Influence of Ionic-Liquid-Tethered Al2O3 Nanoparticle on the

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Influence of Ionic-Liquid-Tethered Al2O3 Nanoparticle on the Nonisothermal Cold Crystallization in Ionic-Liquid-Based Nanofluids Debalina Deb and Subhratanu Bhattacharya* Department of Physics, University of Kalyani, Kalyani, Nadia 741235, West Bengal, India S Supporting Information *

ABSTRACT: A series of ionic-liquid-based nanofluids (ionanofluids) has been prepared by dispersing different wt % of alumina (Al2O3) nanoparticles, covalently tethered with 1-nbutyl-3-(3-(trimethoxysilyl)propyl)imidazolium bis(trifluoromethanesulfonyl)imide [b((MeO)3Sip)im][NTf2], in 1-decyl-3-ethylimidazolium bis(trifluoromethanesulfonyl)imide [Deim][NTf2] ionic liquid (IL) host. Thermophysical properties of the pure-IL and its nanofluids have been studied using transmission electron microscopy, differential scanning calorimetry (DSC) and vibrational spectroscopy. The tethered nanoadditives are dispersed uniformly within the IL host to form stable nanofluids over the entire range of nanoparticle weight fraction studied. Analysis of the heating rate dependent DSC data illustrates that the phase transition and fragility of the nanofluids can be effectively tuned by varying the content of the [b((MeO)3Sip)im][NTf2]-tethered Al2O3 nanoparticles. Moreover, the crystallization and melting transitions of the host can be completely avoided by dispersing merely 10 wt % of the nanoadditives. Analysis of the vibrational spectroscopy data reveals that the guest nanoadditives significantly affect both the relative orientation and the separation of the anion within the host IL through strong intermolecular interactions. The nonisothermal cold crystallization kinetics of the host IL and its nanofluids has been studied using different existing models. The concave downward temperature dependence of the effective activation energy, estimated from the isoconversional analysis of the crystallization data, has been successfully analyzed by standard nucleationbased kinetic model in combination with a power law model. The parameters evaluated from the combined analysis indicate that the tethered nanoparticles within the nanofluids act as heterogeneous nucleation agent, reducing the free energy barrier to nucleation. However, simultaneous large enhancement in diffusion contribution to the nucleation dominates and shifts the process toward higher temperature.

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INTRODUCTION Interest in dilute suspensions of nanometer-sized particles in fluids (nanofluids) has grown in tremendous response to their excellent promise for applications in numerous fields of technology.1 It has been well established that even a small amount of nanoparticle dispersion significantly enhances energy transport process of the base fluid.2 Noble metal nanoparticles such as gold, silver, etc. or ceramic oxide nanoparticles are the most popular nanophases that are used in nanofluids. Recently, multiwalled carbon nanotubes3 (MWCNTs) and graphene4 have also been adopted as the nanoadditive to prepare stable nanofluids with application potentiality. On the contrary, water, synthetic oil, glycols, and polyolefins have been traditionally considered as the base fluids for the suspension of those nanophases. Recently, room temperature ionic liquids (RTILs), comprising bulky organic cations and inorganic or organic anions, have received considerable attention as the base fluid to form nanoparticle dispersed stable nanofluids (ionanofluids). Ionic liquid as a dispersion medium for nanoparticles provide a number of advantages over molecular liquids because of their many attractive physicochemical properties, including ultralow © XXXX American Chemical Society

vapor pressure, high dielectric constant and electrochemical stability with broad electrochemical potential windows over a wide temperature range. The large interface area and the combination of properties of both nanoparticles and ILs, thus obtaining flexible and desirable materials with synergies properties for a wide range of potential applications such as heat-carrier fluids, pigments for solar batteries, electrolytes in batteries, capacitors, magnetic fluids, and so on.2,5,6 Designing nanofluids for a given application, it is essential to understand their thermophysical properties and thermodynamic phase behavior. Specifically for the nanofluids, the defining characteristics are their melting point and crystallinity. However, the thermochemical behavior of IL is not so simple. Some IL may supercool without melting transition. Some of them show freezing transition, crystal formation upon cooling and a melting transition upon heating. Furthermore, some of them do not show any tendency to crystallize upon cooling; however, upon heating, they exhibit cold crystallization. It is Received: November 24, 2016 Revised: March 8, 2017 Published: March 9, 2017 A

DOI: 10.1021/acs.jpcc.6b11845 J. Phys. Chem. C XXXX, XXX, XXX−XXX

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The Journal of Physical Chemistry C

Scheme 1. One-Pot Synthesis of [Deim][NTf2] Room Temperature Ionic Liquid Host with the Corresponding 1H NMR Spectrum

Scheme 2. Surface Modification of Al2O3 Nanopartcles by the IL Precursor [b((MeO)3Sip)im][NTf2]a

a

The 1H NMR and the 29Si NMR of the Al2O3-tethered IL precursor are also shown.

In this research a series of stable nanofluids is prepared by dispersing different wt % of covalently tethered alumina (Al2O3) nanoparticles in an imidazolium-based ionic liquid host. Thermal stability, vibrational properties, and dispersion of nanoparticles within the nanofluids have been studied using conventional thermal gravimetric analysis (TGA), transmission electron microscopy (TEM), and vibrational spectroscopy. To probe the influence of the covalently tethered nanoparticles to the nucleation, phase transition, and crystal growth within the host IL, nonisothermal cold crystallization kinetics of the nanofluids have been studied using differential scanning calorimetry (DSC). It has been observed that the dispersion of as little as 0.5 wt % of tethered nanoparticles dramatically influences the crystallinity of the IL host by slowing down the conversion rate. Moreover, for the nanofluid containing merely 10 wt % of tethered guest the crystallization and melting transition could not be observed during heating from its supercooled state. The differential isoconversional technique of Friedman8 has been implemented to evaluate the temperature dependent effective activation energy for the nanofluids at different degree of conversion. The observed temperature dependence of the activation energy has been analyzed using

worthy to mention that the desirability of crystallinity for a nanofluid depends upon the effective temperature window of the specific properties of the material. A recent study on the thermal properties of nanofluids created by dispersing 1( t r i m e t h o x y s i l y l ) p r o p y l - 3 - m et h y l im i d a z o l i u m b i s ((trifluoromethyl)sulfonyl)imide [Spimim][NTf2]-tethered SiO2 nanoparticles in a 1-butyl-3-methylpyrrolidinium bis((trifluoromethyl)sulfonyl)imide [Bmpyr][NTf2] host has shown that the addition of the SiO2-SpmImNTF2 particles dramatically alters the thermal properties of the IL host at low and high particle loadings.7 The increases in concentration of nanoparticles up to 40 wt % transformed the IL host from a plastic crystalline material into a simple liquid with superior mechanical properties, with no observed melting transition.7 Thus, fundamental understanding of the crystallization phenomena of nanofluids and to get insight into the factors that tune it (e.g., suppression or retardation) is extremely important to support emerging applications of this class of material. However, there has been no report so far in the literature dealing with the mechanisms and kinetics of crystallization of nanofluids. B


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The Journal of Physical Chemistry C the Fisher−Turnbull nucleation-based kinetic model9 in combination with a power law model10 to assess the contribution of diffusion in the overall crystallization process.

