Characterization of bulk metallic glasses via fast ...

Thermochimica Acta 590 (2014) 84–90

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Characterization of bulk metallic glasses via fast differential scanning calorimetry S. Pogatscher *, D. Leutenegger, A. Hagmann, P.J. Uggowitzer, J.F. Löffler Laboratory of Metal Physics and Technology, Department of Materials, ETH Zurich, 8093 Zurich, Switzerland

A R T I C L E I N F O

A B S T R A C T

Article history: Received 29 April 2014 Received in revised form 10 June 2014 Accepted 11 June 2014 Available online 12 June 2014

This study explores the thermophysical properties of Au-based bulk metallic glasses (BMGs) via fast differential scanning calorimetry (FDSC). Using this technique, the glass formation of the alloys Au60 +xCu15.5xAg7.5Si17 (x = 0, 5 and 10) was investigated in situ. The critical cooling rate (Fc) and heating rate (Fh) required to avoid crystallization were analyzed for various sample masses and chip sensor surface materials. The results show that the alloy with the highest Au-content exhibits the lowest resistance against crystallization. Silicon nitride, silicon oxide and graphite used as chip sensor surface material were proven not to influence the measurements. In general, a dependence of crystallization on sample mass was observed for all compositions. Both the critical cooling and critical heating rates increase until a certain mass is reached. This phenomenon is explained via a size-dependent nucleation effect. ã 2014 Elsevier B.V. All rights reserved.

Keywords: Bulk metallic glasses Crystallization Nucleation Kinetics Fast differential scanning calorimetry

1. Introduction Bulk metallic glasses are non-crystalline metallic solids which can be produced by rapid cooling of metallic melts to temperatures below their glass transition [1]. Compared to all other classes of materials BMGs possess unique properties such as high strength and elastic strain limit, good soft-magnetic properties, excellent corrosion resistance and high hardness [2–5]. Their good viscous flow workability in the supercooled liquid and homogeneity and isotropy on a small scale are great advantages, especially in the production of small-scale devices (e.g. micro-electro-mechanical systems, micro-robotics and micro-manipulators) via imprinting, embossing, micro-replication or micro-molding [6–8]. Au-based BMGs [9–12] in particular have been shown to be suitable materials for this emerging field [8]. For metallic systems, BMGs demonstrate extraordinary stability against crystallization, i.e. they exhibit a low critical cooling rate for reaching the glass transition without crystallization during cooling from the equilibrium liquid. Nevertheless, crystallization still occurs rapidly and thus limits many experimental studies in the supercooled liquid region [3–5]. Using conventional thermo-analytical

* Corresponding author. Present address: Laboratory of Metal Physics and Technology, Department of Materials, ETH Zurich, Vladimir-Prelog-Weg 4, 8093 Zurich, Switzerland. Tel.: +41 44 633 64 65; fax: +41 44 633 14 21. E-mail addresses: [email protected] (S. Pogatscher), [email protected] (D. Leutenegger), [email protected] (A. Hagmann), [email protected] (P.J. Uggowitzer), joerg.loeffl[email protected] (J.F. Löffler). http://dx.doi.org/10.1016/j.tca.2014.06.007 0040-6031/ ã 2014 Elsevier B.V. All rights reserved.

methods (e.g. differential scanning calorimetry, DSC) it is not possible to reach constant cooling rates higher than a few K s1, and in situ probing of the glass formation from an equilibrium metallic melt is not feasible. Recent chip-based fast differential scanning calorimeters [13,14] enable thermo-analytical measurements at orders of magnitude higher rates. Heating and cooling with several 104 K s1 and 103 K s1, respectively, can be realized with a recently available commercial instrument (Mettler Toledo Flash DSC 1 [15]). This instrument has generally been used to study polymers [15] and phase-change materials [16,17], but in recent studies it has also been successfully applied to a Au49Ag5.5Pd2.3Cu26.9Si16.3 BMG [18]. Aubased BMGs are ideal candidates for investigation via FDSC because here in situ exploration of the glass formation and crystallization behavior in the whole supercooled liquid region is possible [18]. Compared to most other known BMGs, Au-based BMGs have a low liquidus temperature, which is accessible by FDSC, and are not sensitive to oxidation. However the characterization of BMGs via FDSC is still a new procedure and no work on the measurement conditions and the influence of measurement parameters has so far been published. In this study we explore the crystallization and glass formation of Au60+xCu15.5xAg7.5Si17 (x = 0, 5 and 10) in situ and focus also on the effects of sensor surface material and sample mass. 2. Material and methods 2.1. Alloy production To obtain thin and chemically homogenous samples Au-based glassy ribbons were produced by melt spinning. The elements Au

