, the use of silicone as the base polymer
for making cable insulation at the industrial scale is
unfavorable due to the high production costs associated
with silicone processing. A desirable approach would be
the use of a hydrocarbon based polymer and a filler sys-
tem that would form a ceramic product with relatively
good thermal and mechanical properties [10]. A recently
filed US patent reported a variety of polymers that can be
used as base polymers to form a ceramifiable polymer
composite [15]. It was further reported that, in engineer-
ing a ceramifiable polymer composite, the type of filler
system used takes precedence over the type of base poly-
mer. In summary, the suggested filler system comprised
of inorganic phosphates, aluminosilicate, alkali alumino
silicate, or magnesium silicate, inorganic fillers, such as
metal hydroxides, metal carbonates, and metal oxides.
The base polymers used were a range of typical hydrocar-
bons (thermoplastics, thermosets, and elastomers) which
were blended with minor quantities of a silicone polymer.
It has been reported that experimental design can be
effectively used in optimizing formulation studies where
a set of independent variables can have simultaneous
effects on a given outcome or response [22–24]. How-
ever, optimization of polymer composite formulations
using statistical experimental design for ceramifiable
composites has not been reported on. Therefore, this
study aims to explore this concept by employing ethylene
vinyl acetate (EVA), a low-cost copolymer, as the major
component of the base polymer.
EVA copolymers are known to decompose in two
major steps upon exposure to increased heat. Initially
there is a release of acetic acid gas caused by the deace-
tylation followed by the production of a wide range of
products such as hydroxyl or hydroperoxides, a mixture
of carbonyl products (ketones and a,b-unsaturated
groups), lactones, and a mixture of substituted vinyl prod-
ucts [25]. Inorganic fillers that were said to improve the
flame retardancy of EVA copolymers include metal
hydroxides (Mg(OH)2 and Al(OH)3), mixtures of ammo-
nium polyphosphate, pentaerythritol, and melamine
[26–28].
There are three general types of experimental designs,
namely: screening designs; response surface; and mixture
designs. Screening designs are mainly used for identifying
the factors that have the most significant effect on a
response. Such designs make use of a two-level approach
where each factor is set at a high (11) and a low (21)
level. Response surface designs, on the other hand, gener-
ally make use of three levels which consist of high (11),
mid-point (0), and low (21) levels. These design types
find use in identifying the level(s) with the most signifi-
cant effect on the response. Mixture designs are a type of
response surface designs used to monitor the responses
that are in a mixture, where each factor is a fraction of
the sum of all components in the whole. One specific
type of experimental mixture design, the D-optimal mix-
ture design, makes use of a D-criterion to construct the
final experimental design of a mixture process [29–31].
An experimental design consists of a reduced number
of experiments which are sufficient enough to give an
accurate representation of the relationship between a set
of independent factors and a specified response. This lim-
its the number of experiments that are required to only
those that will show significant effects on the response
and eliminate unnecessary experiments from being per-
formed. The construction and selection of the set of
experiments that will form part of the design involves
many steps where mathematical equations and matrix
algebra are applied to arrive at the full experimental
design. The important steps in designing a set of experi-
ments are the screening of the factors (components in the
formulation) in order to identify those that may have sig-
nificant effect on the response (desired properties) and
the identification of possible interactions of the factors on
a particular response. This then results in an empirical
mathematical function where a set of constraints or limits
for each of the factors can be determined. To complete
the design, experiments are randomized to statistically
allow the elimination of bias and to ensure computation
of valid sampling errors.
The application of experimental design to the current
study was based on the optimization of three properties of
the ceramifiable EVA/PDMS composite, namely the ten-
sile strength, the degree of elongation and the flexural
strength.
EXPERIMENTAL
Materials, Formulation, and Sample Preparation
All raw materials for the preparation of ceramifiable
EVA composites were obtained from local manufacturers
and suppliers in South Africa and are summarized in
Table 1.
D-Optimal mixture design was employed to formulate
a ceramifiable EVA/PDMS composite and is explained in
details in “Experimental Design and Optimization of
Mechanical Properties”. Respective initial quantities of
the raw materials used were chosen within an experimen-
tal domain or weight range for each variable. Each
domain was based on the chemical knowledge of each
variable as well as the suggested amounts from literature
2 POLYMER COMPOSITES—2015 DOI 10.1002/pc

3. [12, 14, 15, 20]. The corresponding variables and concen-
tration constraints are listed in Table 2.