All spectra were externally referenced to liquid tetramethylsilane (TMS) at 0 ppm. The spectrum shows (inset) a sharp peak at δ (ppm) = −108.8 and a shoulder at δ (ppm) = −91.2 corresponding to the Q3 and Q2 siloxane, respectively, similar to the earlier reports of Mijatovic et al.12 Q3 and Q2 describe the possibilities of anchoring of the (MeO)3Si part of the (3chloropropyl)trimethoxysilane to the negatively charged surface of the Al2O3 nanoparticles (shown schematically). The significantly higher intensity of Q3 as compared to that of Q2 resembles that the maximum anchoring of the silane groups to the Al2O3 surface occurs by the Q3 mode. Preparation of Nanofluids. Different wt % (x = 0.5, 1.0, 2.5, 5.0, 7.5, and 10) (corresponding relative volume percentages are included in Table 1) of purified and vacuum-

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EXPERIMENTS Synthesis of Ionic Liquid Host. All chemicals used for the synthesis of nanofluids were procured from Sigma-Aldrich and were used without further purification. The ionic liquid host for the nanofluids, 1-decyl-3-ethylimidazolium bis((trifluoromethyl)sulfonyl)imide, [Deim][NTf2] was synthesized using the general one pot synthesis procedure11 (Scheme 1) and is designated here as “pure-IL”. 1H NMR (400 MHz, DMSO-d6) (inset of Scheme 1): δ (ppm) = 9.165 (s, 1H), 7.792−7.775 (m, 2H), 4.216−4.123 (m, 4H), 1.786 (t, J = 7.2 Hz, 2H), 1.424 (t, J = 7.2 Hz, 3H), 1.242 (s, 14H), 0.856 (t, J = 4.0 Hz, 3H). The as-synthesized IL was dried at a temperature of 100 °C for 24 h under a dynamic vacuum and stored in an argon-filled glovebox. Surface Modification of Al2O3 Nanoparticles by IL Tethering. The surface modification of the alumina nanoparticles by covalent tethering of ionic liquid to their surfaces was performed in two steps. Initially, the IL precursor 1-nbutyl-(3-(trimethoxysilyl)propyl)imidazolium chloride [b((MeO)3Sip)im][Cl] was prepared according to the previously reported procedure7 (Scheme 2). Briefly, (3-chloropropyl)trimethoxysilane and 1-butylimidazole were refluxed under a N2 atmosphere at 120 °C for 2 days. After cooling to room temperature, a honey-like orange viscous liquid [b((MeO)3Sip)im][Cl] was obtained. Any unreacted chemical was removed from the product by column chromatography. 1H NMR (inset of Scheme 2) (400 MHz, DMSO-d6): δ (ppm) = 10.796 (s, 1 H), 7.568 (t, J = 1.8 Hz, 1 H), 7.45 (t, J = 1.2 Hz, 1 H), 4.334 (dt, J = 4.2 Hz, 4 H), 3.571 (s, 9 H), 1.872−2.075 (m, 4 H), 1.352 (dt, J = 8.1 Hz, 2 H), 0.967 (t, J = 7.5 Hz, 3 H), 0.616−0.672 (m, 2H). The excess amount (1.5 times) of as-prepared [b((MeO)3Sip)im][Cl] was allowed to react with the desired amount of deionized water dispersed γ-Al2O3 nanoparticles (40 m2/g (BET)) under a N2 atmosphere. The reaction proceeded for 12 h under continuous stirring at 100 °C, and subsequently, absolute ethanol was added to the mix. Eventually, the surface-functionalized alumina nanoparticles were collected by repeated washing and high speed centrifugation. The [b((MeO)3Sip)im][Cl]-Al2O3 were converted to the [b((MeO)3Sip)im][NTf2]-Al2O3 system by the simple anion exchange reaction. In a typical reaction, 10 g of [b((MeO)3Sip)im][Cl]-Al2O3 was dispersed in 300 mL of DI water to form a clear dispersion. Eight grams of lithium bis((trifluoromethyl)sulfonyl)imide (LiNTf2) salt dissolved in 50 mL of DI water was then added to that clear solution under vigorous stirring. Immediately after the addition, [b((MeOSi)3p)im][NTf2]-functionalized Al2O3 nanoparticles were separated from the water phase and settled to the bottom due to the hydrophobicity of the NTf2 anion. The organic viscous part was collected by centrifuging and washed several times with DI water and acetone to take out the remaining untreated Cl-anion-tethered nanoparticles. Finally, the ILtethered nanoparticle gel was dried at a temperature of 100 °C for 24 h under a dynamic vacuum and stored in an argonfilled glovebox. 29 Si NMR spectra of the dried IL-tethered nanoparticle gel was recorded in DMSO-d6 on a Bruker Avance III 500 MHz spectrometer operating at 500.1 and 99.35 MHz, respectively.

Table 1. Preparation of Different Nanofluids with Relative Weight and Volume Percentages of the Components [Deim][NTf2]

[b((MeOSi)3p)im][NTf2]-Al2O3

wt %

vol %

wt %

vol %

name

100 99.5 99.0 97.5 95.0 92.5 90.0

100 99.582 99.163 97.902 95.786 93.653 91.501

0 0.5 1.0 2.5 5.0 7.5 10.0

0 0.418 0.837 2.098 4.214 6.347 8.499

pure-IL 0.5 Al2O3-IL 1.0 Al2O3-IL 2.5 Al2O3-IL 5.0 Al2O3-IL 7.5 Al2O3-IL 10 Al2O3-IL

dried [b((MeOSi)3p)im][NTf2]-functionalized Al2O3 nanoparticles were dispersed in the [Deim][NTf2] IL host to form different stable nanofluids designated as “xAl2O3-IL” in the manuscript. Characterizations. The thermal stabilities of the pure-IL and its nanofluids were evaluated by TGA (Netzsch TG 209 F3 Tarsus) at a heating rate of 10 °C/min under a nitrogen environment. The samples were scanned from room temperature to 600 °C. Fourier transform infrared (FT-IR) experiments of the samples were performed on a Shimadzu IR affinity 1S FT-IR spectrometer, equipped with a single-reflection diamond attenuated total reflection (ATR) sampling module. The spectra were acquired using 64 scans and 4 cm−1 resolution within a spectral range of 4000−400 cm−1. The Raman spectra were measured with a Bruker RFS 27, multiRAM standalone FT-Raman spectrometer with a resolution of 2 cm−1. Samples were excited by the 1064 nm radiation of a Nd:YAG laser with 100 mW output power. Transmission electron microscopy (TEM) was performed on a JEOL JEM-2100, 200 kV microscope in high resolution mode. The nonisothermal cold crystallization kinetics of the nanofluids were studied using a Netzsch DSC 214, Polyma differential scanning calorimeter (DSC) under a nitrogen atmosphere with temperature calibrated by indium. In practice, ∼10 mg of the sample was encapsulated in concave aluminum crucibles covered by lids. The sample was first heated to 120 °C at 10 °C/min and kept there for 10 min to eliminate any thermal history. The sample was then rapidly cooled (100 °C/ min) to −100 °C and isothermally kept there for 10 min. Afterward, the supercooled liquids were heated at different heating rates up to 30 °C.