S. Pogatscher et al. / Thermochimica Acta 590 (2014) 84–90

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diameter of 5 mm. The tubes were purged several times with Ar (5 N purity) and closed under 200 mbar Ar pressure by melting the tube ends. To produce homogenous pre-alloys the elements were mixed well in the tube, subjected to induction melting at 1273 K [19], and finally quenched in water. The pre-alloys were polished and broken up into small, manageable parts for ribbon production via melt spinning under a 500 mbar He atmosphere (5 N purity). The rotating frequency of the copper wheel used for melt spinning was 25 Hz and its distance from the hole of the graphite crucible containing the melt was 0.2 mm. About 1 g of the pre-alloy was heated to 923 K within 7 min and held for 2 min at this temperature before the ribbons were produced. The overpressure applied to push the melt out of the crucible onto the rotating copper wheel was 150 mbar. The thickness of the ribbons produced ranged from 20 to 30 mm; 20 mm thick ribbons were deployed for the FDSC investigations. 2.2. Thermo-analytical measurements

Fig. 1. DSC traces of Au60+xCu15.5xAg7.5Si17 (x = 0, 5 and 10) metallic glasses measured with a heating rate of 0.33 K s1 and corresponding DHm values.

(purity 99.99 wt.%), Ag (99.99 wt.%), Si (99.999 wt.%) and Cu (99.995 wt.%) were weighed according to the atomic compositions Au60Cu15.5Ag7.5Si17, Au65Cu10.5Ag7.5Si17 and Au70Cu5.5Ag7.5Si17 and inserted into quartz glass tubes with a

Conventional thermal analysis was performed in a differential scanning calorimeter (Mettler-Toledo DSC1) to determine the mass of the small-scale FDSC samples. The DSC measurements were conducted at a heating rate of 0.33 K s1 under Ar atmosphere (5 N purity) at a flow rate of 30 ml min1 and using aluminum pans on the sample and reference platforms. The enthalpy of fusion (DHm) measured by conventional DSC was used as a reference value according to Eq. (1) [20]:

Fig. 2. Micrographs of pre-molten samples with various masses on the sample platform of the sensor. For large sample masses (>10 mg) and samples larger than the small square of the chip sensor (a, b), technical artifacts may occur and can reduce the maximum rates accessible.

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mFDSC ¼


DHm;FDSC mDSC DHm;DSC

(1)

Fig. 1 shows DSC traces of the alloys investigated and the corresponding DHm values. The glass transition (Tg), onset of crystallization (Tx) and onset of melting (Tm) are indicated as examples for Au60Cu15.5Ag7.5Si17. Note that more than one crystallization peak is visible for all BMGs investigated in Fig. 1. FDSC was performed in power compensation mode using the Mettler-Toledo Flash-DSC 1. The sample support temperature of the FDSC was set at 183 K using a Huber intracooler TC90. The furnace was purged with Ar of 5 N purity at a flow rate of 10 ml min1. FDSC samples were prepared by cutting the meltspun ribbons under a stereomicroscope to small pieces with weights of 30 ng to 20 mg and then transferred by an electrostatic manipulator hair onto a conditioned and temperature-corrected MultiSTAR UFS1 sensor (according to the instrument provider’s specification). Fig. 2 illustrates samples of various masses on the sensor. To protect the samples from bouncing due to strains in the material they were pre-melted with a heating rate of 1 K s1 from room temperature to 748 K, which is a temperature accessible for most sensors. For all experiments the samples were heated or cooled between 298 K and 748 K. The exact time–temperature regimes used are displayed in the corresponding heat-flow figures. Reproducibility was always found to be very high, as was judged from comparing the same thermal cycles at the start and end of each measurement series. To explore sensor materials other than the silicon nitride surface provided, the reverse side of the sensors made of silicon oxide was used, and the silicon nitride surface was also coated with a graphite layer of approximately 10 nm thickness. 3. Results 3.1. Sensor surface material Fig. 3 shows FDSC scans at a heating rate of 100 K s1 for Au60+xCu15.5xAg7.5Si17 (x = 0, 5 and 10) on a standard silicon nitride sensor surface, on the reverse silicon oxide side of the sensor, and on a graphite-coated sensor membrane. The samples were amorphized in situ by quenching from 748 K to RT with a cooling rate of 5000 K s1 prior to the measurements. The inserts to Fig. 3 illustrate the time–temperature regime. Clear glass transitions followed by exothermic crystallization peaks and melting of the samples can be observed for all Au-based glasses, and the sensor surface material does not influence the measurements. The curves are not normalized to the mass, which introduces some differences in the size of the peaks only. 3.2. Critical cooling rate Fig. 4 shows typical FDSC scans of Au60+xCu15.5xAg7.5Si17 BMGs with x = 0, 5 and 10 when cooling the equilibrium liquid at different rates. The inserts illustrate the applied temperature–time programs. The exothermic crystallization peaks (400–500 K) shift to lower temperatures and their enthalpy of crystallization decreases with increasing cooling rate until the crystallization peak vanishes. This means that for this and higher cooling rates no crystallization occurs and the critical cooling rate Fc is reached. All alloys also demonstrate a clear glass transition which depends on the cooling rate. Fig. 4(a) shows the curves of the heat flow measured during cooling of a Au60Cu15.5Ag7.5Si17 melt at rates of 425 K s1 up to 675 K s1 and a sample mass of 1.5 mg on the standard silicon nitride sensor membrane. The transition from crystallization in the supercooled liquid region to in situ amorphization is observed at

