Evaluating the Efficacy of Microwave Sanitization in Reducing SARS-CoV-2 Airborne Contagion Risk in Office Environments

Abstract

The COVID-19 pandemic has heightened awareness of airborne disease susceptibility, leading to the development and adoption of various preventive technologies. Among these, microwave sanitization, which inactivates virions through non-thermal mechanical resonance, has gained significant scientific credibility. Laboratory tests have demonstrated its high efficacy, prompting further investigation into its effectiveness in real-world settings. This study employs multi-physical, fluid-dynamic and electromagnetic simulations of office environments to evaluate the reduction of contagion risk. By integrating these simulations with virus inactivation experimental laboratory results, we observed that the introduction of a microwave sanitization device significantly reduces the risk of contamination among individuals in the same environment. These findings suggest potential applications and further studies in other everyday scenarios.

1. Introduction

Airborne pathogens are defined by their mode of transmission through the air. Substantial evidence supports that SARS-CoV-2, the virus responsible for the COVID-19 pandemic, transmitted through the air [1,2]. This mode of transmission has been recognized by global health authorities, based on extensive experimental and epidemiological data [3,4]. Notably, SARS-CoV-2 can be introduced into the environment not only through sneezing and coughing but also through normal breathing and speaking, highlighting the pervasive nature of airborne transmission [5,6,7].

Hence, the COVID-19 pandemic has underscored the critical need for effective methods to mitigate the transmission of airborne pathogens in indoor environments [8,9,10,11]. Among various strategies, microwave (MW) sanitation has emerged as a promising technology due to its potential to inactivate viruses, including SARS-CoV-2, on surfaces and in the air [12,13,14]. Structural resonant energy transfer (SRET) methodology enables the inactivation of pathogens using MWs without inducing thermal effects [15,16,17,18]. Pathogens, which consist of an internal core and an outer envelope made of structural proteins and lipids, can be modeled as nano-dimensional condensed matter with core–shell charge separation. It has been demonstrated that exposure to an electromagnetic (EM) field at specific resonant frequencies can cause external disruption of viruses, leading to their inactivation. This technology forms the foundation of air sanitization E4Shield devices (Elettronica SpA, Rome, Italy) designed to eliminate pathogens such as SARS-CoV-2 and H1N1. Its efficacy has been proven in a laboratory environment [19,20,21,22] but not in a real environment.

This study aims to evaluate the efficacy of microwave sanitation in reducing the risk of the airborne SARS-CoV-2 contagion in office environments, introducing the E4Shield device, which is a microwave (MW) sanitization system. Utilizing advanced electromagnetic and fluid-dynamic simulations, the dispersion and inactivation of viral particles under the influence of microwave radiation has been modeled. In fact, Computational Fluid-Dynamic (CFD) studies are crucial to evaluate pathogen transmission and diffusion [23,24,25]. This approach allows for a detailed analysis of the interaction between microwaves and airborne pathogens, providing insights into the effectiveness of this method in real-world office settings. By integrating experimental data with simulation results, we seek to establish a comprehensive understanding of how MW sanitation can be optimized to enhance indoor air quality and reduce the risk of viral transmission.

2. Evaluation Study and Design

This work combines the modeling of relevant real environments, including human beings, EM simulations to evaluate the propagation of the EM field in real scenarios, MW inactivation principles and fluid dynamic simulations to study particle emission and diffusion. In fact, the main aim of this study is to assess the infection risk for susceptible individuals, both in the absence and presence of an E4Shield device.

2.1. Scenario Design

The first step consisted of the design of the environment, including the disposition of the individuals. The Office Laboratory at the Energy Center Lab of the Polytechnic University of Turin was selected as the reference environment (Figure 1). This laboratory comprises a room approximately 5 × 5 m² in size, including an antechamber (airlock), with a total net volume of about 64 m³.

In the office setting, three occupants were considered: two individuals were positioned at desks placed side by side and facing each other, thereby maximizing the potential for direct transmission of contagions. The last individual was situated separately, positioned against a wall, which reduces their risk of direct exposure, as depicted in Figure 2. Among them, the third individual was the SARS-CoV-2 infected individual, and the other two people were susceptible individuals.

