Thermal Analysis UserCom 40
Thermal Analysis UserCom 40; Table of Contents:
- Curve interpretation Part 3: DSC curves and curves from other thermal analysis techniques
- Characterization of the growth of intermetallic phases by DSC
- Characterization of shape memory alloys by DSC and DMA, part 1: DSC analysis
- Investigation of the bouncing behaviour of two rubber balls
- Characterization of biomass using TGA/DSC coupled to a mass spectrometer
- Determination of the adiabatic Time to Maximum Rate by DSC for thermal safety assessment
- Determination of the water content of an ionic liquid
Characterization of the growth of intermetallic phases by DSC
The method presented in this article shows how DSC measurements of solders in copper crucibles can be used to analyze the formation of intermetallic layers between solder and copper in soldering and annealing processes. After calibration using polished microsections, the average thickness of layers can be determined from the DSC measurements.
In microelectronics, soldering is the most frequently used bonding technology employed to connect the leads of electronic components to tracks on the printed circuit board. The soldering process itself has been known for more than 5000 years .
In this process, a low melting alloy (the solder) connects one or more components together through melting and solidification processes. The formation of an intermetallic compound (a so-called IMC) between the solder and the component is the clearest sign that a connection has been made during the soldering process .
Essential for IMC formation are diffusion processes that take place between the reactants participating in the formation of the alloy. In this study, we investigated a solder alloy based on tin in contact with copper.
In the soldering process, an intermetallic phase is formed consisting of two zones. The zone next to the copper layer is the copper-rich Cu3Sn phase and that next to the solder side is the tin-rich Cu6Sn5 phase. The formation of these two zones can be seen in the polished metallographic microsection shown in Figure 1.
Figure 1. Polished microsection of an annealed solder joint.
The diffusion processes necessary for the formation of the IMC are temperature dependent. They have a low activation energy, ΔE, and can therefore take place both in the melt and in the solid phase after solidification.
An increase in the thickness of the IMC is therefore observed on soldering.
Knowledge of the thickness of the IMC is necessary in order to make predictions about the reliability of solder connections. Since the intermetallic phase is usually more brittle than the alloys involved, the IMC must not become too thick because otherwise the attachment point becomes susceptible to mechanical stresses .
 D. Shangguan, Lead-free solder interconnect reliability. ASM International, Materials Park, OH (2005).
 V. I. Dybkov, The growth kinetics of intermetallic layers at the interface of a solid metal and a liquid solder, JOM (2009) Doi:10.1007/s11837-009-0015-9.
Characterization of shape memory alloys by DSC and DMA, Part 1: DSC analysis
An object made from a shape memory alloy recovers its original shape after deformation when it is heated. This phenomenon is known as the shape memory effect. Objects made from shape-memory alloys also exhibit superelasticity. This is the name given to the property that a strongly deformed object returns to its original shape when the load causing the deformation is removed. In Parts 1 and 2 of this article series, we will describe these properties and show how they can be investigated by DSC and DMA.
Objects made from shape memory alloys (SMA) exhibit two interesting properties: the shape memory effect and superelasticity. In the shape memory effect, a plastically deformed object made from a shape memory alloy recovers its “original” shape when it is heated.
In superelasticity, an object is referred to as superelastic if it recovers its “original” shape after deformation of up to 10% when the load causing the deformation is removed.
For these two effects to occur, the material must have been previously been conditioned in an “original” shape. The shape memory effect and superelasticity are illustrated in Figure 1.
Figure 1. Effects exhibited by shape memory alloys. Left: the shape memory effect. Right: superelasticity.
Well-known shape memory alloys include nickel-titanium alloys and copper alloys such as CuZnAl (copper-zinc- aluminum), CuAlNi (copper-aluminum-nickel) or CuAlBe (copper-aluminum-beryllium). The most widely used nickel-titanium alloys were developed in the early 1960s at the U.S. Naval Ordnance Laboratory and commercialized under the name nitinol (Nickel-Titanium Naval Ordnance Laboratory).
Nitinol combines high corrosion resistance, biocompatibility, the shape memory effect and superelasticity. Due to these properties, nitinol is used in the aerospace industry, automotive engineering, in electronics, and for medical applications.
Practical uses are for example for pipe connections (the collars are stretched in the cold state and shrink-fitted by heating), actuators, medicinal implants (stents) or dental braces.
Investigation of the bouncing behavior of two rubber balls
Experiments were performed with two rubber balls, one black and the other red. Apart from their color, there was no apparent difference. However, when they were allowed to fall onto a hard surface, the red ball bounced back to almost the same height from which it had been dropped while the black ball hardly bounced at all. How can this behavior be explained?
A characteristic property of rubber balls is that they bounce back up when they are dropped onto a hard surface. The two balls investigated in this article were very different in this respect.
The red ball bounced up to about 86% of the height from which it had been dropped. In contrast, the black ball bounced up to only about 2% of the initial dropping height. Can this behavior be predicted from certain material properties and if so how can we measure these properties?
