Upload
lenguyet
View
219
Download
1
Embed Size (px)
Citation preview
CHAPTER-V 100
CHAPTER-V Novel organic-inorganic nanocomposite membranes of
chitosan for pervaporative dehydration of ethanol
ABSTRACT
Preyssler type heteropolyacid, H14[NaP5W30O110], was used as filler to modify
chitosan membranes to develop nanocomposite membranes by solution casting for
use in pervaporation dehydration of ethanol. The physico-chemical properties of the
membranes were investigated. Membrane permeability and selectivity was improved
by adding filler nanoparticles. With increasing filler content, amorphous regions of
the membranes increased due to homogeneous mixing of filler particles in chitosan
matrix. Compared to plain chitosan membrane, nanocomposite membranes exhibited
high thermal stability and improved separation performance. A dramatic
improvement in separation factor of 35,991 for 5 wt.%-loaded nanocomposite
membrane with 10 wt.% feed water mixture at 300C from a base value of 96 observed
for nascent chitosan membrane, is the highest achieved so far using chitosan-based
membrane for ethanol dehydration. Increase of feed water composition and
temperature on pervaporative dehydration of ethanol resulted in a drastic reduction of
membrane performance. Diffusion behavior of water and ethanol through the
membranes was analyzed by Fick’s equation. Pervaporation data were explained in
terms of fractional free volume and porosity, hydrophilicity, surface free energy and
roughness of the membranes. Pervaporation performance was analyzed using the
principles of sorption-diffusion model as well as Flory-Huggins theory.
Results of this chapter are communicated to Journal of Membrane Science
CHAPTER-V 101
V.1. INTRODUCTION
Development of pervaporation (PV) membranes exhibiting unique barrier
properties for ethanol dehydration would profoundly impact the existing
technologies as well as provide entirely new materials. This breakthrough
potential has led to considerable research efforts [1] in developing organic-
inorganic hybrid nanocomposites, which seemingly offer a relatively accessible
means of altering properties of the already existing materials, without recourse to
synthesizing entirely new molecules. Through hybridization, it is possible to
capture the desirable attributes of the organic and inorganic components in a single
composite that possibly offers new synergistic properties. In PV separation,
however, ongoing efforts [2-6] continue to explore the utility of incorporating
nanoparticles into dense membranes for a variety of reasons that mainly includes
improvement in mechanical strength and barrier performance. Incorporation of
nanosized nonporous inorganic fillers increases the diffusion path length for
penetrant molecules to transport across the membrane due to creation of tortuous
path around filler particles [7].
Recent interests in developing nanocomposite membranes (NCMs) have
widely explored the capability of existing nascent membranes used for PV
separation [8-11]. Among the polymers employed, chitosan (CS), which belongs
to a family of polysaccharides and exhibits poor polymer chain packing in part due
to its rigid backbone, low interchain cohesion having hydroxyl as well as amine
reactive groups, high free volume giving low selectivity to liquid penetrants, has
been widely explored in combination with other systems in PV separation [12-14].
Even though CS membrane was first used in ethanol dehydration by Masaru et al
[15], subsequently crosslinking [16,17], blending and polyelectrolyte complex
linkage [18,19] strategies have been used to enhance PV performance of CS
membrane. As of now, no reports are available on NCMs of CS containing
Preyssler type heteropolyacid, H14[NaP5W30O110] i.e., H14-P5 for PV dehydration
CHAPTER-V 102
of ethanol. The available data of flux and selectivity of chitosan or chitosan-based
membranes are not high enough to use them in large-scale operations for ethanol
dehydration. By adding nanofillers, such as H14-P5, low water-selective nature of
amorphous CS can be improved due to the creation of free volume and tortuous
pathways for molecular diffusion to occur [20].
Despite rich hydrophilic hydroxyl and amino groups, CS membrane alone
is not efficient for effective ethanol dehydration. Therefore, its membrane
performance can be improved by combining superior separation performance of
the rigid absorptive inorganic nanoparticles to derive an ideal NCM with desirable
membrane properties. The H14-P5 is chosen as filler because of its strong Bronsted
acidity function with high hydrolytic and thermal stability; having 14 acidic
protons, it is an efficient ‘super acid’. The Preyssler’s anion has a cyclic assembly
of five PW6O22 units, each derived from Keggin’s anion [PW12O40]3− by the
removal of two sets of three corner-shared WO6 octahedra [21]. The bridging and
terminal oxygen atoms are on the periphery of the structure and therefore, are
available to associate with water molecules to form hydrates that are thought to
enhance water selectivity.
Realizing the usefulness of this heteropolyacid to enhance barrier performance,
the NCMs of CS loaded with different amounts of H14-P5 are considered. Membranes
are characterized and used in PV dehydration of ethanol. Ethanol separation is
chosen, since it is the most common renewable biofuel derived from fermentation,
wherein ethanol dehydration has been found to be difficult due to the formation of
azeotropic mixture. The prevailing industrial technologies like azeotropic or
extractive distillation suffer from high energy consumption and the need for an
auxiliary agent. Comparatively, PV has distinct advantages such as a low-energy cost
and ecofriendly nature, which is considered to be a promising alternative [22].
Sorption and diffusion anomalies as well as PV separation data are analyzed based on
the principles of sorption-diffusion model [23] as well as Flory-Huggins theory [24].
Separation optimization of NCMs was investigated by studying the effect of
CHAPTER-V 103
temperature; feed water composition, thermodynamic interaction parameters as well
as surface roughness and free energy, hydrophilicity, proton conductivity, ion
exchange capacity, porosity and free volume effects.
V. 2. RESULTS AND DISCUSSION
V.2.1. X-RD
As displayed in XRD curves of Fig. V.1, crystalline peaks of CS
increasingly diminish by the addition of H14-P5 nanoparticles, giving more of
amorphous regions. Pure CS has peaks at 2θ of 14.6o and 20.3o, but for NCMs,
these have shifted to higher angles of 14.8o to 15.3o and 20.5o to 20.8o, indicating
increasing amorphous nature of CS, enabling high permeation flux. The
interaction between nanoparticles and CS facilitates good dispersion of
nanoparticles.
Fig. V.1. X-RD curves of CS, NCM-5, NCM-10 and NCM-15 membranes.
