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Tutorial 9 — Kymographs

Corporate Research Analytical Laboratory, 3M

A kymograph maps a viscoelastic quantity over two independent variables, for example tan⁡δ(T,ω)\tan\delta(T, \omega) over temperature and angular frequency. It shows at a glance where relaxation processes occur and how they move with temperature or time, information that is spread over many curves in conventional sweep plots.

TORCh experiments

In a Thermally Optimised Ramped Chirp (TORCh) experiment, OWChirps are applied continuously during a temperature ramp at rate T˙\dot T (Vadillo et al., 2026). Each chirp is time-stamped, so one ramp yields a dense (T,ω)(T, \omega) data set. Here the change of the material is imposed by the ramp rather than by its own kinetics. If the viscoelastic properties are single-valued functions of temperature, the mutation number of Tutorial 6 becomes

∣Mu∣=Towc∣∂ln⁡g(ω,T)∂T∣T˙,\lvert \mathrm{Mu} \rvert = T_\mathrm{owc} \left\lvert\frac{\partial \ln g(\omega, T)}{\partial T}\right\rvert \dot T,

(Tutorial 6), so the ramp rate and chirp duration must be chosen to keep ∣Mu∣≲0.15\lvert \mathrm{Mu} \rvert \lesssim 0.15, especially near thermal transitions where gg changes steeply. For low-conductivity samples, the ramp must also be slow compared with heat diffusion through the gap HH: the diffusion time τdiff=H2ρCp/k\tau_\mathrm{diff} = H^2 \rho C_p / k, with density ρ\rho, heat capacity CpC_p and thermal conductivity kk, should be small compared with the mutation time λMu\lambda_\mathrm{Mu}, otherwise the sample develops a temperature gradient.

The Kymograph transform

Kymograph collects the scattered (x,y,z)(x, y, z) points of all measurements and interpolates them onto a regular n_x × n_y grid with scipy.interpolate.griddata; log_y=True interpolates in log⁡10ω\log_{10}\omega. Each axis is looked up first among the dataset columns and then among the measurement conditions, so either variable can go on either axis; log_x and log_y grid and plot an axis in log scale. The result is a piblin TwoDimensionalDataset with names and units attached.

Temperature–frequency map of tan⁡δ\tan\delta

We use the TORCh ramp of Tutorial 6 (−50 to 150 °C, 10 s chirps). The file already contains the moduli of every chirp; for raw chirp waveforms, prepend OWChirpAnalysis() (Tutorial 3). Peaks in tan⁡δ\tan\delta mark relaxation processes such as the glass transition, and their shift to higher frequency with temperature reflects the shift factor aTa_T (Tutorial 4).

<Figure size 650x480 with 2 Axes>

The axes are chosen when the map is built. With frequency on xx, set log_x=True and log_y=False; the glass transition becomes a band that rises in temperature with frequency.

<Figure size 650x480 with 2 Axes>

Other quantities and the colour scale

Any column can be mapped. The moduli span several decades, so log_z=True uses a logarithmic colour scale; vmin and vmax fix the colour range, for example to round decades or to compare several maps on a common scale.

<Figure size 1200x450 with 4 Axes>

Slices

Slices through the grid at fixed TT give isothermal frequency sweeps; slices at fixed ω\omega give isochronal temperature sweeps, as from a dynamic mechanical analysis (DMA) ramp, but both come from a single experiment.

<Figure size 700x400 with 1 Axes>
<Figure size 700x400 with 1 Axes>

Temperature–frequency map of the mutation number

MutationNumber (Tutorial 6) stores temperature and angular frequency with each ∣Mu∣\lvert \mathrm{Mu} \rvert value, so the same transform maps ∣Mu(T,ω)∣\lvert \mathrm{Mu}(T, \omega) \rvert. Setting vmax=0.15 saturates the colour scale at the quasi-static limit: the brightest regions show where the ramp is closest to the limit.

<Figure size 650x480 with 2 Axes>

∣Mu∣\lvert \mathrm{Mu} \rvert is highest across the glass transition, at all frequencies, and stays well below 0.15 elsewhere. The narrow dark line at 25 °C is the turnaround between the two ramps, where the temperature barely changes between consecutive chirps.

References

  • Geri, M., Keshavarz, B., Divoux, T., Clasen, C., Curtis, D. J. and McKinley, G. H., Time-Resolved Mechanical Spectroscopy of Soft Materials via Optimally Windowed Chirps, Physical Review X 8, 041042 (2018). DOI: Geri et al. (2018).

  • Mours, M. and Winter, H. H., Time-resolved rheometry, Rheologica Acta 33, 385–397 (1994). DOI: Mours & Winter (1994).

  • Vadillo, D. C., Perego, A. et al., TORCh: Thermally Optimised Ramped Chirp for time-temperature spectroscopy of polymers, Macromolecules (2026). DOI: Vadillo et al. (2026).

References
  1. Geri, M., Keshavarz, B., Divoux, T., Clasen, C., Curtis, D. J., & McKinley, G. H. (2018). Time-Resolved Mechanical Spectroscopy of Soft Materials via Optimally Windowed Chirps. Physical Review X, 8(4). 10.1103/physrevx.8.041042
  2. Mours, M., & Winter, H. H. (1994). Time-resolved rheometry. Rheologica Acta, 33(5), 385–397. 10.1007/bf00366581
  3. Vadillo, D. C., Perego, A., Whitcomb, K., Behling, R. E., Gill, A. M., Das, M., & McKinley FRS, G. H. (2026). TORCh: Thermally Optimized Ramped Chirps for the Efficient Generation of Rheological Master Curves. Macromolecules, 59(12), 6893–6905. 10.1021/acs.macromol.6c00679