The bouncing behavior of a ball depends on its viscoelastic properties. The red ball is "elastic" – its kinetic energy on hitting a hard surface is largely stored as deformation energy, which is afterward available for bouncing.
In contrast, the deformation energy of the black ball is transformed mainly into heat. This energy is no longer available for bouncing so the black ball bounces to a much lower height (Figure 1).
Figure 1. The bouncing behavior of two rubber balls. In the black ball, the deformation energy is mainly transformed into heat; in the red ball, most of the deformation energy is available for bouncing after it hits the hard surface.
The material parameter that describes this behavior is the modulus of elasticity (Young's modulus). This consists of two components: the storage modulus that describes the energy storage capacity and the loss modulus that characterizes the dissipative processes in the material. The ratio of the loss modulus and the storage modulus is called the loss factor or tan δ. The loss factors can be used to estimate the ratio of the energies transformed into heat in the two balls, E reddiss and E blackdiss, according to equation 1 .
Bouncing experiments with the two balls yielded a value of 0.14 for this ratio. This means that when the balls hit the surface, about seven times more energy is dissipated in the black ball than in the red ball.
 C. Wrana, Introduction to Polymer Phyiscs, Lanxess AG, 2009, ISBN 978-3-941343-19-1.
Characterization of biomass using TGA/DSC coupled to a mass spectrometer
During the last ten to twenty years, there has been a marked change to the use of more renewable and sustainable energies. This change has been catalyzed by the prospect of limited resources of fossil fuels, the greatly increased awareness of environmental problems, and unsolved problems associated with the operation of nuclear power plants. Alternative or renewable energies encompass a very large field. Besides wind energy and photovoltaic power, biomass is an important possibility for generating energy. Two examples from the field of biomass illustrate the use of thermal analysis for the characterization of biomass. This work was performed using a TGA/DSC coupled to a mass spectrometer.
Biomass refers to renewable organic substances from which energy can be obtained. This includes products such as wood, straw, corn (maize), eucalyptus, rape, sugar cane and many other non-fossil raw materials.
Before biomass can be used as an energy resource, it must be suitably processed - it can be gasified, liquefied or solidified. There are many complex procedures for doing this .
This article describes how corn (maize) and eucalyptus biomass was characterized using a TGA/DSC and a TGA/DSC coupled to a mass spectrometer (MS).
The main purpose was to determine the moisture content, the dry mass, and the nature of the gases produced in pyrolysis.
 Thomas Bührke, Roland Wengenmayr: Erneuerbare Energien- Konzepte für die Energiewende, Wiley-VCH, 3. Auflage 2011.
Determination of the adiabatic Time to Maximum Rate by DSC for thermal safety assessment
The identification and assessment of possible hazards and risks in chemical processes is of major practical importance. It is essential for developing and controlling chemical reactions both on the laboratory scale and in an industrial environment. Frequently chemical accidents are due to loss of control and incorrect handling. The result of this is often a so-called runaway reaction that can lead to an explosion.
The earliest possible identification of possible hazards and risks is of major importance for product development. DSC measurements are very useful for this because only small amounts of sample are needed to quickly measure the enthalpies and rates of chemical reactions.
In this connection, the maximum adiabatic temperature increase and the Time to Maximum Rate (TMR) are important quantitative criteria. They describe the conditions under which a substance or a process becomes thermally unstable or a thermal runaway, for example an explosion, could occur.
Kinetic descriptions of chemical reactions can be used to estimate their thermal behavior under any temperature profile. This procedure can be applied for the risk analysis of chemical compounds or processes. The most important objective is to ensure safe working conditions and to minimize possible risks.
Risk assessment can be carried out to introduce methods and measures to ensure that a specified level of safety is maintained and that control strategies are implemented.
Risk profiles mostly serve as the basis for classifying acceptable scenarios versus unacceptable situations. Such profiles are generally described and are linked to severity and probability.
Determination of the water content of an ionic liquid
The moisture content is an important quality criterion of ionic liquids. In this article, we show how the water content of 1-ethyl-3-methylimidazolinum methyl sulfate can be determined using a thermogravimetric analyzer (TGA) interfaced to a mass spectrometer. The result was confirmed by titration measurements.
Ionic liquids are salts that are liquid at room temperature. They exhibit many properties that make them extremely attractive for a number of applications: they are thermally stable, non-flammable, non-volatile, conduct electricity, have a relatively high heat capacity, and have very good dissolving properties for many substances.
It is therefore not surprising that they are used in numerous applications, for example as electrolytes in fuel cells or batteries; thermofluids; solvents, etc. An important quality parameter of an ionic liquid is its water content. In this article, we show how the water content of 1-ethyl-3-methylimidazolinum methyl sulfate (EMIMS, CAS 516474-01-4) can be determined using a thermogravimetric analyzer coupled to a mass spectrometer. The water content determined using this method was confirmed by titration measurements.