Study on electromagnetic protection performance of pine pollen-derived carbon-iron oxide cement mortar
Source abstract
Abstract
The rapid proliferation of wireless communication technologies and electronic devices has intensified concerns regarding electromagnetic pollution and information security, presenting serious environmental and public health challenges. In this study, we developed a cement mortar composite material that integrates high mechanical strength with broadband electromagnetic wave (EMW)-absorbing properties, employing biomass-derived carbon (C) (pine pollen-derived C) and iron oxide (Fe 2 O 3 ) as functional fillers. The electromagnetic absorption performance of the composite was evaluated across the 2–18 GHz frequency range, and its underlying protective mechanisms were systematically elucidated. Using the finite-difference time-domain (FDTD) method, we investigated the influence of filler content and specimen thickness on the absorption efficiency of the pine pollen-derived C-Fe 2 O 3 cement mortar. The results reveal that a 25 mm-thick specimen co-doped with 10 % pine pollen-derived C and 15 % Fe 2 O 3 achieves an effective absorption bandwidth of 1.8 GHz (2.7–4.5 GHz) and a minimum reflectivity of −14.7 dB at 3.7 GHz. Increasing the thickness to 30 mm extends the bandwidth to 15.1 GHz (2.9–18.0 GHz), with a peak reflectivity of −20.9 dB at 5.9 GHz. Additionally, a specimen singly doped with 15 % pine pollen-derived C demonstrates nearly full-band absorption at 30 mm thickness, attaining a minimum reflectivity of −20.4 dB. These findings suggest that optimizing specimen thickness enables targeted enhancement of absorption performance within specific frequency ranges, facilitating broadband electromagnetic protection.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Electromagnetic wave absorption materials · Electromagnetic Fields and Biological Effects · Advanced Antenna and Metasurface Technologies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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