Design of tailor-made surfactants for industrial applications using a molecular modelling approach

Colloids and Surfaces

A: Physicochemical and Engineering Aspects 205 (2002) 139–148

Design of tailor-made surfactants for industrial applicationsusing a molecular modelling approach

Pradip *, Beena RaiTata Research De�elopment and Design Centre, 54B, Hadapsar Industrial Estate, Pune 411013, India

Received 12 November 2001; accepted 12 December 2001

Abstract

A molecular modelling approach to design surfactants for industrial applications is presented. Adsorption ofsurfactants at interfaces is modelled using a force field approach. The examples for illustration include fatty acids forselective flotation separation amongst calcium minerals, design of phosphonic acid based corrosion inhibitors,dicarboxylic acids as crystal growth inhibitors for calcium fluoride, optimisation of molecular architecture ofcarboxylic acid type dispersants for titania based paints and fatty acids and benzoic acid dispersants for zirconia andalumina ceramic suspensions, respectively. The proposed methodology based on theoretical molecular modellingcomputations, requiring no experimental data can thus be utilised to screen/select the most promising molecules froma large set of possible architectures, thus saving enormous time and resources otherwise needed to design novelsurfactants. © 2002 Elsevier Science B.V. All rights reserved.

Keywords: Molecular modelling; Surfactants; Force field; Interaction energy

www.elsevier.com/locate/colsurfa

1. Introduction

Certain chemicals exhibit the characteristicproperty of enhanced adsorption at interfaces andhence possess the important attribute of beingable to modify interfacial interactions. Suchchemicals known as surface-active agents or ‘sur-factants’ are widely used in a variety of modernindustrial processes. Areas of application are asdiverse as motor oils and other lubricants, laun-dry detergents, personal care products, cosmetics,petroleum recovery, corrosion inhibitors, pharma-

ceutical formulations, building materials, mineralsand ceramic processing and effluent treatment[1–3].

Design/development and/or selection of surfac-tants tailor-made for a given application is achallenging task. Most of the industrial surfac-tants currently being used in the industry aredesigned by and large, through trial and errormethods based on rules of thumb and past experi-ence. With increasing demands of the end-userson the efficacy and selectivity of performancechemicals needed for achieving enhanced produc-tivity, quality and yield in the industry, the biggestchallenge for professionals involved in surfactantdesign is to reduce the time in developing new

* Corresponding author.E-mail address: [email protected] (P. radip).

0927-7757/02/$ - see front matter © 2002 Elsevier Science B.V. All rights reserved.

PII: S0 927 -7757 (01 )01153 -0

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P. radip, B. Rai / Colloids and Surfaces A: Physicochem. Eng. Aspects 205 (2002) 139–148140

products. Currently this search space is very lim-ited since trial and error methodology is indeedexpensive both time wise and in terms of resourcerequirements.

However, recent advances in our understandingof molecular level phenomena governing surfac-tant adsorption at interfaces, the accessibility ofapplication oriented molecular modelling toolsand availability of rather inexpensive computingpower has brought the whole field of surfactantdesign on the threshold of a major breakthrough.Two key features of this emerging paradigm shiftfrom conventional trial and error methodology torational, scientific approach to arrive at the pre-ferred molecular architecture of the tailor-madesurfactant for a specified industrial applicationare: first, identification of the molecular recogni-tion mechanisms underlying selectivity of surfac-tant adsorption at the interface [4,5] and thesecond, utilising molecular modelling techniquesfor quantitatively assessing the relative intensityof surfactant interaction amongst different surfac-tant–surface combinations based on completelytheoretical computations.

A quantitative methodology to screen out/iden-tify the appropriate molecular architectures basedon determination of the relative efficacy of vari-ous structures based on theoretical computations,is evidently an economically attractive and elegantmethodology as compared with the conventionalapproach. Selecting the most promising moleculesfrom a wide variety of possibilities based on com-puter-aided design tools for subsequent synthesis,characterisation, testing and pilot plant–plant tri-als will certainly save enormous costs in time andefforts to arrive at new formulations. It is in thiscontext that we have been exploring the use ofmolecular modelling tools for quantifying the in-teractions of surfactants with interfaces. We haveelucidated the building blocks of the above-men-tioned novel paradigm through our recent work inthis field [4–7]. Several other research groups arealso engaged in studying surfactant–surface inter-actions using molecular modelling tools [8–18].We present in this paper a few examples based onour own work, illustrating the validity as well asthe power of such an approach in the design ofindustrial surfactants.

