Materials Map

Discover the materials research landscape. Find experts, partners, networks.

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The Materials Map is an open tool for improving networking and interdisciplinary exchange within materials research. It enables cross-database search for cooperation and network partners and discovering of the research landscape.

The dashboard provides detailed information about the selected scientist, e.g. publications. The dashboard can be filtered and shows the relationship to co-authors in different diagrams. In addition, a link is provided to find contact information.

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The Materials Map is still under development. In its current state, it is only based on one single data source and, thus, incomplete and contains duplicates. We are working on incorporating new open data sources like ORCID to improve the quality and the timeliness of our data. We will update Materials Map as soon as possible and kindly ask for your patience.

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in Cooperation with on an Cooperation-Score of 37%

Topics

Publications (3/3 displayed)

  • 2021Probing structural and chemical evolution in (AlxGa1−x)2O3 using atom probe tomography: A review10citations
  • 2010Solvothermal synthesis of group 5 and 6 nitrides via reactions using LiNH2 and ammonia nitrogen sources13citations
  • 2009Solution phase preparative routes to nitride morphologies of interest in catalysis21citations

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Chart of shared publication
Chirico, Pietro
1 / 1 shared
Hector, Andrew Lee
2 / 50 shared
Chart of publication period
2021
2010
2009

Co-Authors (by relevance)

  • Chirico, Pietro
  • Hector, Andrew Lee
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article

Probing structural and chemical evolution in (AlxGa1−x)2O3 using atom probe tomography: A review

  • Mazumder, Baishakhi
Abstract

<jats:title>Abstract</jats:title><jats:p>(Al<jats:sub><jats:italic>x</jats:italic></jats:sub>Ga<jats:sub>1−<jats:italic>x</jats:italic></jats:sub>)<jats:sub>2</jats:sub>O<jats:sub>3</jats:sub> is a novel ultra‐wide bandgap semiconductor with the potential to dominate future power electronics industries. High‐performance devices demand high Al content in (Al<jats:sub><jats:italic>x</jats:italic></jats:sub>Ga<jats:sub>1−<jats:italic>x</jats:italic></jats:sub>)<jats:sub>2</jats:sub>O<jats:sub>3</jats:sub> but are limited by crystallinity degradation resulting from phase separation. Additionally, the solubility limit of Al is still under debate, and conclusive research is in progress. (Al<jats:sub><jats:italic>x</jats:italic></jats:sub>Ga<jats:sub>1−<jats:italic>x</jats:italic></jats:sub>)<jats:sub>2</jats:sub>O<jats:sub>3</jats:sub> is also limited in high‐frequency applications owing to low carrier mobility and requires n‐type doping. For commercializing this material, the major obstacle is understanding dopant's behavior in the host (Al<jats:sub><jats:italic>x</jats:italic></jats:sub>Ga<jats:sub>1−<jats:italic>x</jats:italic></jats:sub>)<jats:sub>2</jats:sub>O<jats:sub>3</jats:sub>. To investigate these issues, an advanced characterization technique, atom probe tomography (APT), was employed to analyze the structural‐chemical evolution of (Al<jats:sub><jats:italic>x</jats:italic></jats:sub>Ga<jats:sub>1−<jats:italic>x</jats:italic></jats:sub>)<jats:sub>2</jats:sub>O<jats:sub>3</jats:sub>. In this review, we summarized our recent works on the structure‐chemistry investigation of (Al<jats:sub><jats:italic>x</jats:italic></jats:sub>Ga<jats:sub>1−<jats:italic>x</jats:italic></jats:sub>)<jats:sub>2</jats:sub>O<jats:sub>3</jats:sub> with alloy composition and doping interaction. We introduced machine learning algorithms on APT data to reveal unrivaled knowledge, previously not achievable with conventional methodologies. The outstanding capabilities of APT to study (Al<jats:sub><jats:italic>x</jats:italic></jats:sub>Ga<jats:sub>1−<jats:italic>x</jats:italic></jats:sub>)<jats:sub>2</jats:sub>O<jats:sub>3</jats:sub> with Al composition and doping will be considered significant for the wide bandgap semiconductors community.</jats:p>

Topics
  • impedance spectroscopy
  • phase
  • mobility
  • semiconductor
  • crystallinity
  • atom probe tomography
  • machine learning
  • alloy composition