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Study of moisture absorption in natural ﬁber plastic composites

W. Wang, M. Sain

*

, P.A. Cooper

Centre for Biocomposites and Biomaterials Processing, Faculty of Forestry, University of Toronto, Canada

Received 20 July 2005; accepted 20 July 2005

Available online 26 September 2005

Abstract

Moisture absorption of natural ﬁber plastic composites is one major concern in their outdoor applications. Traditionally diﬀu-

sion theory is applied to understand the mechanism of moisture absorption; but it cannot address the relationship between the

microscopic structure-inﬁnite 3D-network and the moisture absorption. The purpose of this study is to introduce percolation theory

into this ﬁeld and conduct some preliminary work. First, two new concepts, accessible ﬁber ratio and diﬀusion-permeability coef-

ﬁcient, were deﬁned; secondly, a percolation model was developed to estimate the critical accessible ﬁber ratio; ﬁnally, the moisture

absorption and electrical conduction behavior of composites with diﬀerent ﬁber loadings were investigated. At high ﬁber loading

when accessible ﬁber ratio is high, the diﬀusion process is the dominant mechanism; while at low ﬁber loading close to and below

percolation threshold, percolation is the dominant mechanism. The over-estimate of accessible ﬁber ratio led to discrepancies

between the observed and model estimates.

2005 Elsevier Ltd. All rights reserved.

Keywords: Natural ﬁber reinforced composites; Diﬀusion; Percolation theory; Electrical conductivity

1. Introduction

Natural ﬁber plastic composites (NFPCs) have

gained increasing interest and bro ader application dur -

ing the past decade due to the desirable properties of

natural ﬁbers [1]. Presently, the main application of

NFPCs is as construction materials [2,3], such as deck-

ing and railing products. However, natural ﬁbers also

have an undesirable property, namely, hygroscopicity

because of their chemical constituents. The moisture

absorption by composites containing natural ﬁbers has

several adverse eﬀects on their properties and thus af-

fects their long-term performance. For example, in-

creased moisture decreases their mechanical properties

[4,5], provides the necessary condition for biodegrada-

tion, and changes their dimensions [6]. Numerous eﬀorts

have been made to address this issue. Coupling agents,

compatibilizers or other chemical modiﬁcations are used

to improve the moisture resistance of composites [7–9].

However, moisture absorption of composites is still

one a major concern especially for their outdoor

application.

Moisture absorption increases with increasing ﬁber

loading. Thomas et al. [10] investigated the relationship

between the moisture absorption of pineapple-leaf ﬁber

reinforced low density polyethylene (LDPE) composites

and the ﬁber loadings (10%, 20%, and 30% by weight).

They found that the moisture absorption increased al-

most linearly with the ﬁber loading. Stark [4] found that

wood ﬂour-pol ypropylene (PP) composites with 20 wt%

wood ﬂour reached equilibrium after 1500 h in a water

bath and absorbed only 1.4% moisture while composites

with 40 wt% loading reached equilibrium after 1200 h

water submersion and absorbed approximately 9.0%

0266-3538/$ - see front matter 2005 Elsevier Ltd. All rights reserved.

doi:10.1016/j.compscitech.2005.07.027

*

Corresponding author. Present address: Department of Chemical

Engineering and Applied Chemistry, Earth Science Center, 33 Will-

cocks Street, University of Toronto, Toronto, Ont., Canada M5S 3B3.

Tel.: +1 416 946 3191; fax: +1 416 978 3834.

E-mail address: m.sain@utoronto.ca (M. Sain).

Composites Science and Technology 66 (2006) 379–386

COMPOSITES

SCIENCE AND

TECHNOLOGY

www.elsevier.com/locate/compscitech

moisture. After the analysis, she concluded that the

wood ﬂour is inhibited from ab sorbing moisture due

to encapsulation of the wood ﬂour by the PP matr ix

and that the degree of encapsulation is greater for the

20% wood ﬂour composite than that for the 40 % wood

ﬂour composite.

Traditionally, the diﬀusion theory has been applied to

understand the mechanism of moisture absorption in

composites [11–14]. A model based on diﬀusion theory

was developed to understand the relationship between

the ﬁber loading/ﬁber orientation and apparent diﬀu-

sion coeﬃcient [15].