2. Molecular modelling methodology

While we have utilised both the quantum-chem-ical and force field computations in our work[6,7], we will present the latter only in this paper.The CERIUS2 program [19] was used to modelthe surface–surfactant interactions by force fieldapproach (Universal force field, UFF). Thoughthere are many well-parameterised force fields(Drieding II, MM2 and CFF) available to modelorganic and polymer molecules, very few can han-dle inorganic materials such as minerals. Wechose to use UFF as it offers a versatile eventhough approximate parameterisation for a widerange of atoms including calcium, titanium, bar-ium, aluminium and zirconium, atoms of particu-lar interest to this study. Coveney and Humphries[16] have reported their results on UFF computa-tions for the interactions of diphosphonate addi-tives with cement (ettringite) surface. Morerecently Sieval et al. [20] have used UFF to modelthe adsorption of alkyl monolayers on silica (111)surface.

Universal force field (UFF) is a purely diagonaland harmonic force field in which bond stretchingis described by a harmonic term, angle bending bya three-term Fourier cosine expansion and torsion& inversion by cosine-Fourier expansion terms.The van der Waals interactions are described bythe Lennard–Jones potential. The electrostatic in-teractions are described by atomic monopoles anda screened (distance-dependent) coulombic term[21–24].

The method followed was similar to that re-ported recently by Oliver et al. [25]. A surface cellwas created from the unit cell of mineral at agiven Miller plane (cleavage plane). This surfacecell was further extended to periodic super latticeof �25×25 A� in x and y directions. The atomsin top layer of the cluster were allowed to relaxwith respect to energy and an equal number ofatoms in bottom layers were kept fixed. In orderto ensure that the modelled mineral surfaces wererepresentative of those observed experimentally,the references were taken from literature [26–28]for creating the correct surface. However,wherever no experimental data were available,several initial surface cells (pertaining to the dif-

P. radip, B. Rai / Colloids and Surfaces A: Physicochem. Eng. Aspects 205 (2002) 139–148 141

ferent cleaving locations and depth) were createdand clusters thus obtained were minimised withrespect to the energy. The most stable surfacecluster with the highest negative energy was cho-sen for further computations.

The surfactant molecule was geometry opti-mised and docked on this most stable surfacecluster. The initial geometry of surface–surfac-tant complex was obtained by molecular graph-ics and fitting method taking into considerationthe interactions of surfactant functional groupwith surface atoms. The surfactant molecule wasthen allowed to relax completely on the surface.Several initial conformations (�20) were con-sidered so as to locate the minimum energy con-formation of the mineral–reagent complex.

The interaction energy was calculated usingthe following equation:

Interaction energy (�E)

=E(complex)− [E(surfactant)+E(surface)]

where E(complex), E(surfactant) and E(surface) are thetotal energies of optimised surface–surfactantcomplex, surfactant molecule and surface clus-ter, respectively. It is worth noting that themore negative magnitude of interaction energy(�E) indicates more favourable interactions be-tween the surface and the surfactant.

Interaction energy so obtained was correlatedwith experimentally measured macroscopic prop-erty of the system. For instance, we have suc-cessfully correlated the flotation behaviour ofminerals, the extent of dispersion of ceramicsuspensions and the corrosion inhibition of inor-ganic compounds, all in the presence of a widevariety of surfactants with corresponding inter-action energy trends as computed by above-mentioned methodology. We present a fewselected examples in the following section.

3. Results and discussion

3.1. Validation of UFF approach

To establish the validity of the UFF for mod-elling mineral surfaces, we optimised their crys-

Table 1A comparison of UFF optimised crystal structures with exper-imentally observed crystal structures of minerals

Minerals Lattice parameters (in degrees and A� )

UFF predicted Experimental (after[29,30])

�=�=�=90,�=�=�=90,Fluoritea=b=c=5.13 a=b=c=5.46�=�=�=90, �=�=�=90,Baritea=8.82, b=5.05, a=8.88, b=5.45,c=7.15 c=7.15

Rutile �=�=�=90,�=�=�=90,a=b=4.39, c=3.02 a=b=4.59, c=2.96�=�=90, �=120,Alumina �=�=90, �=120,

a=b=4.75, c=12.90a=b=4.32,c=11.64�=�=�=90, �=�=�=90,Zirconia

a=b=c=5.13a=b=c=5.02

tal structures and compared with the experi-mentally observed values [29,30]. As summarisedin Table 1, the UFF predicted values (latticeparameters) are comparable to experimentalmeasurements. Further, we calculated the inter-action energies for adsorption of some reagentson alumina {0001} surface and compared theresults with adsorption energies reported in liter-ature. The results are summarised in Table 2.The energies calculated by our method are com-parable to that reported in literature (obtainedeither by more accurate ab initio simulations orexperiments) [31,32].

