Mount-Learn Wu

567 citations
49 papers · 443 indexed · h-index 12

Impact in

Papers in

Mount-Learn Wu

46 papers receiving 418 citations

Peers

Mount-Learn Wu
Comparison fields: 5 of 33
  • Surfaces, Coatings and Films 154
  • Electrical and Electronic Engineering 378
  • Atomic and Molecular Physics, and Optics 156
  • Condensed Matter Physics 45
  • Biomedical Engineering 113
Replace Juha Tommila with:
Juha Tommila Finland
F.S. Walters United States
Alex Hartsuiker Netherlands
Fanglu Lu United States
Christof Klein Austria
Pascal Xavier France
T. Werner Germany
K. Seo South Korea
Ngoc Diep Lai Taiwan
J. Daleiden Germany
Mount-Learn Wu relative to Juha Tommila Finland Juha Tommila's profile →
Citations per field
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Juha Tommila · 1×
Citations per year

Countries citing papers authored by Mount-Learn Wu

Since Specialization
Citations

This map shows the geographic impact of Mount-Learn Wu'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 Mount-Learn Wu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mount-Learn Wu more than expected).

Fields of papers citing papers by Mount-Learn Wu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Mount-Learn Wu. 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 Mount-Learn Wu. The network helps show where Mount-Learn Wu may publish in the future.

Co-authors

The 25 scholars most cited alongside Mount-Learn Wu, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Mount-Learn Wu Line = papers co-authored together Mount-Learn Wu links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 49 papers — load more, or switch the sort, to bring in the rest.

#Work
1 200885
2 200628
3 199727
4 200924
5 201121
6 201018
7 200716
8 200914
9 201514
10 201213
11 200413
12 199612
13 200611
14 200811
15 201011
16 200610
17 20099
18 20109
19 20019
20 20038

About Mount-Learn Wu

Mount-Learn Wu is a scholar working on Electrical and Electronic Engineering, Atomic and Molecular Physics, and Optics, Surfaces, Coatings and Films, Biomedical Engineering and Condensed Matter Physics, having authored 49 papers that have together received 443 indexed citations. Recurring topics across this work include Photonic and Optical Devices (39 papers), Semiconductor Lasers and Optical Devices (19 papers), Photonic Crystals and Applications (13 papers), Optical Coatings and Gratings (13 papers), GaN-based semiconductor devices and materials (7 papers), Advanced Fiber Optic Sensors (5 papers), Advanced Photonic Communication Systems (4 papers) and Microwave Engineering and Waveguides (4 papers). The work is most often cited by research in Surfaces, Coatings and Films (154 citations), Electrical and Electronic Engineering (378 citations), Atomic and Molecular Physics, and Optics (156 citations), Condensed Matter Physics (45 citations) and Biomedical Engineering (113 citations). Mount-Learn Wu has collaborated with scholars based in Taiwan, United Kingdom and United States. Frequent co-authors include Jenq-Yang Chang, Chih‐Ming Wang, Chien-Chieh Lee, Ching-Ting Lee, Jui‐Ming Hsu, Jin‐Wei Shi, F.-M. Kuo, Chia‐Chi Chang, Te-Yuan Chung and Ching‐Cherng Sun. Their work appears in journals such as IEEE Photonics Technology Letters, Optics Express, Journal of Lightwave Technology, Japanese Journal of Applied Physics and Optics Letters.

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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