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RESULTS AND DISCUSSION Thermal Stability. Thermal stabilities of Al2O3-[b((MeOSi)3p)im][NTf2], pure-IL, and its three nanofluids C


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Morphology of Tethered Nanoparticles and Nanofluids. Surface functionalization of the Al2O3 nanoparticles and their distribution within the IL host play the key roles to alter the properties of the host. Both of these have been analyzed by TEM imaging, as shown in Figure 2a,b for the [b((MeO)3Sip)im][NTf2]-tethered Al2O3 nanoparticles and 5.0 Al2O3-IL nanofluid, respectively. Figure 2a along with the high resolution image (inset) clearly shows the effectiveness of [b((MeO)3Sip)im][NTf2] to functionalize the surface of the Al2O3 nanoparticles that limits their aggregation. The corresponding SAED pattern (inset) demonstrates the diffraction spots of tethered γ-Al2O3 nanoparticles. Figure 2b indicates that the nanoparticles are uniformly dispersed within the host IL to form stable nanofluid. Differential Scanning Calorimetry and Phase Transition. The effect of uniformly dispersed, surface-functionalized nanoparticles at different loading on the thermodynamic phase transition of the host IL has been observed by differential scanning calorimetry (DSC). DSC has been used to measure the glass transition temperature (Tg), cold crystallization temperatures, and melting temperature (Tm) of pure-IL and its nanofluids during heating at different heating rates from the supercooled states of the systems. Parts a−e of Figure 3 depict the nonisothermal cold crystallization thermograms for pure-IL and its nanofluids at various heating rates (φ). The endotherms of pure-IL and different nanofluids at a fixed heating rate of 5 °C/min have been compared in Figure 4. Each of the plots shows a glass transition temperature, an exothermic event attributed to the cold crystallization followed by an endothermic event corresponding to the melting of the crystallites. The glass transition temperature (Tg) the onset (Tocc), peak (Tpcc), and end (Tecc), temperature of cold crystallization and corresponding melting temperature Tm at different heating rates are collected in Table 2. Parts a−c of Figure 5 depict the variation of Tg, Tpcc, and Tm of different samples as the function of φ. With increasing heating rate, the time for nucleation and growth of the crystallites decreases. Subsequently, the process is appreciably shifted to higher temperature. The respective glass transition (Tg) and melting peak (Tm) are also shifted toward higher temperature, but at a slower rate (Figure 5 and Table 2). Above a particular heating rate the crystallization and melting curve tend to merge. With increasing Al2O3 loading, this

have been compared in Figure 1. Respective derivative thermogravimetric (DTG) curves for pure-IL and nanofluids

Figure 1. Comparison of the thermal decomposition behavior of different nanofluids with host IL and Al2O3-[b((MeOSi)3p)im][NTf2]. The derivative thermogravimetric (DTG) curves for pure-IL and different nanofluids are shown at the inset.

are also included in the inset. The absence of any weight loss below 390 °C for each of the samples rules out the possibility of any solvent or unreacted small molecules within them. As observed, Al2O3-[b((MeOSi)3p)im][NTf2] shows better thermal stability than others. From the TGA trace for the Al2O3tethered [b((MeOSi)3p)im][NTf2] the grafting density of the ligand to the nanoparticle surface has been estimated by ∑ = NA(1 − Wnano)/(Wnano × anano × ρnano)/(6Mligand) and is found to be ∼0.71 ligand/nm2 and a spacing of around 0.23 nm between the IL chain tethered to the Al2O3 nanoparticle surface. The thermal stability of nanofluids increases at higher nanoparticle loading and the DTG curves depict a systematic shift in the decomposition onset temperature and the temperature range of the nanofluids with respect to host IL with increasing guest loading. From the residual of the decomposed 2.5, 5, and 10 wt % nanoparticle-loaded nanofluids the percentage of nanoparticles has been estimated as 2.3%, 4.8%, and 8.6%, respectively. Such a close agreement to the above two percentages indicates the uniformity of dispersion of the nanoparticles within the nanofluids.

Figure 2. TEM micrographs of (a) [b((MeO)3Sip)im][NTf2]-tethered Al2O3 nanoparticles. The inset shows the high resolution image and corresponding SAED pattern. (b) Dispersion of nanoadditives within the 5.0Al2O3-IL nanofluid. D


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Figure 3. Nonisothermal cold crystallization endotherms of (a) pure-IL and (b)−(e) different nanofluids.

of the nanofluid containing as little as 0.5 wt % of functionalized Al2O3 nanoparticles has substantially shifted to higher temperature. Furthermore, both the enthalpy change due to crystallization from the supercooled state and corresponding melting transition is considerably reduced. For the nanofluid containing 10 wt % of nanoadditives, both the crystallization and the melting transitions almost disappear and the material exhibits only a glass transition temperature. The temperature range for nonisothermal cold crystallization ΔTcc = Tecc − Tocc and the corresponding difference between the crystallization and melting peaks ΔT = Tpcc − Tm for different samples are plotted in Figure 5e as the function of φ. As can be observed, with increasing heating rate, ΔTcc for nanofluids significantly increases with respect to the pure-IL. However, that follows a sharp decrease in corresponding ΔT value. Moreover, at a particular heating rate, with increasing nanoparticle loading, ΔTcc gradually widens with a simultaneous narrowing in ΔT. Relatively higher Tpcc and wider ΔTcc at any heating rate for the nanofluids with respect to the pure-IL imply the requirement of a higher amount of energy for the relaxation of the amorphous fraction within these, inhibiting the cold crystallization process. Consequently, at a 5 wt % loading, the crystallization and melting phenomena can sustain only up to φ ≤ 5 °C/min (Figure 5d). These clearly indicate that at higher nanoparticle loading (10 Al2O3-IL), even at a very low rate of heating the time for crystal growth between the beginning of cold crystallization and corresponding melting becomes so short that the materials do not get any chance to