Fig. 3. FDSC scans when heating (a) Au60Cu15.5Ag7.5Si17, (b) Au65Cu10.5Ag7.5Si17, and (c) Au70Cu5.5Ag7.5Si17 metallic glasses on a standard silicon nitride, silicon oxide, or graphite sensor membrane surface. The inserts illustrate the applied temperature– time programs and Tg, Tx and Tm represent the onsets of glass transition, crystallization and melting.


Fig. 4. Typical FDSC scans when cooling the melt in the region of Fc for (a) Au60Cu15.5Ag7.5Si17, (b) Au65Cu10.5Ag7.5Si17, and (c) Au70Cu5.5Ag7.5Si17. The sample masses are 1.5, 3.3, and 1.3 mg, respectively. In all cases a standard silicon nitride sensor membrane was used. The inserts illustrate the applied temperature–time programs, and Tg and Tx represent the onsets of glass transition and crystallization.

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Fig. 5. Typical FDSC scans when heating the glass in the region of Fh for (a) Au60Cu15.5Ag7.5Si17, (b) Au65Cu10.5Ag7.5Si17, and (c) Au70Cu5.5Ag7.5Si17. The sample masses are 1.5, 3.3, and 1.3 mg, respectively. In all cases a standard silicon nitride sensor membrane was used. The inserts illustrate the applied temperature–time programs, and Tg and Tx represent the onsets of glass transition and crystallization.

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Fc 575 K s1. Fig. 4(b) shows the cooling curves of a Au65Cu10.5Ag7.5Si17 melt (sample mass 3.3 mg) at rates of 200–375 K s1. The critical cooling rate is observed at around 350 K s1. Au70Cu5.5Ag7.5Si17 (sample mass 1.3 mg) was investigated between 500 K s1 and 1200 K s1 and exhibits a higher Fc value of around 1200 K s1 (Fig. 4(c)). Note that Fig. 4 is also a good example of the instrument’s performance at different cooling rates. The signal-to-noise ratio is higher in Fig. 4(c) than in Fig. 4(a) and (b). 3.3. Critical heating rate The samples used to determine Fc (Fig. 4) were also investigated for their critical heating rate Fh, at which the glass (obtained by in situ quenching from 748 K at a rate of 5000 K s1) transforms on heating into the supercooled and finally equilibrium liquid without detectable crystallization (Fig. 5). Below Fh an exothermic crystallization peak and a melting peak can be seen. Fig. 5(a) shows curves for the heat flow measured during heating of a glassy Au60Cu15.5Ag7.5Si17 sample at rates of 750–2500 K s1 on the standard silicon nitride sensor membrane. At rates higher than around 2300 K s1 neither crystallization nor melting are visible and a direct transition from the supercooled to the equilibrium liquid occurs with no detectable heat flow. Fig. 5(b) shows curves for glassy Au65Cu10.5Ag7.5Si17 at heating rates between 800 K s1 and 2500 K s1. The critical heating rate Fh is again observed at around 2300 K s1. Fig. 5(c) displays FDSC scans of Au70Cu5.5Ag7.5Si17 at heating rates of 7000–12,000 K s1. This alloy exhibits with a Fh of around 11,000 K s1 the highest critical heating rate of all samples investigated. 3.4. Mass dependency of Fc and Fh

Fig. 6. Mass dependence of the critical cooling rates for (a) Au60Cu15.5Ag7.5Si17, (b) Au65Cu10.5Ag7.5Si17, and (c) Au70Cu5.5Ag7.5Si17 measured on a standard silicon nitride sensor membrane. Results for samples on silicon oxide and graphite are also shown (marked by arrows). The dashed lines are guides for the eye.