10 20 30 40 50
H14
-P5
2θ
CS
NCM-5
NCM-10
Cou
nts
NCM-15
CHAPTER-V 104
100 200 300
0.0
0.2
0.4
0.6
61.7 oC
60.2 oC55.2 oC
54.3 oC
294 oC291 oC
288 oC
287 oC
NCM-15
NCM-10
NCM-5
CS
Hea
t flo
w (m
W)
Temperature (oC)
V.2.2. DSC
DSC curves of CS and NCMs presented in Fig. V.2, CS shows the first
endothermic melting point due to the release of moisture at 54.3oC, whereas
NCM-5, NCM-10 and NCM-15 release moisture at 55.2, 60.2 and 61.7oC,
respectively, suggesting increased water retention capacity of NCMs compared to
CS. Exothermic melting point due to polymer degradation is observed at 287oC for
CS, while for NCM-5, NCM-10 and NCM-15 at 288, 291 and 294oC, respectively,
indicating improvement in thermal stability after incorporation of H14-P5
nanoparticles into CS matrix.
Fig. V.2. DSC thermograms of CS, NCM-5, NCM-10 and NCM-15 membranes.
CHAPTER-V 105
V.2.3. DMTA
DMTA is used to investigate microstructure of the membranes. The tan δ
plot as a function of temperature displayed in Fig. V.3 suggests that Tg is the peak
temperature, while relaxation strength corresponds to the height of tan δ peak.
Storage modulus, E′, cross-linking density, νe and number of moles of elastically
effective network chains/cm3, Mc (molar mass between crosslinks) of the
membranes are calculated as: RTE mI
e Φ= ρυ 3 and emcM υρ= where modulus
EI is determined from DMTA analysis at a frequency of 1 Hz at 30oC taking ρm for
membrane density, φ as front factor (φ=1) and RT the energy term.
The systematically increasing values (see Table V.1) of νe for increasing
loading of CS membrane as in NCMs indicate tighter networks compared to CS;
for the latter, Mc is highest, which decreased with increasing filler loading,
suggesting increasingly stronger membranes. Notice that E′ values of NCMs are
higher than CS, which increased with increasing H14-P5 content, while the
corresponding tan δ peaks are shifted to higher temperatures, showing broader
peaks. The peak values increased from 131.56 to 152.48, 152.6 and 154°C for CS,
NCM-5, NCM-10 and NCM-15, respectively, suggesting that the interaction
between hydroxyl groups of CS and PO4 and WO6 of H14-P5 are responsible for
increasing the crosslink density as well as Tg of NCMs with increasing loading.
The observed lower relaxation strength at higher H14-P5 content indicates that
more of CS chain segments are chemically bonded or entrapped within the H14-P5
cluster, restricting the mobility of CS chains.
CHAPTER-V 106
Table V.1. Calculated parameters of the membranes from DMTA data at 30oC and details
of PALS.
V.2.4. SEM
The distribution of nanoparticles in the CS matrix exerts influence on
transport characteristics. Typical SEM images of CS and NCMs displayed in Fig.
V.4(a) – V.4(e) did not clearly reveal the surface roughness, since smooth surfaces
are observed for both CS and NCM-5 with no phase separation or aggregation of
H14-P5 particles. In case of NCM-15, one can visualize a slight roughness of the
surface. A typical cross-section of NCM-15 suggests the homogenous distribution
of filler particles with no agglomeration and no cracks in the bulk of NCMs.
Tg EI
(Pa)
x 10-9
ρ
(g/cm3)
νe
(mol/cm3)
Mc
(g/mol) τ3
±0.02
ns
I3
±0.2
%
Vf
± 1.5
Å3
fv
± 0.12
%
CS 131.6 0.96 0.8079 0.41 1.95 1.78 8.74 77.51 1.22
NCM-5 152.5 1.58 0.8071 1.48 0.55 1.74 10.8 73.9 1.44
NCM-10 152.6 1.35 0.8085 1.68 0.48 1.73 11.2 73.11 1.47
NCM-15 154.0 1.40 0.8116 1.74 0.47 1.70 12.3 70.5 1.56
CHAPTER-V 107
Fig. V.3. DMTA curves for EI, EII and tan δ of CS, NCM-5, NCM-10 and NCM-15
membranes.
50 100 150 200
-3.0G
0.0
3.0G
6.0G
9.0G
12.0G
15.0GCS
Temperature (oC)
EI /
EII (d
ynes
/cm
2 )
0.06
0.09
0.12
0.15
0.18
0.21Tg = 131.56oC
tan
δ
50 100 150 200
0
10G
20G
30G
40G
50G
tan δ
EII
EI
Tg= 153.14oC
NCM-15
Temperature (oC)
EI / EII
0.10
0.12
0.14
0.16
0.18
0.20
0.22
tan
δ
50 100 150 2000.0
5.0G
10.0G
15.0G
20.0G
25.0G
30.0G
35.0G
40.0G
45.0G
50.0GNCM-10
Tg=152.6 oC
EI
EII
tan δ
Temperature (oC)
EI / EII
0.1
0.2
0.3
0.4
0.5
tan
δ
50 100 150 200
5.0G
10.0G
15.0G
20.0G
25.0G Tg = 152.48 oC
EII
EI
NCM-5
Temperature (oC)
EI / EII
0.100
0.125
0.150
0.175
0.200
tan
δ
CHAPTER-V 108
Fig. V.4. SEM images of (a) CS, (b) NCM-5, (c) NCM-10, (d) NCM-15 and
(e) cross sectional view of NCM-15 membrane.
(a) (b)
(c) (d)
(e)
CHAPTER-V 109
V.2.5. AFM
From the AFM images, surface roughness of the membrane was calculated
in terms of Rq, a classical amplitude parameter used to assess the surface texture.
The Rq actually measures the average length between peaks and valleys, and
deviation from the mean line on the entire surface within the sampling length.
Hence, it is a good general description of the height variation, but is insensitive to
wavelength and occasional high peaks and low valleys. The AFM pictures (see
Fig. V.5.)show increasing surface roughness with increasing H14-P5 content,
reflecting increased hydrophilicity. However, SEM images (Fig. V.4) did not
reveal clearly the surface roughness between different membranes.