Mixing of components was done using a 80 mL Bra-
bender internal mixer fitted with a water-cooling system
that kept the mixing temperature below 408C. The initial
rotor speed was 60 rpm for 3 min; milled on a Schwaben-
than two-roll mill with a nip size of 5 mm; and remixed in
the mixing head for a further 3 min at a rotor speed of
60 rpm. A Monsato Rheometer-100 was used to determine
the curing time required in order to achieve the optimal
rheological torque plateau (T90). A temperature adjustable
hydraulic heating press was used for pressing and curing
2-mm thick flat sample sheets at about 1508C. At the end
of the run samples were quickly removed from the press
and put in cold water to quench any reactions.
Preparation of ceramified samples was done by heating
precut 30 3 13 3 2 mm rectangular samples in a furnace
from room temperature to 1,0008C, and kept isothermal at
1,0008C for 60 min before cooling them back to room
temperature.
Experimental Design and Optimization of Mechanical
Properties
An assumption was made that the magnitude of
mechanical properties such as tensile strength, degree of
elongation, and flexural strength, were directly affected
by the variation in the concentrations of the starting mate-
rials. The objective was to find a mathematical model,
using the collected experimental data, which can best
describe the relationship between the independent varia-
bles and the EVA/PDMS composite responses or depend-
ent variables. The model would then be a predictive
equation within the variable limits that can be optimized
in terms of each of the responses of the ceramifiable
EVA/PDMS composite. Analysis and optimization of the
experimental design was achieved using statistical multi-
ple regression using ordinary least squares analysis. In
principle, the regression analysis for a composite that
consists of five variables and their interactions can be
summarized by Eq. 1.
Yi5b01b1A1b2B1b3C1b4D1b5E1b6AB
1 . . . 1bnABCDE1Ei
(1)
where Yi dependent variable (response) – tensile, % elon-
gation and flexural strength; A–E: independent variables.
Each letter denotes the corresponding component in the
EVA composite formulation (Table 2); b0: intercept; b1 –
bn: coefficients representing the rate of change or gradient
with respect to its variable and or their possible interac-
tion; Ei: error or residual associated with variations in Yi
The coefficients (b0 – bn) are the unknowns and hence
must be estimated using the experimental data (response
values and variable concentrations) collected by the set of
experiments done. The most commonly used method to
estimate b0 – bn values is ordinary least squares (OLS)
which was based on obtaining the minimum sum of the
squared errors (SSE) (Eq. 2).
SSE5
X
ðYi2ðb01b1A1b2B1b3C
1b4D1b5E1b6AB1 . . . 1bnABCDEÞÞ
2 (2)
TABLE 1. Names and chemical composition of raw materials.
Reagent/material Description Supplier
Ethylene vinylacetate (EVA) 45% vinyl acetate content
Pellets coated with Talc powder
Plastichem
Polydimethylsiloxane (PDMS) Nonvulcanized silicone rubber Carst and Walker
Muscovite mica KAl2(AlSi3O10)(OH)2
Phyllosilicate mineral
Gelletic mine, South Africa
Calcium carbonate CaCO3
0.4% MgCO3 and 0.1% Fe2O3 impurities
Idwala carbonates, South Africa
Aluminium hydroxide Al(OH)3 Idwala carbonates, South Africa
Kaolinite Al2Si2O5(OH)4
Calcined powder
Layered silicate mineral
CPS chemicals
Diammounium hydrogen phosphate (di-AHP) (NH4)2HPO4
Inorganic phosphate salt
Water soluble
Saarchem
Dicumyl peroxide (DCP) C18H22O2
Crosslinking agent for polymer curing
Fluka chemicals
TABLE 2. An outline of the independent variables and their predeter-
mined concentration constraints in formulating a ceramifiable EVA/
PDMS composite.
Independent variables
Concentration constraints
(w/w%)
Upper limit Lower limit
A 95%EVA/5%PDMS blend 56.7 44.0
B Muscovite mica 18.0 10.6
C Calcined kaolinite 13.0 9.0
D CaCO3 15.4 10.6
E Al(OH)3 13.0 9.7
DOI 10.1002/pc POLYMER COMPOSITES—2015 3

4. The coefficients and response for the estimated param-
eters are denoted by bi and Yˆi, respectively, and the model
then becomes:
^Yi5b01b1A1b2B1b3C1b4D1b5E1b6AB1 . . .
