Machine learning-assisted performance prediction of graphene-silicon twin-port band-notched wideband antenna for THz 6G communication systems.
- DOI
- 10.1038/s41598-026-60977-x
- Published
- 2026 Jul 21
- Container
- Scientific reports
- Publisher
- Not recorded
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1038/s41598-026-60977-x,
title = {Machine learning-assisted performance prediction of graphene-silicon twin-port band-notched wideband antenna for THz 6G communication systems.},
author = {Datta G and Narayanaswamy NK and Verma A and Singh P and Jadapalli S and Saini NK and Tripathi S and Pandey A},
year = {2026},
journal = {Scientific reports},
doi = {10.1038/s41598-026-60977-x},
url = {https://doi.org/10.1038/s41598-026-60977-x}
}RIS
TY - JOUR TI - Machine learning-assisted performance prediction of graphene-silicon twin-port band-notched wideband antenna for THz 6G communication systems. AU - Datta G AU - Narayanaswamy NK AU - Verma A AU - Singh P AU - Jadapalli S AU - Saini NK AU - Tripathi S AU - Pandey A PY - 2026 JO - Scientific reports DO - 10.1038/s41598-026-60977-x UR - https://doi.org/10.1038/s41598-026-60977-x ER -
APA
G, D., NK, N., A, V., P, S., S, J., NK, S., S, T., & A, P. (2026). Machine learning-assisted performance prediction of graphene-silicon twin-port band-notched wideband antenna for THz 6G communication systems.. Scientific reports. https://doi.org/10.1038/s41598-026-60977-x
Source records
- pubmed · retrieved 2026-09-26T12:58:20.105Z