The Rq values of the membranes show increasing trends with increased
loading, namely, CS (365 nm) < NCM-5 (513 nm) < NCM-10 (704 nm) < NCM-
15 (948 nm) and the highest Rq for NCM-15 indicates greater surface roughness.
Therefore, NCM-15 selectively sorbed more water from the feed than others, but
due to its high fractional free volume (fv=1.56 %), along with water molecules,
some ethanol may also be permeated, thereby reducing membrane selectivity to
water, but increasing the flux and permeance. The 3-D optical images of Fig. V.6
show similar trends to AFM pictures.
CHAPTER-V 112
V.2.6. Surface free energy from contact angle
Membrane surface property has an impact on its separation performance. A
smooth surface with lower surface energy has higher contact angle, θ for water
and higher hydrophobicity. Our measurements show decreasing contact angle with
increasing filler loading, namely, CS (88) > NCM-5 (83) > NCM-10 (80) > NCM-
15 (76) (see Fig. V.7), which indicates increasing hydrophilicity. From the contact
angle data, surface free energy was calculated using Young’s equation. [25]:
slsvlv cos γγθγ −= (V.1)
where γlv is surface tension of water in equilibrium with its saturated vapor, γsv is
surface tension of membrane in equilibrium with saturated vapor of water and γsl is
interfacial tension between membrane and water. As per Neumann’s theory of
equation of state, surface tension is expressed as [26]:
)( 2000124702 svlv.svlvsvlvls e γγγγγγγ −−×−+= (V.2)
upon solving and simplifying using Taylor series, we get,
22
4421
2sec
41
2sec
41
lvlv
lvlv
lvsv γω
θωγ
γθωγ
γγ −−
+±
+= (V.3)
where ω = 0.0001247. Thus, we have calculated using the known values of
γlv, ω and θ . Now rewriting eq. (V.3) for the best fit γsv, we get,
( ) ( ) ( )ω
γωγ
θγωγ
θγγ21
42sec
42sec 2
244−−
+−+= lv
lvlv
lvlvsv
(V.4)
Contact angle data and the calculated γSV values for different membranes are listed
in Table V.2. The γsv values of the NCMs increased with increasing filler loading,
but contact angle decreased.
CHAPTER-V 113
Table 2. Some relevant data on membrane performance and their PV performances in 10 wt. % feed water mixture at 30oC.
σ = Proton conductivity; θ = Contact angle in degree; γSV = Surface energy; IEC= Ion exchange capacity; a† = not
determined.
Membranes IEC
(meq/g)
σ
(S/cm) x 103
θ (o) γSV
(mJ/m2)
Porosity
(%)
Vf
(%)
DS
(%)
J
(kg/m2h)
Permeance
(g/m2hkPa)
βij αij
Jw Je
CS 1.35 a† 88 28.4 30.2 1.22 15.2 0.09 6.8 3.5 96 8
NCM-5 1.64 1.04 83 31.8 43.8 1.47 22.4 0.11 10.2 0.01 35991 2725
NCM-10 1.80 2.52 80 33.8 45.0 1.50 29.9 0.13 11.6 0.07 8173 613
NCM-15 2.10 2.56 76 36.5 48.0 1.56 44.4 0.18 17.3 0.11 6914 549
CHAPTER-V 114
Fig. V.7. Contact angle and degree of swelling vs. amount of H14-P5 loading at
300C.
V.2.7. Membrane performance
V.2.7.1. Influence of filler nanoparticles on extent of swelling, ion exchange
capacity and proton conductivity
Close proximity in solubility parameter, δ of 43.04 J1/2/cm3/2 for CS to that
of water (δ = 47.8 J1/2/cm3/2) [27] contributes to its high water selectivity and flux
during ethanol dehydration. Unfortunately, high hydrophilicity of CS encounters
high degree of swelling, DS, at high water concentration, thereby reducing its
0 5 10 15
76
78
80
82
84
86
88
H14-P5 loaded (wt. %)
Con
tact
ang
le (d
eg)
15
20
25
30
35
40
45
Deg
ree
of sw
ellin
g (%
)
CHAPTER-V 115
membrane performance. Some studies in the literature attempted to improve the
PV performance of chitosan-based membranes for ethanol dehydration [28-30],
but the results are best of academic interests and little of practical significance due
to the observed low separation factors and fluxes. The present study aims to
improve the PV performance of chitosan-based membranes by combining its
favorable properties, namely, flexibility and processability with those of H14-P5
nanoparticles as assessed by proton conductivity and ion exchange capacity data
of NCMs. Increase of filler concentration intensifies the ionic interaction with CS
polymer to boost its performance. However, only the appropriate amount (5 wt.%)
of filler loading out of the two other combinations (10 and 15 wt.% of filler)
investigated have produced NCMs with optimum efficiency for ethanol
dehydration.
It is visualized that H14-P5 nanoparticles used as inorganic fillers into the
dense CS matrix will help to adjust the chain packing density by creating tortuous
pathways and free volume space for easy transport of liquids. By incorporating the
nanoparticles, it is shown that the crystalline domains of CS would diminish with
the appearance of amorphous phase regions (see XRD profiles in Fig. V.1), due to
the disruption of intermolecular hydrogen-bonding of the original CS crystallites,
as a result of favorable interaction between H14-P5 nanoparticles and the CS
chains. From a close examination of degree of swelling, DS data of Table V.1, we
find an increase from 15.2 % for 10 wt. % feed water mixture at 30oC for the
unfilled CS to 22.4 for NCM-5, reaching a maximum value of 44.4 % for NCM-
15, which indicates increasing water sorption capacity of NCMs than the CS. Such
an increase in swelling is due to favorable interaction of the nanoparticles with the
CS matrix. This dependence is supported by increased membrane hydrophilicity
(due to decreasing contact angle) and increase in DS of NCMs with increasing
filler loading for 10 wt.% water-ethanol feed mixture at 300C as displayed in Fig.
V.7.
CHAPTER-V 116
From the results of Table V.2, one can also observe close interdependencies
between extent of swelling, ion exchange capacity, IEC and proton conductivity.
