Dielectric properties of polymer nanocomposite interphases from electrostatic force microscopy using machine learning

Gupta, Praveen Kumar; Schadler, Linda S.; Sundararaman, Ravishankar

doi:10.1016/j.matchar.2021.110909

Cited by 11 publications

(15 citation statements)

References 20 publications

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“…Finally, for each position, 14, the force gradient at a specific lift height, l, was generated from the finite difference derivative of the force scans generated at two lift heights, l − δ and l + δ, where l is the lift height, with δ chosen between 2 and 5 nm to balance errors in subtraction of simulations (small δ) versus nonlinearity errors (large δ). See ref 14 for further details on the finite-element simulations to calculate the force gradient profiles 3.1. ML to Predict Interfacial Properties.…”

Section: Resultsmentioning

confidence: 99%

“…ML models (SVR(t) PANI-modified Particle 1) also predict the interfacial region's thickness with high accuracy when particle depth is known as expected based on simulated measurements in ref. 14 3.2. Prediction of ML Model on the Experimental Data.…”

Section: Resultsmentioning

confidence: 99%

“…The topography was used to calculate the size of the particle and its submersion depth in the composite. The dependence of the measured EFM phase shift δϕ at each spatial location was used to extract the quadratic coefficient (with respect to V ) of the force gradient d F /d z . A large data set of simulated force gradients using finite-element solutions of the Poisson equation for the potential applied between the tip and a single nanoparticle within a polymer matrix were generated. All known parameters such as nanoparticle radius and permittivity of the filler and matrix were constrained to their known values, while unknown parameters such as interface thickness and permittivity were sampled to cover a range of possible values. A machine learning (ML) model was fit to this data set to predict interface thickness and permittivity, given the force gradient profile along a line scan passing above the center of the nanoparticle as the features (input) to the model. The ML model, which was trained to the simulated data set, was applied to the experimental force gradient data to extract the interface permittivity from the experimental data. …”

Section: Methodsmentioning

confidence: 99%

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Dielectric Properties of Polymer Nanocomposite Interphases Using Electrostatic Force Microscopy and Machine Learning

Gupta

Ruzicka

Benicewicz

et al. 2023

ACS Appl. Electron. Mater.

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Knowing the dielectric properties of the interfacial region in polymer nanocomposites is critical to predicting and controlling dielectric properties. They are, however, difficult to characterize due to their nanoscale dimensions. Electrostatic force microscopy (EFM) provides a pathway to local dielectric property measurements, but extracting local dielectric permittivity in complex interphase geometries from EFM measurements remains a challenge. This paper demonstrates a combined EFM and machine learning (ML) approach to measuring interfacial permittivity in 50 nm silica particles in a PMMA matrix. We show that ML models trained to finite-element simulations of the electric field profile between the EFM tip and nanocomposite surface can accurately determine the interface permittivity of functionalized nanoparticles. It was found that for the particles with a polyaniline brush layer, the interfacial region was detectable (extrinsic interface). For bare silica particles, the intrinsic interface was detectable only in terms of having a slightly higher or lower permittivity. This approach fully accounts for the complex interplay of filler, matrix, and interface permittivity on the force gradients measured in EFM that are missed by previous semianalytic approaches, providing a pathway to quantify and design nanoscale interface dielectric properties in nanodielectric materials.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Dielectric Properties of Polymer Nanocomposite Interphases Using Electrostatic Force Microscopy and Machine Learning

Gupta

Ruzicka

Benicewicz

et al. 2023

ACS Appl. Electron. Mater.

Self Cite

View full text Add to dashboard Cite

show abstract

“…At present, in most cases, the empirical method is used to assume the initial parameters of the algorithm, which increases the randomness of the results of the model algorithm. If the initial parameters are given irregularly, the prediction quality of the network model will be weakened and the accuracy of prediction information cannot be guaranteed [7,8]. Taking the initial parameters as the optimization starting point, the chaos theory is introduced to combine it with the machine learning system to build a new prediction model of sports results.…”

Section: Introductionmentioning

confidence: 99%

Sports Performance Prediction Based on Chaos Theory and Machine Learning

Sun

2022

Wireless Communications and Mobile Computing

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In order to combine chaos theory and machine learning technology to predict sports performance, a research on sports performance prediction based on chaos theory and machine learning is proposed. This paper takes the sports performance as the goal to predict the data; introduce the chaos theory algorithm, and combine the neural network system and particle swarm optimization algorithm to actively train sports results and ensure the quality of performance prediction. The comparison between shot put data prediction and real data shows that the prediction results given by the model have little deviation and can provide technical services for performance prediction for special sports training. When predicting the sports performance of college students, the prediction accuracy of sports performance of each subject is no less than 90%, which proves that this system can be used in college sports management. The delay time of the model data is short, which is mainly related to the correlation coefficient. When the coefficient is determined to be 0.02, the prediction delay time is 5 s, which can effectively complete the prediction and analysis of sports performance. The combined model has various technical advantages such as chaos theory, neural system, and particle optimization. It has strong sports performance prediction ability and can provide technical support for performance prediction for athletes’ training, college sports management, and other related sports industries.

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“…Polymer informatics has been applied to essentially every aspect of the polymer lifecycle. It has been used to design new monomers for various applications 12,15,16 ; engineer reactions 17 ; model processing conditions and parameters [18][19][20] ; identify and predict polymer conformations and phases [21][22][23][24][25][26] ; predict materials properties [27][28][29][30][31][32][33][34][35] ; and finally offer insight into wear and end of life. 4,[36][37][38][39] Most polymer informatics literature focuses on property prediction, but recently other aspects of polymer synthesis, processing and lifetime have been gaining attention.…”

Section: Introductionmentioning

confidence: 99%

Machine learning for polymeric materials: an introduction

Cencer

Moore

Assary

2021

Polymer International

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Polymers are incredibly versatile materials and have become ubiquitous. Increasingly, researchers are using data science and polymer informatics to design new materials and understand their structure–property relationships. Polymer informatics is an emerging field. While there are many useful tools and databases available, many are not widely utilized. Herein, we introduce the field of polymer informatics and discuss some of the available databases and tools. We cover how to share polymer data, approaches for preparing a dataset for machine learning and recent applications of machine learning to polymer property prediction and polymer synthesis. © 2021 Society of Industrial Chemistry.

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Dielectric properties of polymer nanocomposite interphases from electrostatic force microscopy using machine learning

Cited by 11 publications

References 20 publications

Dielectric Properties of Polymer Nanocomposite Interphases Using Electrostatic Force Microscopy and Machine Learning

Dielectric Properties of Polymer Nanocomposite Interphases Using Electrostatic Force Microscopy and Machine Learning

Sports Performance Prediction Based on Chaos Theory and Machine Learning

Machine learning for polymeric materials: an introduction

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