7.3 Thermal analysis methods (DSC, TGA)

Thermal Analysis Methods

Thermal analysis methods help you understand how polymers respond to temperature changes. By tracking heat flow and mass loss as a polymer is heated or cooled, you can identify key transitions (like glass transition and melting), assess thermal stability, and figure out what a polymer sample is actually made of. The two workhorses in this area are Differential Scanning Calorimetry (DSC) and Thermogravimetric Analysis (TGA).

Principles of Differential Scanning Calorimetry

DSC works by heating (or cooling) a polymer sample and an empty reference pan at the same controlled rate, then measuring the difference in heat flow between them. When the polymer undergoes a thermal transition, it absorbs or releases extra heat compared to the reference, and that difference gets recorded.

• A typical heating rate is 10–20 °C/min.
• Endothermic transitions (like melting) absorb heat, so the sample's heat flow dips relative to the reference.
• Exothermic transitions (like crystallization) release heat, so the sample's heat flow rises relative to the reference.

The output is a DSC thermogram, a plot of heat flow vs. temperature. Thermal transitions show up as peaks or step changes on this plot. The area under a peak is proportional to the enthalpy change ($\Delta H$) of that transition, which tells you how much energy was involved.

Applications of Thermogravimetric Analysis

TGA measures how a sample's mass changes as it's heated at a controlled rate (typically 5–20 °C/min) under a specific atmosphere (nitrogen for inert conditions, air for oxidative conditions). Any time the polymer decomposes, evaporates, or reacts, you see a drop in mass.

Thermal stability assessment:

• The onset temperature of decomposition tells you how thermally stable the polymer is.
• Distinct mass loss steps reveal different decomposition stages. For example, a filled polymer might show one step for the polymer matrix decomposing and a residual mass corresponding to the inorganic filler.

Composition and purity analysis:

• TGA can detect volatile components, residual moisture, or solvents (they evaporate at lower temperatures).
• The height of each mass loss step gives you the mass fraction of that component, so you can quantify how much polymer, additive, or filler is present.

Degradation kinetics:

• By running TGA at several different heating rates and applying kinetic models, you can calculate the activation energy and pre-exponential factor for decomposition. This gives insight into how fast and by what mechanism the polymer breaks down.

Interpretation of Thermal Analysis Data

Interpreting DSC thermograms:

Three key transitions to look for:

• $T_g$ (glass transition temperature): Appears as a step change in the baseline heat flow. This is where the polymer shifts from a hard, glassy state to a softer, rubbery state. It's not a peak; it's a gradual shift, and you typically report the midpoint of the step.
• $T_m$ (melting temperature): Appears as an endothermic peak. This is where crystalline regions melt. Only semicrystalline polymers show a $T_m$; fully amorphous polymers don't.
• $T_c$ (crystallization temperature): Appears as an exothermic peak during cooling. This is where polymer chains organize into crystalline structures.

The enthalpy values $\Delta H_m$ and $\Delta H_c$ (from peak areas) relate directly to the degree of crystallinity. You can estimate percent crystallinity by comparing $\Delta H_m$ to the theoretical enthalpy of melting for a 100% crystalline sample of that polymer.

Comparing thermograms across different samples or formulations lets you see how changes in composition affect thermal behavior.

Interpreting TGA thermograms:

• Identify the onset temperature of each mass loss step and the temperature range over which it occurs.
• Read the mass fraction of each component from the height of each step (the percentage of mass lost in that step).
• Compare thermograms across samples to see how composition changes affect thermal stability.
• For kinetic analysis, TGA data collected at multiple heating rates can be fit to the Arrhenius equation to extract activation energy and pre-exponential factor values.

Thermal Properties vs. Polymer Structure

Understanding how structure affects thermal behavior is one of the most useful things thermal analysis can teach you.

Molecular weight and distribution:

• Higher molecular weight generally increases $T_g$, $T_m$, and overall thermal stability, because longer chains have more entanglements and intermolecular interactions.
• A broader molecular weight distribution tends to broaden thermal transitions, since chains of different lengths undergo transitions at slightly different temperatures.

Branching and crosslinking:

• Both branching and crosslinking restrict chain mobility, which raises $T_g$ and improves thermal stability.
• Heavily crosslinked polymers (like thermosets) may not show a distinct $T_g$ or $T_m$ at all, because the network structure prevents the large-scale chain motion those transitions require.

Crystallinity and morphology:

• Higher crystallinity means a higher $T_m$ and a larger heat of fusion ($\Delta H_m$).
• Some polymers exhibit polymorphism (multiple crystal forms), which can produce multiple melting peaks or more complex thermal behavior on a DSC scan.

Copolymer composition:

• Random copolymers typically show thermal properties intermediate between those of the two corresponding homopolymers.
• Block and graft copolymers can show separate, distinct thermal transitions for each block or component, because the different segments phase-separate.

Blends and composites:

• Miscible blends show a single $T_g$ that falls between the $T_g$ values of the individual components. This single $T_g$ is actually a common test for miscibility.
• Immiscible blends retain separate thermal transitions for each component.
• Fillers and reinforcements can shift decomposition temperatures and alter the overall thermal stability profile.

Using structure-property relationships in practice:

Thermal analysis data feeds directly into material design. You can tailor thermal properties by selecting specific monomers, adjusting polymerization conditions, or modifying the polymer after synthesis. For predicting thermal behavior before running experiments, group contribution methods and empirical correlations offer quick estimates of $T_g$ and other properties based on the polymer's repeat unit structure. More advanced approaches use molecular modeling and simulation to understand thermal transitions and decomposition pathways at a molecular level.