Ni(OH)2 as an novel shell layer material for core-shell dielectric filler based on barium titanate and their dielectric polymer composites in P(VDF-HFP) matrix
“…Experimental E b , ε r , and U e values belonging to various nanocomposites with different polymer matrixes and nanofillers were collected from existing literatures − ,,− ,− ,− to train the ML models, as shown in Figure . It should be noted that the database only contains composites uniformly filled with a single type of filler and does not consider complex doping schemes such as hybrid fillers and multilayer structures.…”
Rationally designing polymer composite structures, including physical parameters of nanofillers, nanofiller−matrix interface characteristics, and geometric distribution of nanofillers, is thought to be an effective approach to achieve the desired dielectric properties such as breakdown strength (E b ), permittivity (ε r ), and energy density (U e ) in wide applications. However, the work is difficult to complete through merely high-cost and timeconsuming trial-and-error experiments. A machine learning (ML) driven approach, trained on hundreds of experimentally measured data, is presented to rationally design polymer composites with desired properties. The doping scheme of nanofillers is fingerprinted with a string of characters considering the physical parameters, shape, distribution of fillers, and shell properties in core−shell structures, and then the Gaussian process regression algorithm is trained to establish the linkage between the filler doping scheme and the dielectric properties. The dielectric properties of the randomly generated tens of millions of candidate composites are calculated with the resulting ML model, and representative composites with high E b , ε r , and U e are presented. The results indicate that the effects of nanofiller permittivity and bandgap on E b and ε r follow exactly the opposite trend, hence it is difficult to simultaneously improve E b and ε r by choosing the type of nanofiller. Fortunately, the trade-off between E b and ε r can be improved by tailoring the shape, orientation, and distribution of the nanofillers, for instance, by using horizontally orientated nanosheets and orthotropic nanowires with high permittivity.
“…Experimental E b , ε r , and U e values belonging to various nanocomposites with different polymer matrixes and nanofillers were collected from existing literatures − ,,− ,− ,− to train the ML models, as shown in Figure . It should be noted that the database only contains composites uniformly filled with a single type of filler and does not consider complex doping schemes such as hybrid fillers and multilayer structures.…”
Rationally designing polymer composite structures, including physical parameters of nanofillers, nanofiller−matrix interface characteristics, and geometric distribution of nanofillers, is thought to be an effective approach to achieve the desired dielectric properties such as breakdown strength (E b ), permittivity (ε r ), and energy density (U e ) in wide applications. However, the work is difficult to complete through merely high-cost and timeconsuming trial-and-error experiments. A machine learning (ML) driven approach, trained on hundreds of experimentally measured data, is presented to rationally design polymer composites with desired properties. The doping scheme of nanofillers is fingerprinted with a string of characters considering the physical parameters, shape, distribution of fillers, and shell properties in core−shell structures, and then the Gaussian process regression algorithm is trained to establish the linkage between the filler doping scheme and the dielectric properties. The dielectric properties of the randomly generated tens of millions of candidate composites are calculated with the resulting ML model, and representative composites with high E b , ε r , and U e are presented. The results indicate that the effects of nanofiller permittivity and bandgap on E b and ε r follow exactly the opposite trend, hence it is difficult to simultaneously improve E b and ε r by choosing the type of nanofiller. Fortunately, the trade-off between E b and ε r can be improved by tailoring the shape, orientation, and distribution of the nanofillers, for instance, by using horizontally orientated nanosheets and orthotropic nanowires with high permittivity.
“…36,37 In addition, the obvious decrease of dielectric constant in the low-frequency range(10 2 Hz–10 4 Hz) can be ascribed to the dipole polarization in BTCNs/PMIA composites was hardly catch up with frequency change of external electric field. 38 Figure 7.( a ) Dielectric constant, ( b ) dielectric loss, and ( c ) conductivity of BTCNs/PMIA composites.…”
This study reports that a novel high-temperature poly (m-phenyleneisophthalamide) (PMIA) composite with enhanced dielectric constant and thermal conductivity was prepared by filling with BaTiO3 nanowires–carbon nanotubes (BTCNs) fillers. Due to effective functionalization of BaTiO3 nanowires (BTNWs) and multi-wall carbon nanotubes (MWCNTs), the fabricated BTCNs fillers were homogeneously dispersed in PMIA matrix. The consequence displays that the dielectric constant of PMIA composite with 15 wt % BTNWs fillers increases to 27.6 at 103 Hz, which is about nine times higher than that of pure PMIA. Moreover, owing to the high thermal conductivity of MWCNTs, the thermal conductivity of PMIA with 15 wt% BTNWs fillers increases to 1.01 W/(mK). The enhanced thermal conductivity is beneficial for BTCNs/PMIA composite to dissipate the generated heat by dielectric loss. Considering these merits, this research would provide new methods and ideas for preparation of high-temperature dielectric polymer composites and reducing the internal thermal effect.
“…Polymer dielectric materials have attracted more and more attention because of their good processability and flexibility, but their applications are limited by their low dielectric constant and poor thermal conductivity. Inorganic ceramic materials [1,2] and conductive particles [3,4] are usually required for modification. Among them, graphene has been studied more because of its excellent electrical properties.…”
ABS / multilayer graphene (MLGN) composites were prepared by melt blending ABS and multilayer graphene masterbatch,and their dielectric and thermal properties were studied. The results show that the dielectric constant, dielectric loss, conductivity, thermal decomposition temperature and thermal conductivity of ABS / MLGN composites increase with the increase of MLGN content. The content of MLGN in ABS / MLGN composites with conductive percolation is between 7-8%. When the mass content of MLGN increased to 7%, the dielectric constant of the composite increased from 3.01 to 103.23, the dielectric loss increased from 0.058 to 0.83 at 100Hz, the 5% mass loss temperature of the composite increased by 17.0°C and the thermal conductivity increased by 1.9 times. The research results can provide reference for the preparation of thermally conductive polymer dielectric composites.
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