| Literature DB >> 36212679 |
Stefan Schwabe1,2, Klara Lünser1,2,3, Daniel Schmidt4,5, Kornelius Nielsch1,2, Peter Gaal4,5, Sebastian Fähler3.
Abstract
Structural martensitic transformations enable various applications, which range from high stroke actuation and sensing to energy efficient magnetocaloric refrigeration and thermomagnetic energy harvesting. All these emerging applications benefit from a fast transformation, but up to now their speed limit has not been explored. Here, we demonstrate that a thermoelastic martensite to austenite transformation can be completed within 10 ns. We heat epitaxial Ni-Mn-Ga films with a nanosecond laser pulse and use synchrotron diffraction to probe the influence of initial temperature and overheating on transformation rate and ratio. We demonstrate that an increase in thermal energy drives this transformation faster. Though the observed speed limit of 2.5 × 1027 (Js)1 per unit cell leaves plenty of room for further acceleration of applications, our analysis reveals that the practical limit will be the energy required for switching. Thus, martensitic transformations obey similar speed limits as in microelectronics, as expressed by the Margolus - Levitin theorem.Entities:
Keywords: Martensitic phase transitions; magnetocaloric refrigeration; shape memory alloys; thermomagnetic energy harvesting; time-resolved synchrotron diffraction
Year: 2022 PMID: 36212679 PMCID: PMC9542621 DOI: 10.1080/14686996.2022.2128870
Source DB: PubMed Journal: Sci Technol Adv Mater ISSN: 1468-6996 Impact factor: 7.821
Figure 1.Detector images taken while heating with a laser pulse reveal the time-dependent transformation from martensite to austenite. The images in the first column are taken in the martensite state, 5 ns before the pulse (a, d); those in the second column at the end of the pulse at 7 ns, where both phases coexist (b, e); and those in the third column after the more or less complete transformation of the sample region at t = 20 ns (c, f). Both rows show the raw data in detector pixel coordinates. The pictures in the first row (a–c) show the area around the (0 0 4)A and neighboring ()MM reflections; the second row shows the area around ()MM , where no overlap with the austenite occurs (d–f). These exemplary diffractograms have been taken at an initial sample temperature of T0=312 K while heating with a laser pulse of 60 mJ cm−2, which results in a temperature increase of ΔT*=177K. A full time series of a larger region around (004)A is available as a supplementary video. Indexing follows [20] where the index A denotes an austenite reflection and MM one of modulated martensite. The intensity in all images is scaled linearly according to the scale given on the right side for both rows.
Figure 2.Probing the time dependency of a martensitic transformation while heating with a laser pulse and subsequent cooling. The summed up intensity of the ()MM peak allows to determine the martensite fraction, which reduces sharply during heating with the 7 ns laser pulse and increases afterwards when the sample cools down. Two series with different experimental conditions were investigated: (a) Nearly the same base temperature of around T0 = 329 K was used for all measurements and the laser fluence was varied to obtain temperature rises ΔT* between 60 K and 177 K. (b) the laser fluence and therefore ΔT* was kept constant at 177 K, but the base temperature was varied between 312 K and 354 K.
Figure 3.Driving a martensite to austenite transformation as fast as possible. To complete the transformation in a short time interval Δt, a sufficient overheating ΔT above the austenite start temperature by the 7 ns laser pulse is required (bottom axis). The laser pulse drives the transformation by adding thermal energy to the sample, which is plotted as additional top axis. An increase of driving energy increases the transformation rate r (right axis). This graph contains data obtained for series 1 (solid symbols) and series 2 (open symbols); the latter exhibits a larger experimental error, as described in the text. Therefore, we use only data from series 1 for the linear fit.