As one theory regarding mass transfer, diﬀusion

theory is based on the assumption that the medium

structure is homogeneous and that the mass transfer is

only the result of the random molecular motion of the

diﬀusion agent whether it is in gaseous or liquid state

[16]. As the result of this molecular motion, the overall

trend is that diﬀusion agent moves in the direction from

higher to lower concentration as stated in FickÕ s Law

[17].

Diﬀusion theory does shed light on the mechanism of

moisture absorption and provides a framework for

quantifying the mass transfer; however, it fails to

address the structural complexity of composites. Since

natural ﬁbers and polymer matrix exhibit diﬀ erent prop-

erties in terms of moisture absorption, the ﬁber distribu-

tion in polymer matrix is a key to the overall moisture

absorption of composites. Hence, all the factors that

aﬀect the ﬁber distribution will ﬁnally aﬀect the moisture

absorption ability. These fact ors, includi ng ﬁber concen-

tration, size, shape, need to be addressed individually to

get a full understanding of the mechanism of moisture

absorption.

The problem raised above falls into the domain of

percolation theory which deals with the randomness of

the medium [18]. At the same time, the network model-

ing which takes into account the randomness of both

diﬀusion agent and medium signiﬁcantly promotes the

development of percolation theory. Thus, to some

extent, the apparent diﬀusion coeﬃcient is integrated

into percolation theory. Moreover, it provides further

understanding of composites which is lacking in diﬀu-

sion theory. One successful application of percolation

theory is the development of a percolation model con-

cerning controlled drug release system [19–25].

The present research is trying to introduce percola-

tion theory to the NFPC ﬁeld, to better understand

the mechanism of moisture absorption in natural ﬁber

reinforced composites. To this end, the moisture absorp-

tion and electrical conductivity of composites with

diﬀerent ﬁber loadings were investigated. Two new con-

cepts based on percolation theory were deﬁned: accessi-

ble ﬁber ratio and diﬀusion-permeability coeﬃcient. The

objectives are: (i) to develop a model to estimate critical

accessible ﬁber ratio; (ii) to understand the moisture

absorption and electrical cond uctivity behaviors using

estimated critical accessible ﬁber ratio and other perco-

lation concepts.

2. Theoretical approach

2.1. Diﬀu sion and Fick’s laws

Diﬀusion is the process by which matter is transported

from one part of a system to another as a result of ran-

dom molecular motions [16]. Fick ﬁrst put diﬀusion on

a quantitative basis by adopting the mathematical equa-

tion of heat conduction after realizing the analogy be-

tween the heat conduction and the diﬀusion process

F ¼D

oC

ox

; ð1Þ

where F is the rate of transfer per unit area of section, C

is the concentration of diﬀusion substance, x is the space

coordinate measured normal to the section, and D is the

diﬀusion coeﬃcient. Eq. (1) is referred to as FickÕ s ﬁrst

law.

If the diﬀusion coeﬃcient is constant, and if the con-

centration gradient is only along the x-axis, in other

words, if diﬀusion is one -dimensional, the fundamental

diﬀerential equation of diﬀusion in an isotropic medium

is derived by considering a rectangular whose sides are

parallel to the axes of coordinates as follows:

oC

ot

¼ D

o

2

C

ox

2

. ð2Þ

Eq. (2) is nor mally referred to as FickÕs second law.

Its solution for a plane sheet with uniform initial dis-

tribution and equal initial surface concentrations under

non-steady-state can be expressed by

M

t

M

1

¼ 1

X

1

n¼0

8

ð2n þ 1Þ

2

p

2

exp½ðDð2n þ 1Þ

2

p

2t

=ð4l

2

ÞÞ;

ð3Þ

where M

t

denotes the total amount of diﬀusion sub-

stance entering the sheet at time t, M

1

denotes the cor-

responding quantity after inﬁnite time, and l denotes the

half thickness of sheet.

At initial absorption stage, moisture absorption (M

t

)

increases linearly with

ﬃﬃ

t

p

; hence Eq. (2) can be simpli-

ﬁed to the following equation:

M

t

¼

2M

1

ﬃﬃﬃﬃ

D

p

ﬃﬃﬃ

p

p

ﬃﬃ

t

p

l

. ð4Þ

Thus, the average diﬀusion coeﬃcient can be calculated

as follows:

D ¼

p

4

M

2

1

l

2

h

2

; ð5Þ

where h is the slope of M

t

vs.

