First-principles identification of localized trap states in polymer nanocomposite interfaces

Shandilya, Abhishek; Schadler, Linda S.; Sundararaman, Ravishankar

doi:10.1557/jmr.2020.18

Cited by 6 publications

(5 citation statements)

References 44 publications

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“…Out of chemical defect-containing polymers, the technique was applied to probe electronic properties of various polymers as Polystyrene, Poly(methyl methacrylate), or Poly(ethylene terephthalate) [227], fluorinated polymers [228] and Polyimide [229] that contain unsaturated bonds. It was also extended to interfaces in nanocomposites [230,231]. A step forward with modelling approaches is being achieved with combining density functional theory and GW-BSE (GW-Belthe-Salpeter equation) in order to compute the optical properties of materials, in particular energy levels involved in excited states [232,233].…”

Section: Discussionmentioning

confidence: 99%

Charge trap spectroscopy in polymer dielectrics: a critical review

Teyssèdre¹,

Zheng²,

Boudou³

et al. 2021

J. Phys. D: Appl. Phys.

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Trapping phenomena are essential features controlling the transport properties of insulating materials. Depending on the energy depth, traps can either assist transport or lead to long-lasting storage of charges. The consequences of charge trapping are non-linear phenomena and electric field distribution distortion in the dielectric bulk. The important characteristics about traps are the nature of the levels, their depth in energy, and their density. In this review, we discuss the different techniques available to probe the energetics of traps, particularly in insulating polymers. The methods implemented for approaching the characteristics of traps range from atomistic simulation based on known physical/chemical defects, identification by spectroscopic techniques, and coupled opticalelectrical or thermal-electrical techniques. The review is focused on methods involving thermal or optical excitation coupled to detection using electrical or luminescence response with questioning about the physical hypotheses behind the analysis and the difference in response obtained through the various approaches. The technical implementation of these methods is described, along with examples of application. The differences in trap depth estimation from optical and thermal methods is discussed as well as the impact of having distributed trap depths. The input of luminescence techniques, which provide a fingerprint of chemical groups involved in charge recombination, is put forward.

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Section: Discussionmentioning

confidence: 99%

Charge trap spectroscopy in polymer dielectrics: a critical review

Teyssèdre¹,

Zheng²,

Boudou³

et al. 2021

J. Phys. D: Appl. Phys.

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“…Trap states critically impact electronic transport in polymers and nanocomposites but can only be indirectly inferred from experimental measurements such as transient depolarization current (TSDC) and luminescence spectroscopy, making it difficult to account for their influence on breakdown strength in material design. We previously developed a framework for systematic first-principles predictions of trap states at filler-polymer interfaces and extracted the multi-modal distribution of trap states at polyethylene-silica interfaces expected to significantly influence the carrier transport [39].…”

Section: First-principles Predictions Of Trap Statesmentioning

confidence: 99%

“…Here, we extend that approach to predict trap states at the extrinsic interfaces of functionalized fillers, focusing on molecules thiophene, terthiophene, and ferrocene at the inorganic-polymer interface (Figure 3). We create an ensemble of 15 amorphous interfaces containing each molecule, starting from random-walk polymer structures and classical molecular dynamics quench simulations followed by first-principles structure optimization to account for the randomness in the interfacial structure [39]. We then perform electronic density-functional theory calculations using the JDFTx plane-wave basis code [40], PBE-GGE exchange-correlation functional [41] with DFT-D2 dispersion corrections, GBRV ultrasoft pseudopotentials [42], and a kinetic energy cutoff of 20 and 100 Hartrees on the wavefunction and charge density, respectively, for each of these 45 large interfacial structures, each with approximately 300 atoms and 1000 valence electrons.…”

Section: First-principles Predictions Of Trap Statesmentioning

confidence: 99%

Design of Polymer Nanodielectrics for Capacitive Energy Storage

Prabhune,

Comlek,

Shandilya

et al. 2023

Nanomaterials

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Polymer nanodielectrics present a particularly challenging materials design problem for capacitive energy storage applications like polymer film capacitors. High permittivity and breakdown strength are needed to achieve high energy density and loss must be low. Strategies that increase permittivity tend to decrease the breakdown strength and increase loss. We hypothesize that a parameter space exists for fillers of modest aspect ratio functionalized with charge-trapping molecules that results in an increase in permittivity and breakdown strength simultaneously, while limiting increases in loss. In this work, we explore this parameter space, using physics-based, multiscale 3D dielectric property simulations, mixed-variable machine learning and Bayesian optimization to identify the compositions and morphologies which lead to the optimization of these competing properties. We employ first principle-based calculations for interface trap densities which are further used in breakdown strength calculations. For permittivity and loss calculations, we use continuum scale modelling and finite difference solution of Poisson’s equation for steady-state currents. We propose a design framework for optimizing multiple properties by tuning design variables including the microstructure and interface properties. Finally, we employ mixed-variable global sensitivity analysis to understand the complex interplay between four continuous microstructural and two categorical interface choices to extract further physical knowledge on the design of nanodielectrics.

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“…The breakdown strength was estimated using an approach described in [15] which investigated the role of dispersion and volume fraction of spherical fillers on breakdown strength; that approach generalizes straightforwardly to fillers of arbitrary geometry. Briefly, this approach takes advantage of first-principles calculations of ensembles of amorphous polymer-nanofiller interfaces generated using molecular dynamics to provide critical information regarding trap distributions [18]. For example, we have shown that defects at polyethylene-silica interfaces exhibit a bimodal distribution of shallow and deep traps (Fig.…”

Section: Ecs Transactions 108 (2) 51-60 (2022)mentioning

confidence: 99%

“…(c) The carrier mobility is inversely correlated with the experimental breakdown strength, allowing us to calibrate breakdown strength predictions from mobility estimates[15]. (Adapted with permission from (a) Ref [18]. and (c) Ref [15].…”

mentioning

confidence: 99%

(Invited) Combining Machine Learning, DFT, EFM, and Modeling to Design Nanodielectric Behavior

Schadler¹,

Chen²,

Brinson³

et al. 2022

ECS Trans.

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Predicting and designing the properties of polymer nanodielectrics is challenging due to the number of parameters controlling properties and the breadth of scale (from electronic to mm). This paper summarizes a preliminary study using elongated semiconducting nanoparticles with an extrinsic interface that enhanced carrier trapping to attempt to find a parameter space that allows for improved permittivity and breakdown strength without increasing loss. We combine finite element modeling of dielectric constant with a Monte Carlo multi-scale simulation of carrier hopping to predict break down strength. Filler dispersion, filler geometry, isotropy and interface trapping properties are explicitly taken into account to compute design objectives associated with dielectric constant and mobility. Ultimately, we trained a latent variable Gaussian Process (LVGP) metamodel that can take both qualitative (e.g., orientation and dispersion states) and quantitative variables (e.g., microstructure descriptors) as inputs to predict properties over a broader range with observed tradeoffs.

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First-principles identification of localized trap states in polymer nanocomposite interfaces

Abstract: Abstract

Cited by 6 publications

References 44 publications

Charge trap spectroscopy in polymer dielectrics: a critical review

Charge trap spectroscopy in polymer dielectrics: a critical review

Design of Polymer Nanodielectrics for Capacitive Energy Storage

(Invited) Combining Machine Learning, DFT, EFM, and Modeling to Design Nanodielectric Behavior

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