ArticleScientific reports2026
Dual-band graphene-based THz MIMO antenna for 6G-enabled biomedical application using supervised regression machine learning validation.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
The exponential increase in wireless data traffic and the growing demand for biomedical sensing have driven the advancement of sophisticated antenna technologies, particularly within the terahertz (THz) frequency range. This research presents an innovative graphene-based microstrip patch antenna featuring a slotted design and MIMO configuration, specifically designed for the high-speed needs of 6G communications and sensitive biomedical applications. The proposed antenna features a compact dual-band structure with a narrowband of 0.2839 THz (1.7654 to 2.0493 THz) and a wideband of 3.8201 THz (2.6412 to 6.4613 THz), achieving a peak gain of 12.83 dB, high efficiency (93.1%), and excellent isolation (-38.8 dB). A novel slot geometry, including hexagonal, triangular, and circular trous slots, enhances impedance matching, bandwidth, and radiation properties. To address the limitations of conventional THz antenna design, the study integrates an RLC equivalent circuit model and machine learning (ML)-driven regression algorithms to optimize performance. The ML-based prediction model validated against simulated data demonstrates high accuracy and significant reductions in design iteration time. Material analyses confirm graphene and polyimide as optimal choices for high-efficiency, miniaturized THz devices. The design progression from single-element to MIMO configurations with silver-based decoupling structures ensures low mutual coupling, making the antenna suitable for high-data-rate, low-latency wireless systems. The comprehensive evaluation encompassing ECC, DG, CCL, TARC, MEG, and radiation characteristics highlights the antenna's potential in both wireless communications and biomedical applications.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.