Table 2Interaction energies for adsorption of some reagents on alu-mina {0001} surface

Adsorption/bindingReagent Interaction energyenergy (kcal mol−1)(kcal mol−1)(after [31,32])(calculated by our

method)

−31.9 −33.2, −32.5 andWater−23.3−8.4Butane −10.7

−15.9 −14.6Octane


Table 3Calculated interaction energies (in kcal mol−1) for oleic acidand water adsorption on calcium mineral surfaces

FluorapatiteFluorite Calcite {110}{100}{111}

−46.8 −40.2Oleic acid −52.6−42.9−23.6 −32.2Water

magnitude of interaction energies one can predictthe order of response for oleic acid adsorption oncalcium minerals as:

Fluorite�fluorapatite�calcite

We thus correlated the interaction energies withexperimental flotation response of the mineralsfor oleate (Fig. 1). It is indeed noteworthy thatthe order of adsorption response as predicted bymolecular modelling calculations (entirely theoret-3.2. Fatty acids as flotation collectors for calcium

minerals

Fatty acids are currently being used in theindustry for flotation separation amongst calciumminerals [33–36]. Due to their similar surfaceproperties, semi-soluble nature and calcium beingthe common chelating cation on all the mineralsurfaces separation of one calcium mineral fromanother is a difficult task. Sodium oleate is one ofthe common flotation reagents, currently beingused in the industry. It does exhibit selectivity inflotation of one calcium mineral from another.Interestingly, the experimentally observed (andindustrially practised) differences in the responseof fluorite (CaF2), calcite (CaCO3) and fluorap-atite [Ca10(PO4)6F2] to oleate flotation for exam-ple remain unexplained in literature [33–36].

We have done force field calculations to modelthe interactions of oleic acid molecule with cal-cium minerals. The selected mineral surfaces werecreated having the cleavage plane exposed foroleate adsorption, that is {111} for fluorite, {110}for calcite and {100} for fluorapatite, respectively.The optimised oleic acid molecule was docked onthe mineral surface and the geometry of the com-plex was optimised with respect to energy. Theinteraction energy was calculated for each mineralsurface–oleic acid complex and the results aresummarised in Table 3.

We have also calculated the interaction energyfor adsorption of water on each surface and com-pared with that of oleic acid. As reported in Table3, comparatively lower magnitude of interactionenergies for water as compared with that of oleicacid indicates that oleic acid will replace waterfrom the mineral surface.

As illustrated in Table 3, based on the relative

Fig. 1. Comparison of theoretically computed interaction en-ergy (our method) with experimental flotation response ofoleate for calcium minerals as reported by Pugh and Stenius[33].


Fig. 2. Optimised molecular model of the diphosphonate molecule found to be geometrically compatible with barite {011} surface.

ical) is the same as observed during experimentalflotation tests. It is well known that selectiveadsorption of the flotation collector at the min-eral–water interface is the key to determining theselectivity in flotation separation. The mineralconstituent with larger surface coverage of thecollector becomes relatively more hydrophobicand hence more floatable.

The flotation response of a mineral is depen-dent on the pH of the solution. For example,mineral flotation with oleate is found to be maxi-mum at around pH 8. The relative order offlotation response of different minerals to oleatewill remain same even though the magnitude offlotation recovery will vary with pH. We havecompared our results with the published dataavailable in literature that is at pH 10, as shownin Fig. 1.

We have also reported recently our molecularmodelling computations on the interactions ofother flotation collectors such as octylimino-bis-methylene-diphosphonic acid (IMPA-8), 1-hy-droxy-octylidene-1, 1-diphosphonic acid(Flotol-8) [6] and octyl hydroxamates [7] with thesame three calcium minerals. The predicted orderof flotation response of these three minerals to theselected reagents was observed to correlate re-markably well with the experimentally observedtrends.