To characterize the influence of different sample masses, the mass dependence of the critical cooling (Fc) and heating (Fh) rates was investigated for Au60+xCu15.5xAg7.5Si17 (x = 0, 5 and 10). Fc and Fh are defined as the rates at which no crystallization peak is detectable in the heat flow curves. Note that the error in the corresponding Figs. 6 and 7 is generated by the step-size in F. A dependence of crystallization on sample mass was observed for all compositions investigated. The critical cooling rate increases until a certain mass is reached. This saturation starts at roughly above 1 mg. The critical cooling rates within this constant regime are at around 600 K s1 for Au60Cu15.5Ag7.5Si17 (Fig. 6(a)), 400 K s1 for Au65Cu10.5Ag7.5Si17 (Fig. 6(b)) and 1700 K s1 for Au70Cu5.5Ag7.5Si17 (Fig. 6(c)). No technical artifacts due to large sample sizes (see Fig. 2(a) and (b)) were observed within the range of cooling rates investigated. Results for samples on silicon oxide and graphite as sensor surface material are also shown (marked by arrows). No influence of the sensor material on Fc can be deduced from Fig. 6. However, although scattered, masses 1 mg generated significantly lower Fc values. The same trend was found for the critical heating rate to avoid crystallization for all compositions investigated (Fig. 7), although the values of Fh are much higher than those of Fc. The critical heating rate Fh is also more difficult to deduce from FDSC curves than Fc, because the transition (crystallization of the supercooled liquid vs. no crystallization) is more blurred. Above 1 mg of sample mass, Fh was found to be around 2300 K s1 for Au60Cu15.5Ag7.5Si17 (Fig. 7(a)), 2400 K s1 for Au65Cu10.5Ag7.5Si17 (Fig. 7(b)) and 13,000 K s1 for Au70Cu5.5Ag7.5Si17 (Fig. 7(c)). An investigation of Fh for large heating rates is restricted to masses smaller than 10 mg (see Fig. 7(c)) due to limitations of the setup. Again no effect of the sensor surface material was observed.


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4. Discussion

Fig. 7. Mass dependence of the critical heating rate for (a) Au60Cu15.5Ag7.5Si17, (b) Au65Cu10.5Ag7.5Si17, and (c) Au70Cu5.5Ag7.5Si17 measured on a standard silicon nitride sensor membrane. Results for samples on silicon oxide and graphite are also shown (marked by arrows). The dashed lines are guides for the eye.