2.2. Electromagnetic Simulations

The purpose of the electromagnetic simulations, conducted using CST Microwave Studio 2024 (Dassault Systèmes, Velizy-Villacoublay, France), was to assess the distribution of the EM field within the laboratory office based on the placement of devices. This evaluation aimed to determine the optimal positioning of the device within the actual reference site.

2.2.1. Device Design and Electromagnetic Simulation in Free Space

Initially, we conducted an analysis of the electromagnetic device within a simulation environment. The E4Shield device (Elettronica SpA, Rome, Italy) was meticulously modeled in CST to replicate the actual design of the object, as illustrated in Figure 3.

The antenna designed is a miniaturized patch antenna fed by a microstrip (Figure 4). The pattern of the device was simulated in free space and is shown in Figure 4.

To acquire the near-field data, the field source was evaluated. Utilizing the field source in place of the actual device can significantly streamline subsequent simulations conducted in complex environments.

2.2.2. Electromagnetic Simulation in Real Environment

The previously assessed field source was subsequently integrated into the office environment. Two distinct device locations were evaluated, with the primary aim of the study being to determine the optimal placement to minimize contagions among the individuals present in the office. These two positions, shown in Figure 5, were

• On the main wall, among the two individuals (the infected one and the susceptible one) at 1.7 m from the ground;
• In the middle of the main table, among the two individuals (the infected one and the susceptible one).

The materials for the various elements within the room were selected from the electromagnetic software library. For the structural components, concrete was utilized for the walls and floor, while wood was chosen for the table and seating. Regarding the occupants of the environment, who were represented schematically, muscle tissue was selected as the sole representative tissue due to its primary significance. The conductivity, relative permeability, and tangent loss values of the muscle at various frequencies were obtained from the Institute For Applied Physics ‘Nello Carrara’ (IFAC)-provided library [26] (

Table 1. Dielectric properties of human muscle at different frequencies (from IFAC-CNR).

Frequency [GHz]	Conductivity [S/m]	Relative Permittivity	Loss Tangent
8	7.7978	45.497	0.38511
9	9.1923	44.126	0.41607
10	10.626	42.764	0.44666
11	12.083	41.419	0.47672
12	13.55	40.101	0.50616

).

2.2.3. SAR Evaluation

One of the notable characteristics of this technology is its ability to be used in the presence of humans, thereby effectuating real-time sanitation and reducing the risk of contagion in the event of an infected individual. To validate this approach, the Specific Absorption Rate (SAR) value was evaluated under these specific conditions and compared to the International Commission on Non-Ionizing Radiation Protection (ICNIRP) guidelines [27].

Simulations were conducted to determine the maximum field amplitude at the edge of the nearest subject in two scenarios: the device placed on a table or mounted on a wall. The maximum field amplitude in the subject’s head and trunk was estimated to be 0.48 V/m. Considering conductivity and muscle density, the SAR was calculated to be 0.002 W/kg under these conditions. According to ICNIRP regulations, the SAR should not exceed 2 W/kg for the head and trunk. As the calculated SAR value is significantly below this threshold, the device is compliant with the ICNIRP guidelines.

2.3. Fluid-Dynamic Methods and Simulations

The methodology involves the use of CFD software (STAR-CCM+ software v.2410) to simulate the distribution of viral load emitted by an infected individual within a confined environment. This simulation specifically calculates the viral dose potentially absorbed by a reference susceptible individual over a defined time interval. Following the determination of the viral dose, this data is applied to the Airborne Risk Indoor Assessment (ARIA) calculation model, which has been developed and published by the World Health Organization (WHO) [28]. The ARIA model enables the association of an average probability of infection with the potentially absorbed viral dose (Figure 6).

CFD simulations were conducted using the Star-CCM+ software v.2410 developed by Siemens and licensed to the Department of Energy of the Polytechnic University of Turin. These simulations employed Reynolds-Averaged Navier–Stokes (RANS) equations in a three-dimensional (3D) framework. The study focused on modeling aerosol transport and diffusion within a confined environment to understand the spread of airborne viruses from an infected individual. The simulations were categorized into two main groups based on the modeling approach: the first group utilized a Lagrangian method, while the second group employed an Eulerian method. Simulations employing both methodologies were conducted. The primary objective was to enhance security levels, while the secondary aim was to validate the reliability of the Eulerian approach. This approach is notably faster and demands less computational power, has already been proposed and is a promising candidate for future research extensions [29].