Furthermore, molecular modelling computa-tions can also be used as a basis for the prediction

of the most favourable configuration of the ad-sorbed molecule on the surface. For example, wehave recently reported our detailed computationson the adsorption of oleate collector on fluoritesurface [37]. The orientation angles (for the func-tional group as well as the alkyl chain) of theadsorbed oleate molecule in the preferred configu-ration on the fluorite surface were thus predictedbased on these force field computations. Miel-czarski et al. [38] have also measured these anglesthrough a sophisticated in-situ infrared externalreflection spectroscopy technique. The remarkablematch between experiment and theory suggeststhat molecular modelling technique can be em-ployed to investigate preferred orientation of theadsorbed molecules at interface [37].

3.3. Design of corrosion inhibitors

Black et al. [11] and Davey et al. [12] havereported extensively on the possibilities of design-ing highly efficient corrosion inhibitors belongingto diphosphonate family of surfactants (Fig. 2)taking into account the geometric compatibilitybetween the structure of the molecules and thedistribution of surface binding sites on the surfaceof barite (BaSO4) crystals (Fig. 3). We have mod-elled the interaction of phosphonic acids (possess-ing maximum link length C0 to C12) with barite{011} surface and compared in Fig. 4, the interac-tion energies computed by force field method with


Fig. 3. Projection of barite surface at [011] plane showing the relationship between neighbouring like oriented sulphate ions anddiphosphonate ions replacing sulphate ions in adjacent sites (after [12]).

the experimentally observed values of the degreeof corrosion inhibition as reported by Davey et al.[12]. Needless to add, the theoretical predictionsbased on molecular modelling computations arecertainly consistent with the observed trends.

3.4. Dicarboxylic acids as crystal growthinhibitors for calcium fluoride

Grases et al. [13] studied the influence of dicar-boxylic acids on the growth of calcium fluoridecrystals and interpreted their efficiency in terms ofLangmuir type adsorption isotherms. To relatethe chemical structure of the carboxylic acidmolecules with their inhibitory capacity, they alsoperformed MNDO geometry optimisation calcu-lations on acid molecules. The results indicatedthat the presence of carboxylic acid and a hy-droxy group in the � position, in addition toanother carboxylic acid group in the moleculesimparted better inhibitory activity due to a goodgeometrical matching with structural features ofthe calcium fluoride crystal surface.

We performed force field calculations on thesemolecules and calculated interaction energy valuesfor different carboxylic acids adsorbing on cal-cium fluoride {111} surface. A comparison of ourtheoretical results (interaction energies) with cor-

Fig. 4. Comparison of interaction energy (our method) ofdiphosphonates on barite surface with their experimental in-hibitory activity as reported by Davey et al. [12].


Fig. 5. Correlation of interaction energy (our method) ofdicarboxylic acids on fluorite surface with their experimentalinhibitory activity as reported by Grases et al. [13]. The bars inthe figure represent the spread of experimentally measuredvalues of extent of inhibition.

(flocculation factor) were defined as a measure ofthe degree of dispersion. The lower the slope,better was the dispersion and hence more effectivethe dispersant.

We have computed the interaction energies ofthe same fatty acid molecules with titania {110}surface and compared them with experimentalflocculation factors, as reported by Doroszowskiand Lambourne [39] earlier, (Fig. 7). The remark-able match between theory and experiment sug-gests that molecular modelling computations,admittedly with several simplifying assumptions,does capture the essence of the phenomena, weare trying to describe. The dispersion of the sus-pensions is directly related to the adsorption ofdispersants at solid– liquid interface. The adsorp-tion, in turn is related to the affinity of theparticular dispersant to the pigment surface, forexample, titania. That is the reason the interactionenergies correlate well with the extent ofdispersion.

Furthermore, in this case, using molecularmodelling tools, it is possible to study (in greatdetail) the effect of molecular architecture on thedispersion efficiency of the molecules; screen/se-lect the most promising structures, thus synthesiseonly a smaller subset and confirm the enhanceddispersion behaviour through experiments. Thepractical implications of this kind of methodologyin designing tailor-made dispersants are self-evident.