ﬃﬃ

t

p

plot.

380 W. Wang et al. / Composites Science and Technology 66 (2006) 379–386

2.2. Basic percolation concepts

Many properties of a macroscopic system are essen-

tially determined by the connectivity of the system ele-

ments. The properties of a system which emerge at the

onset of macroscopic connectivity within it are known

as percolation properties, e.g., conductivity.

2.2.1. Inﬁnite cluster and percolation threshold p

c

The following square lattice (Fig. 1) is a simpliﬁed

representation of the dispersion of one component in

another component. Suppose that each square repre-

sents either one unit of natural ﬁber or one unit of plas-

tic, regardless of their diﬀerences in morphology.

Natural ﬁbers are hygroscopic and ready to absorb

moisture while the plastics discussed here are highly

hydrophobic. The square is marked dark if occupied

by ﬁber; otherwise white if occupied by plastics.

Suppose that the ﬁber content here is deﬁned as the

percentage of ﬁber units over total units (ﬁber and plas-

tic) and is designated as p. Then the probability (P) that

one individual square is occupied by ﬁbers is equal to

the ﬁber content p.

At very low ﬁber content, individual ﬁbers are com-

pletely encapsulated by plastic. At a little bit higher ﬁber

content, ﬁber clusters, which are deﬁned by one ﬁber

square having at least one neighboring ﬁber square,

are formed. Fig. 1(a) shows the ﬁber distribution at

low ﬁber content. Fibers either exist individually or form

ﬁnite clusters. Since both individual ﬁbers and ﬁnite ﬁ-

ber clusters are still encapsulated by plastics, they are

un-accessible to moisture except the ﬁbers exposed on

the approximate surface of the lattice. Hence, if only

the horizontal top surface of this lattice is open to mois-

ture while two lateral surfaces are sealed, water mole-

cules cannot penetrate the lattice through ﬁbers to

reach the bottom of the lattice.

Fig. 1(b) exhibits another scenario of ﬁber distribu-

tion in the lattice. At high ﬁber content, all ﬁber squares

connect together and plastics fail to encapsulate these

ﬁbers. Now if we repeat the same experiment, water

molecules easily ﬁnd their ways to penetrate the lattice

and reach its bottom.

As shown in Fig. 1, the ﬁber clusters become large r

with increasing ﬁber content. Between the two above-

mentioned scenarios, there must be a ﬁber content at

which the ﬁber cluster ﬁrst spans from one side of the

lattice to the opposite side. This spanning cluster is

called an ‘‘inﬁnite cluster’’ ; the ﬁber content at this point

is called the ‘‘percolation threshold’’ or ‘‘critical ﬁber

content’’ and is designated as p

c

(%). Above this thresh-

old, the higher the ﬁber content, the more ﬁbers connect

to this inﬁnite cluster. This cluster serves as passages for

water molecules to travel through the lattice from one

side to the other.

2.2.2. P

1

P

1

denotes the probability that a ﬁber square

belongs to the inﬁnite cluster. In other words, it denotes

the proportion of ﬁbers belonging to the inﬁnite cluster.

Within this inﬁnite cluster, two types of ﬁbers are diﬀer-

entiated to facilitate the discussion of conductivity. As

shown in Fig. 2, ﬁbers marked with ‘‘e’’ are the dangling

ends of inﬁnite cluster; they absorb moisture but do not

serve as moisture passages and so do not contribute to

the conductiv ity of the lattice. Fibers marked with ‘‘l’’

form loops inside the inﬁnite cluster and also do not in-

crease the conductivity of the lattice. They have exactly

the same eﬀect as ÔeÕ ﬁbers. ‘‘F’’ represents ﬁbers which

form continuous passages for moisture and thus con-

tribute to the conductivity of the lattice.

If the ﬁber content is lower than p

c

, there is no inﬁnite

cluster and P

1

is zero. Otherwise, its value increases

above p

c

as follows:

P

1

ðp p

c

Þ

b

; ð6Þ

where p is the ﬁber content (%), p

c

is the percolation

threshold, and b is a critical static exponent. The value

of b is universal. It only depends on the dimension of

the lattice. For a three-dimensional lattice, b is approx-

imately 0.42 [26].