Figure 4. DSC thermograms of pure [Deim][NTF2] IL and different wt % of [b((MeO)3Sip)im][NTf2]-tethered Al2O3 nanoparticles dispersed in nanofluids during heating at 5 °C/min from the supercooled state.

overlapping occurs at a substantially lower heating rate, as can be observed from Figure 4. It is worthy to observe from Figure 4 that at a fixed heating rate the cold crystallization temperature E


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The Journal of Physical Chemistry C Table 2. Nonisothermal Crystallization Kinetic Parameters for Pure-IL and Its Nanofluids xAl2O3-IL

0.0

0.5

1.0

2.5

5.0

φ (°C/min)

Tg (°C)

Tocc (°C)

Tpcc (°C)

Tecc(°C)

Tm (°C)

t1/2 (min)

1.5 2.5 5.0 10 15 20 1.5 2.5 5.0 7.5 10 15 1.5 2.5 5.0 7.5 10 1.5 2.5 3.5 5.0 1.5 2.5 3.5 5.0

−87.1 −86.0 −84.7 −83.3 −82.6 −81.8 −86.3 −85.1 −83.9 −83.4 −82.8 −82.4 −84.6 −84.0 −83.4 −82.8 −82.3 −83.9 −83.4 −83.2 −82.8 −83.5 −83.1 −82.8 −82.5

−64.8 −63.5 −61.7 −59.0 −54.9 −50.8 −64.7 −63.2 −60.3 −56.6 −52.8 −43.3 −64.4 −58.3 −53.2 −46.7 −43.2 −59.6 −56.0 −50.2 −47.6 −59.0 −55.4 −50.5 −40.8

−60.9 −58.7 −55.3 −52.0 −47.7 −43.1 −60.7 −58.5 −53.8 −49.5 −45.0 −32.4 −60.4 −52.8 −44.3 −36.4 −32.1 −53.9 −49.0 −41.6 −38.0 −53.1 −48.4 −41.6 −29.9

−55.7 −54.0 −51.0 −45.2 −40.0 −31.2 −57.1 −53.8 −47.6 −42.0 −36.0 −23.9 −57.0 −50.0 −37.0 −22.8 −19.5 −49.6 −44.8 −35.0 −29.7 −48.6 −43.4 −33.5 −22.0

−14.5 −14.3 −13.8 −13.2 −12.5 −12.0 −14.4 −14.2 −13.3 −12.4 −11.8 −11.3 −14.0 −13.5 −12.7 −12.0 −11.3 −13.8 −13.1 −12.8 −12.1 −13.7 −13.5 −13.2 −12.0

2.62 1.84 1.27 0.70 0.46 0.39 2.64 1.88 1.30 0.85 0.79 0.59 2.66 2.12 1.74 1.38 1.12 3.81 2.80 2.53 1.93 3.96 2.84 2.55 2.19

recrystallize. All these observations designate that Al2O3 nanoparticles with a [b((MeO)3Sip)im][NTf2]-functionalized surface play a retardant role in the cold crystallization process, which might be ascribed to the nature of interactions between the functionalized Al2O3 particles and the host IL. Similar phenomena were also observed earlier for [Bmpyr] [NTf2] ILhosted nanofluid at a much higher loading (40 wt %) of [Spimim][NTf2] IL-grafted SiO2 nanoparticles.7 Thus, the covalently tethered Al2O3 nanoparticles are found to be effective to tune the thermodynamic phase transition of the [Deim][NTF2] IL-hosted nanofluids. Glass Transition and Fragility. The variation of Tg with heating rate manifests the kinetic nature of the glass transition. To characterize the temperature dependent dynamics of the nanofluids at their supercooled state near the glass transition, subsequent determination of the fragility parameter is important. Fragility (m) is a measure of the thermal sensitivity of the liquid structure and is described as the rate at which the structure of a supercooled liquid around the glass transition temperature relaxes with heating.13 It is an important concept to measure the magnitude of deviation from conventional Arrhenius kinetics for supercooled liquids and glasses. Accordingly, glass forming liquids can be classified as their position between two extremes; the so-called strong/fragile pattern.14 Strong liquids that show almost Arrhenius behavior have small m values (m ≈ 16), whereas fragile liquids with larger m values (m > 100) collapse under weak perturbations, leading to fast dynamical rearrangement and non-Fickian diffusion, giving rise to non-Arrhenius behavior.14 Fragile liquids have been extensively studied because of their unusual relaxation behavior. However, what exactly makes a liquid fragile is still unclear. The fragility is often obtained from the fit of the data to the Vogel−Fulcher−Tammann (VFT) equation15−17 for viscosity (η) written as

η = η0 exp[DT0/(T − T 0)]

(1)

where η0, D, and T0 are adjustable parameters, and small D indicates large fragility. However, if the range of viscosity data corresponding to far above Tg are used in that fitting, it may yield T0 values higher than Tg, which is unphysical. The more reliable measure is the fragility determined from the slope of the Tg-scaled Arrhenius plot for viscosity as18 m = [d logη /d(Tg /T )]T = Tg = −Eg (Tg)/(RTg) ln 10 = 16 + 590/D

(2)

where Eg is the glass transition activation energy or the activation enthalpy for structural relaxation, assuming a value of 10−4 poise for η0.18 The activation energy Eg is determined using the theory developed by Moynihan19 on the basis of the glass transition kinetics and structural relaxation to a high degree of approximation as d ln(φ)/d(1/Tg) = −Eg /R

(3)

Another method used to find the glass transition activation energy is the Kissinger method,20 and the equation for it is given as20 d ln(φ /Tg 2)/d(1/Tg) = −Eg /R

(4)

Parts a and b of Figure 6 depict the activation plots corresponding to Moynihan and Kissinger method ln(φ) and ln(φ/Tg2) as a function of 1000/Tg for all the samples, respectively. The activation energy values (calculated from the slope of the activation plots according to eqs 3 and 4) and the respective F


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Figure 5. Variation of (a) Tg, (b) Tpcc, and (c) Tm with φ for different samples. (d) Heating rate dependent variation of ΔTcc = Tecc − Tocc and ΔT = Tpcc − Tm for pure-IL and its nanofluids.