1bnABCDE
(3)
The residuals (ei) were obtained by determining the
differences of Yi – Yˆi for each response. Using OLS, the
best fit model to the experimental data was predicted to
be when the sum of the estimated residuals would
approximate zero (Eq. 4):
X
i
e 5
X
i
Yi2 ^Yi
À Á
0 (4)
The values obtained for bi are only estimates of the
true values of bi and therefore there must be an interval
within which the true value lies. This was obtained by
calculating (1 2 a) 3 100% confidence intervals for the
true coefficients, where a was the significance level
which was set to 5 % or 0.05. The confidence intervals
are calculated from the standard deviations of the resid-
uals and a t-statistic associated with the number of coeffi-
cients being estimated [24]. When the 95% confidence
intervals pass through zero, it meant that it was possible
that the true value of the coefficient (bi) can be zero and
therefore possible for the independent variable associated
with the particular coefficient to have no effect on the
response.
In multiple regression analysis for an experimental
design, the objective was to obtain a refined mathematical
model that was as accurate as possible to represent the
actual relationship between the variables and the
response. The refinement of the model was obtained by
eliminating those terms (in Eq. 1) found to be insignifi-
cant or alternatively have confidence intervals that passed
through zero. The p value was used as a measure of
whether an estimated value was significantly different
from zero.
To test whether an estimated coefficient was signifi-
cantly different to zero or not, a null hypothesis (Ho),
which states that bi 5 0, was used. The null hypothesis
can either be accepted or rejected. The decision to accept
or reject the null hypothesis depended on the p value. In
most cases, 95% confidence intervals were used in multi-
ple regressions, which meant that the cutoff p value
would be 0.05. The meaning of a p value of 0.05 was
that the null hypothesis (Ho) can only be rejected if the p
value associated with bi was less than 0.05 and cannot be
rejected if the p value exceeded 0.05.
Characterization
Preparation of samples for X-ray analyses involved
grinding ceramic samples with a mortar and pestle; and
the powder placed in standard polycarbonate sample
holders. For surface structure analysis, samples were sim-
ply cut to fit into a suitable sample holder. Crystalline
phase analysis was done using X-ray powder diffraction
(PXRD) fitted with a Bruker D2 phaser which uses Cu
radiation, and within the scan range of 5–708 2h. Identifi-
cation of phases was determined using the Bruker EVA
software [32].
Thermal degradation studies were carried out in a TA
SDT Q600 instrument and analyzed using TA Universal
Analysis v4.5 software. Approximately 5 mg samples
were heated from 25 to 1,0008C at 108C min21
. Synthetic
air (containing 22% oxygen) was used as purge gas at a
flow rate of 50 mL min21
.
The microstructural analyses of samples were done at
103 magnification on a JEOL JSM 6380 scanning elec-
tron microscope (SEM). Samples were coated with Au
before analyses.
Tensile testing was done according to the ASTM D638
using an Instron 4411/H2034 tensiometer at room temper-
ature. A 1 kN load cell was used with an extension speed
of 250 mm min21
. A minimum of six dog-bone shaped
specimen were tested per sample and average values of
the ultimate tensile strength (MPa) and elongation at
break (%) were recorded.
Flexural testing was done using the same Instron 4411/
H2034 tensiometer fitted with a 10 N load cell and using
a downward speed of 0.5 mm min21
(ASTM D790). The
ceramic samples were rested between two fixed support
beams that were separated by a span length of 18 mm
with the downward load applied to the center of the
ceramic sample.
The self-supporting properties of the ceramic product
were determined by placing 30 3 13 3 2 mm rectangular
specimen on a refractory support firebrick with 13 mm of
its length hanging over the edge of the brick. The ceramic
sample would be assumed to be self-supporting if it did
not bend over the edge with an angle of more than
158 [15].
The flammability of the ceramifiable EVA/PDMS
composite was determined in terms of its limiting oxygen
index (LOI) which is a measure of the volume of oxygen
required by a material to maintain a self-propagating
flame onto its surface for a given amount of time (sec-
onds) or length (mm). The method employed was based
on a method specified in the ISO 4589-2 [33].
