STUDY OF ACETYLSALICYLIC ACID ADSORPTION USING MOLECULAR SIEVES

7/23/2019 STUDY OF ACETYLSALICYLIC ACID ADSORPTION USING MOLECULAR SIEVES

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STUDY OF ACETYLSALICYLIC ACID ADSORPTION BY

MOLECULAR SIEVES

G. A. Assumpção1, J. G. R. Poço2, R. Fernández-Felisbino1, D. Cardoso3, E. L.

Gomes1,* (1) Department of Exact and Earth Sciences, Chemical Engineering Sector, Federal

University of São Paulo (UNIFESP), R. Artur Riedel, 275, Diadema – SP, 09972-270, Brazil.

(2) Department of Chemical Engineering, Faculty of Industrial Engineering – University Center

(FEI), Av. Humberto Alencar Castelo Branco, 3972 – São Bernardo do Campo – SP, 09850-

901, Brazil. (3) Department of Chemical Engineering, Federal University of São Carlos

(UFSCar), Rod. Washington Luis, Km 235 – São Carlos – SP, 13565-905, Brazil.

*Author to whom all correspondence should be addressed. E-mail: [email protected]

(E. L. Gomes). Phone: +55 11 3319-3545 – Fax: +55 11 3319-3400.



ABSTRACT: Molecular sieves can be used as a matrix host to encapsulate several drugs. Here

we studied the adsorption of acetylsalicylic acid (ASA) in HUS-Y, HZSM-5 and silicalite-1

zeolites, and in the mesoporous sieve Si-MCM-41, in order to compare their adsorption

capacities. The materials used presented generally good performance for ASA adsorption.

Among the zeolites, HZSM-5 adsorbed relatively more drug than the others. The Si-MCM-41

samples synthesized with fumed silica presented better performance for adsorption than those

ones synthesized with TEOS. The best mesoporous sample for adsorption, synthesized with

silica fumed, was chosen for the kinetic assay. The adsorption kinetics of ASA for this sample

fitted well to a model of second order. The initial loading rate and diffusion coefficient for the

ASA-(mesoporous sieve) system were 0.153% (mg ASA)/(mg solid.min) and 5.02 x 10 -17 m2/s,

respectively. The materials were characterized by XRD, SEM and TEM, N2 physisorption and

infrared spectroscopy.

1. INTRODUCTION

Recently the use of inorganic matrices associated to drugs, in order to modify its

adsorption or release in the human body or even improve their adsorption from environmental,

is a very common theme in the area of pharmaceutical sciences (Rakic et al. 2013). Studies

involving several drugs have been reported, such as ibuprofen (Horcajada et al. 2004; Gonzalez

et al. 2013; Charnay et al. 2004), acetylsalicylic acid (Beninati et al. 2008), and cancerigen

agents (Zhou and Zhu, 2005), all associated to inorganic matrices such as mesoporous sieves,

activated carbon or clays.

Among the drug delivery systems, porous inorganic materials are emerging as a new

category of systems "host/guest" (Moller and Bein, 1998): for example, we can mention

synthetic zeolites, silica xerogel and porous ceramics (Unger et al . 1983; Uglea et al , 1994). The

advantages of zeolites and mesoporous silicas for biomedical applications include

biocompatibility, low toxicity, large surface areas and the ability to control the physicochemical



properties (Charnay et al . 2004). Due to these interesting features, there is a significant and

growing interest in these potential supports.

The incorporation of active substances in inorganic materials has many advantages such

as reducing the risk of dosage errors, diminution of the necessary number of administrations and

avoiding allergic reactions (Amos et al. 1971), besides of a better use of the active compounds

located in the digestive tract, providing greater convenience and comfort to patients (Nema et

al. 1997).

Zeolites were discovered by Swedish mineralogist Cronstedt in 1756. Their selective

adsorption properties led to the known term "molecular sieve" which describe this class of

materials that have the ability to separate molecules (or adsorb them from a solution) based on

its size (Giannetto 1990). However, zeolites have a diffusional limitation for some applications

due to the diameter of their micropores (Rakic et al . 2013).

In the 90s, researchers at Mobil Co. patented the materials of M41S family (MCM-41,

MCM-48 and MCM-50), with well-defined pore diameters in the range of mesopores and high

surface area (> 1000 m2/g) (Beck et al . 1992). The larger pore diameter enables the diffusion of

bulky molecules to their interior. MCM-41, for example, has been extensively studied and

allows the occlusion of several kinds of drugs into their pores, offering an alternative as a drug

delivery system (Mollo et al . 1997; Vallet-Regi et al . 2001). In contrast to some minerals that

have disordered pores with a wide range of sizes and irregular shapes, MCM-41 exhibits

excellent structural characteristics, such as high surface area and cylindrical ordered mesopores

with a narrow diameter distribution (Cheng et al . 1997). By the appropriate choice of surfactant

or by inserting swelling agents which increase the size of their micelles (Beck et al . 1992; Blin

et al . 2002), these mesopores can be obtained in a range from 20 to 100 Å. Moreover, it is

possible to chemically modify the mesopores surface through the "grafting" process, that is,

anchoring organic functional groups on them (Brunel et al ., 2000; Derrien et al . 1998 ). Another

advantage is that fumed silica is widely used as a pharmaceutical excipient. Thus, it would be

interesting to use mesoporous silica formulations for the controlled adsorption and release of

drugs.