Based on the described geometry, an analysis was conducted for a summer scenario utilizing a mixed radiant cooling and primary air system (mixing ventilation). The boundary conditions used for the simulations are expressed in

Table 2. Boundary conditions for summer conditions.

Season	Characteristic	Value
Summer	T_Human_Head [K]	310
	T_Human_Body [K]	300
	Air_Density [kg/m3]	1.1762
	T_Windows [K]	302
	T_Walls [K]	300
	T_Office [K]	300
	T_Air [K]	298
	Volume_Flow_Rate_X_Fan [m3/s]	0.0333–0.0666

.

The emission from the infected individual was modeled in constant and sinusoidal mode according to the schedule shown in

Table 3. Infected individual’s emission conditions.

Activity	Normal Speaking	Breathing	Normal Breathing
Exhale Velocity [m/s]	Sinusoidal (0–1) and peak of 5	1	Sinusoidal 0–1
Duration [s]	9.2	12.5	40 (20 top of the area)

.

2.3.1. Lagrangian Method

In the Lagrangian approach, the motion and temperature fields within the environment are initially simulated under steady-state conditions. Subsequently, particle input conditions are imposed, with the particle trajectories derived from a temporal dynamic calculation (i.e., using a transient model) based on the balance of forces acting on the particles, specifically aerodynamic forces. This simulation method allows for a more accurate reproduction of the distribution and diffusion of the aerosol particles emitted by an infected individual through breathing, talking, and coughing. However, as the duration of the simulation increases, the computational power and time required grow exponentially.

The particle distribution considered was characterized with the spectrum summarized in

Table 4. Dimensional spectrum of bio-aerosol emission from the infected individual.

Diameter [µm]	<1	1–3.8	3.8–6.6	6.6–14.7	14.7–19.2	90–120	120–800
Emission rate (particle/s)	217.6	20.3	3.1	5.7	1.8	1.6	4.6

, as described in [30,31].

Respiratory droplets are inherently complex, comprising mixtures of water, salts, proteins (e.g., mucin), and biological materials such as virions. Nevertheless, the modelling approach employed in this study simplifies droplets as single-component spherical particles to concentrate on their aerodynamic transport and dispersion behavior rather than their intricate internal composition or biological viability.

The representation of respiratory droplets as spherical particles is widely accepted in the literature, particularly for the purposes of airflow interaction and transport, as demonstrated in previous studies [7,32,33,34,35]. These models have effectively captured the fundamental dynamics of droplet sedimentation and airborne suspension.

Moreover, due to the rapid evaporation of the water content [32,36], the residual droplet nuclei primarily consist of non-volatile materials and virions. Previous studies [5,37] have shown that the trajectory and suspension time of these nuclei are predominantly governed by their aerodynamic size and environmental conditions rather than their detailed chemical composition. Once the water has evaporated, the particle’s effective size stabilizes, and its subsequent motion can be reasonably approximated using a spherical model.

This simplification may constrain the ability to predict virion viability or detailed droplet-phase behavior. However, the primary focus of this study is on modeling transport pathways and exposure potential. In this context, the assumption of spherical droplets offers a computationally efficient and physically reasonable representation.

By employing the Lagrangian approach, the trajectory of emitted aerosol particles can be accurately tracked. This method necessitates transient simulations, which are constrained by the requirement for substantial computational power and extended processing times. In these transient models, the emission from the infected individual is represented using a sinusoidal function, meaning the digital dummy emits particles at intervals with modulated intensity.

2.3.2. Eulerian Method

In the Eulerian approach, the diffusion and distribution of inert gas are simulated by tracking individual particles. The software calculates the steady-state mass balance of the inert gas within the discretized volumes of space. In this method, the observation point for the balances is a control volume through which traverse flows. This approach allows for the simulation and calculation of long-term dynamics under steady-state conditions, i.e., at the end of the transient phase. However, it does not permit the resolution of individual particle trajectories. The behavior of a reference gas molecule is assumed to be unaffected by gravity or aerodynamic forces, diffusing instead by Brownian motion and being transported by advection through the air.

