Abstract
The glass transition temperature (Tg) is a key property that dictates the applicability of conjugated polymers. The Tg demarks the transition into a brittle glassy state, making its accurate prediction for conjugated polymers crucial for the design of soft, stretchable, or flexible electronics. Here we show that a single adjustable parameter can be used to build a relationship between the Tg and the molecular structure of 32 semiflexible (mostly conjugated) polymers that differ drastically in aromatic backbone and alkyl side chain chemistry. An effective mobility value, ζ, is calculated using an assigned atomic mobility value within each repeat unit. The only adjustable parameter in the calculation of ζ is the ratio of mobility between conjugated and non-conjugated atoms. We show that ζ correlates strongly to the Tg, and that this simple method predicts the Tg with a root-mean-square error of 13 °C for conjugated polymers with alkyl side chains.
Highlights
The glass transition temperature (Tg) is a key property that dictates the applicability of conjugated polymers
Tg appears to increase with both chain stiffness and bulkier side groups[14,15,16,17], and decreases as alkyl side group length increases[1], but universal molecular models that connect the Tg to a given repeat unit structure are lacking
Signatures of Tg in differential scanning calorimetry (DSC) scans are thereby suppressed[33], some unambiguous values have been summarized by Müller[34]
Summary
The glass transition temperature (Tg) is a key property that dictates the applicability of conjugated polymers. The Tg demarks the transition into a brittle glassy state, making its accurate prediction for conjugated polymers crucial for the design of soft, stretchable, or flexible electronics. Predicting the glass transition temperature of conjugated polymers from the chemical structure remains a challenge. Tg appears to increase with both chain stiffness and bulkier side groups[14,15,16,17], and decreases as alkyl side group length increases[1], but universal molecular models that connect the Tg to a given repeat unit structure are lacking. Other approaches have used data to build empirical correlations based on the quantitative structure-property relationships (QSPR) method[26], group contributions approaches[27], and machine learning[28] to predict and match with experimentally measured Tgs of many polymers. Rheological measurements are sensitive to changes in mechanical properties, and can unambiguously locate the Tg of conjugated polymers as the peak in the loss modulus as a function of temperature[8]
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