Acetylsalicylic acid (ASA), also called 2-acetoxybenzoic acid (Figure 1) was first

prepared in 1893 by Charles Frederic Gerhardt by reaction of acetyl chloride with salicylic acid

(Beninati 2008). In 1898, the Bayer Co. patented this drug under the name Aspirin ®. It is one of

the most successful synthetic compounds in the field of pharmaceutical chemistry and it is

employed as an analgesic for pain of low intensity, with mild sedative action and excellent

antipyretic action. Additionally, the anticoagulant effect of aspirin has been widely studied

(Watson and Berquist 2000).

INSERT FIGURE 1

This paper presents an exploratory study of the adsorption of acetylsalicylic acid on

molecular sieves HUS-Y, HZSM-5, silicalite-1 and Si-MCM-41, and had as main objectives

relate the textural properties of micro- and mesoporous sieves with their adsorption capacities,

determine the adsorption kinetics and estimate the diffusion coefficient for the

AAS/mesoporous molecular sieve.

2. EXPERIMENTAL

2.1. Molecular sieves

The zeolites used in these experiments, microporous molecular sieves in the acid form,

were previously characterized with respect to their elemental composition, specific surface area

and micropore volume (Table 1). The silicalite-1 sample was synthesized according to the

method proposed by Prakash et al . (1998), using tetrapropylammonium hydroxide as structural

template and tetraethyl ortosilicate (TEOS).

INSERT TABLE 1

The mesoporous adsorbents used in these experiments were Si-MCM-41 molecular

sieves. Samples T1, T2 and T3 were prepared based on a modification of the methodology



proposed by Cai et al . (1999), synthesized at 100 oC during 2 days, using tetraethyl ortosilicate

(TEOS, Aldrich, 98%) as silica source, hexadecyltrimethylammonium bromide (CTABr, Sigma,

98%) and amoonium hydroxide (Cinética Química, 25% w/w sol.), with the composition

1 SiO2: 0.15 CTABr: 19.5 NH3: 82.5 H2O. The samples A1, A2 and A3 were prepared

according to the method proposed by Cheng et al . (1997), synthesized at 150 oC during 2, 4 and

10 days using fumed silica (Aerosil 380, Evonik, 99.99 %) as silica source,

tetramehylammonium hydroxide (TMAOH, Aldrich, 97%) with the composition 1 SiO 2:

0.27 CTABr: 0.19 TMAOH: 40 H2O.

2.2. Characterization

A. Small Angles X-ray Diffraction. The diffractograms were obtained from a diffractometer

Bruker, D8 Advanced model, employing CuK radiation and nickel filter, operating at 40 kV

and 40 mA with goniometer speed of 1º/min. The samples were scanned in the range 2θ from 1

to 10o.

B. N 2 Physisorption. The physisorption isotherms were obtained using a Quantachrome

Corporation (Nova-1200e) equipment. Before each analysis, about 0,05 g of the sample was

treated at temperature of 150º C for 2 h under vacuum.

C. Thermogravimetric analyses (TG). A thermobalance DTG-60H da Shimadzu was employed,

using 10 mg of the sample, under synthetic air flow, from 25 to 600 ºC, with a temperature

heating rate of 10 ºC/min.

D. Scannig Electron Microscopy. The analyses were performed in a scanning electron

microscope Philips, XL30-FEG model, operating at 30 kV, with 2 nm resolution and maximum

magnification of 1.000.000 X. Some samples were also analyzed in a FEI Company SEM,

Quanta 3D FEG model, operating at 30 kV, and magnification from 1500 to 100.000 X.

E. Transmission Electron Microscopy. A TEM Philips CM-120 with 0,2 nm resolution and

maximum magnification of 660.000 X, was used operating at 120 kV.

2.3. Acetilsalicilic acid (ASA) adsorption measurements



2.3.1. Total adsorption capacity

This experiment aimed to fully load the drug in the molecular sieve in a period of 24 h.

First, 150 mg of a calcined sample, previously activated for 24 hours at 120° C, was weighed.

After that, the solid was added to a solution of acetylsalicylic acid (1 mg/mL ethanol) under

stirring (Charnay et al. 2004). After 24 h, the suspension was centrifuged; the solid obtained

was dried for 24 hours at 110 °C and finally submitted to thermal analysis.

2.3.2. Adsorption kinetics

This experiment was carried out aiming to follow the loading of the molecular sieve

during 4 h, taking aliquots of 5 mL from the suspension, at predetermined time intervals. The

aliquots were centrifuged and the solid was dried for 24 hours at 110 °C and, after that,

submitted to thermal analysis.

3. RESULTS AND DISCUSSION

3.1. Zeolites and Si-MCM-41

The X-ray diffractograms (XRD) of HZSM-5 zeolites, HUS-Y and Silicalite-1 (not

shown here) proved the formation of the desired phases, showing their characteristic diffraction

peaks and presenting good crystallinity, without the presence of undesirable phases.