Fig. 1. Fiber distributions in plastic at diﬀerent ﬁber contents: (a) low ﬁber content; (b) high ﬁber content.

W. Wang et al. / Composites Science and Technology 66 (2006) 379–386 381

2.2.3. Percolation factor f

Flow properties, such as electrical conductivity, of

the lattice are of interest only above the percolation

threshold. Flow properties are related to the connectiv-

ity of the lattice. Lee et al. [27] deﬁned percolation factor

(f) as a representation of connectivity as follows:

f ¼ ppR

2

; ð7Þ

where p is the ﬁber content (%) of composites and R is

the average radius of the ﬁber.

Suppose that the ﬁber content is higher than percola-

tion threshold and that the inﬁnite cluster has already

formed. The low connectivity implies that the number

of moisture passages is low and that the passages are tor-

tuous and lengthy (Fig. 2(a)). Hence, it takes a long time

for mo isture molecules to travel through the lattice. The

conductivity of the lattice is also low. Fig. 2(b) shows a lat-

tice with higher connectivity. The higher number of ﬂow

passages increases the conductivity. Moreover, the more

ﬂow passages there are, the more direct (shorter) these

passages will be. This shortens the moisture travel time

from one side of composites to the other.

2.2.4. Apparent diﬀusion coeﬃcient D

The diﬀusion problem in a heterogeneous composite

is mathematically equivalent to the electrical conduction

problem [26]. In the previous session, we have intro-

duced the concept describing the relationship between

the conductivity of a composite and physical parameters

of its components. Next, the conduction properties will

be understood from another perspective by looking into

the microscopic network formed by its components.

As discussed before, the inﬁnite percolation cluster

has dangling ends and loops. These parts of the cluster

absorb the diﬀusing substance – moisture – but do not

contribute to the conduction property. In fact, accord-

ing to random walk theory of diﬀusion, these dangling

ends and loops act as ‘‘sinks’’ to slow down the trans-

port of moisture in the diﬀusing direction. By being ta-

ken into account this fact, together with other

percolation concepts, the apparent diﬀusion coeﬃcient

D holds the following equation close to the percolation

threshold [28]:

D ¼ vD

0

ðp p

c

Þ

l

; ð8Þ

where vD

0

represents a scali ng factor, and l the conduc-

tivity exponent, which is 2.0 for a three-dimensional lat-

tice. This value (2) will be adopted in present research.

3. Application of percolation theory in NFPCs

In NFPCs, the inﬁnite ﬁber cluster is a 3D-network.

In order to facilitate the application of percolation the-

ory in NFPC system, the following assumptions are

essential before applic able new concepts can be de ﬁned.

3.1. Assumptions

(1) Plastic is hydrophobic. Moisture can only pene-

trate into the composite through natural ﬁbers.

(2) The composite is void free; the moisture absorp-

tion can be quantiﬁed by ﬁber saturation point

of the accessible ﬁbers (FSP, assumed to be 30%,

dry mass basis).

(3) The samples are thin so moisture can penetrate

through the whole inﬁnite 3D-network which is

present above percolation threshold during a spe-

ciﬁc time period; all the natural ﬁbers belonging

to the inﬁnite 3D-ne twork can reach FSP after

water submersion.

(4) Free ﬁbers, which are not connected to the inﬁnite

3D-network, are categorized into two types. Type

I ﬁbers have no access to moisture as they are

dominantly entrapped in the bulk; Type II ﬁbers

are on the proximate surface, and they absorb

moisture if not on the sealed surfaces.

3.2. Acce ssible ﬁber ratio r

The accessible ﬁber ratio r was theoretically derived

from the percolation concept P

1

, which denotes the

probability that a ﬁber square belongs to the inﬁnite

cluster. According to this deﬁnition, the accessible ﬁbers

only are those belonging to the inﬁnite 3D-network in

Fig. 2. Flow passages in lattice with diﬀerent ﬁber contents: (a) with one ﬂow passage; (b) with two ﬂow passages.

382 W. Wang et al. / Composites Science and Technology 66 (2006) 379–386

NFPCs. However, the application of this theoretical ra-

tio in small samples is limited, since the moisture

absorption by free ﬁbers on the proximate surface is sig-

niﬁcant at low ﬁber loading.

