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Meryl D. Stoller

Researcher at University of Texas at Austin

Publications -  34
Citations -  30061

Meryl D. Stoller is an academic researcher from University of Texas at Austin. The author has contributed to research in topics: Graphene & Graphene oxide paper. The author has an hindex of 26, co-authored 34 publications receiving 27501 citations. Previous affiliations of Meryl D. Stoller include University of Texas System.

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Graphene-Based Ultracapacitors

TL;DR: CMG materials are made from 1-atom thick sheets of carbon, functionalized as needed, and here their performance in an ultracapacitor cell is demonstrated, illustrating the exciting potential for high performance, electrical energy storage devices based on this new class of carbon material.
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Carbon-based Supercapacitors Produced by Activation of Graphene

TL;DR: This work synthesized a porous carbon with a Brunauer-Emmett-Teller surface area, a high electrical conductivity, and a low oxygen and hydrogen content that has high values of gravimetric capacitance and energy density with organic and ionic liquid electrolytes.
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Chemical analysis of graphene oxide films after heat and chemical treatments by X-ray photoelectron and Micro-Raman spectroscopy

TL;DR: In this paper, several nanometer-thick graphene oxide films were exposed to nine different heat treatments (three in Argon, three in Argon and Hydrogen, and three in ultra-high vacuum), and also a film was held at 70°C while being exposed to a vapor from hydrazine monohydrate.
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Graphene, related two-dimensional crystals, and hybrid systems for energy conversion and storage

TL;DR: Graphene and related two-dimensional crystals and hybrid systems showcase several key properties that can address emerging energy needs, in particular for the ever growing market of portable and wearable energy conversion and storage devices.
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Best practice methods for determining an electrode material's performance for ultracapacitors

TL;DR: In this paper, the authors review and validate best practice test methods that accurately predict a material's performance, yet are flexible and quick enough to accommodate a wide range of material sample types and amounts.