Increased swelling resulted in increased IEC as well as proton conductivity with
increasing filler loading; this is due to increased mobility of ions in the water
phase. A high membrane swelling leads to high mobility of water molecules, both
of which enhance flux or permeance to water. Due to the formation of higher
number of nano-trapping levels in NCMs, interaction of solvent molecules with
the CS matrix will be facilitated, thus contributing to higher proton conduction. In
other words, protons available in H14-P5 take maximum advantage of the
polymeric voids in the NCM matrix to exhibit high conductivity.
As shown in Fig. V.8, higher proton conductivity of NCM-10 and NCM-15
membranes than NCM-5 is a clear evidence of the increase in DS with proton
conductivity. The presence of H14-P5 ameliorates membrane hydrophilicity by
forming hydrogen-bonding between CS chains and the Keggin anion, thus
increasing proton conductivity with increasing temperature, since the process is
thermally activated. The IEC also varies similar to proton conductivity, since both
are influenced by the extent of swelling and that water influences the cluster and
channel size, plasticizes and modifies the membrane properties such that the
observed increase in DS is the result of water retention capacity of filler
nanoparticles due to H-bonding interaction of H2O with PO4 or WO6 groups of
H14-P5. These effects contribute to the improved membrane performance with
filler loading, as will be explained further in section V.2.7.2.
Conductivity increases with increasing temperature due to increased
mobility of water as well as structural reorientation of the matrix. Proton transport
in these systems occurs by the Grotthuss mechanism in which proton forms
hydrogen-bond with water molecules to exit as H3O+, hopping from one active
component to other through a tunneling mechanism. The fact that both proton
conductivity and IEC increase with increasing H14-P5 loading as well as with
temperature supports the observations of swelling results. Notice that IEC is only
CHAPTER-V 117
an indirect, but reliable approximation of proton conductivity. The IEC results
shown in Table V.2 increase with filler loading, and also, proton conductivity
increases to double compared to NCM-15 (6.3 x 10-3 S/cm at 110oC) becomes
twice higher than NCM-5 (3.1 x 10-3 S/cm), suggesting increased interactions of
nanoparticles at higher loading. Almost identical optimum proton conductivities of
NCM-10 and NCM-15 suggest the threshold loading capacity of the membranes.
Arrhenius activation energies calculated from proton conductivity vs temperature
plots shown in Fig. 8 are higher for NCM-5 (32.7 kJ/mol) than for NCM-10 (22.6
kJ/mol) and NCM-15 (25.9 kJ/mol), and these values are in the range observed for
Grotthuss mechanism. High activation energy of NCM-5 suggests its high water
content and hence, high separation ability, since some excess water molecules
present in the membrane are likely to be involved in hydrating H14-P5 and that
water molecules available for hopping mechanism are lower, thereby increasing
the permeation flux and selectivity to water.
V.2.7.2. Influence of filler nanoparticles on separation factor, flux, selectivity
and permeance
As per DMTA data given in Table V.1, introduction of H14-P5 nanoparticles
restricts the chain segmental mobility and increases the membrane strength.
Crosslink densities increase with increasing wt.% loading of H14-P5, while molar
mass between crosslinks decreases, due to chain packing, but storage modulus
increases. Separation factor and flux along with selectivity and permeance results
for 10 wt.% feed water composition at 300C are displayed in Fig. V.9 as a function
of filler loading. Notice that a separation factor of 96 and selectivity of 8 for CS
increased dramatically to its highest separation factor of 35,991 and a selectivity
of 2725 with only a moderate increase in flux from 0.090 kg/m2h to 0.113 kg/m2h
for NCM-5, while permeance of 1.05 x 108 kg/m2s for CS increased to 1.05 x 108
kg/m2s for NCM-5 compared to the much lower separation factors of 8,173 and
6,914 and selectivities of 613 and 549 for NCM-10 and NCM-15 membranes;
however, water permeances of 1.83 x 108 and 2.56 x 108 kg/m2s for NCM-10 and
CHAPTER-V 118
NCM-15 are observed as well as a slight increase in fluxes of 0.130 and 0.182
kg/m2h.
Notice the sudden increases in separation factor and selectivity values for
NCM-5, which declined very fast with higher loadings, namely, 10 and 15 wt.%,
probably due to particle agglomeration, leading to a more heterogeneous
structure. Also, the large amount of Keggin anion disrupts the diffusion path
length by blocking the voids of NCM, but at lower loading, the polymer chain will
readjust to allow more water molecules to transport across the membrane, thus
increasing selectivity to water. In general, all the NCMs showed increasing
selectivity and separation factor as well as flux and permeance compared to
unfilled CS membrane. From a molecular viewpoint also, ethanol is a bigger and
lesser polar molecule than water, will have restricted permeability, thus favoring
more water permeation.
In a previous study by Liu et al [31], silica nanoparticles modified with
sulfonic groups provide extra free volume to the chitosan chains, consequently
altering the spaces for water molecules to permeate through. With the addition of 5
parts per hundred of functionalized silica into CS, the resulting membrane
exhibited a separation factor of 919 (a much lower value than ours) and a flux of
0.410 kg/m2.h, which is quite larger than what we observed for PV dehydration in
10 wt.% water feed mixture with ethanol at 700C. A similar effect was observed
by Chen et al., [29], wherein separation factor of water increased to a maximum
value of 597 at 10 wt.% loading of 3-aminopropyl-triethoxysilane for 15 wt.% of
feed water mixture with ethanol at 500C, and later decreased with increasing filler
loading, but permeation flux kept increasing to reach a value of 0.887 kg/m2.h.
Wang et al., [32] investigated the effect of clay content on pervaporation
performance of 90 wt.% aqueous ethanol solution through polyamide/clay
nanocomposite membrane, wherein they observed a sharp decrease of separation
factor upon increasing the clay content. However, compared with the typical
literature work [33], our results represented by 33,991 for separation factor with a
CHAPTER-V 119
somewhat low flux of 0.113 kg/m2.h for 10 wt.% aqueous ethanol solution in feed
at 300C, which that compared to many other membranes, the present NCMs
showed relatively much higher separation factors with comparable fluxes to water.
Efforts are underway to increase the fluxes. Both high separation factor and
permeation flux are expected for a permeation dehydration operation, and most
case, a trade-off exists between these two factors.
Fig. V.8. Plots of (a) proton conductivity vs temperature and (b) lnσ vs. 1000/T.
