Daylond Hooper

767 citations
13 papers · 581 indexed · h-index 7
Topics
Carbon Nanotubes in Composites (4 papers)Graphene research and applications (3 papers)Machine Learning in Materials Science (3 papers)
Partner nations
United States

In The Last Decade

Daylond Hooper

12 papers receiving 566 citations

Peers

Daylond Hooper
Comparison fields: 5 of 75
  • Materials Chemistry 382
  • Biomedical Engineering 130
  • Electrical and Electronic Engineering 104
  • Mechanical Engineering 98
  • Computational Theory and Mathematics 68
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Daylond Hooper relative to Aldair E. Gongora United States Aldair E. Gongora's profile →
Citations per field
00.5×10×14.3×
Aldair E. Gongora · 1×
Citations per year

Countries citing papers authored by Daylond Hooper

Since Specialization
Citations

This map shows the geographic impact of Daylond Hooper's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Daylond Hooper with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daylond Hooper more than expected).

Fields of papers citing papers by Daylond Hooper

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Daylond Hooper. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Daylond Hooper. The network helps show where Daylond Hooper may publish in the future.

Co-authorship network of co-authors of Daylond Hooper

This figure shows the co-authorship network connecting the top 25 collaborators of Daylond Hooper. A scholar is included among the top collaborators of Daylond Hooper based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Daylond Hooper. Daylond Hooper is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

13 of 13 papers shown
#WorkIndexed citations
1 0
2 91
3 11
4 277
5
A Taxonomy for Improving Dialog between Autonomous Agent Developers and Human-Machine Interface Designers.
1
6
Fusion: A Framework for Human Interaction with Flexible-Adaptive Automation Across Multiple Unmanned Systems
6
7 2
8 82
9 53
10
Coalition Formation under Uncertainty
1
11 2
12
HAMR: A Hybrid Multi-Robot Control Architecture
3
13 52

About Daylond Hooper

Daylond Hooper is a scholar working on Automotive Engineering, Materials Chemistry and Management Information Systems, having authored 13 papers that have together received 581 indexed citations. Recurring topics across this work include Carbon Nanotubes in Composites (4 papers), Graphene research and applications (3 papers) and Machine Learning in Materials Science (3 papers). The work is most often cited by research in Materials Chemistry (382 citations), Structural Biology (10 citations) and Information Systems and Management (33 citations). Daylond Hooper has collaborated with scholars based in United States. Frequent co-authors include Benji Maruyama, Pavel Nikolaev, Rahul Rao, Michael Krein, Frederick Webber, Kevin Decker, Rick Barto, Mauricio Terrones, Néstor Perea‐López and Mark A. Pitt. Their work appears in journals such as ACS Nano, The Journal of Physical Chemistry C and Scripta Materialia.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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