2021
DOI: 10.1007/s00397-021-01262-8
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Time-temperature-superposition analysis of diverse datasets by the minimum-arclength method: long-term prediction with uncertainty margins

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“…For systems with a single dominant evolution mechanism (i. e., involving a single activation barrier) the acceleration factor is easy to determine. This assumption is behind methods such as time-temperature superposition (TTS) that involves rigid-shifting of elevated-temperature isotherms along logarithmic timescales to create long-term prediction curves under ambient storage conditions [29][30][31][32]. However, for any real multicomponent systems, as is the case with either EBW or EFI detonators, one needs to be cognizant of the fact that many different mechanisms are at play, and there may not be a single dominant activation barrier.…”
Section: Thermal Conditioning Effect On Initiation Threshold Voltagementioning
confidence: 99%
“…For systems with a single dominant evolution mechanism (i. e., involving a single activation barrier) the acceleration factor is easy to determine. This assumption is behind methods such as time-temperature superposition (TTS) that involves rigid-shifting of elevated-temperature isotherms along logarithmic timescales to create long-term prediction curves under ambient storage conditions [29][30][31][32]. However, for any real multicomponent systems, as is the case with either EBW or EFI detonators, one needs to be cognizant of the fact that many different mechanisms are at play, and there may not be a single dominant activation barrier.…”
Section: Thermal Conditioning Effect On Initiation Threshold Voltagementioning
confidence: 99%