In the present study, accessible ﬁbers include both the

ﬁbers belonging to the inﬁnite 3D-network and the ﬁ-

bers on the proximate surface of composite. Ratio r

can be computed as follows:

r ¼

M

1

C

FSP

; ð9Þ

where M

1

denotes the maximum moisture absorption

of sample, C denotes the ﬁber loading in composites

(percentage by weight, the name ‘‘ﬁber loading’’ is used

to diﬀerentiated the name ‘‘ﬁber content’’ in previous

section). In this research, the FSP of natural ﬁbers is as-

sumed to be 30%.

The percolation threshold of r is called the critical

accessible ﬁber ratio, designated as r

c

. It is the ratio at

which the inﬁnite 3D-network formed for the ﬁrst time.

3.3. Diﬀu sion-permeability coeﬃcient DP

From the proceeding introduction, it can be concluded

that the moisture absorption behavior of NFPCs depends

on both diﬀusion coeﬃcient and percolation network. In

this context, we deﬁne a new property of composite from

diﬀusion coeﬃcient and designate it as diﬀusion-perme-

ability coeﬃcient (DP). DP is deﬁned as follows:

DP ¼

h

ﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ

2

C r

p

; ð10Þ

where h is the slope of Fickian M

t

vs.

ﬃﬃ

t

p

plot, C is the

ﬁber loading of composite, and r is the accessible ﬁber

ratio. The unit for DP is s

0.5

.

3.4. Modeling

DP is a derivative of diﬀusion coeﬃcient D correlated

through slope h. By combining Eqs. (8) and (10), we have

DP ¼

h

ﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ

2

C r

p

¼ kðr r

c

Þ; ð11Þ

where k is a constant.

By investigating the moisture absorption behavior of

composites with diﬀerent ﬁber loadings near the thres h-

old, DP values can be calculated; then, the critical acces-

sible ﬁber ration r

c

can be obtained through the linear

regression by plotting DP vs. r.

4. Experimental

4.1. Mater ials

Rice hulls (40 mesh size), HDPE, and maleic anhy-

dride polyethylene (MAPE) were commercial products.

4.2. Sampl e preparation

HDPE and rice hulls were compounded at 185 Cand

3200 rpm. 2% MAPE was added to improve the compat-

ibility of hydrophobic polymer and hydrophilic rice hulls

and thus improve the interfacial bonding. Discharged

dumps were hot pressed in a Wabash press at 185 C

and 30 ton for 5 min, and cooled for 20 min. The dimen-

sions of the mould are 12 cm · 12 cm · 2.4 cm. Five

sheets of diﬀerent rice hull loadings, i.e., 40%, 50%,

55%, 60%, 65% by weight, were prepared.

Four blocks, 2 cm · 2 cm, were cut from each sheet.

All the samples were dried in an oven at 53 C for

24 h and then cooled down in desiccators. The edges

of these samples were sealed by unsaturated polyester.

After being cured, their dry weights were measured

before water immersion.

4.3. Experimental procedure

Samples were immersed in distilled water at room

temperature, i.e., 23 C. After speciﬁc time intervals,

samples were removed from water, their surface mois-

ture was removed by tissue paper, and their weights

(±0.0001 g) and electrical conductivities (to be intro-

duced in Section 4.5) were measured immediately.

4.4. Moisture content (M

t

)

Moisture absorption was determined by the weight

gain relative to the dry weight of the samples. The mois-

ture content of a sample was computed as follows:

M

t

¼

W

t

W

0

W

0

100%; ð12Þ

where W

0

and W

t

denote the dry weight of the sample

and the weight at any speciﬁc time t, respectively.

Equilibrium moisture absorption of samples, M

1

,

was assumed to be reached when the daily weight gain

of samples was less than 0.01%.

4.5. Calculation of electrical conductivity S

t

Initially the dry NFPC has no measurable electrical

conductivity. After water submersion, natural ﬁbers

belonging to an inﬁnite cluster absorb moisture and thus

impart electrical conductivity to composites even though

it is very low. This conductivity depends more on the

number of conduction passages rather than the ratio

of ﬁbers belonging to the inﬁnite 3D-network (Fig. 2).

Since the electrical resistance of samples was extre -

mely high and so could not be measured at the earlier

stage of moisture absorption, electrical current A

t

in

mA was measured whenever the weight measurement

took place. The electrical current was measured by dig-

ital multi-meter to the accuracy of 0.1 mA.