To model the bio-aerosol emission from an infected individual, carbon dioxide (CO₂) gas was utilized, constituting 4% of the total exhaled volume [29,38,39]. A stationary approach was employed to simulate a long-term scenario, maintaining a constant emission velocity of air of 3 m/s from the individual’s mouth.

2.4. The Process and Evaluation of the Electromagnetic Incativation of Viruses

To evaluate the device’s effectiveness in real-world environments, it is crucial to assess the inactivation effectiveness over time, as particle diffusion in the environment is inherently time dependent. For this purpose, experimentally obtained SARS-CoV-2 inactivation results [19,20,21,22] were analyzed as a function of time. The experimentally obtained inactivation percentages are associated with EM field amplitudes greater than 6 V/m for 1, 5 and 10 min of MW exposure and shown in Figure 7.

3. Results

3.1. Electromagnetic Numerical Results

Figure 8 and Figure 9 show the field amplitude values obtained in simulations of the two conditions previously described (device on wall and device on table).

As illustrated in the images, both positions evaluated are critical for achieving an optimal EM field distribution. This optimal distribution is essential for ensuring the field’s effectiveness in pathogen inactivation.

3.2. Fluid-Dynamic Results

This model allows for the determination of the transit time for each individual particle within the electromagnetic wave field generated by the E4Shield device, taking into account the relative probability of virus inactivation in the particle. A sphere with a diameter of 0.5 m, clearly depicted in grey in Figure 10, was used as a reference for the proximity area of the susceptible individual.

The experimentally derived deactivation temporal function (reported in Figure 7) exhibits a very fast action. Nonetheless, it was necessary to investigate the fate of the virus-laden particles during the very first minute of emission from the infected individual’s mouths. During the first minute, the exposure to the MW field is assumed to deactivate from 10% to 90% of the virus particles.

With the Lagrangian model, it was possible to mark a single particle and track its fate, “measuring” the time from emission to the moment it enters the 50 cm diameter respiratory sphere. Focusing on the particles that entered the respiratory sphere in the 120 s since their emission, in Figure 11 the distribution of travel times is depicted for the cooling and heating scenarios. It can be observed that the average time to the respiratory volume is close to 60 s and that a non-negligible proportion of the particles reaches the volume in a shorter time period. The deactivation effect after 40 s of exposure to MWs is assumed to be around 60% (40% survival rate). The analysis of Figure 11 is critical to the evaluation of the reduction in the risk of contagion.

The Eulerian simulations generated concentration contours of the gas in various regions of the room. A limitation of this approach lies in the homogenous dispersion assumption that correlates gas concentration (solved as a scalar transport in the simulations) to the particle spectral concentration and the viral loads. In fact, only the smallest diameter particles have a diffusion and advection behavior very close to the gas. At a short distance and time after emission, the respiratory jet might generate a different distribution of particles than in the case of gas. Nonetheless, the Eulerian approach allows for much longer transient simulations and even steady-state ones, which can be used to analyze the ventilation efficiency of the room.

Figure 12 shows the strong deflection effect of the thermal plume of the emitter individual due to the motion field imposed by the air diffuser.

Table 5. Gas concentrations in the proximity of the near and far susceptible individuals, with and without an active E4Shield placed on the wall or table.

Scenario	E4Shield Position	Nearby Individual		Distant Individual
Summer: radiant system with primary air		CO2 passive scalar	CO2 passive scalar after device operation	CO2 passive scalar	CO2 passive scalar after device operation
	Wall	8.1 × 10−4	0.20 × 8.1 × 10−4	7.2 × 10−4	0.6 × 7.2 × 10−4
	Table	8.1 × 10−4	0.02 × 8.1 × 10−4	7.2 × 10−4	0.6 × 7.2 × 10−4

reports the gas concentrations in the proximity of the two susceptible individuals with and without the E4Shield device (in both placements). Although using gas as a proxy is appropriate for respiratory particles with diameters below 5 microns, it should be noted that the particles analyzed in this study were considered to be up to 800 microns in diameter. This assumption does not contradict the use of gas as a proxy for respiratory particles. More than 94% of the particles had diameters less than 5 microns, while the remaining particles quickly fall to the ground due to gravity. These larger particles do not become suspended in the air and therefore do not contribute to the risk of infection [29,38,40,41].