∼3000 cm−1) groups, whereas the one in the lower frequency region (Figure 7b) corresponds mostly to the IR pattern of the conformational isomerism of NTf2 anion. Within the spectral region 3215−3050 cm−1, the spectra of pure-IL and its nanofluids are deconvolated (Figure 7c−f) using a Gaussian function into four Voigt profiles with maxima at ∼3179 , ∼3154, ∼3123, and ∼3100 cm−1, respectively. The pair of bands at the higher wavenumbers are assigned to the symmetric C(4)−H and antisymmetric C(5)−H stretching of the imidazolium ring, while those at the lower wavenumbers are attributed to the C(2)−H stretching modes of the ring and Fermi resonances of the C−H stretching vibrations with overtones of in-plane ring deformations.23 The deconvolated spectra (Figure 7c−f) clearly illustrate that there is a direct interactions between IL and the oxide nanoparticles within the nanofluids, which significantly influences the characteristic CH···anion H-bonding modes between the imidazolium cations and NTf2 anions.23,24 It has been established earlier that in the imidazolium-based IL system the C(2)−H and C(4,5)−H modes can either form isolated ion pairs by hydrogen boding with the anion or form larger networks where cation can be completely H-bonded via C(2)−H and C(4,5)−H.23−26 In the latter case the H-bonds are weaker than those in isolated ion pairs because of the anticooperative charge transfer within the imidazolium ring.24 As the stronger H-bonded species give larger vibrational intensities, the deconvulated spectrum of pure-IL (Figure 7c)

heating rate dependent fragility index (m) are depicted in Figure 6c,d, respectively. As observed from Figure 6c,d, both methods provide similar and almost identical results for the activation energy and fragility index, respectively. Furthermore, it is interesting to notice that with increasing Al2O3 content within the host IL, the fragility of the resulting nanofluids significantly enhances. This may be attributed to the development of strong intermolecular interactions21 between the host IL and tethered Al2O3 within the nanofluid medium that actually perturbs both the local and global dynamics of host IL chain matrix. This perturbation essentially leads to an increase of the particle relaxation time relative to the host in a “layer” about the particles,22 resulting in a gradual increase in both Tg and fragility of the nanofluids with increasing nanoparticle dispersion. Intermolecular Interactions. The observed variation in phase transition and higher fragility index of the nanofluids clearly indicate the possibility of strong intermolecular interaction between the host and guest. To investigate the nature of intermolecular interactions, vibrational spectra, including both infrared and Raman of different samples have been analyzed in different spectral regions. Parts a and b of Figure 7 depict the ATR-FT-IR spectra of pure-IL and three different nanofluids in two different regions, viz., 3215−2820 and 1650−1020 cm−1, respectively. The high frequency region (Figure 7a) represents the IR impressions of C−H vibrational bands of aliphatic (below ∼3000 cm−1) and aromatic (above G


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Figure 6. (a) Moynihan and (b) Kissinger plots to estimate the glass transition activation energy (Eg) for pure-IL and different nanofluids. (c) Composition variation of Eg obtained from different method. (d) Heating rate dependent fragility index (m) for pure-IL and different nanofluids.

the shoulder at 1143 cm−1 corresponds to either the out-ofphase asymmetric CF3 stretch of the trans conformer or the asymmetric stretch on only one side of the cis conformer. It evolves with significantly higher absorption intensity for the cis conformation than that of the trans. The intense peak at 1216 cm−1 corresponds to asymmetric CF3 vibration, which couples strongly to the symmetric SO2 stretching mode. The shoulder associated with the above band around 1230 cm−1 is assigned to the symmetric CF3 stretch mode. The doublet feature at 1333 and 1359 cm−1 is allocated to the out-of phase asymmetric SO2 stretch of the trans conformer or to the asymmetric stretch of only one side of the cis conformer. In the trans conformation the absorption intensity of the 1359 cm−1 band is substantially higher than cis. Finally, the absorption bands around 1578 cm−1 is attributed to the vibrations of the CC double bond overlapping with the vibrations of the NCN group. In Figure 7b, the spectrum of pure-IL stipulates the dominating feature of the energetically stable trans-anion conformation. In the trans conformation the NTf2 preferably displaces slightly and establishes more specific interactions with the C(2,4,5)−H sites (in particular with C(2)−H) favoring the ion pair formation within the pure-IL. However, with increasing tethered Al2O3 dispersion, the intensity of the absorption bands exclusive to the cis-NTf2 conformation increases with simultaneous suppression of the corresponding exclusive trans-vibration bands. For the nanofluid containing 10 wt % nanoparticles, the intensity of trans-vibration bands significantly reduces and the cis conformation dominates. The issue of intermolecular interaction has been further addressed by employing Raman spectroscopy. In Figure 8a, the Raman spectrum of the pure-IL and its nanofluids are shown within 250−800 cm−1. The spectral range shown here contains useful information on both the strength of interaction

clearly indicates the presence of significant amount of ion pairs within it. With tethered Al2O3 nanoparticle loading, although the frequency envelope of the higher wavenumber bands does not change in energy, a significant blue shift in the lower wave bands can be observed. Furthermore, a variation of the relative intensities of the bands can also be observed with increasing Al2O3 nanoparticle content. The observed blue shift in C(2)− H stretching bands and simultaneous increase in intensities of the C(2)−H and C(4,5)−H vibrational bands of the network structure clearly designate a gradual weakening of the strong isolated ion pair H-bonds and simultaneous transformation of the ion pairs to larger H-bonded network configuration.24 Apart from the above bands, the intensity of the peaks at the frequency ∼2960, ∼2940, ∼2900, and ∼2855 cm−1, corresponding to the fundamental C−H stretching vibrational bands of the alkyl chain of cation bound to the nitrogen atoms of the imidazolium ring are also gradually diminished with increasing concentration of Al2O3 within the nanofluids. The observed changes in the delicate balance between Coulombic attraction and H-bonding within the nanofluids are also associated with simultaneous changes in the conformation of the NTf2 anion, which has been demonstrated by the infrared spectra within 1650−1020 cm−1 range in Figure 7b. The NTf2 anion within the IL can adopt two different conformations viz. the cisoid (cis or C1) and the transoid (trans or C2), which differ only by a few kJ/mol in energy.25 The absorption bands corresponding to these conformations are assigned as follows.27,28 The band around 1061 cm−1 corresponds to the antisymmetric SNS stretching mode of the NTf2 anion, which is strongly coupled to the symmetric stretch of the CF3 group. The absorption around 1138 cm−1 with a shoulder at 1143 cm−1 occurs due to coupled asymmetric CF3 stretching of different phases. Specifically, H


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Figure 7. FT-IR spectra of [Deim][NTF2] IL and different wt % of [b((MeO)3Sip)im][NTf2]-tethered Al2O3 nanoparticles dispersed nanofluids in the regions (a) 3215−2820 cm−1 and (b) 1650−1020 cm−1, respectively. (c−f) Deconvoluted spectra of bulk IL and different nanofluids respectively, in the region 3215−3050 cm−1.