The crystallization and vitrification behavior of three different Au-based BMGs was invested in situ by FDSC with a focus on the effects of sensor surface material and sample mass. Using different surface materials for the chip sensor membrane does not influence the FDSC results. For silicon nitride, silicon oxide or graphite as surface material FDSC traces appear quite similar (Fig. 3) and are also comparable to conventional DSC traces of the alloys analyzed (Fig. 1). Note that the exothermic crystallization peaks in Fig. 3 are at higher temperatures than in Fig. 1 because of the higher heating rates used in the FDSC measurements. The used sensor surface material also does not influence the critical cooling and heating rates measured (see Figs. 6 and 7). In principle these results confirm that the standard sensor surface is suitable for an investigation of Au-based BMGs, but we found that several practical issues arise. Using the silicon oxide surface on the reverse side of the sensor allows rotation of the sensor during sample transfer, which was found to be quite useful. However, we propose a graphite-coated chip sensor as the best possibility. Samples are much easier to position on this sensor surface material because they do not tend to jerk (as they do on silicon nitride or oxide) when pushed by the manipulator. In addition, it is much easier to remove a sample from a graphite-coated chip sensor. This means that the sensors can be re-used for multiple samples. A dependency of crystallization kinetics on sample mass was observed for all compositions (Figs. 6 and 7). Nevertheless, it is quite convenient that there is a broad range of masses (1–20 mg; 1–10 mg samples showed the best handling conditions), which can be used expediently. The possibility to determine Fc in situ by using FDSC is very important for future BMG development, because Fc is related to the size to which BMG products can be made of. The observation that the alloy with the highest Au-content clearly exhibits the highest Fc is predictable because this trend has been also reported for the critical casting diameter at which an amorphous sample can still be obtained [10,11]. However, Au65Cu10.5Ag7.5Si17 showed a slightly lower Fc than Au60Cu5.5Ag7.5Si17 in FDSC, which contrasts with the trend found in [10,11] for the critical casting diameter. The methodology for determining the critical casting thickness in [10,11] was rather rough and parameters other than the critical cooling rate (e.g. thermal conductivity of the alloy, casting setup etc. [21]) also influence the critical casting thickness. Thus, determining Fc by FDSC is believed to be a more sensitive method than casting rods of certain diameters (usually in mm steps) and checking whether the samples are amorphous via X-ray diffraction. This underlines the importance of FDSC for future BMG development. The observation that the critical cooling rate for reaching the glassy state upon cooling from the melt without crystallization decreases at small sample masses is very interesting. During this study, however, it mainly helped to determine an optimal FDSC working range for Au-based BMGs. Nevertheless, one possible explanation for the decrease in Fc with decreasing sample mass may be the FDSC setup itself: in FDSC only the sensor platform serves as a heat source, while the ambient Ar atmosphere stays at 183 K. This may introduce a temperature gradient in the sample, which could enhance crystallization on the colder sample side. Very small samples will not retain the thickness of the BMG ribbons (20 mm) due to cutting issues, i.e. they may be thinner and therefore exhibit a reduced temperature gradient, which would result in a decrease of Fc. Fig. 8 shows the temperature gradient in a large Au60Cu15.5Ag7.5Si17 sample of mass 8.5 mg measured on the standard silicon nitride sensor membrane. Indium as a reference material is located on the reference platform (melting apparently occurs exothermically) for the first heating run of the sensor. In a

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Optimal practical working conditions for Au-based bulk metallic

glasses are obtained with sample masses of 1–10 mg on sensors coated with graphite.

Most importantly, the demonstrated capability of fast differential scanning calorimetry to determine the critical cooling rate of BMGs in situ is expected to be a great advantage in future BMG development. Acknowledgments The authors thank Fabio Krogh at LMPT and Jürgen Schawe at Mettler Toledo AG for fruitful discussions. Support by the Swiss National Science Foundation (SNF Grant No. 200020-135100) is gratefully acknowledged. References

Fig. 8. Investigation of a potential temperature gradient in an Au60Cu15.5Ag7.5Si17 sample of mass 8.5 mg measured on a standard silicon nitride sensor membrane. An indium sample is located on the reference platform (melting then occurs exothermically) in the first run. In a second run it is also located on top of the Au60Cu15.5Ag7.5Si17 sample on the sample platform. In both cases melting occurs at the same temperature.

second run a larger amount of In is additionally located on top of the Au60Cu15.5Ag7.5Si17 glass on the sample platform. The onset of both melting peaks is similar (within 1 K) and temperature gradients in 20 mm thick Au-based BMG samples are therefore negligible. Thus temperature gradients cannot be made responsible for the decrease of Fc at small sample masses. A decreasing Fc with decreasing sample size (mostly in dispersed systems) of BMGs [22], or similarly an increasing undercooling in pure metals with decreasing sample size [23,24], has also been observed previously and discussed in literature. Schroers et al. [22] reported that small particles of Pd-based BMGs exhibit a lower critical cooling rate and related this observation to the lower probability of impurities acting as nucleation sites. In principle this is also possible in our case. In addition to this heterogeneous nucleation effect, Wilde et al. [25] reported an increasing undercooling for a reduced sample size of pure Ni and discussed this effect by homogenous nucleation within the framework of classical nucleation theory. While it was not within the scope of this work to clarify the mechanisms behind the decrease in Fc with decreasing sample size, it is likely that it can be linked to a decrease in the probability of nucleation. The general observation that Fc is always lower than Fh is expected because for metallic glasses the maximum in the nucleation rate always appears at a lower temperature than the maximum in the growth rate (see, e.g., Ref. [18,22]). 5. Conclusions In summary, we have shown that crystallization and vitrification of bulk metallic glasses can be characterized well by fast differential scanning calorimetry. The most important findings are: The sensor surface material used (silicon nitride, silicon oxide or

graphite) does not influence the FDSC measurements of Aubased bulk metallic glasses. Critical cooling and heating rates to avoid crystallization depend on sample mass. Both values decrease below 1 mg, which may be explained by size-dependent nucleation.

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