CHAPTER-V 120
Fig. V.9. Separation factor, flux, selectivity and permeance of water as a function
of filler loading at 300C for 10 wt.% water-containing feed mixture.
0 5 10 150
5000
10000
15000
20000
25000
30000
35000
40000(a)
H14-P5 loading
Sepa
ratio
n fa
ctor
,βij
0.08
0.10
0.12
0.14
0.16
0.18
Flux
(kg/
m2 h)
0 5 10 15
0
1000
2000
3000
4000
5000
6000
H14-P5 loading
Sele
ctiv
ity,α
ij
0.8
1.2
1.6
2.0
2.4
2.8(b)
J i x 1
08 (kg/
m2 s)
CHAPTER-V 121
V.2.7.3.Influence of filler nanoparticles on free volume and surface morphology
Free volume in NCMs plays a major role in determining the overall
performance of the membranes. In this work, positron annihilation lifetime
spectroscopic analysis (PALS) was used for the analysis of free volume in
polymers. As per free volume theory, diffusion is not a thermally activated process
as in the molecular model, but is assumed to be the result of random redistribution
of free volume voids within a polymer matrix [34]. Voids are formed during the
statistical redistribution of free volume within the matrix. The effect of filler
loading on free volume, fractional free volume and porosity of the membranes, is
shown in Fig. V.10, while numerical data are presented in Table V.2. It is
observed that fractional free volume and porosity increase in a similar manner
with filler loading.
The decrease in free volume is due to the restricted mobility of chain
segments in the presence of filler particles, resulting in reduced free volume
concentrations of the matrix, since the contact surface area between the filler and
the matrix is higher in NCMs, thus reducing free volume concentration. It is also
observed that % fractional free volume increases with filler loading, which can be
attributed to the aggregation of filler nanoparticles with a consequent additional
void formation. The impact of nanoparticles on free volume and the barrier
performance has been discussed before [35], wherein it was concluded that
permeability of NCMs is mainly influenced by the fractional free volume effects.
Thus, at H14-P5 content > 5 wt. %, higher fractional free volume or lower free
volume originated from the phase separation of organic phase and inorganic phase
becomes non-selective, resulting in a reduced water selectivity and separation
factor. Such effects were also observed earlier on other membrane systems [4,5].
Overall, the data from PALS indicate that addition of filler increases the accessible
free volume in NCM and that the nanoparticles are able to disrupt the packing of
rigid, bulky CS chains, thereby subtly increasing the free volume available for
molecular transport.
CHAPTER-V 122
As displayed in Fig. V.10, increase of filler loading has a systematic
increase in % porosity and % fractional free volume, due to thermodynamic or
kinetic considerations. This trend is also reflected in AFM and SEM pictures as
well as optical images. Surface free energies calculated from contact angle data
and presented in Table V.2 also show increasing trend with increasing filler
loading. For instance, a surface energy of 28.4 mJ/m2 found for CS increased to
36.5 mJ/m2 for NCM-15. The surface energy of CS calculated by atomistic
simulations [36] using the minimized amorphous CPK model of CS (Fig. V.11)
also gave at higher loadings. The height profiles of optical images are based on
automatically captured images; height, width, and height difference on the surface
are plotted in Fig. V.6 in which color bars indicate the heights of 3D images. The
highest position is displayed in red, while the lowest is in blue. NCM-5 shows the
lowest height profile compared to NCM-10 and NCM-15, suggesting its favorable
surface properties and interaction of filler nanoparticles with the CS matrix to
enhance the barrier performance. However, due to poor compatibility of CS with
H14-P5 nanoparticles at higher loading, the membrane performance also becomes
lower. Moreover, due to a decreased chain-packing relaxation ability of NCMs at
higher filler loading, lower values of αs and αd are observed for NCM-10 and
NCM-15 compared to NCM-5.
V.2.7.4. Influence of filler nanoparticles on sorption and diffusion selectivity
Figure V.12 displays the dependence of sorption selectivity and diffusion
selectivity on filler loading for 10 wt.% feed water composition mixture at 300C.
Notice that sorption selectivity curve is higher than diffusion selectivity, which
suggests the dominant effect of sorption process than the diffusion. At increased
filler loading, both these parameters increase, reaching maxima at 5 wt.% of H14-
P5, followed by a steep decline of diffusion selectivity and a somewhat steady
decline of sorption selectivity with increasing filler content of the matrix. At lower
filler loading, however, the observed high selectivity is due to increased favorable
interaction of water molecules with nanoparticles in the NCM-5 matrix.
CHAPTER-V 123
0 5 10 15
30
40
50
60
70
80
Free volume size in Å3 (Vf ) Porosity (%) Fractional free volume (%) (fv)
H14-P5 loading (wt. %)
Poro
sity
(%) /
Vf
1.2
1.3
1.4
1.5
1.6
f v (%)
Fig. V.10. Plots of porosity, free volume and fractional free volume vs amount of
H14-P5 loading at 30oC.
Fig. V.11. Amorphous (CPK) model of chitosan (colors: carbon atoms - grey,
hydrogen – white, oxygen – red and nitrogen – blue).
CHAPTER-V 124
On comparing the theoretically calculated sorption selectivity curve (dotted
line) with the experimental sorption selectivity curve (solid line) as in Fig. V.12,
we find that the theoretical curve falls lower than the experimental curve
throughout the filler composition. However, the changes observed in experimental
results of NCMs are due to surface adsorption of H14-P5 particles. Sorption
selectivity is always higher for NCMs than the unfilled CS due to higher water
selectivity of NCMs, meaning large amount of water molecules get adsorbed by
the hydrophilic H14-P5 particles, making it more hydrophilic, thereby extracting
higher amount of water on permeate side, consequently enhancing the water
selectivity.
The above-mentioned effects can also be explained on the basis of free
volume effects in polymers, since higher fractional volume or lower free volume
are originated from the phase separation of organic polymer phase and inorganic
phase, which becomes non-selective at higher filler loading, resulting in a
reduction of sorption selectivity as well as diffusion selectivity with increasing
filler loading; similar trends were also observed in the literature [4,5]. The two
main factors that influence sorption selectivity of the films are the availability of
free volume in the polymer matrix and chemical compatibility between the
polymer chain and the solvent mixture. A higher material volume accessible for
liquid sorption due to higher flexibility of NCM-5 than NCM-10 and NCM-15
(Table V.1), would increase the water permeation in NCM-5 compared to NCM-
10 and NCM-15. However, the decline in sorption and diffusion selectivity at high
filler loadings is due to the tortuous pathways for permeating molecules to travel
across the nanocomposite matrix.