W. Wang et al. / Composites Science and Technology 66 (2006) 379–386 383

Fig. 3 shows the set-up of electrical current measure-

ment. Each of two identical thin steel plates was tightly

attached to the surfaces of rubber stoppers. One end of a

wire was welded to the steel plate and the other end

passed through the middle hole of the stopper and then

connected to the circuit. The upper stopper was ﬁxed to

a stand, while the lowe r one was attached to an adjust-

able platform. After the sample was put on the top of

the steel plate on the lower stopper, the height of plat-

form was adjusted until the sample touched the upper

steel plate at suﬃcient pressur e.

The output of the adapter is 13.6 V DC. The ﬁxed

resistance R

0

of this set-up is 27 X. The electrical con-

ductivity S

t

can be computed by following equation:

S

t

¼

A

t

V A

t

R

0

¼

A

t

13.6 27A

t

. ð13Þ

5. Results and discussions

5.1. Moisture absorption and calculations

Fig. 4 shows the moisture absorption as a function of

ﬃﬃ

t

p

. The values of diﬀusion coeﬃcient and accessible ﬁ-

ber ratio for each composite were calculated and are

shown in Table 1. It should be noted that the composites

with 40%, 50%, 55%, and 60% ﬁber loadings had not

showed the level-oﬀ which signals the equilibrium mois-

ture absorption. However, their electrical conductivities

(Fig. 5) already reached maximum, which suggest that

the moisture absorption was near to or already reached

equilibrium. Hence, the most recent moisture absorp-

tions of composites were adopted as M

1

.

In addition, in order to vilify the assumption 1,

HDPE ﬁlms were made and submersed in the distilled

water at room temperature. After one year, no weight

gain was detected. This observation suggests that mois-

ture only penetrate into composites through rice hulls.

5.2. Estimate of critical accessible ﬁber ratio r

c

The critical accessible ﬁber ratio r

c

can be assessed by

plotting DP against r and extrapolating to DP = 0 as

shown in Fig. 6. The value of r

c

is 0.44.

For the composite with 65% ﬁber loading, the acces-

sible ﬁber ratio was 0.91. At this ﬁber loading , ﬁbers

were highly connected. HDPE failed to encapsulate

these ﬁbers and led as high as 91% ﬁbers accessible to

moisture. As discus sed above, higher connectivity re-

sults in shorter ﬂow passages and higher conductivity.

In this case, the ﬂow passages are almost all vertical ly

running through the one side of sample to the opposite

side, rather than the tortuous ones shown in Fig. 2.

Hence, this composite reached equilibrium signiﬁcantly

Fig. 3. Experimental set-up of electrical current measurement.

0

5

10

15

20

0

600 1200

SQRT(t)(Sec)

Mt (%)

40% 50%

55% 60%

65%

Fig. 4. Moisture absorption behavior of composites with diﬀerent ﬁber

loadings.

Table 1

Values of slope of moisture absorption curve (h), diﬀusion coeﬃcient

(D), diﬀusion-permeability coeﬃcient (DP), and accessible ﬁber ratio

(r) for composites with diﬀerent ﬁber loading

Fiber

loading (%)

h (%/s

0.5

) D (cm

2

/s · 10

9

) DP (/s

0.5

· 10

4

) r

40 0.0070 3.96 1.67 0.62

50 0.0117 4.63 2.45 0.77

55 0.0124 4.50 2.11 0.75

60 0.0142 4.67 2.18 0.78

65 0.0241 8.42 3.84 0.91

0

10

20

30

0 600 1200

SQRT(t)(sec)

Conductivity(S*10

-3

)

Conductivity(S*10

-3

)

0

1

2

3

65%-First axis

40%-Second axis

50%-Second axis

55%-Second axis

60%-Second axis

Fig. 5. Electrical conductivity of composites.

384 W. Wang et al. / Composites Science and Technology 66 (2006) 379–386

earlier than other composites with lower ﬁber loadings

(Fig. 4).

Fig. 7 shows the increment of elect rical conductivity

with the increasing moisture content of composite wi th

65% ﬁber loading. The composite started showing con-

ductivity after it absorbed approximately 50% of maxi-

mum moisture. After this, conductivity increased

quickly with further moisture absorption. The pattern

of the increment of electrical conductivity suggests a dif-

fusion pro cess of moisture absorption.