3.6. Dispersants in ceramic processingapplications

Edirisinghe et al. [40] studied the effect of fattyacid dispersants namely stearic acid, oleic acidand poly(12-hydroxystearic acid) (polyHSA) orSolsperse3000, on the dispersion of zirconia(ZrO2) in non-aqueous system. The dispersantswere compared for their ability to produce low-viscosity suspensions of zirconia in kerosene byrheological and sediment packing density mea-surements. The study showed that polyHSA orSolsperse3000 was a much better dispersant thaneither oleic or stearic acid due to the presence ofa long polymeric chain in the molecule and alsoperhaps a better molecular architecture.

responding capacities to inhibit the crystal growthof calcium fluoride is presented in Fig. 5. Theinhibition efficiency values were taken from theexperimental data on the growth of calciumfluoride in the presence of these acids, as reportedby Grases et al. [13]. Once again, the trendspredicted by our molecular modelling calculationsare consistent with experimental findings.

3.5. Design of dispersants for titania based paints

Doroszkowski and Lambourne [39] had pub-lished the results of their excellent experimentalinvestigation on the effect of molecular architec-ture of long chain fatty acids (Fig. 6) on thedispersion properties of titania (TiO2) in non-aqueous media. They reported remarkable trendson the effect of changes in the molecular architec-ture that is, the chain length, the branching, thelength of branch, the position of branch etc. onthe degree of dispersion of titania suspensions.Through their excellent experimental work, theydemonstrated that the dispersion of titania couldbe enhanced almost by an order of magnitudeprovided an appropriate dispersant is employed.The slopes of experimentally measured viscosityof suspensions as a function of root of strain rate


We have modelled the interactions of all theabove-mentioned dispersants with zirconia {001}surface. The interaction energies thus calculatedwere correlated with packing density of the sedi-ments as obtained experimentally by Edirisingheet al. [40] (Fig. 8). It is interesting to note that ourtheoretical predictions match well with experi-ments though the experimental property we aretrying to correlate with, is not a direct/absolutemeasure of adsorption of dispersants on the zirco-nia surface. It only suggests that the strong inter-action of dispersant molecules with zirconiasurface is the key factor in controlling dispersionwhich in turn determines the final packing densityobtained after consolidation. The experimentallymeasured quantity is called the green density andis represented as a fraction or percentage of thetheoretical material density with no pores.

We have also employed this methodology tomodel the effect of benzoic acid dispersants (Fig.9) on alumina (Al2O3) dispersion as studied exper-imentally by Sumita et al. [41]. The percent theo-retical density achieved on alumina sediment wasdependent primarily on the type and concentra-tion of dispersant used. A correlation of theoreti-cally computed interaction energies, calculated forthe interactions of benzoic acid dispersants withalumina {0001} surface (as described in earliersection), with percent theoretical density of alu-mina sediments as obtained by experiments [40] ispresented in Fig. 10. The theoretical predictionsare consistent with experimental findings, validat-ing therefore, our proposed approach of design-ing/screening molecules, based on molecularmodelling computations, for various industrialapplications.

Fig. 6. Chemical structures of fatty acid dispersants used in titania dispersion.


Fig. 7. Correlation of interaction energy (our method) of fattyacid dispersants with their experimental flocculation factor asreported by Doroszkowski and Lambourne [39].

Fig. 9. Chemical structures of benzoic acid dispersants usedfor dispersion of alumina suspensions.

4. Concluding remarks

Based on our extensive work with a wide varietyof industrial surfactant systems, some of which arepresented in this paper, we find our design method-ology based on molecular modelling of surfactant–surface interactions extremely promising. It isworth noting that the theoretical computationspresented in this paper did not require any exper-

imental data. The knowledge of the structure of thesurfactant molecule as well as the surface withwhich it is interacting, was sufficient to predict therelative affinity of the particular molecule withdifferent surfaces and/or the relative strength ofdifferent surfactant molecules for a given surface.The approach has thus been successfully demon-strated to be useful in screening/designing surfac-tants for targeted applications.

Fig. 8. Correlation of interaction energy (our method) of fattyacid dispersants with experimental sedimentation density ofzirconia as reported by Edirisinghe et al. [40].

Fig. 10. Correlation of interaction energy (our method) ofbenzoic acid dispersants with experimental green density ofalumina as reported by Sumita et al. [41].


Acknowledgements

Authors are grateful to Professor M. Josephand Professor E.C. Subbarao for their constantsupport and encouragement during the course ofthis work.

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Design of tailor-made surfactants for industrial applications using a molecular modelling approach

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