The reduced sorption and diffusion selectivity at higher loadings is,
therefore, influenced by the geometry of the filler as well as molecular level
interaction of the matrix with the filler, resulting in a high volume. Similarly,
decrease in diffusion selectivity with extent of filler loading is because of the
increasing aggregation of nanoparticles at higher concentrations, resulting in the
CHAPTER-V 125
0 5 10 150
100
200
300
400
500
600
αs exptal αs calc αd
Amounts of H14-P5 loaded (wt. %)
Sorp
tion
sele
ctiv
ity (α
s )
0
3
6
9
12
15
Diff
usio
n se
lect
ivity
(αd )
weakening of polymer chains, thus facilitating slower diffusion selectivity.
However, the penetrant molecules can easily pass through the inter-phase between
the polymer matrix and the filler particles at lower loadings, but at higher
loadings, they seem to experience a difficult pathway because of the molecular
level dispersion of nanoparticles in the matrix as also observed by PALS data.
Fig. V.12. Sorption selectivity and diffusion selectivity vs. H14-P5 loading (dotted
curve for sorption selectivity represents theoretical calculations)
V.2.7.5. Influence of feed water composition on pervaporation performance and
diffusion coefficient
Feed water composition exerts a considerable effect on membrane
performance as seen from the plots of separation factor, flux, selectivity and
permeance vs feed water composition at 300C displayed in Fig. V.13. Feed water
composition was varied from 10 to 40 wt.% by maintaining the feed temperature
300C. Both separation factor and selectivity show a drastic decline by increasing
CHAPTER-V 126
water composition of the feed mixture. The selective separation of water through
NCMs at higher feed water composition is attributed to increased hydrophilic-
hydrophilic interactions between H14-P5 nanoparticles and the CS matrix.
In a general case of increasing water content, permeation flux also
increases, while separation factor decreases because of the enhanced swelling of
NCMs and such increased membrane swelling is likely to exert a negative effect
on separation factor because the swollen and plasticized upstream membrane layer
permeates a large amount of water molecules by rejecting most of ethanol. On the
other hand, ethanol permeance is much smaller by 2-3 orders of magnitude than
water, but not displayed graphically, to reduce the number of presentations.
Swelling characteristics of NCMs are determined in terms of equilibrium swelling
ratio. Fig. V.14 illustrates the swelling behaviors of all the membranes in varying
amounts of feed water composition at 300C. A smaller degree of swelling of CS
suggests that its permeation flux in pervaporation separation of water is small and
that swelling increases in the order: CS > NCM-5 > NCM-10> NCM-15.
Diffusion coefficient plays an important role in understanding the transport
across the membrane during pervaporation and hence, diffusion coefficients were
estimated from the Ficks theory. These data plotted in Fig. V.15 as a function of
feed water composition, suggest decreasing trends for water diffusion in case of
CS, NCM-5 and NCM-10 up to 30 wt.% of water, beyond which they slightly
increase with increasing feed water concentration. On the other hand, for ethanol,
diffusion coefficients increased continuously with increasing feed water
composition. Diffusion of water is much higher than ethanol by two orders of
magnitude throughout the entire range of feed composition, but increase with
increasing feed water content, thus contributing to increased water flux and
permeance. For an ideal permeation where penetrant molecules do not plasticize
the membrane, but independently permeate through the membrane, then
permeation of each liquid component would be independent of feed water
composition, thus leading to constant flux over the entire feed water composition.
CHAPTER-V 127
On the other hand, increasing water sorption capacity of NCMs is the result of
increased diffusion path lengths for the penetrant molecules as a result of
increased free volume or porosity as well amorphous regions of the matrix as
discussed before.
Fig. V.13. Separation factor, flux, selectivity and permeance of water as a function
of feed water composition at 30oC for 10 wt. % water-containing feed mixture.
0 5 10 150
5000
10000
15000
20000
25000
30000
35000
40000(a)
H14-P5 loading
Sepa
ratio
n fa
ctor
,βij
0.08
0.10
0.12
0.14
0.16
0.18
Flux
(kg/
m2 h)
0 5 10 15
0
1000
2000
3000
4000
5000
6000
H14-P5 loading
Sele
ctiv
ity,α
ij
0.8
1.2
1.6
2.0
2.4
2.8(b)
J i x 1
08 (kg/
m2 s)
CHAPTER-V 128
10 20 30 40
20
40
60
80
100
DS
(%)
Feed water composition (wt. %)
CS NCM-5 NCM-10 NCM-15
10 20 30 400.0
0.1
0.2
0.3
0.4 CS
10 20 30 400
5
10
15
De x
1012
(m2 /s)
Feed water composition (wt. %)
NCM-10
NCM-15
NCM-5
Dex
1012
(m2 /s)
Feed water composition (wt. %)10 20 30 40
0.75
1.00
1.25
1.50
1.75
2.00 NCM-15
NCM-10
NCM-5
CS
Dw x
1010
(m2 /s)
Feed water composition (wt. %)
Fig. V.14. Swelling vs. feed water composition at 30oC
Fig. V.15. Effects of feed water composition on diffusion coefficients.
CHAPTER-V 129
V.2.7.6. Influence of temperature on pervaporation performance and diffusion Temperature has a significant effect on membrane performance. The
influence of temperature over 30o-60oC on separation factor, selectivity, flux and
permeance is shown in Fig. V.16 for 10 wt. % feed water mixture. Increase of
temperature increases the chain segmental mobility and thereby the flux and
permeance will increase. On the other hand, separation factor and selectivity
curves show a decline with increase of temperature as was also studied before
[30,31]. At higher temperatures, diffusion of water is high, which restricts
membrane separation ability. In case of NCM-5, a drastic reduction in separation
factor of 1,087 is observed at 60oC compared to its original value of 33,651 at
300C. Similar observations can be seen with NCM-10 and NCM-15 membranes.