RESULTS AND DISCUSSION
Physical changes in sample dimensions were immedi-
ately observed after heating the EVA/PDMS composite to
1,0008C; and these were analyzed using 30 3 13 3
2 mm samples. Direct measurements showed an average
contraction of 4–5% in their respective widths and
lengths; as well as about 55% increase in their thickness
(Table 3). Shrinkage of the sample could be attributed to
the decomposition of the organic polymer material and
other volatile components of the fillers; whereas an
4 POLYMER COMPOSITES—2015 DOI 10.1002/pc

5. increase in thickness could be as a result of the formation
of gas bubbles entrapped on the outer ceramic surface.
All samples showed a LOI value greater than
26.6 6 0.2%, which is within the required specifications
for cable insulating material [33]. From the flexural
strength results listed in Table 4, a quadratic equation
was fitted to the observed data and refined to obtain a
best-fit model using multiple linear regression analysis
where only two-factor interactions of the independent var-
iables were considered (Eq. 5).
^Y 5 0:172A20:466B11:040C10:417D20:224E
21:75031022
AC21:00531022
AD
12:55531022
BE22:66531022
CE
(5)
Calculations showed that A, B, C, and D had p values
lower than 0.05, whereas E had a p value greater than
0.05. This suggests that the variation in the concentration
of Al(OH)3 does not have a significant effect on the
ceramic flexural strength within the specified upper and
lower concentration limits. However, the coefficient value
obtained for E could not be excluded from the proposed
model since Al(OH)3 was a component of the ceramifi-
able EVA/PDMS composite and also showed to have
interactions with variables B (muscovite mica) and C (cal-
cined kaolinite) whose p values were lower than 0.05. On
the other hand, coefficients for BE and CE were found to
be 12.555 3 1022
and 22.665 3 1022
, respectively;
which suggests that at any fixed level of variables A, C,
and D, the ceramic flexural strength would increase by an
average of 2.555 3 1022
units for every 1% w/w increase
in the concentration of Al(OH)3 and muscovite mica.
Also, for every 1% w/w increase in the concentration of
calcined kaolinite and Al(OH)3 at fixed levels of A, B,
and D, the ceramic flexural strength would decrease by
TABLE 4. Results of the average ceramic flexural strength (MPa), the tensile strength (MPa), and the degree of elongation at break (%) for the group
of samples studied by a D-optimal mixture experimental design.
Composite
sample
Concentration (w/w%) Flexural
strength
(MPa 6 r)
Tensile
strength
(MPa 6 r)
Degree of
elongation
(% 6 r)A B C D E
1 52.2 13.6 13.0 10.6 10.6 0.82 6 0.03 5.23 6 0.18 730 6 68
2 52.2 13.6 13.0 10.6 10.6 0.84 6 0.08 5.18 6 0.05 600 6 57
3 52.0 10.6 13.0 15.4 9.0 1.21 6 0.08 5.51 6 0.28 683 6 62
4 44.0 18.0 11.3 15.4 11.3 1.00 6 0.03 5.70 6 0.26 710 6 71
5 48.6 18.0 9.0 15.4 9.0 0.54 6 0.03 7.01 6 0.13 658 6 44
6 52.3 14.3 9.0 15.4 9.0 0.70 6 0.13 5.44 6 0.12 547 6 43
7 53.4 18.0 9.0 10.6 9.0 0.58 6 0.04 5.96 6 0.22 753 6 66
8 49.0 18.0 9.0 13.0 11.0 0.65 6 0.04 6.01 6 0.27 545 6 60
9 49.4 18.0 9.0 10.6 13.0 0.69 6 0.04 5.59 6 0.18 740 6 73
10 49.4 18.0 13.0 10.6 9.0 0.56 6 0.03 5.20 6 0.14 730 6 63
11 52.0 10.6 9.0 15.4 13.0 1.03 6 0.10 5.50 6 0.12 753 6 75
12 48.3 14.3 13.0 15.4 9.0 1.46 6 0.09 7.28 6 0.28 752 6 71
13 56.0 10.6 9.0 15.4 9.0 1.46 6 0.04 6.57 6 0.22 731 6 68
14 56.7 14.7 9.0 10.6 9.0 0.87 6 0.09 6.34 6 0.22 593 6 41
15 48.0 10.6 13.0 15.4 13.0 1.02 6 0.07 6.35 6 0.17 745 6 69
