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Multifunctional structural design of graphene thermoelectrics by Bayesian optimization.

ABSTRACT: Materials development often confronts a dilemma as it needs to satisfy multifunctional, often conflicting, demands. For example, thermoelectric conversion requires high electrical conductivity, a high Seebeck coefficient, and low thermal conductivity, despite the fact that these three properties are normally closely correlated. Nanostructuring techniques have been shown to break the correlations to some extent; however, optimal design has been a major challenge due to the extraordinarily large degrees of freedom in the structures. By taking graphene nanoribbons (GNRs) as a representative thermoelectric material, we carried out structural optimization by alternating multifunctional (phonon and electron) transport calculations and Bayesian optimization to resolve the trade-off. As a result, we have achieved multifunctional structural optimization with an efficiency more than five times that achieved by random search. The obtained GNRs with optimized antidots significantly enhance the thermoelectric figure of merit by up to 11 times that of the pristine GNR. Knowledge of the optimal structure further provides new physical insights that independent tuning of electron and phonon transport properties can be realized by making use of zigzag edge states and aperiodic nanostructuring. The demonstrated optimization framework is also useful for other multifunctional problems in various applications.

SUBMITTER: Yamawaki M

PROVIDER: S-EPMC6003749 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature

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Multifunctional structural design of graphene thermoelectrics by Bayesian optimization.

Yamawaki Masaki M Ohnishi Masato M Ju Shenghong S Shiomi Junichiro J

Science advances 20180615 6

Materials development often confronts a dilemma as it needs to satisfy multifunctional, often conflicting, demands. For example, thermoelectric conversion requires high electrical conductivity, a high Seebeck coefficient, and low thermal conductivity, despite the fact that these three properties are normally closely correlated. Nanostructuring techniques have been shown to break the correlations to some extent; however, optimal design has been a major challenge due to the extraordinarily large d ...[more]

PMID: 29922713

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Project description:Realization of graphene-based sensors and electronic devices remains challenging, in part due to integration challenges with current fabrication and manufacturing processes. Thus, scalable methods for in situ fabrication of high-quality graphene-like materials are essential. Low-cost CO2 laser engravers can be used for site-selective conversion of polyimide under ambient conditions to create 3-D, rotationally disordered, few-layer, porous, graphene-like electrodes. However, the influences of non-linear parameter terms and interactions between key parameters on the graphitization process present challenges for rapid, resource-efficient optimization. An iterative optimization strategy was developed to identify promising regions in parameter space for two key parameters, laser power and scan speed, with the goal of optimizing electrode performance while maximizing scan speed and hence fabrication throughput. The strategy employed iterations of Design of Experiments Response Surface (DoE-RS) methods combined with choices of readily measurable parameters to minimize measurement resources and time. The initial DoE-RS experiment set employed visual response parameters, while subsequent iterations used sheet resistance as the optimization parameter. The final model clearly demonstrates that laser graphitization through raster scanning is a highly non-linear process requiring polynomial terms in scan speed and laser power up to fifth order. Two regions of interest in parameter space were identified using this strategy: Region 1 represents the global minimum for sheet resistance for this laser (∼16 Ω/sq), found at a low scan speed (70 mm/s) and a low average power (2.1 W) . Region 2 is a local minimum for sheet resistance (36 Ω/sq), found at higher values for scan speed (340 mm/s) and average power (3.4 W), allowing ∼5-fold reduction in write time. Importantly, these minima do not correspond to constant ratios of average laser power to scan speed. This highlights the benefits of DoE-RS methods in rapid identification of optimum parameter combinations that would be difficult to discover using traditional one-factor-at-a-time optimization. Verification data from Raman spectroscopy showed sharp 2D peaks with mean full-width-at-half-maximum intensity values <80 cm-1 for both regions, consistent with high-quality 3D graphene-like carbon. Graphene-based electrodes fabricated using the parameters from the respective regions yielded similar performance when employed as capacitive humidity sensors with hygroscopic dielectric layers. Devices fabricated using Region 1 parameters (16 Ω/sq) yielded capacitance responses of 0.78 ± 0.04 pF at 0% relative humidity (RH), increasing to 31 ± 7 pF at 85.1% RH. Region 2 devices (36 Ω/sq) showed comparable responses (0.88 ± 0.04 pF at 0% RH, 28 ± 5 pF at 85.1% RH).

Dataset Information

Multifunctional structural design of graphene thermoelectrics by Bayesian optimization.

Publications

Multifunctional structural design of graphene thermoelectrics by Bayesian optimization.

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