In Figure 2 the diffractograms of Si-MCM-41 synthesized with TEOS are presented.

Comparing these XRD patterns with bibliography data (Meynen and Cool 2005) it can be said

that these materials correspond to the desired product and there was no formation of other

phases. For all samples (Tn) is observed the peak related to the plan (100), more intense. The

peaks related to secondary plans (110) and (200) are present in the samples T2 and T3, and less

obvious, in the sample T1.

INSERT FIGURE 2



Regarding to the structural organization of these samples Tn, we can observe that the

peak (100) of samples T2 and T3 are narrower and more intense, while the peak (100) of T1

sample is wider and less intense. According to Brinker (1996), we could say that, starting from

the material less organized for the more organized, we would have the sequence T1<T3<T2

based only on this parameter.

In Figure 3 the diffractograms of Si-MCM-41 samples synthesized with Aerosil 380, with

different thermal treatment times, are presented. The materials (An) obtained also correspond to

the Si-MCM-41 without parallel phases, despite of the A1 sample being very disorganized. For

these samples we observe the peak (100) more intense (A2 and A3) and almost imperceptible

for A1. The secondary peaks (110) and (200) are present only for sample A2.

INSERT FIGURE 3

Regarding to the structural organization of these samples An, the sample A2 presents a

peak (100) narrower and more intense, while the sample A3 presents the peak (100) wider and

less intense. We would have the sequence of increasing organization as A1<A3<A2. The

thermal treatment time effect for these samples is evident in these diffractograms: with only 2

days there is no sufficient time for the formation of the Si-MCM-41; with 4 days the material is

more organized; with 10 days the phase begins to dismount and disorganize.

In Table 2 we have the textural parameters for the Si-MCM-41 samples obtained from the

diffractograms. The LMA organization parameter is consistent with the previous discussion.

However, when we analyze only the parameter organization degree (Go), it is not possible do a

good comparison in this sense since the areas under the peaks (100) depend heavily on the

amount of organized or disorganized pores.

INSERT TABLE 2



A parameter that could minimize this distortion would be Go’ = Go/LMA that takes into

account the full width at half maximum (FWHM). Thus, analyzing the values of Go, FWHM

and Go’, we could say that the samples A2, T2, A3 and T3 are well organized, with hexagonally

structured pores. Sample A1 shows no arranged pores and sample T1 shows a lot of less

organized pores. In Table 2 we also have the hexagonal framework parameter (aH), ranging

between 39 and 66 Å. These values are consistent with those found by other authors (Meinen

and Cool 2005; BECK et al . 1992).

In Figure 4a is presented the Si-MCM-41 SEM micrograph (sample A2) synthesized with

Aerosil 380 (thermal treatment: 4 days at 150 oC). It is observed that it consists of irregularly-

shaped clusters ranging between 5 and 15 m. A more careful analysis shows that these clusters

are formed of small crystallites of ~ 0.1 m. Carvalho (1997), when synthesizing Si-MCM-41

with fumed silica, observed in the same sample irregular particles and others with hexagonal

section, claiming that irregular particles indicated an incomplete "crystallization" of the reaction

gel, justifying the low specific areas and the diffraction patterns with a low degree of

organization achieved.

Figure 4b shows a SEM micrograph of Si-MCM-41 synthesized with TEOS (sample T2).

The synthesis with TEOS produces particles in geometries that ranging from globules to rods,

with sizes between 1 and 10 m. This morphology is related to monomeric silica (TEOS) that

facilitates the production of non-agglomerated particles and with a tendency to form hexagonal

particles.

INSERT FIGURE 4a

INSERT FIGURE 4b

In Figure 5 shows a TEM micrograph in bright field of sample A2 (Si MCM-41, Aerosil).

We can observe a pattern of parallel lines in the particle which correspond to pores seen along



direction [010], confirming the formation of mesopores. Analyzing the scale and considering a

measurement error, we have mesoporous diameter of approximately 2.6 nm.

INSERT FIGURE 5

3.2. ASA Adsorption

3.3.1. Total adsorption capacity

Figures 6 to 8 show the N2 physisorption isotherms of the representative samples for

each group of molecular sieves before and after loading of ASA for 24 h. Table 3 summarizes

the textural parameters obtained for each case.

The HZSM-5 zeolite (Figure 6), used for the adsorption of ASA, showed a behavior

similar to HUS-Y zeolite and Silicalite-1. They show type I isotherms as expected. The

maximum specific volume in physisorption of HZSM-5 original sample was 114 cm3/g, value

also compatible with those observed by Giannetto (1990). It is observed that the isotherm, even

after the ASA loading, presents N2 physisorption isotherms of type I, however shifted down, i.e.

the amount adsorbed was lower because the pores were filled by AAS and the solvent (ethanol).

From the data of Table 3 it can be said that, among the zeolites, Silicalite-1 adsorbed less

ASA and solvent (2.1 %) and that HZSM-5 showed the best performance (38.6 %) in relation to

the volume available for adsorption. HUS-Y zeolite also offers good performance (33.1 %) and

can be applied with good efficiency for this process.