low frequency range ∼250−450 cm−1 (Figure 8b) are the best candidates to investigate the conformational isomerism of the NTf2 anion within the aprotic ionic liquids.29 The vibrational bands found within the above-mentioned range have previously been assigned to the twisting (τ) and rocking (ρ) modes of the SO2 and CF3 groups of the NTf2 anion.29 The bands at around 408, 333, 326, and 310 cm−1 are the best indicators for the C1 conformation, whereas the C2 conformer can be suitably recognized by the bands near 398, 339, 314, and 297 cm−1. Furthermore, the shoulder at 351 cm−1 is also due to the C1 conformer.12,29 As shown in Figure 8b, the spectrum of pure-IL mainly contains the modes corresponding to the trans (C2) form (see arrows). However, with increasing nanoparticles the

experienced by the NTf2 anion (expansion−contraction mode at 740 cm−1) and its conformational state (240−450 cm−1). The vibrational mode at 740 cm−1 has been assigned to the most characteristic Raman signature for the NTf2 anion.29 It has been demonstrated earlier that the 740 cm−1 feature is the sum of two components arising from both the cisoid (C1) at 738 cm−1 and the transoid (C2) at 741 cm−1.12 It is interesting to observe that, with increasing nanoparticle loading, this mode gradually shifts toward lower frequencies, indicating the increasing proportion of the C1 conformer than C2 within the nanofluids. It has been reported earlier that the series of interaction independent deformation Raman modes centered within the I


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Figure 8. (a) Raman spectra of pure-IL and its nanofluids in the spectral range 250−800 cm−1. The inset shows the enlarged portion of the strongest Raman mode (∼740 cm−1) corresponding to the NTF2 anion. (b) Low frequency NTF2 anion sensitive Raman signature of different samples. Arrows indicate the band position corresponding to different anion conformations.

Figure 9. Conversion (Xt(T)) with increasing temperature within (a) 1.0 Al2O3-IL nanofluid recorded for various heating rates and within (b) different samples at a particular heating rate. Corresponding temperature variation of the rate of crystallization (dXt(T)/dt) are depicted in (c) and (d), respectively.

intensity of the modes associated with the cisoid (C1) form increases with respect to those of the transoid (C2). For the 10 Al2O3-IL nanofluid, the modes corresponding to the C2 form almost diminish and the spectrum shows the characteristics of the C1 form only. A similar trans to cis transformation was also observed during adsorption and desorption of 1-butyl-3methylimidazolium bis(trifluoromethylsulfonyl)imide ionic liquid on the of Al2O3/Ni Al(110) surface.27,30

From the above discussion it is clear that the [b((MeO)3Sip)im][NTf2]-tethered Al2O3 nanoparticles within the nanofluids significantly affect both the relative orientation and the separation of the anion from the imidazolium ring, which hinders the ion-pair formation and induces the NTf2 anion to gradually rearrange and establish a conformational equilibrium different from that in the host. Both of these rearrangements gradually alter the thermophysical properties J


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Figure 10. Plots of log φ vs log t from Liu’s method for the nonisothermal cold crystallization of pure-IL and different nanofluids.

same sigmoid shape, indicating that only the heating rate dependent retardation effect in relaxation of the amorphous fraction separates them from each other. The transformation from temperature to time is performed using eq 6 for different heating rates. Figure S2 shows the variation of Xt with the crystallization time for pure-IL and different nanofluids at different heating rates. The temperature changes of the crystallization rate dXt(T)/dt in a representative nanofluid, calculated for various φ, are presented in Figure 9c. As can be seen, the rate of crystallization increases significantly with φ. Figure 9d compares the temperature dependent crystallization rates for different samples at a specific heating rate. With increasing Al2O3 nanoparticle dispersion, the rate of crystal growth within the nanofluids significantly diminishes as compared to that of the pure-IL. Thus, at a specific heating rate, higher temperature is necessary to achieve same degree of crystallinity within the nanofluids. The half cold crystallization time (t1/2), defined as the time taken to reach the relative crystallinity at the value 50%, can be obtained from the Xt vs t curve (Figure S2), as summarized in Table 2 at different heating rates. The t1/2 value gradually decreases with increasing heating rate, as the crystallization proceeds faster. However, higher t1/2 for the nanofluids with respect to the pure-IL at any particular heating rate again indicates the decelerating effect of the guest nanoadditives to the overall crystallization process. In isothermal crystallization kinetics, the degree of crystallinity Xt(T) is usually described in terms of the Avrami model31 as

such as phase transition and fragility of the nanofluids as compared to the case for the host. Kinetics of Crystallization. To further investigate the influence of the IL-tethered nanoadditives to the nucleation and growth of the crystallites within the IL host, the kinetics of the cold crystallization for different samples have been studied. As the crystallization and melting endotherms for the 7.5Al2O3-IL nanofluid coincide at φ ≤ 5 °C/min, the data for the nanofluids up to 5.0Al2O3-IL are considered for the kinetics study. In the dynamic crystallization mechanism, it is generally considered that the development of crystallinity is linearly proportional to the evolution of heat released during the crystallization. On the basis of the above consideration, the thermal growth of mass fraction of crystalline state in the sample i.e., the degree of conversion as a function of temperature Xt(T), is defined as the ratio of crystallinity at any arbitrary temperature to the crystallinity as the temperature approaches infinity and can be expressed as X t (T ) =

ΔH = ΔH∞

∫T

T

o cc

⎛ dHc ⎞ ⎜ ⎟ dT / ⎝ dT ⎠

∫T

T∞ ⎛ dH ⎞ c

o cc

⎜ ⎟ dT ⎝ dT ⎠

(5)

where dHc/dT is the rate of heat evolution, ΔH is the total heat evolved at any arbitrary temperature T, and ΔH∞ is the heat evolved at ultimate crystallization temperature T∞ (at t = ∞). Tocc is the initial (at t = 0) crystallization temperature. In the nonisothermal experiments the crystallization time is related to the cooling rate φ as t = |Tcc0 − T | /φ

(6)

X t(T ) = 1 − exp( −Ztt n)

Figure 9a illustrates the degree of conversion Xt(T) with temperature at different heating rates for a representative nanofluid. Similar Xt(T) vs T plots for other nanofluids are depicted in Figure S1. The compositional variations of temperature dependent Xt(T) at a particular heating rate (ϕ = 5 °C/min) are shown in Figure 9b. All the curves have the

(7)

where n is the Avrami constant, associated with the nucleation mechanism and the geometry of crystal growth, and Zt is the overall (macroscopic) rate constant involves both nucleation and growth rate parameters. To describe nonisothermal crystallization kinetics in the experiment with constant heating K


