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Ian Broadwell

Researcher at Xiamen University

Publications -  12
Citations -  1371

Ian Broadwell is an academic researcher from Xiamen University. The author has contributed to research in topics: Nanomaterials & Electrochemical energy conversion. The author has an hindex of 10, co-authored 12 publications receiving 1230 citations.

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Nanomaterials of high surface energy with exceptional properties in catalysis and energy storage

TL;DR: This critical review presents a review of the progress made for producing shape-controlled synthesis of nanomaterials of high surface energy using electrochemical and wet chemistry techniques and discusses important nanommaterials such as nanocrystal catalysts based on Pt, Pd, Au and Fe, metal oxides TiO(2) and SnO( 2), as well as lithium Mn-richMetal oxides.
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Anion exchange membranes based on quaternized polystyrene-block-poly(ethylene-ran-butylene)-block-polystyrene for direct methanol alkaline fuel cells

TL;DR: The Nature Science Foundation of China [20976145] as discussed by the authors, Nature Science foundation of Fujian Province, China [2009J01040], [2005038401].
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Structure Design and Performance Tuning of Nanomaterials for Electrochemical Energy Conversion and Storage.

TL;DR: Significant progress has already been made in the electrocatalytic field, and likely emerging techniques based on NCs enclosed with high-energy surfaces and high-index facets could provide a promising platform to investigate the surface structure-Catalytic functionality at nanoscale, thus shedding light on the rational design of practical catalysts with high activity, selectivity, and durability for energy conversion and storage.
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In-situ infrared spectroscopic studies of electrochemical energy conversion and storage.

TL;DR: The body of work summarized here has substantially advanced the knowledge of electrode processes and represents the forefront in studies of EECS at the molecular level.
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Quaternized polyepichlorohydrin/PTFE composite anion exchange membranes for direct methanol alkaline fuel cells

TL;DR: In this article, the authors proposed a method to solve the problem of artificial neural networks in the field of ecology and evolutionary biology, which was proposed by the National Nature Science Foundation of China (NNFSCFG).