It should be noted that the good performance of HZSM-5 and HUS-Y in relation to

Silicalite-1 can be possibly justified by the presence of aluminum in their structures, which

provides a charged framework that must be compensated, probably adsorbing ASA more easily

in these materials. Another aspect to take in account is the kinetic diameter of ASA (0.59 x 0.31

x 0.80 nm) that allows a better diffusion in HUS-Y pores than other zeolites, confirming its

good capacity adsorptive.

INSERT FIGURE 6



Figure 7 presents the physisorption isotherms of sample A2 (Si-MCM-41, Aerosil) used

for ASA adsorption during 24 h and that present similar behavior as sample A3. We can observe

type IV isotherms with H2-type hysteresis (slit-pores). The maximum specific physisorbed

volume for sample A2 was 541 cm3/g, value consistent with that is observed in bibliography.

Analyzing Table 3, the sample A1, which presented an isotherm with behavior next to the type

II (solid with few pores) and a diffractogram without peaks, showed that these pores were

efficient for the adsorption of ASA. The sample A3, despite being less organized, presented

good efficiency in adsorption. Possibly there is an influence of thermal treatment time on

particle size, because the sample A3, synthesized 10 days, provided a material with smaller

particle size, favoring the diffusion to the interior of the mesopores and increasing the ASA

sorption.

Based on the graphics and in Table 3 it can be said that the mesoporous material that

adsorbed less ASA and solvent was sample A2 (17.1%) and the best performance in this set was

the sample A3 (61.1 %) in relation to the volume available for adsorption. Despite the large

available volume, the sample A2 was not so efficient in ASA adsorption possibly because its

particle size is very large, with a long average free path to go, hindering the diffusion of ASA

and solvent molecules and not fully using the volume of the particle.

INSERT FIGURE 7

Figure 8 presents the physisorption isotherms for the sample T2 (Si-MCM-41),

synthesized with TEOS. The samples exhibit type IV isotherms with an unclear hysteresis. The

adsorbed volume was 470 cm3/g, the smallest among the Tn samples. Although the sample T1

presents smaller organization (FWHM), i.e, mesopores not ordered hexagonally, its volume is

significantly larger than the structurally organized samples T2 and T3. After loading of ASA,

we can observe that the isotherm is still of type IV, reproducing the behavior already discussed

for these kind of materials. Based on Table 3 it can be said that sample T1 was the material that



adsorbed less ASA and solvent (6.6%) and the sample T3 presented the best performance

(12.7%) in relation to the volume available for adsorption. Despite the large volume available,

the sample T1 was not effective in adsorption of ASA, possibly because their mesopores are not

arranged hexagonally, presenting tortuosity and irregular pores diameters, which difficults the

ASA and solvent molecules diffusion inside the pores.

The low loading of the sample T1 shows that the area and the volume are not the only

parameters to be considered to perform the adsorption of ASA, since the organization should

also be considered. Once the kinetic diameter of ASA is much smaller than the pore diameter

(DP), diffusional restrictions are not important, but cannot be neglected. This diffusion would be

of Knudsen, where the molecules adsorption is still influenced by the pore dimension.

INSERT FIGURE 8

INSERT TABLE 3

Figure 9 presents the TG curves of samples An, representing the relative mass loss of

each sample. The lost mass contains ASA and solvent (ethanol). The TG curves for the other

samples are similar. Thus, for a better view of the data, Table 4 summarizes the data obtained.

INSERT FIGURE 9

INSERT TABLE 4

From Table 4, in relation to zeolites, HUS-Y presents the largest total mass loss (29,9%),

and consequently higher adsorbed mass of ASA (11,71 %). This result is in agreement with Datt

(2012), who found that the total loading of ASA on zeolite HUS-Y was 23.5%, considering

ASA and solvent. Among the samples An, sample A3 presented higher total mass loss (9,47 %),

while among the samples Tn, sample T1 was that with the higher total mass loss (10,98%).



From TG curves of the samples and standard TG of ASA, it was possible to identify how much

of the mass loss corresponds to the ASA and solvent.

Observing Table 4, although much of the lost mass is solvent, it is confirmed that the

HUS-Y zeolite was material that most adsorbed ASA (11.71 %). In contrast, Silicalite-1

adsorbed less ASA and solvent, confirming the result obtained by physisorption. However, the

HZSM-5, in this study, did not have the same result as the physisorption, because the accuracy

of this analysis allows this discrepancy. We also have confirmed that the largest diameter of the

pore HUS-Y justifies its higher adsorption capacity.

Analyzing the mesoporous molecular sieves in Table 4, it was confirmed that the samples

A3 and T1 were the ones that most adsorbed ASA (4.73 % and 5.24 %, respectively). Similar

explanation for the HZSM-5 low performance can be given with respect to the low value

observed for sample T3, in this analysis.

The solvent used in experiments adsorbs aplenty, interfering in the ASA adsorption

process, which is the compound that we wish to occlude in the molecular sieves. The solvent

represent up to 60 % of the adsorbed material in the case of zeolites and up to 67 % for

Si-MCM-41 samples.

