Archive
12 postsIncreasing Incidence of Thyroid Cancer and Use of Smart Phones [Health Matters]
This magazine article discusses the rising incidence of thyroid cancer and raises the possibility of an association with increased smartphone use and related RF EMF exposure near the head and neck. It characterizes EMF exposure from personal electronics as a growing public health concern. The piece calls for more research, monitoring, and public awareness, and mentions precautionary measures.
Rouleaux in Real Time: Ultrasound Evidence, Red Blood Cells, and the S4–Mito–Spin Mechanism
RF Safe argues that red blood cell (RBC) “rouleaux” (stacking/aggregation) could be a visible, testable endpoint for investigating potential short-term physiological effects from wireless device exposure. The post highlights a 2025 report by Brown & Biebrich describing ultrasound observations interpreted as rouleaux-like aggregation after 5 minutes of smartphone placement near the popliteal vein, and contrasts this with earlier, more-criticized “live blood analysis” videos. It frames rouleaux as an electrostatic/zeta-potential phenomenon and calls for mechanistic testing and exposure mitigation, while presenting the ultrasound observation as a key shift toward more clinically standard imaging.
How RF Safe Will Serve Humanity in 2026
RF Safe founder John Coates outlines a 2026 advocacy plan focused on increasing enforcement of U.S. federal radiation-control law, pushing consumer technology toward Li‑Fi as a safer baseline for children, and demanding accountability from companies marketing wireless products to children. The post argues that current RF safety rules rely too heavily on “thermal-only” assumptions and that cumulative exposure from smartphones is a preventable risk. It also states RF Safe intends to pursue legal action to compel HHS to fulfill duties under the Radiation Control for Health & Safety Act (Public Law 90-602).
This piece does not argue that radiofrequency (RF) electromagnetic fields “cause” any single disease.
An RF Safe commentary argues that persistent, pulsed “non-native” RF electromagnetic noise can disrupt biological “timing coherence,” leading to downstream “fidelity losses,” particularly in electrically active tissues. It also emphasizes that smartphones are adaptive RF systems that change transmit power and modulation, so accessories that detune antennas or distort near-field conditions may cause phones to transmit harder. The piece cites FTC warnings that partial-shield products can be ineffective and may increase emissions by interfering with signal quality, and it argues that material shielding claims do not directly translate to real-world exposure outcomes.
Density‑Gated Spin Engines: Why the 5G Skin‑Cell Null Fits the Heme/Spin Extension
This RF Safe commentary argues that non-thermal RF/5G effects may vary by tissue based on the density of specific biological “targets,” such as voltage-gated channel S4 helices, mitochondrial/NOX ROS capacity, and heme/flavin “spin chemistry” substrates. It claims that reported null findings in 5G mmWave skin-cell studies can be reconciled with reported red blood cell (RBC) rouleaux observations by proposing a “density-gated” mechanism where spin-related effects are more detectable in heme-dense cells like RBCs. The post cites an ultrasound study (named “Brown & Biebrich”) as showing in-vivo rouleaux changes within minutes near a smartphone, but provides limited methodological detail in the excerpt.
Why the 2025 “5G Skin-Cell Null” Actually Confirms the Density-Dependence of Both Pillars of the Unified Framework
RF Safe comments on a 2025 PNAS Nexus study (Jyoti et al., 2025) reporting no detectable changes in gene expression or methylation in 5G millimeter-wave–exposed human skin cells. The post argues that this “null” result does not indicate biological inertness, but instead supports the site’s proposed “dual-pillar” framework in which effects depend on cell-specific cofactor density and frequency-window/coupling conditions. It contrasts skin-cell findings with claims about rapid blood (RBC) effects from smartphone exposure, presenting this as consistent with differential susceptibility across tissues.
Associations between Individual and Geospatial Characteristics and Power of 4G Signals Received by Mobile Phones
This exposure assessment study analyzed smartphone-logged 4G LTE RSSI and GPS data from adults in France to identify determinants of downlink signal strength. RSSI varied with geospatial factors (distance to antennas, antenna density, urbanicity) and time of day, and was also influenced by technical smartphone parameters. The study reports an estimated electric field strength derived from RSSI, but notes high uncertainty in this conversion.
Smartphone Usage Patterns and Sleep Behavior in Demographic Groups: Retrospective Observational Study
This retrospective observational study analyzed Murmuras app data from 1074 participants in 2022 to examine demographic differences in smartphone use and nocturnal smartphone inactivity duration (a proxy for sleep-related behavior). Nighttime smartphone use increased, especially for social media and entertainment, and usage patterns varied by gender, age, education, and employment status. Most demographic groups showed no significant correlation between usage duration and nocturnal inactivity, although some subgroups showed correlations in either direction. The authors frame excessive nighttime smartphone use as potentially adverse for sleep and link this behavioral exposure to electromagnetic fields with sleep health risks.
Impact of magnetic fields from tablets, laptops, smartphones, and household/leisure magnets on cardiac implantable electronic devices
This study tested magnetic fields from tablets, laptops, smartphones, and household/leisure magnets against 13 cardiac implantable electronic device (CIED) models to assess magnet mode activation. It reports that these consumer devices can trigger magnet mode when in close proximity, with median activation distances of 5–6.5 mm for phones/tablets/laptops and mainly contact-level activation for household/leisure magnets. None of the tested devices activated magnet mode at distances of 20 mm or more, and the authors emphasize patient awareness of proximity-related risk.
Determining the relationship between mobile phone network signal strength and RF-EMF exposure: protocol and pilot study to derive conversion functions
This protocol and pilot study evaluated whether smartphone signal strength indicators can be converted into RF-EMF exposure estimates using derived formulas and regression models. The study reports a positive log-linear association between LTE RSSI and far-field (base station) exposure aggregated by location, while handset-related exposure at the ear and chest during data transmission showed negative log-linear trends with improving signal quality. The authors conclude the ETAIN 5G-Scientist app may support large-scale RF-EMF exposure assessment, but emphasize the need for more data to improve accuracy and address uncertainties in individual measurements.
RF-EMF exposure assessment with add-on uplink exposure sensor in different microenvironments in seven European countries
This exposure assessment study introduces a cost-efficient add-on sensor attached to a smartphone to quantify auto-induced uplink RF-EMF transmission across 100–6000 MHz in multiple microenvironments. Activity-based surveys were conducted in seven European countries under non-user, maximum downlink, and maximum uplink scenarios. Reported power levels were lowest for non-user scenarios and higher during active use, with variation by country, urbanization, and setting. The authors frame the work as supporting future epidemiological research and planned validation against other tools.
The Effect of Proximity Sensor & Grip Sensor Use on Specific Absorption Rate (SAR) in Smartphones
This engineering study examined how smartphone proximity and grip sensors affect SAR during LTE and 5G NR operation in a 3D measurement environment. The abstract reports that enabling these sensors reduces SAR relative to being turned off, with reductions varying by sensor and frequency. The authors attribute the reduction to sensor-driven power management and transmission power adjustment.