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Acene Ring Size Optimization in Fused Lactam Polymers Enabling High n-Type Organic Thermoelectric Performance.

Hu ChenMaximilian MoserSuhao WangCameron JellettKarl J ThorleyGeorge T HarrisonXuechen JiaoMingfei XiaoBalaji PurushothamanMaryam AlsufyaniHelen BristowStefaan De WolfNicola GaspariniAndrew WadsworthChristopher R McNeillHenning SirringhausSimone FabianoIain McCulloch
Published in: Journal of the American Chemical Society (2020)
Three n-type fused lactam semiconducting polymers were synthesized for thermoelectric and transistor applications via a cheap, highly atom-efficient, and nontoxic transition-metal free aldol polycondensation. Energy level analysis of the three polymers demonstrated that reducing the central acene core size from two anthracenes (A-A), to mixed naphthalene-anthracene (A-N), and two naphthalene cores (N-N) resulted in progressively larger electron affinities, thereby suggesting an increasingly more favorable and efficient solution doping process when employing 4-(2,3-dihydro-1,3-dimethyl-1H-benzimidazol-2-yl)-N,N-dimethylbenzenamine (N-DMBI) as the dopant. Meanwhile, organic field effect transistor (OFET) mobility data showed the N-N and A-N polymers to feature the highest charge carrier mobilities, further highlighting the benefits of aryl core contraction to the electronic performance of the materials. Ultimately, the combination of these two factors resulted in N-N, A-N, and A-A to display power factors (PFs) of 3.2 μW m-1 K-2, 1.6 μW m-1 K-2, and 0.3 μW m-1 K-2, respectively, when doped with N-DMBI, whereby the PFs recorded for N-N and A-N are among the highest reported in the literature for n-type polymers. Importantly, the results reported in this study highlight that modulating the size of the central acene ring is a highly effective molecular design strategy to optimize the thermoelectric performance of conjugated polymers, thus also providing new insights into the molecular design guidelines for the next generation of high-performance n-type materials for thermoelectric applications.
Keyphrases
  • systematic review
  • machine learning
  • molecular dynamics
  • quantum dots
  • photodynamic therapy
  • big data
  • multidrug resistant