At this point we can discuss the preferential adsorption of ASA in the molecular sieves

which have aluminum in their structures. The sieves that adsorbed more ASA were HUS-Y and

HZSM-5 (acid forms). Aspirin is more soluble in ethanol (0,06 mol ASA/mol ethanol) than in

water (0,00066 mol ASA/mol water) (Grunwald and Berkowitz 1951). Ethanol is a solvent less

polar than water and so is able to more easily dissolve low-polarity substances. Possibly the

interaction of ethanol with ASA generates a clathrate that is an inclusion compound in which

the guest molecule (ASA) is in a cage formed by the host molecule (EtOH) or by a lattice of

host molecules. This clathrate [ASA-EtOH]+ interacts with the negative molecular sieve lattice,

adsorbing both molecules and explaining the high amount of solvent adsorbed. The almost

neutral lattice without aluminum of Silicalite-1 and Si-MCM-41 generates few sites for this kind

of interaction (silanol groups), justifying their low performance.



3.3.2. Adsorption kinetics

In this study we aimed to obtain preliminary adsorption kinetics of ASA for a

mesoporous molecular sieve. The sample A3, with the highest adsorption capacity among the

Si-MCM-41 (Aerosil) synthesized was selected for this application and determination of kinetic

parameters. Another reason that led to the selection of this sample is that the cost of preparation,

involving fumed silica, is much lower than the samples synthesized with TEOS, enabling their

potential use in the pharmaceutical industry.

Table 5 presents the data of adsorption kinetics experiment for ASA at 36.5 °C in a

period of 240 min, obtained by thermogravimetric analysis. The kinetic curve is shown in

Figure 10. The experimental data obtained show excellent consistency, because a nonlinear

exponential fit shows no significant deviations (R 2 = 0.9994), which proves the good quality of

the analysis method. The loading was progressive until the relative mass of 7.5% of AAS. After

60 min, the loading has reached its maximum, becoming constant from that time.

INSERT FIGURE 10

INSERT TABLE 5

The time required to reach equilibrium was graphically estimated as 240 min. The data

were then adjusted mathematically for kinetic models of adsorption of first and second order.

Starting up from the general order n equation, represented by equation (1):

(1)

In equation (1), qe is the adsorption capacity in equilibrium, n is the order of the kinetic

model, qt is the sorption capacity at a defined time t and k n is the constant rate of adsorption.

For the 1st order model proposed by Lagergrem, n takes the unit value and equation (1)

becomes the equation (2):

= . ( − )



(2)

Thus, the rate of temporal variation in adsorption capacity would be linearly proportional

to the gradient of the adsorption capacity. A linearization is given in equation (3):

(3)

Equation (3) suggests that the relationship between log(qt-qe) and time is linear. However,

qe is unknown, and adopt the highest value of q t for this value leads to the elimination of an

experimental point because equation (3) is not defined at this point. Thus it was assumed that q e

would be 7.5%, corresponding to the longest time of analysis. The linearization and these

estimations were made for the experimental data obtained. From t and ln(qe-qt), k 1 values were

calculated for each time, and subsequently using the average (k 1m) of these values in the model

evaluation. In Table 6 the data obtained for the kinetic model of adsorption of first order are

presented.

INSERT TABLE 6

If we discard the points related to equilibrium and to time t = 0 min (i.e., using

10 < t < 60 min), we can obtain a very good fit to the data, and the model of first order becomes

log (7.5 - qt) = 0.564 - 0.020 t, with a residue parameter R 2 = 0.9981. Thus, this model is

acceptable to describe the kinetics of ASA adsorption by the sample Si-MCM-41. Figure 11

presents the fits for this case.

INSERT FIGURE 11

For the kinetic model of second order, equation (1) becomes equation (4):

(4)

= . ( − )

log − = −1 +

=

2. (

− )2



A linearization is also possible, and is represented by equation (5):

(5)

Thus, Table 7 was built in order to obtain the value of k 2 and replace it in the model. The

value of qe was maintained as previously done. The procedure, as applied to experimental data

gave the results shown in Figure 12.

INSERT TABLE 7

INSERT FIGURE 12

The values obtained for the residue (R 2 = 0.99988), very close to the unit, indicated an

excellent fit of the data obtained by the second order model. Thus, it can be concluded that the

adsorption kinetics of ASA on the Si-MCM-41 is a kinetic process which is well described by

the model (t / qt) = 4.318 + (t / 7.5). Thus, the kinetic model of second order is slightly more

suited to data that the model of order 1. More experiments to confirm this result would be

needed. If the model of 2nd order is accepted, this suggests that the step limiting of velocity is

the adsorption on the adsorbent surface, and points to the chemical nature of the adsorbate-

adsorbent interactions.

3.3.3. Effective diffusion coefficient for the AAS / Si-MCM-41 system

To calculate the effective diffusion coefficient of the AAS in the molecular sieve

Si-MCM-41 (sample A3), we followed the procedures proposed by Valencia (2013), who

performed the adsorption of vitamin B12 on a metal-organic mesoporous system (MOF).