3.3. Risk Contamination Outcome

By integrating data from both simulations, the aerosol concentrations within the reference volumes of susceptible individuals were determined. From these concentrations, the inhalable amounts over a specified time frame were estimated and the corresponding absorbed doses were calculated, both with and without the effects of the E4Shield device. Utilizing the ARIA model, these doses were then correlated with the probability of infection, which was defined as the risk under analysis. The risk rates, characterized by a margin of uncertainty, are presented as a range encompassing the values derived from both the Eulerian and Lagrangian simulations.

The results obtained are summarized in

Table 6. Summary of E4Shield system de-activation results on the considered scenarios.

Environment Configuration	ARIA Model Risk	ARIA Model Risk—E4Shield on Table	ARIA Model Risk—E4Shield on Wall
Lab Office: mixed radiant ceiling and primary air system, summer case	32%	6–7%	7–8%

.

4. Discussion

The inhalation of aerosols and droplets emitted by infected individuals is the predominant route of respiratory virus transmission for viruses such as SARS-CoV-2. The spread of these droplets is affected by their size and the surrounding environmental conditions.

In this study, fluid-dynamics simulations were employed to examine the dispersion of particles emitted by an infectious individual. Additionally, mathematical models were utilized to assess the infection potential for susceptible individuals. A real scenario has been modeled, and the risk of infection has been proved to be significant.

Two CFD modeling approaches were used: the Lagrangian and Eulerian approaches. The Lagrangian method, which tracks the motion of individual particles within the study environment, provides the detailed descriptions necessary to evaluate the deactivation effect of the E4Shield when the particles have travelled for a short time from mouth to mouth, which characterizes the direct route of the contagion. The Eulerian method is an alternative computational approach, the results of which showed a good agreement with the Lagrangian approach at a much lower computational costs, which allows for longer transient simulations. Nonetheless, the approach lacks detailed descriptions of short distance phenomena.

To estimate the risk of infection with the introduction of an E4Shield sanitizing device into the environment, a series of preliminary electromagnetic simulations has been conducted. The E4Shield device utilizes microwave irradiation to inactivate the respiratory virus responsible for airborne COVID-19 transmission. The microwaves emitted by this device are compatible with the presence of humans, therefore this device can be used in these kinds of scenarios.

Our investigations involved evaluating the electromagnetic field emitted by the device, which was then integrated with fluid-dynamic studies, the ARIA contamination risk model, and experimentally obtained inactivation rates. Two different positions of the E4Shield device were studied, both of which ensured a substantial reduction in the estimated risk of infection thanks to the virucidal effect achieved by the electromagnetic waves. The reduction in risk between these two positions is not significant. This is because, in both scenarios, the device is positioned between the two individuals who are facing each other. Nevertheless, the risk of infection is drastically lowered by the inclusion of the device.

5. Conclusions

In this study, the effectiveness of the E4Shield device in reducing the risk of SARS-CoV-2 contamination was evaluated using simulations of real-world scenarios, with a particular focus on office environments. The evaluation was conducted under a single environmental condition, specifically a summer scenario with a mixed ventilation system.

For future developments, it would be valuable to evaluate various thermofluid-dynamic scenarios within the same type of real environment (i.e., winter cases), considering different emission types from infected individuals. Additionally, assessing environments of different natures, including those with higher crowd densities, could provide further insights. Regarding electromagnetic considerations, more detailed experimental studies on the inactivation of pathogens within subminute timescales are necessary. In addition, it is of particular relevance to this study to conduct experiments that replicate the characteristics of this environment. These experiments will allow for an analysis of the accuracy and validity of the simulations performed.

Regardless, the findings of this work have significant implications for public health, particularly in the context of maintaining safe and healthy workspaces during and beyond the current pandemic. Our research contributes to the growing body of knowledge on innovative disinfection technologies and offers practical solutions for mitigating airborne contagion risks in office environments.