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The Journal of Physical Chemistry C rates ϕ, the Avrami model was generalized by Ozawa32 and Liu and co-workers.33 In the Ozawa32 model, the nonisothermal conditions were considered as the result of an infinite number of isothermal steps and the following equation was proposed 1 − X t(T ) = exp[−K (T )/φm]

significantly with increasing Al2O3 content within the IL host. This again confirms that the crystallization rate of nanofluids gradually becomes slower with increasing Al2O3 content and the tethered nanoparticles essentially retard the overall crystallization process. Effective Activation Energy for Cold Crystallization. To characterize the nonisothermal cold crystallization kinetics of the pure-IL and different nanofluids from their supercooled state determination of the effective activation energy (EX), the energy required to transport molecular segments to the crystallization surface is very essential. In this regard, the reaction model independent isoconversional methods are advantageous as compared to the other methods.37 The isoconversional methods also enable us to evaluate the dependence of the EX on conversion and temperature, which is quite helpful in detecting and elucidating complex kinetics and crystal growth mechanism in any system.37 Among the different existing isoconversional methods the differential method of Friedman8 and the advanced integral method of Vyazovkin37 are most appropriate and in the current investigation the method of Friedman has been used to evaluate EX for different samples. In the cold crystallization process above the glass transition, the supercooled glassy material relaxes and turns into its metastable liquid form. As the temperature continues to rise, the molecular mobility increases, promoting nucleation and crystallization of the supercooled liquid. Cold crystallization normally occurs below a certain temperature Tmax up to which the nucleation rate increases with increasing temperature. Above Tmax, as the process is solely diffusion controlled, a dramatic decrease of the nucleation rate occurs.37 On the basis of the energetics involved in the nucleation process, the maximum rate of nucleation can be expressed according to the Fisher and Turnbull model as9

(8)

where K(T) is a function of temperature, which relates to nucleation style, nucleation rate, and crystal growth rate, and m is the Ozawa exponent, which depends on the dimensions of the crystal growth. However, the Ozawa model was found to be inadequate to properly explain the nonisothermal crystallization kinetics for different materials34−36 as the secondary crystallization and the impingement of spherulites has been neglected in this model.32 Liu et al.33 combined the Avrami and Ozawa equations to give a relationship between Zt, K(T), and t as log Zt − m log φ = log K (T ) + n log t

(9)

Thus, a new equation for a description of crystallization appears log φ = (1/m) log[Zt /K (T )] − (n/m) log t = log F(T ) − λ log t

(10) 1/m

where the kinetic parameter F(T) = [Zt/K(T)] represents the necessary value of heating rate per unit crystallization time to reach the systems at a distinct degree of crystallinity, i.e., the parameter that specifies the polymer crystallization rate, and λ is the ratio of the Avrami exponent (n) to the Ozawa exponent (m). As shown in Figure 10a−e, the linear dependence of log φ vs log t is obtained for each degree of crystallinity for each of the samples, indicating that the observed crystallization is a well-defined process for all φ applied in the experiment. The values of λ and F(T) estimated from the slope and intercept of the linear fitting are summarized in Table 3. F(T) is

w(T ) = w0 exp( −ΔG*/RT ) exp(−E D/RT )

where ΔG* and ED are considered as the free energy barrier to nucleation and the activation energy for diffusion across the phase boundary, respectively. The product of these two exponential terms yields a temperature dependence that demonstrates a maximum in the nucleation rate.9 The free energy barrier is infinity near the glass transition temperature, but quickly decreases with heating as

Table 3. Dynamic Crystallization Kinetic Parameters at Different Degrees of Crystallinities by the Liu Method Xt (%) sample pure-IL 0.5 Al2O3-IL 1.0 Al2O3-IL 2.5 Al2O3-IL 5.0 Al2O3-IL

parameters

20

40

60

80

F(T) λ F(T) λ F(T) λ F(T) λ F(T) λ

3.71 1.67 4.23 1.80 6.03 2.04 7.53 1.93 13.8 2.11

5.24 1.69 6.60 1.81 9.72 2.14 11.6 2.05 23.1 2.21

7.24 1.72 9.21 1.87 15.4 2.31 17.9 2.21 40.2 2.41

9.93 1.73 13.1 1.89 24.5 2.51 30.1 2.33 62.1 2.47

(11)

ΔG* =

16πσ 3T0 2 2

2

3(ΔHf ) (ΔT )

=

A (ΔT )2

(12)

where σ is the surface energy, ΔT = T0 − T (T0 is the temperature where nucleation is zero), ΔHf is the heat of fusion, and A is a constant. Equations 11 and 12 can be used to predict a variation in the effective activation energy with temperature.37 By definition, the effective activation energy is determined by the logarithmic derivative of the rate constant with respect to the reciprocal temperature:

considered as a parameter that indicates the cold crystallization rate. A lower F(T) value corresponds to the higher crystallization rate under nonisothermal crystallization. Constant λ together with F(T) growing with Xt(T) rules out the appearance of any extra crystallization in the experiment and it further indicates that at a particular crystallization time, to achieve a higher degree of crystallinity a higher heating rate is necessary. It is worthy to observe from Table 3 that for an identical degree of crystalline transformation the value of F(T) increases

E X = −R

∂ ln w(T ) ∂T −1

(13)

Combining eqs 11 and 12 give the following equation ⎞ ⎛ ⎛ E ⎞ A w(T ) = w0 exp⎜ − exp⎜ − D ⎟ 2⎟ ⎝ RT ⎠ ⎝ RT (ΔT ) ⎠

(14)

Substitution of eq 14 into eq 13 yields L


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Figure 11. (a) Effective activation energies, obtained using the differential isoconvertional method, as a function of conversion for cold crystallization of pure-IL and its nanofluids (open symbols) along with corresponding variation of average temperature (solid symbols). (b) Dependence of the effective activation energy on average temperatures, for different samples. The solid lines represent best fits to eq 20.

Table 4. Nucleation and Diffusion Parameters for Different Samples Obtained from the Fitting of the Temperature Dependent EX Data Using Eq 20 samples

ED (kJ mol−1)

T0 (K)

A (K2 kJ mol−1)

ε (kJ mol−1)

n

ED + ε (kJ mol−1)

ΔG* (at Tc) (kJ mol−1)

pure-IL 0.5 Al2O3-IL 1.0 Al2O3-IL 2.5 Al2O3-IL 5.0 Al2O3-IL

−68.3 −38.7 −16.3 −4.9 3.5

183 190 199 203 211

7423.3 3811.6 1389.2 165.9 15.3

30.7 35.4 28.4 26.9 24.3

9.0 4.6 3.5 2.5 2.0

−37.6 −3.3 12.1 22.0 27.8

6.16 4.46 1.57 1.36 0.40

⎛ 2T 1 ⎞ E X = ED − A⎜ − ⎟ 3 (ΔT )2 ⎠ ⎝ (ΔT )

nature (Figure S3). The values of EX for each sample, obtained from the slopes of the straight lines achieved for each conversion and for different φ are plotted in Figure 11a as a function of Xt(T). Temperature variation of the average relative crystallinity for the respective samples is also included in the figure. The EX dependencies for all the samples have a similar concave downward shape, and its value is always positive irrespective of conversion, which is in line with the Turnbull and Fisher crystal nucleation model (eq 15) up to a certain extent. With increasing tethered nanoparticle dispersion, the EX value for a particular conversion substantially decreases with respect to pure-IL. However, it is worthwhile to notice that for each of the sample that above a certain conversion the value of EX shows a reverse trend. Such an increase in EX in the later stages of crystallization may be attributed to the changing conditions of diffusion within the materials during heating. Figure 11b depicts the average temperature dependence of the activation energy for different samples. With increasing nanoadditives the temperature range of crystallization process widens and gradually shifts toward higher temperature. Furthermore, the diffusion controlled region, i.e., the region where EX varies inversely, gradually increases. Such a strong Xt(T) dependence of EX clearly indicates that the consideration of constant activation energy for diffusion (ED) in eq 15 is inadequate to demonstrate the complete crystallization process and should be replaced by a conversion dependent function as37,38