Here, higher molecular mobility and vapor pressure of ethanol under a temperature
above its boiling point should be taken into consideration. Further, the change of
free volume impacts ethanol diffusivity more remarkably than water diffusivity
resulting in lower selectivity at higher operation temperature.
As per free volume theory, increase in temperature will increase the thermal
mobility of polymer chains generating extra void space with increased sorption
and diffusion. The driving force for permeation is concentration gradient, which
results from a difference in partial vapor pressure of permeant molecules between
feed and permeate mixtures. As the feed temperature increases, vapor pressure in
the feed compartment also increases, but vapor pressure at permeate side will not
be affected, resulting in increasing driving force, since the latter is closely related
to phase transition in PV process, but is dependent of temperature. These
phenomena are caused by the fact that the flux and separation factor depend on
both intrinsic membrane properties and influence of the experimental operating
conditions, while permeance and selectivity exclude the effect of experimental
operating conditions.
CHAPTER-V 130
In the present work, one can observe a similar changing trend for both
permeances and fluxes with increasing temperature. It is reasonable because the
temperature is an important parameter affecting the intrinsic properties of the
membranes [37]. As per sorption-diffusion principles [23], permeance and
selectivity reflect the true membrane performance; while selectivity is the
selectivity of the membrane and the separation factor is the separation factor of the
pervaporation process [38]. Thus, using permeance and selectivity instead of flux
and separation factor can significantly decouple the effect of operating conditions
on performance evaluation. As a result, normalizing the flux with respect to the
driving force would clarify and quantify the contribution by the nature of the
membrane to separation performance.
Fig. V.17 displays increasing diffusion coefficients of water as well as
ethanol with increasing the temperature from 30oC to 60oC. Notice smaller values
of diffusion coefficients for ethanol than water. Arrhenius equation was used to fit
diffusivity and permeation flux data to calculate activation energies. From the
estimated results presented in Table V.3, we find positive values of activation
energy, which suggests increase of permeation flux and diffusion coefficient with
increasing temperature. Activation energies of permeation and diffusion for water
(Epw) are much lower than those of ethanol (Epe), suggesting easy permeation of
water molecules through the membranes as well as water selective nature of the
NCMs compared to CS. In case of NCMs, the activation energy values are higher
than unfilled CS, but for NCM-5, the activation energies of flux and diffusion are
lower than those of NCM-10 and NCM-15, suggesting efficient separation by the
NCM-5 membrane. Activation energies thus increase with increasing wt%
loading, which reveals that the rate at which water molecules transport through
nanocomposite membranes increases much faster than ethanol and that water is
separated first.
CHAPTER-V 131
30 40 50 600
5000
10000
15000
20000
25000
30000
35000
40000
NCM-10
NCM-15
NCM-5
CS
Sepa
ratio
n fa
ctor
, αij
30 40 50 6040
50
60
70
80
90
100
Sepa
ratio
n fa
ctor
, αij
Temperature (oC)30 40 50 60
0
1000
2000
3000
4000
5000
6000
Sele
ctiv
ity, α
ij
CS
30 40 50 606
8
10
12
14
16
NCM-10
NCM-15
NCM-5
Sele
ctiv
ity, α
ij
Temperature (oC)
30 40 50 600.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
CS
Flux
(kg/
m2 h)
30 40 50 600.08
0.10
0.12
0.14
0.16
0.18
0.20
NCM-10
NCM-15
NCM-5
Flux
(kg/
m2 h)
Temperature (oC)
30 40 50 600
3
6
9
12
15
18
21
J i x 1
08 (kg/
m2 s)
30 40 50 601.0
1.5
2.0
2.5
3.0
Ji x
108 (k
g/m
2 s)
NCM-10
NCM-5
NCM-15
Temperature (oC)
Fig. V.16. Separation factor, flux, selectivity and permeance of water as a function
temperature.
CHAPTER-V 132
30 40 50 60
2
4
6
8
10
Dw x
1010
(m2 /s
)
NCM-10
NCM-15
NCM-5
30 40 50 600.8
1.0
1.2
1.4
1.6
1.8
2.0
CS
Dw x
1010
(m2 /s)
Temperature (oC)30 40 50 60
0.0
0.5
1.0
1.5
2.0
2.5
De x
1012
(m2 /s)
30 40 50 601
2
3
4
5
De x
1012
(m2 /s)
NCM-15
NCM-10
NCM-5
CS
Temperature (oC)
Fig. V.17. Diffusion coefficient vs. température.
Table V.3. Activation energies of all the membranes calculated from Arrhenius
plots.
Activation energies in kJ/mol
CS NCM-5 NCM-10 NCM-15
Ep 21.01 40.24 47.69 46.09
Epw 19.41 40.08 47.49 45.82
Epe 38.19 133.6 109.8 111.6
Ed 19.72 40.11 47.53 45.87
Edw 19.34 40.08 47.49 45.82
Ede 39.48 133.7 109.9 111.8
CHAPTER-V 133
V.4. CONCLUSIONS
Developments of novel membrane materials with improved performances
would reduce the cost of membrane systems, making them a more attractive
option in difficult separation problems than the conventional technologies.
Compared to unfilled chitosan membrane, the nanocomposite membrane prepared
by incorporating 5 wt. % of H14-P5 nanoparticles has shown significantly
improved pervaporation performance as its separation factor increased remarkably
with a moderate increase of flux for 10 wt.% water in the feed at 30oC. These
effects are attributed to increasing surface energy, fractional free volume and
hydrophilicity due to favorable interactions between nanoparticles and the
polymer matrix. Sorption experiments show that sorption selectivity and diffusion
selectivity as well as diffusion coefficients of water and ethanol were greatly
influenced by feed concentration and temperature. At higher feed water
compositions and temperatures, the membrane performance declined. Sorption
selectivity, being the dominant effect over diffusion, suggests water-selective
nature of the membranes; these observations also fit into the explanations
advanced by sorption-diffusion model. Flory-Huggins theory enabled accurate
assessment of binary interaction parameters used to understand the pervaporation
process. Feed composition has played a significant role in permeation flux,
whereas it has only a slight influence on ethanol. The sorption, diffusion, and
permeation parameters were assessed on the basis of sorption-diffusion model.