Composites with 50%, 55%, and 60% ﬁber loading

exhibited similar absorption behaviors and all eventu-

ally had measurable electrical conductivities (Fig. 5).

Their accessible ﬁber ratios were almost identical

(0.76). This observation suggests that the 3D-network

inside composites did not change much within this range

of ﬁber loading and that the percolation threshold may

lie just below 50% ﬁber loading.

However, this observation deviated signiﬁcantly from

the model-estimated value 0.44. One reason causing this

discrepancy may be the over-estimate of accessible ﬁber

ratio. As made clear in the deﬁnition, accessible ﬁbers in-

clude both the ﬁbers belonging to the inﬁnite 3D-network

and the ﬁbers on the proximate surface of composite. This

inclusion of proximate surface ﬁbers more greatly aﬀects

the total accessible ratio of the composite with lower ﬁber

loading. For composite with low ﬁber loading, since the

ﬁber connectivity is low and moisture would not go far

into the composite, the proximate surface ﬁbers consist

of a higher proportion of total accessible ratio.

Fig. 4 shows that all the curves (except one with 65%

ﬁber load ing) underwent slight change of slopes. If the

above proposal is valid, then the reason causing diﬀerent

degrees of slope change is explicit. For composite with

65% ﬁber loading, the accessible ﬁber ratio is as high

as 0.91; compared to this high ratio, those proximate

surface ﬁbers which do not belong to the inﬁnite 3D-net-

work con sist of a minor proportion and are negligible.

Hence, there was no observable slope change during

the initial absorption period.

For composite with 40% ﬁber loading, no electrical

conductivity was detected. This impl ies that this ﬁber

loading was lower than percolation threshold and that

there was no inﬁnite 3D-network. Here, the observed

accessible ﬁber ratio (0.62) was even higher than its critical

value, inconsi stent with the implication from the observa-

tion of electrical conductivity. Again, the over-estimate of

accessible ﬁber ratio can be applied to explain this dis-

agreement. At low ﬁber loading, the ﬁber connectivity is

low, so the proximate surface ﬁbers not belongi ng to inﬁ-

nite network consist of a signiﬁcant proportion of the

accessible ﬁbers. This eﬀect decreases with increasing ﬁber

loading. The higher eﬀect on composite with 40% ﬁber

loading resulted in greater slope change compared to

composites with 50%, 55%, and 60% ﬁber loadings.

It is believed that, in NFPCs, natural ﬁbers are encap-

sulated by hydrophobic plast ics. From this research, it

can be concluded that the plastics do encapsulate the

natural ﬁbers and prevent the moisture of composites

at low ﬁber loading. However, at high ﬁber loading,

e.g., 65%, this function decreases. Precautions are

needed to prevent moisture absorption when high ﬁber

loading is applied.

6. Conclusions

Through the proceeding discussion, this study led to

the following conclusions:

1. At high ﬁber loading when ﬁbers are highly con-

nected, the diﬀusion process is the dominant mecha-

nism; while at low ﬁber loading close to and below

the percolation threshold, the formation of a contin-

uous network is key and hence percolation is the

dominant mechanism.

2. The model can be used to estimate the threshold

value which can be in turn used to explain moisture

absorption and electrical conduction behavior. How-

ever, it is still on the preliminary stage; further work

is needed to improve the accuracy of its prediction.

0

5

10

15

20

0 600 1200

SQRT(t)(sec)

M

t

(%)

0

10

20

30

Electrical conductivity

(S*10

-3

)

Moisture content Electrical conductivity

Fig. 7. The moisture absorption and electrical conductivity for

composite with 65% natural ﬁber.

y = 0.0765x - 0.0341

R

2

= 0.8612

0

0.01

0.02

0.03

0.04

0 0.5

1

Accessible fiber ratio r

DP (/sec

0.5

)

Fig. 6. Diﬀusion-permeability coeﬃcient vs. accessible ﬁber ratio plot.

W. Wang et al. / Composites Science and Technology 66 (2006) 379–386 385

Acknowledgments

We are grateful to Natural Science and Engineering

Research Council of Canada (NSERC) for funding the

present study. We are also grateful to Composite Build-

ing Products IntÕl. Inc.

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