Initially a mathematical adjustment was made for the loading curve of ASA molecular

sieve (Figure 10), yielding the following equation:

=

1

.2

+



(6)

which residue was R 2 = 0.9994, x is the loading time for ASA and F(x) the relative mass

adsorbed. Using equation (6) it is possible to determine the initial loading rate and the

instantaneous rate for any time. The shape of the curve suggests that the initial loading rate is

constant until about 15 min. We can, in this way, from the differentiation of this equation with

respect to time to calculate this rate (F’(x)). Then

(7)

To determine the initial loading rate we use the point (t=10 min, 5.2% mgASA/mgsolid):

F’(10 min) = 0,153 % mgASA/(mgsolid.min)

According to Chowdhury et al . (2005), the diffusion coefficient of a molecule diffusing

through a mesopores structure can be found from measurements of solution concentration as a

function of time. In this study we used a spherical geometry for the particle for the resolution of

the model, which is an approximation, because the particles have irregular shapes as seen in the

micrographs of Figures 4a and b.

An approximation of the true value of the diffusion coefficient for this system can be

calculated from the following equation (Bird et al. 2006):

(8)

In this model, the equation has been cast in dimensionless form through the use of

Laplace transforms. The variables in this equation are defined as follows:

(9)

where Ct is the ASA concentration in ethanol solution at time t, C0 is the initial ASA

concentration in the solution, Vf is the volume of the solution, V b is the average volume of the

particle size of Si-MCM-41, Deff is the effective diffusion coefficient and R is the radius of the

= −4,175. (−3,200 ) − 3,286. (−

21,664)+ 7,461

′() = 1,305. −3,200

+ 0,152. (−21,664)

= + 1

+ 6−2

91 + + 22∞

=1

=0 , =

, =

.2



particle. It was assumed in this case that the partition coefficient () equals 1. In this model i

are the solutions of equation (10):

(10)

Once is determined, can be found as the zeros of the function

(11)

which is the Laplace transform of equation (10). From equations (8) and (9) we have:

(12)

And when

and the system reaches the equilibrium and the infinite sum in the second term on the

right side of equation (12) tends to zero and then / ( +1). It was assumed the

concentration after 240 min of loading as the equilibrium concentration. It provides:

92,5 / 100 = / ( +1) and = 12,333 (13)

Using this value for , the i values were calculated from equation (9). Figure 13 shows

the graph of s versus G(s) and Table 8 presents the 7 first approximations for i with G(s) = 0.

INSERT FIGURE 13

INSERT TABLE 8

With the values of i and , we can calculate the value of Deff from equation (12).

Substituting the known values and using the data collected for the initial loading step, we have:

tan = 33 +

2

= tan − 3

3 + 2

0 =

+ 1

+6(

)

(− 2

2 )

9 1 + + (

)

2. 2

∞

=1

→ ∞, − 2

2 → ∞



(14)

(15)

(16)

Using the equation (16), the value of the effective diffusion coefficient, Deff , was obtained

from successive approximations and interpolations in the spreadsheet.

Finally we have for ASA/Si-MCM-41 system under the conditions of this work:

Deff = 5,02 x 10-17 m2/s

This value is consistent with the data found by Polakovic et al . (2005) who performed the

adsorption of aspirin on activated carbon at 30 oC, finding a value of 0.8 x 10-17 m2/ s. Pendleton

and Park (2011) when adsorbing allyl isothiocyanate in SBA-15 found a value for Deff between

0.3 x 10-17 and 0.44 x 10-17 m2/s. As we have different materials in different conditions, this data

is acceptable.

4. CONCLUSION

All the molecular sieves employed in this study are suitable for the adsorption of ASA.

Among the zeolites, HUS-zeolite Y adsorbs higher ASA due to their larger specific area and

volume, but in relation to the available area, was less efficient. Silicalite-1 showed to be less

efficient for this process possibly due to absence of aluminum and loading sites.

Among the Si-MCM-41samples, An and Tn, the samples synthesized with Aerosil (An) were most

effective for the adsorption of ASA, probably due to the higher number of silanol groups that this kind of

silica source generates in these samples, which seems to be related with a better adsorption of ASA.

94,8

100 =

12,333

13,333+ 6(12,333)

(− 210. )

502

91 + 12,333 + 12,3332. 2

∞

=1

0,948 =12,333

13,333+ 6(12,333)

(

−

2

10

.

)

502

91 + 12,333 + 12,3332. 2∞

=1

0,0230 = 73,998 . (−0,004. 2 )

119,997 + 152,103. 2

∞

=1



Thermogravimetric analyses proved that the HUS-Y zeolite adsorbs more ASA among

the zeolites and A3 and T1 samples of Si-MCM-41 were the better for this function. The

behavior of the samples An and Tn demonstrates that the interaction between the ASA and the

Si surface MCM-41 is not directly linked to the specific area or volume available for adsorption,

but by the amount of adsorption sites that have important role in the adsorptive behaviour of the

sample. Probably the surface silanol groups more abundant in samples An, are the key to

explain the improved performance of these samples.