The membranes exhibited significantly lower Arrhenius activation energies for
water than ethanol, suggesting higher separation ability and water selective nature
of the membranes. The hybrid membranes of this study are very promising in
pervaporation applications and possibly in gas separation as well.
CHAPTER-V 134
V.5. REFERENCES
[1] P. Shao, R.Y.M. Huang, J. Membr. Sci. 287 (2007) 162-179.
[2] M. Sairam, M.B. Patil, R.S. Veerapur, S.A. Patil, T.M. Aminabhavi, J.
Membr. Sci. 281 (2006) 95-102.
[3] S.G. Adoor, M. Sairam, L.S. Manjeshwar, K.V.S.N. Raju, T.M.
Aminabhavi, J. Membr. Sci. 285 (2006) 182–195.
[4] V.T. Magalad, G.S. Gokavi, K.V.S.N. Raju, T.M. Aminabhavi, J. Membr.
Sci. 354 (2010) 150-161.
[5] V.T. Magalad, A.R. Supale, S.P. Maradur, G.S. Gokavi, T.M.
Aminabhavi, Chem. Eng. J. 159 (2010) 75-83.
[6] S.G. Adoor, B. Prathab, L.S. Manjeshwar, T.M. Aminabhavi, Polymer 48
(2007) 5417.
[7] R.M. Barrer, in Diffusion in polymers, J. Crank. G.S. Park, Eds.
Academic press, London, 1968, p 165-217.
[8] B.V.K. Naidu, M. Sairam, K.V.S.N. Raju, T.M. Aminabhavi, J. Membr.
Sci. 260 (2005) 142-155.
[9] L.Y. Jiang, T.S. Chung, J. Membr. Sci. 327 (2009) 216-225.
[10] S.W. Kang, J. Hong, J.H. Park, S.H. Mun, J.H. Kim, J.C.K. Char, Y.S.
Kang, J. Membr. Sci. 321 (2008) 90-93.
[11] S.J. Lue, D.T. Lee, J.Y. Chen, C.H. Chiu, C.C. Hu, Y.C. Jean, J.Y. Lai, J.
Membr. Sci. 325 (2008) 831-839.
[12] R.S. Veerapur, K.B. Gudasi, T.M. Aminabhavi, J. Membr. Sci. 304
(2007)102-111.
[13] K.S.V. Krishna Rao, M.C.S. Subha, M. Sairam, N.N. Mallikarjuna, T.M.
Aminabhavi, J. Appl. Polym. Sci. 103 (2007) 1918-1926.
[14] D. Yang, J. Li, Z. Jiang, L. Lu, X. Chen, Chem. Eng. Sci. 64 (2009) 3130-
3137.
[15] M. Masaru, I. Reikichi, M. Seiich, Y. Shuzo, M. Akira, T. Yoshinobu,
Kobunshi Ronbunshu 42 (1985) 139–142.
CHAPTER-V 135
[16] D. Anjali Devi, B. Smitha, S. Sridhar, T.M. Aminabhavi, J. Membr. Sci.
280 (2006) 45-53.
[17] D. Anjali Devi, B. Smitha, S. Sridhar, T.M. Aminabhavi, J. Membr. Sci.
262 (2005) 91-99.
[18] X.H. Zhang, Q.L. Liu, Y. Xiong, A.M. Zhu, Y. Chen, Q.G. Zhang, J.
Membr. Sci. 327 (2009) 274-280.
[19] J.J. Shieh, R.Y.M. Huang, J. Membr. Sci. 127 (1997) 185–202.
[20] K. Ghosal, B.D. Freeman, Polym. Adv. Technol. 5 (1994) 673-697.
[21] M.H. Alizadeh, T. Keramani, R. Tayebee, Monatshefte fur Chemie. 138
(2007) 165-170.
[22] T.M. Aminabhavi, R.S. Khinnavar, S.B. Harogoppad, U.S. Aithal, Q.T.
Nguyen, K.C. Hansen, J. Macromol. Sci. Rev. Macromol. Chem. Phy. C.
34 (1994) 139-204.
[23] J.G. Wijmans, R.W. Baker, J. Membr. Sci. 107 (1995) 1-21.
[24] P.J. Flory, Principles of Polymer Chemistry, Cornell University Press,
Ithaca, New York, 1953.
[25] M. Khayet, M.M. Nasef, J.I. Mengual, J. Membr. Sci. 263 (2005) 77-95.
[26] C.M. Chan, Polymer surface modification and characterization, Hanser:
Cincinnati (1994) 35- 62
[27] R. Ravindra, K.R. Krovvidi, A.A. Khan, Carbohydr. Polym. 36 (1998)
121-127.
[28] T. Uragami, Y. Tanaka, S. Nishida, Desalination 147 (2002) 449-454.
[29] J.H. Chen, Q.L. Liu, X.H. Zhang, Q.G. Zhang, J. Membr. Sci. 292 (2007)
125-132.
[30] X. Chen, H. Yang, Z. Gu, Z. Shao, J. Appl. Polym. Sci. 79 (2001) 1144-
1149.
[31] Y. L. Liu, C.Y. Hsu, Y.H. Su, and JY. Lai, Biomacromolecules, 6 (2005)
368–373.
CHAPTER-V 136
[32] Y.C. Wang, S.C. Fan, K.R. Lee, C.L. Li, S.H. Huang, H.A. Tsai, J.Y. Lai,
J. Membr. Sci., 239 (2004) 219-226.
[33] D. Xu, S. Hein, K. Wang, Mater. Sci.Techn, 24 (2008) 1076-1087.
[34] D. Turnbull, M.H. Cohen, J Chem. Phys., 34 (1961)120-125.
[35] S. Anilkumar, M.G. Kumaran, S. Thomas, J. Phys. Chem. B. 112 (2008)
4009-4015.
[36] B. Prathab, R Parthasarathi, P. Manikandan, V. Subramanian, T.M.
Aminabhavi, Polymer. 47 (2006) 6914-6924.
[37] J. G. Wijmans, J. Membr. Sci., 220 (2003) 1-3.
[38] H.M. Guan, T.S. Chung, Z. Huang, M. L. Chng, S. Kulprathipanja, J.
Membr. Sci., 239 (2004) 219-226.