Flexible dielectric polymers that can withstand high electric field and simultaneously have high dielectric constant are desired for high-density energy storage.Here,we systematically investigated the impact of oxygen...Flexible dielectric polymers that can withstand high electric field and simultaneously have high dielectric constant are desired for high-density energy storage.Here,we systematically investigated the impact of oxygen-containing ether and carbonyl groups in the backbone structure on dielectric properties of a series of cyclic olefin.In comparison to the influence of the-CF3 pendant groups that had more impact on the dielectric constant rather than the band gap,the change of the backbone structure affected both the dielectric constant and band gaps.The one polymer with ether and carbonyl groups in the backbone has the largest band gap and highest discharge efficiency,while it has the lowest dielectric constant.The polymer without any ether groups in the backbone has the smallest band gap and lowest discharge efficiency,but it has the highest dielectric constant.Polymers that have no dipolar relaxation exhibit an inversely correlated dielectric constant and band gap.Enhancing the dipolar relaxation through rational molecular structure design can be a novel way to break through the exclusive constraint of dielectric constant and band gap for high-density energy storage.展开更多
The dielectric constant(ϵ)is a critical parameter utilized in the design of polymeric dielectrics for energy storage capacitors,microelectronic devices,and high-voltage insulations.However,agile discovery of polymer d...The dielectric constant(ϵ)is a critical parameter utilized in the design of polymeric dielectrics for energy storage capacitors,microelectronic devices,and high-voltage insulations.However,agile discovery of polymer dielectrics with desirableϵremains a challenge,especially for high-energy,high-temperature applications.To aid accelerated polymer dielectrics discovery,we have developed a machine-learning(ML)-based model to instantly and accurately predict the frequency-dependentϵof polymers with the frequency range spanning 15 orders of magnitude.Our model is trained using a dataset of 1210 experimentally measuredϵvalues at different frequencies,an advanced polymer fingerprinting scheme and the Gaussian process regression algorithm.展开更多
基金supported by the Office of Naval Research through a multidisciplinary university research initiative(MURI)grant(N00014-17-1-2656)a capacitor program grant(N00014-19-1-2340)。
文摘Flexible dielectric polymers that can withstand high electric field and simultaneously have high dielectric constant are desired for high-density energy storage.Here,we systematically investigated the impact of oxygen-containing ether and carbonyl groups in the backbone structure on dielectric properties of a series of cyclic olefin.In comparison to the influence of the-CF3 pendant groups that had more impact on the dielectric constant rather than the band gap,the change of the backbone structure affected both the dielectric constant and band gaps.The one polymer with ether and carbonyl groups in the backbone has the largest band gap and highest discharge efficiency,while it has the lowest dielectric constant.The polymer without any ether groups in the backbone has the smallest band gap and lowest discharge efficiency,but it has the highest dielectric constant.Polymers that have no dipolar relaxation exhibit an inversely correlated dielectric constant and band gap.Enhancing the dipolar relaxation through rational molecular structure design can be a novel way to break through the exclusive constraint of dielectric constant and band gap for high-density energy storage.
基金This work is supported by the Office of Naval Research through N0014-17-1-2656,a Multi-University Research Initiative(MURI)grant.
文摘The dielectric constant(ϵ)is a critical parameter utilized in the design of polymeric dielectrics for energy storage capacitors,microelectronic devices,and high-voltage insulations.However,agile discovery of polymer dielectrics with desirableϵremains a challenge,especially for high-energy,high-temperature applications.To aid accelerated polymer dielectrics discovery,we have developed a machine-learning(ML)-based model to instantly and accurately predict the frequency-dependentϵof polymers with the frequency range spanning 15 orders of magnitude.Our model is trained using a dataset of 1210 experimentally measuredϵvalues at different frequencies,an advanced polymer fingerprinting scheme and the Gaussian process regression algorithm.