The solvent is adsorbed in large proportion in relation to the ASA. A study with different

solvents is necessary to determine what would be the best option, because the interaction of the

solvent with ASA competes in adsorption. The formation of a possible clathrate ASA-Ethanol

positively charged could explain the better affinity with the samples with aluminum in the

lattice.

Regarding the kinetics of adsorption of ASA, it was observed that the loading reaches its

peak only after 15 min of contact with Si-MCM-41 (sample A3). The methodology employed

data obtention was very appropriate and the experiment was considered valid. The adsorption

kinetics using a first or second order models fits well to the proposed experiment, but second

order model is the better, providing the following equation: (t/q t) = 4.318 + (t/7.5), with an

residue parameter of R 2 = 0.99988.

In the experiment for determining the effective diffusion coefficient for the

ASA/Si-MCM-41, the initial rate of loading (0.153% mgASA/mgsolid.min), the equation for the

rate of adsorption (eq. 7) and the effective diffusion coefficient (Deff = 5.02 x 10-17 m2/s) were

obtained. This indicates that the Si-MCM-41 is a porous material that has great capacity for

ASA adsorption and low hindering to diffusion of the drug.

Whereas the model used for the calculation of the effective diffusion coefficient is only

an approximation, a number of recommendations can be made to provide a more accurate result,

since it was adopted a spherical geometry to particles and a perfect interaction of the solution

ASA-ethanol. The first aspect can be improved by using data from particle laser analyser to

more accurately measure the particle sizes and their distribution. The adsorption experiment



could be carried out under the action of an ultrasound system, which could be coupled in a

circulating system with a sampling point to prevent the agglomeration of particles and formation

of dead zones.

5. ACKNOWLEDGMENTS

We would like to thank the Catalysis Laboratory (LabCat-UFSCar) and Electronic

Microscope Laboratory (LME-UFSCar), both located in the Federal University of São Carlos

(Brazil), for the XRD measurements and for SEM and TEM analyses.



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LIST OF FIGURES

Figure 1. Molecule of acetylsalicylic acid and its dimensions calculated by the method MM2(adapted from Beninati et al., 2008).

Figure 2. Diffraction patterns of calcined samples of Si-MCM-41 synthesized with TEOS at

100 °C for 2 days.

0 2 4 6 8 10

0 2 4 6 8 10

0 2 4 6 8 10

1000 cps

Sample T2

1000 cps

2/degrees

Sample T1

Sample T3

1000 cps



Figure 3. Diffraction patterns of calcined samples of Si-MCM-41 synthesized with Aerosil,

T = 150 °C (thermal treatment (days): A1 = 2 , A2 = 4, A3 = 10).

(a)

(b)

Figure 4. (a) Si-MCM-41 (Sample A2) synthesized with Aerosil. (b) Si-MCM-41 (sample T2)synthesized with TEOS.

0 2 4 6 8 10

0 2 4 6 8 10

0 2 4 6 8 10

Sample A31000 cps

2/degrees

Sample A1

4000 cps

Sample A2

20000 cps



Figure 5. Micrograph (TEM) of sample A2 synthesized with Aerosil, view along [010] axis.

Figure 6. Physisorption isotherms of HZSM-5 before and after loading (24 h).

0,0 0,2 0,4 0,6 0,8 1,00

50

100

150

200

A d s o r b e d

V o l u m e ( c m 3 / g ) - S T P

P/Po

ZSM-5

ZSM-5+ASA



Figure 7. Physisorption isotherms of sample A2 before and after loading (24 h).

Figure 8. Physisorption isotherms of sample T2 before and after loading (24 h).

0,0 0,2 0,4 0,6 0,8 1,00

100

200

300

400

500

600

A d s o r b e d V o l u m e ( c m 3 / g ) -

S T P

P/Po

A2

A2 + ASA

0,0 0,2 0,4 0,6 0,8 1,00

100

200

300

400

500

600

0,0 0,2 0,4 0,6 0,8 1,00

100

200

300

400

500

600

T2

P/Po

A d s o r b e d V o l u m e ( c

m 3 / g ) - S T P

T2+ASA



Figure 9. Thermogravimetric curves for samples An (Si-MCM-41, Aerosil).

Figure 10. ASA adsorption curve as a function of time - adsorption kinetics.

100 200 300 400 500 600 700

90

95

100

100 200 300 400 500 600 700

90

95

100

100 200 300 400 500 600 700

90

95

100

R e l a t i v e M a s s L o s s

( % )

Temperature (oC)

A1

A3

A2

0 50 100 150 200 250

0

1

2

3

4

5

6

7

8

A d s o r b e d

M a s s A S A ( % )

Time (min)



Figure 11. First order kinetic model of ASA adsorption applied to the experimental data:.

Transient adjustment range.

Figure 12. Second order kinetic model of ASA adsorption applied to the experimental data.