16 44.0 18.0 13.0 12.0 13.0 0.83 6 0.04 5.39 6 0.40 513 6 49
17 44.0 18.0 11.3 15.4 11.3 1.11 6 0.10 5.84 6 0.25 563 6 47
18 44.0 18.0 13.0 12.0 13.0 1.07 6 0.10 5.47 6 0.17 487 6 39
19 56.7 10.6 11.0 12.7 9.0 1.22 6 0.05 8.55 6 0.16 498 6 46
20 44.6 18.0 9.0 15.4 13.0 1.18 6 0.11 6.17 6 0.23 447 6 51
21 44.6 18.0 9.0 15.4 13.0 1.10 6 0.11 6.18 6 0.21 562 6 46
22 53.1 14.4 9.0 10.6 13.0 1.12 6 0.06 7.15 6 0.18 623 6 67
23 56.7 10.6 9.0 10.7 13.0 0.97 6 0.05 8.53 6 0.30 620 6 62
24 46.5 14.4 10.7 15.4 13.0 1.27 6 0.08 6.67 6 0.16 508 6 65
25 56.7 10.6 11.0 12.7 9.0 1.17 6 0.05 8.44 6 0.24 530 6 61
TABLE 5. Maximum filler concentrations predicted for an optimized
flexural strength and tensile strength using Eqs. 5 and 6, respectively.
Sample
name
Variable concentration (w/w%)
A: 95%
EVA/
5%PDMS
B:
Muscovite
mica
C:
Calcined
kaolinite
D:
CaCO3
E:
Al(OH)3
Low filler 52.7 18.0 9.0 10.6 9.7
High filler 44.0 15.5 12.1 15.4 13.0
TABLE 3. A summary of the average of expansion and contraction of
the ceramifiable composite upon heating to 1,0008C.
Length
(mm)
Width
(mm)
Thickness
(mm)
At room temperature 30.0 13.0 2.0
After heating to 1,0008C 28.8 12.3 3.1
Change (%) 24.0 25.4 155
DOI 10.1002/pc POLYMER COMPOSITES—2015 5

6. an average of 2.665 3 1022
units. Consistently, coeffi-
cients of A, C, and D on one hand, showed a positive
contribution on the ceramic flexural strength whereas
their interactions, AC and AD, were found to contribute
negatively. On the other hand, coefficients of B and E
showed negative contribution on the ceramic flexural
strength whilst their interaction, BE, was found to contrib-
ute positively.
Tensile strength and degree of elongation (results sum-
marized in Table 4) also gave a best best-fit model using
multiple linear regression analysis with only two-factor
interactions. However, the best-fit set of results was a nat-
ural log for the tensile strength (Eq. 6) and a quadratic
equation for the degree of elongation (Eq. 7).
ln ^Y
À Á
5 4:56331022
A20:116B29:72031022
C
22:699D12:706E13:22812:55531022
BE
22:66531022
CE 1022
AD
23:30931022
AE14:64631022
BD23:70631022
BE
14:55831022
CD23:73731022
CE
(6)
^Y 5 2 56:4A1151:6B138:81C1167:4D
2405:3E18:59AE28:56BD
(7)
The variation in the concentration of variable C
(calcined kaolinite), at fixed levels of the remaining varia-
bles, had no effect on the ultimate tensile strength within
its specified upper and lower concentration limits. This
was based on the p value of C being greater than 0.05.
The variables which were found to have a significant
effect on the tensile strength were A (polymer matrix), B
(muscovite mica), D (CaCO3), and E (Al(OH)3) as these
had p values smaller than 0.05. The magnitude of their
respective coefficients suggested that an increase of about
1% w/w of CaCO3 at fixed levels of A, B, C, and E
would cause the tensile strength to decrease by 2.699
units; for fixed A, B, C, and D levels, the increase in the
concentration of Al(OH)3 by about 1% w/w would
increase the tensile strength by 2.706 units and similarly
muscovite mica was observed to decrease the tensile
strength by 0.1161 units. The interactions of A with D
and E showed that the effect of A on the tensile strength
was affected differently by both D and E. AD was found
to have a positive contribution whereas AE contributed
negatively towards the tensile strength. The same was
observed for the interactions of C with D and E; where
CD had a positive contribution and CE a negative
contribution.