(15)

As the crystallization occurs on heating ΔT < 0, the value in the parentheses of eq 15 is negative and increases asymptotically to zero with increasing temperature. Thus, for cold crystallization the value of EX should be positive and expected to decrease monotonically throughout the process.37 The dependence of EX upon the conversion Xt(T) can be obtained using the isoconversional principle, which states that at a constant extent of conversion, the reaction rate is a function only of the temperature.10 Thus, the rate of the nucleation driven crystallization can be expressed by the basic rate equation as d X t (T ) = w(T ) f (x) dt

(16)

where f(x) is the reaction model. The isoconversional activation energy can be estimated as10 ⎡ ∂ ln(dX t(T )/dt ) ⎤ E X = −R ⎢ ⎥ ⎣ ⎦X ∂T −1

t

(17)

According to the differential isoconversional method of Friedman,8 using eqs 16 and 17, different effective activation energies (EX) can be determined for each degree of conversion as ⎛ d X (T ) ⎞ EX ln⎜ t ⎟ = ln(A X t f (x)) − ⎝ dt ⎠ X , i RTX ti t

ε(X t ) = E D + εX t n

(19)

where ε are the activation energies of diffusion after complete conversion (Xt = 1) and n determines the strength of the contribution of diffusion in the crystallization process.38 Replacing ED with ε(Xt) in eq 15 the expression for the activation energy with contribution of both nucleation and diffusion becomes

(18)

where the subscript i is used for different heating rates. The variations of dXt(T)/dt at a specific Xt(T) for different heating rates (φ) with the corresponding crystallization temperature (TXti) for Xt(T) within 0.2−0.98 are found to be Arrhenius in M


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The Journal of Physical Chemistry C ⎞ ⎛ 2T 1 ⎟ E X = E D + εX t n − A ⎜ − 3 2 (T0 − T ) ⎠ ⎝ (T0 − T )

of both nucleation and diffusion was used to estimate different nucleation parameters. The variation of the estimated parameters with the wt % of tethered nanoparticles demonstrated that in the presence of the nanoadditives, although the free energy barrier within the nanofluids was substantially reduced, it progressively enhanced the diffusion contribution to the overall nucleation process. This diffusionmediated hindrance effect essentially delayed the process toward higher temperature, and for the nanofluid containing higher nanoadditives the conversion could not be occurred.

(20)

The parameter A designates the contribution of the nucleation process to the temperature dependent conversion, which is effective at a lower contribution of diffusion, i.e., within the region of decreasing EX dependence upon T. The increasing portion of the dependence resembles the strong diffusion contribution that is represented by the parameters ε and n. Solid lines in Figure 11b show that eq 20 simulates the experimental data reasonably well with parameters given in Table 4. The steady decrease in the value of both A and free energy barrier to nucleation at respective onset of crystallization Tocc (Table 2) (ΔG* = A/(T0 − Tocc)2) with increasing tethered nanoparticle dispersion indicates the heterogeneous nucleating effect of the nanoparticles. However, a simultaneous significant increase in ε + ED, which reflects the total diffusion effect, corresponds to the hindrance effect of the guest toward the diffusion across the phase boundary during nucleation. These two complementary effects essentially shift the process toward the higher temperature (increasing T0). The constantly decreasing value of n further indicates that cumulative nanoparticle dispersion enhances the diffusion contribution in the crystallization process. The observed feature directs that for the nanofluid containing higher content of tethered nanoparticles (>10 wt %), the diffusion contribution may become so large that the required temperature for the initiation of nucleation supersedes the melting temperature of the crystallites. That circumvents the process and a transition from hard, glassy supercooled state to a soft rubbery amorphous liquid state occurs.

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ASSOCIATED CONTENT

S Supporting Information *

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.jpcc.6b11845. Degree of conversion with temperature and time for different samples, and Arrhenius plots of Friedman differential isoconversional method for different samples. (PDF)

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AUTHOR INFORMATION

Corresponding Author

*S. Bhattacharya. Phone: (+91 33) 2582 0184. Fax: (+91-33) 2582 8282. E-mail: [email protected]. ORCID

Subhratanu Bhattacharya: 0000-0001-9601-034X Author Contributions

The manuscript was written through the contributions of all authors. All authors have given approval to the final version of the manuscript.

■

Funding

CONCLUSIONS The effects of the cumulative dispersion of IL-tethered Al2O3 nanoparticles to the thermophysical properties, crystallization, and phase transition of [Deim][NTf2] IL-hosted nanofluids were studied using TGA, vibrational spectroscopy, and differential scanning calorimetry. TEM and DSC analysis confirmed the uniform dispersion of the tethered nanoparticles within the host IL to form stable nanofluids with different thermophysical properties as compared to the host. For the nanofluid containing more than 10 wt % of nanoadditives, the crystallinity was completely suppressed with significantly higher fragility as compared to that of the host IL. The observed alteration of the thermophysical properties of the nanofluids with respect to their host was corroborated by strong intermolecular interactions between the host and guest, as evidenced by vibrational spectroscopy including both ATR-FTIR and Raman. This intermolecular interaction was found to affect both the relative orientation and the separation of the anion from the imidazolium ring, intervening the ion-pair formation with a simultaneous increase of the cisoid forms relative the transoid forms of the anion within the nanofluids. Analysis of nonisothermal cold crystallization kinetics data by different models identified that the tethered nanoparticles considerably retard the overall crystallization process within the nanofluids. The effective activation energy for conversion was obtained from the isoconversional technique at different temperature. The nucleation model of Fisher and Turnbull was found to be inadequate to represent the complete temperature dependence of the nucleation rate within the nanofluids and a modified model comprising the contribution

Council of Scientific and Industrial Research, Government of India. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS Authors thankfully acknowledge the Council of Scientific and Industrial Research (Govt. of India) for the financial support under the Extra Mural Research Scheme No: 03(1327)/14/ EMR-II Dated 03.11.2014. Also, the authors thankfully acknowledge the DST-FIST scheme of the Department of Physics, DST-SAIF and University of Kalyani for providing the instrumental facilities.

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REFERENCES

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