0 20 40 60 80 100 120 140 160 180 200

-1,0

-0,5

0,0

0,5

1,0

l o g (

q e

- q t

)

t(min)

Equation y = a + b*

Adj. R-Square 0,99818

Value Standard Error

B Intercept 0,5648 0,01794

B Slope -0,02097 5,16667E-4

0 5 10 15 20 25 30 35

0

5

10

15

20

25

30

35

t

/ q

e

t / qt

Equation y = a + b*x

Adj. R-Square 0,99988

Value Standard Error

B Intercept -0,57342 0,084

B Slope 1,01597 0,00497



Figure 13. Graph of G(s) as a function of s defined by equation (11).

s



LIST OF TABLES

Table 1 – Data from microporous molecular sieves employed in this work.

Cat. SiO2/Al2O3 Vm (cm3/g) S (m2/g) MSA (m2/g)

HZSM-5 26,3 0,134 385 91HUS-Y 5,62 0,279 664 65

Vm = specific micropore volume; S = specific surface area; MSA = mesoporous surface area.

Table 2. Properties of the samples of Si-MCM-41 from their respective diffraction patterns.

Sample Go (%) FWHM G0’ 2100 2110 2200 aH (Å)

T1 69 0,50 136 2,30 - - 44,4

T2 61 0,18 336 2,46 4,32 5,00 41,4

T3 62 0,20 311 2,27 3,93 4,52 45,0

A1 37 - - 1,90 - - 53,6

A2 100 0,50 201 2,63 4,44 5,19 38,8

A3 100 0,93 108 1,55 - - 65,8

Go = organization degree = (I100/I100 pattern)x100; FWHM = full width at half maximum (plane (100));Go’ = Go/FWHM; 2100 = position 2 (plane (100)); 2110 = position 2 (plane (110)); 2200 = position 2 (plane(200)); aH = hexagonal framework parameter.

TABLE 3. Textural parameters of the samples before and after loading with ASA.

Sample So(m2/g) S(m2/g) S(m2/g) Vo(cm3/g) V(cm3/g) V(cm3/g) V(%) DP(Å)

Silicalite-1 370 323 47 146 143 3 2,1 ~ 5,6

HZSM-5 280 146 134 114 70 44 38,6 ~ 5,6

HUS-Y 593 387 206 236 158 78 33,1 ~7,4

T1 1478 1387 91 617 576 41 6,6 33,5

T2 1340 1219 121 470 428 42 8,9 37,2

T3 1147 1030 117 511 446 65 12,7 37,8

A1 23 26 3 50 35 15 30,0 37,1

A2 1400 1162 238 541 448 93 17,2 37,6

A3 571 191 380 329 128 201 61,1 33,4

So = specific surface area before loading; S = specific surface area after loading; Vo = specific volume total of pores before loading; V = specific volume total of pores after loading; S = variation in specific surface area (S-So);V = variation in specific volume total of pores (V-Vo); V (%) = 100 x V / Vo; DP = diameter of pores ofmolecular sieve.



Table 4. Percentage of adsorbed ASA and solvent in the molecular sieves from TG analyses.

Molecular sieve Sample ASA (%) Solvent (%) Total Mass Loss (%)Silicalite – 1 2,26 0,67 2,93

Zeolites HZSM – 5 5,87 4,68 10,55HUS – Y 11,71 18,19 29,90

A1 4,10 4,10 8,20A2 4,48 2,61 7,09A3 4,73 4,74 9,47

Si-MCM-41 T1 5,24 5,74 10,98T2 3,11 6,19 9,30T3 4,51 3,83 8,34

Table 5. Data obtained by thermogravimetry for the adsorption kinetics experiment.

Time (min) m0 (mg) m120 (mg) mf (mg) mAAS (mg) mAASrel (%)0 - - - - 0

10 1,454 1,418 1,348 0,071 5,2

15 0,843 0,834 0,788 0,046 5,8

30 0,715 0,698 0,655 0,043 6,6

60 0,802 0,821 0,765 0,056 7,3

180 0,858 0,840 0,782 0,058 7,4

240 2,181 2,205 2,058 0,153 7,5

m0 = initial mass of the loaded sample; m120 = m0-msolvent; mf = final mass; mASA = mass of ASA;mASArel = mass relative of ASA = mASA / mf .

Table 6. Parameters obtained from the kinetic model of adsorption of first order.

t (min) log(qe-q) k 1 (min-1)0 0,875 -10 0,362 0,11815 0,230 0,09930 -0,046 0,07160 -0,699 0,060180 -1 0,024240 - 0,008

k 1m = 0,047



Table 7. Parameters obtained from the kinetic model of adsorption of second order .

t (min) t/qt (min.mgsolid/ mgAAS)

t/qe

(min.mgsolid/ mgAAS)k 2

(mgAAS/mgsolid)

0 - 0 -10 1,923 1,333 0,07415 2,586 2,000 0,05430 4,545 4,000 0,03060 8,219 8,000 0,016180 24,324 24,000 0,006240 32,000 32,000 0,004

k 2m = 0,031

Table 8. i values determined from equation (11) for G (s) = 0.

i 1 2 3 4 5 6 7

i 0,50 4,38 7,53 10,50 13,50 20,00 23,00

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STUDY OF ACETYLSALICYLIC ACID ADSORPTION USING MOLECULAR SIEVES