The overall results for the degree of elongation (Table
4) showed that a high loading of the inorganic fillers was
consistent with a low postcure tensile strength and a high
TABLE 6. Summary of the ultimate tensile strength, degree of elongation and the ceramic flexural strength obtained for the samples made with the
predicted low and high filler concentrations.
Predicted response with 95% prediction intervalsa
Average experimental responseb
Sample
Tensile strength
(MPa)
Flexural strength
(MPa)
Elongation
(%)
Tensile strength
(MPa)
Flexural strength
(MPa)
Elongation
(%)
Low filler 9.02 6 1.25 0.70 6 0.32 664 6 176 9.03 6 0.35 0.63 6 0.03 671 6 62
High filler 4.00 6 1.26 1.06 6 0.36 696 6 182 6.60 6 0.25 1.34 6 0.05 567 6 51
a
The 6 error was based on 95% prediction intervals.
b
The 6 error was based on standard deviation.
FIG. 1. Thermogravimetric curves for unfilled and filled 95%EVA/5%PDMS blends.
6 POLYMER COMPOSITES—2015 DOI 10.1002/pc

7. ceramic flexural strength. Conversely, low inorganic filler
loading was found to be consistent with a lower ceramic
flexural strength and a high postcure tensile strength.
In order to verify the predicted model, two ceramifi-
able EVA/PDMS composite samples were made by con-
sidering a low and a high filler range determined using
Eqs. 5 and 6 only. This was done by limiting the changes
of the independent variables within the predetermined
upper and lower limits (Table 2) and calculating the larg-
est flexural strength using Eq. 5. The resulting variable
amounts for A to E were then used in Eq. 6 to determine
to corresponding tensile strength (Table 4). Similarly, a
corresponding high tensile strength within the variable
limits was calculated using Eq. 6. The resulting values
for A–E were then used to determine a corresponding
flexural strength using Eq. 5. It was noted that if a low
filler amount was determined; giving a correspondingly
higher tensile strength, a lower flexural strength of the
ceramic was then predicted. Vice versa, for a suitably
optimized higher filler value that would give an optimum
flexural strength, a comparatively poorer tensile strength
was predicted with Eq. 6. Duplicate samples were made
with the indicated filler concentrations (Table 5) and their
measured tensile strength, degree of elongation and the
ceramic flexural strength were then compared to predicted
model results (Table 6).
The results showed reasonably good agreements
between the predicted and experimental results for the
samples made with the “low filler” formulation. On aver-
age, the predicted and measured values for the ceramic
FIG. 2. PXRD patterns observed for the top ceramic skin layer, bottom ceramic skin layer, and the bulk
ceramic composition respectively. [Color figure can be viewed in the online issue, which is available at
wileyonlinelibrary.com.]
FIG. 3. Side view of the outer surface and edges of the ceramic product under SEM.
DOI 10.1002/pc POLYMER COMPOSITES—2015 7

8. flexural strength values differed only by about 10%. For
samples made with the “high filler” formulation, higher
values of tensile and flexural strengths were observed for
the experimentally determined responses when compared
to the values predicted by the mathematical model. This
was about 26% higher for the ceramic flexural strength
and 65% higher for the ultimate tensile strength. As for
the elongation at break, the model predicted a value that
is about 18% higher that the experimental value.
Prediction of probable changes in the physical charac-
teristics of the composite during the firing process were
studied using unfilled EVA/PDMS blend as a reference,
and a filled EVA/PDMS composite that contained 44.0%
EVA/PDMS/13.0% Al(OH)3/15.4% CaCO3/12.1% cal-
cined kaolinite/15.5% muscovite mica. These were ana-
lyzed by TGA and the results are shown in Fig. 1.
It can be seen that the onset of decomposition of the
unfilled 95%EVA/5%PDMS sample is at about 2918C,
and that of the filled composite is at about 3128C. This
slight shift in the onset temperature is clearly due to the
presence of fillers in the composite. However, the first
distinctive decomposition step occurs in both samples at
about 3488C, with about 33% mass loss for unfilled sam-
ple and about 18% mass loss for the filled sample. This
can be attributed to the removal of acetate moieties, such
as acetic acid, from the EVA [34]. A second major
decomposition step in both samples occur at about 4408C;
with about 55% loss in mass for the unfilled sample and
about 39% mass loss for the filled sample. This can be
associated with the final breakdown of the polyunsatu-
rated hydrocarbon chain [34]. Although the filled com-
posite sample shows a final decomposition step between
500 and 7508C, which may relate to the decomposition of
the PDMS, it is highly likely that the decomposition of
Al(OH)3 and CaCO3 also occurs. It has been reported that
CaCO3 typically decomposes to calcium oxide (CaO) at
about 7008C and as illustrated below, the last of the three
steps in the decomposition process of Al(OH)3 occurs
between 380 and 6008C [35, 36].
1: Al OHð Þ3 ! OHð Þ2Al-O-Al OHð Þ2 1 H2O 190–220
C
2: OHð Þ2Al-O-Al OHð Þ2 ! OHð ÞAl-O2-Al OHð Þ 1 H2O 220–380
C
3: OHð ÞAl-O2-Al OHð Þ ! Al2O3 1 H2O 380–600
C
It can also be seen that beyond 7508C, the EVA/
PDMS matrix decomposed completely, leaving a residue
of about 2% for unfilled sample and about 37% for the
filled sample. For the unfilled sample, the residue may be
attributed to the formation of silica in the form of SiO2;
whereas for the filled sample it may be credited to the
final ceramic forming components which include musco-
vite mica, calcined kaolinite, CaO (from the decomposi-
tion of CaCO3) and Al2O3 (from the decomposition of
Al(OH)3.
Powder X-ray diffraction (PXRD) was employed to
study crystalline phases of the cross-sectional area of the
ceramic. Significant difference in patterns between the
outer top (side of the ceramic which was exposed more to
the environment), middle (bulk), and the outer bottom
(side which was in direct contact with the fire brick sur-
face) layers of the solid ceramic product can immediately
be seen in Fig. 2. The intensities of the diffraction peaks
associated with the top outer surface are very small and
broad, which is characteristic of amorphous or nanotype
FIG. 4. Top view of underlying inner ceramic surface under SEM.
8 POLYMER COMPOSITES—2015 DOI 10.1002/pc

9. material. A more pronounced diffraction pattern was
observed for the bulk ceramic material with the bottom
part of the ceramic sample showing again relatively small
and broad diffraction peaks. This therefore implies that
the three layers of the ceramified material are asymmetric
or nonhomogeneous in their composition distribution
across the thickness of the ceramic strip. These observa-
tions are in accordance with the findings of Mansouri
et al. [20] who suggested that during the heating of a
ceramifiable silicone–mica polymer composite to high
temperatures, inorganic filler particles accumulated near
the polymer surface, and formed a continuous ceramic
skin-like layer. In a similar study, a multilayered ceramic
structure was also reported and their qualitative analyses
of the samples showed that the different layers contained
different quantities of calcium silicates [37, 38]. It was
therefore suggested that during the formation of the
ceramic product, the mica particles remained predomi-
nantly in the middle of the sample and that the calcium
silicates were predominant near the bottom of the ceramic.
These findings were further confirmed by scanning
electron microscopy (SEM). Micrographs of the cerami-
fied samples show flake like particles, which are most
likely mica, largely located near the inner bulk of the sam-
ple’s cross section (Fig. 3); and a large sections of molten
fused layer linking various particles together (Fig. 4).
CONCLUSIONS
The study showed that D-optimal mixture design with
multiple regression analysis can be used to optimize the
various properties of EVA/PDMS composites. However,
the accuracy of the prediction models was found to have
some limitations. For example, the difference between the
predicted and experimental tensile strength values could
be from 0.78%, for the predicted values of 8.00–9.00
MPa, to about 57% for predicted lower values of about
6.00 MPa. To a large extent though, pre- and postcerami-
fication properties desired for cable insulation purposes
were achieved in this study.
Microstructural analyses of the ceramic showed that
there was an asymmetric structural distribution across the
thickness of the ceramic. Thin skin-like layers formed
towards the outermost surfaces whereas the underlying
bulk structure was found to contain numerous micropores
as well as uniformly distributed crystal-like material. It
was also found that muscovite mica played an important
role in the composition as well as in keeping the ceramic
physically stable.
ACKNOWLEDGMENTS
The authors thank Powertech Aberdare Cables South
Africa for the supply of some of the materials used in the
study and for financial support. The authors thank the
HRTEM facility at the NMMU for the SEM image analysis.
The authors than Coos Bosma from Innoventon at the
NMMU with help in the statistical analysis.
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