The use of an ultrasonic scaler in the dental diagnosis and treatment process can produce a large number of droplets due to splashing, which can be transformed into droplet nuclei in the form of aerosols suspended in the air of the clinic, and droplets and aerosols may carry pathogenic microorganisms that pose a great threat to the health of clinical staff and patients. This paper takes a dental clinic as the research object and adopts experimental and numerical simulation methods to study the splashing droplet flow during ultrasonic dental cleaning surgery. It explored the similarities and differences in the spatial flow characteristics of droplets caused by different operation modes in the ultrasonic dental cleaning process. The results showed that the large droplets (particle size >100 μm) caused by the ultrasonic dental cleaning machine could be spread to the dangerous range of the patient's surroundings at 2.5 m. The droplets could be spattered to the patient's oral cavity at 1.5 m above the room, and the room was contaminated with a space of 17.5 m3. The droplets were concentrated, and the smaller droplets particles were more concentrated in the 0.75 m of the patient's oral cavity. The concentration of the small droplet particles (particle size 1–100 μm) of the splash height can be up to the clinic's ceiling in the air for a long time, and the contamination range can be expanded to the entire clinic. This study will provide strong guidance for developing microbial adsorption and elimination equipment for dental clinics and formulating new prevention and control opinions for dental clinics.

The world has been threatened by several new and re-emerging major infectious diseases in the last decade, such as tuberculosis, severe acute respiratory syndrome (SARS), avian influenza, swine influenza (H1N1), and a novel coronavirus (COVID-19). The outbreak of the novel coronavirus and its variants at the end of 2019, which is still ravaging the world, has claimed millions of lives, and the seizures triggered by the disease have imposed a severe public health and socioeconomic burden (Jalili , 2021). Dental clinics are health-care organizations particularly vulnerable to respiratory infectious diseases (Komperda , 2021). The use of ultrasonic scalers, a commonly used therapeutic instrument during dental practice, generates a large amount of droplets (Gambhir , 2016), as shown in Figs. 1(a) and 1(b). The number of bacteria in the air increases by 30 during ultrasonic scaling (Larato, 1967). If a patient carries blood-borne viruses or respiratory-borne viruses in the oral cavity, these viruses are transmitted in the air using droplets as carriers. Viruses can survive over 3 h after leaving the body (Vuorinen , 2020). Dentists and nurses will be close to patients and completely exposed to these droplets for a long time when performing oral scaling on patients. Even if a small amount of the virus enters the respiratory tract of a healthy patient, it is very likely to cause an infection (Jayaweera , 2020; Vuorinen , 2020). It, therefore, has a significant impact on the health of health-care workers (Zhou , 2018).

FIG. 1.

(a) Ultrasonic scaling machine; (b) large number of droplets caused by the operator performing ultrasonic scaling; (c) the operator performing the procedure in the dental office wearing only normal protective clothing; and (d) additional adsorption decontamination equipment in the office.

FIG. 1.

(a) Ultrasonic scaling machine; (b) large number of droplets caused by the operator performing ultrasonic scaling; (c) the operator performing the procedure in the dental office wearing only normal protective clothing; and (d) additional adsorption decontamination equipment in the office.

Close modal

Currently, the standard protective measures in healthcare facilities are wearing protective equipment by health-care workers, hanging of ultraviolet light disinfection equipment in the clinic (Klompas , 2022), and increasing ventilation (Somsen , 2020). Due to the relatively closed nature of dental offices and the large number of droplets that are consistently generated during procedures, the ability of these generic protective measures to reduce the risk of infection is minimal. To reduce nosocomial infections, a dental hospital in Guiyang City improvised and put into use a droplet adsorption and elimination device for dental offices during the spread of a novel coronavirus (COVID-19), as shown in Figs. 1(c) and 1(d). However, due to the lack of research on the mechanism of droplet flow, the equipment was used with problems such as incomplete droplet adsorption, high noise level, and a non-ergonomic design that affected the doctor's surgery.

Bacterial contamination from splashes is often analyzed in medical research using air sampling (Zemouri , 2017) or culture media collection. To further reveal the extent of contamination of doctor's surfaces by splash particles during dental treatment, Biradar (2014) also analyzed the extent of contamination caused by droplets based on the degree of adherence of the spatter to the paper sheet by using fluorescein labeling method. Fluorescein labeling experiments are generally prohibited for human subjects, and the above methods mainly focus on the extent of contamination at a few sampling points in the clinic and lack of studies on the flow characteristics of droplets of different particle sizes. In medical protection, the use of mouthwash such as chlorhexidine (0.12%–0.20%) before treatment can reduce the number of bacteria by more than 60% (Retamal-Valdes , 2017); the use of strong suction equipment during treatment can reduce aerosols by more than 90%; and the simultaneous use of the two measures can effectively reduce the bacterial contamination of the clinic, but it is still higher than the limit value of 500 CFU/m3 (Marui , 2019).

It is widely recognized that no single measure can completely control dental splash contamination, and developing adsorption and abatement equipment for dental practice are essential. Characterizing the spatial flow of droplets in the dental office environment plays a crucial role in developing more effective droplet adsorption and abatement equipment and guiding infection control policies and practices in the dental practice environment. The CFD numerical approach provides a convenient framework for studying the spread of virus-carrying droplets. As a result, many researchers have attempted to detect virus transmission pathways through numerical modeling (Chen , 2020; Meccariello and Gallo, 2020; Shah , 2020; Rockett , 2020; Enserink and Kupferschmidt, 2020; Ivorra , 2021; Chaudhuri , 2020; and Talaat 2021). Table I presents an overview of recent studies on aerosol and droplet flows in different environments using numerical CFD methods. Most of these studies have focused on droplet and aerosol dispersion induced by human breathing and coughing in public settings using discrete phase modeling (DPM). In contrast, fewer studies have been conducted on droplet sprays caused during dental procedures. Droplet splashes induced by powered machinery in dental practice differ in flow trajectory, splash duration, particle size range, and spatial distribution compared to the droplet flow caused by natural human breathing and coughing (Klompas , 2020).

TABLE I.

List of the most recent investigations that employed a combination of CFD and particle science.

No. Model Droplet size (μm) Domain Turbulence model Target Reference
Unsteady DPM  1–50  Fever clinic  RNG k–ε  Breathing simulations  Zhou and Ji (2021)  
Unsteady DPM  1–50  Classroom  RNG k–ε  SARS-CoV-2transmission  Abuhegazy (2020)  
Unsteady DPM  2.5  School buses  RNG k–ε  Particle flows  Li (2015)  
Unsteady DPM  1–100  Air-conditioned room  Wale  Breathing simulations  Zhang (2019)  
Unsteady DPM  1–50  A large dental clinic  Realizable k–ε  Aerosol delivery  Komperda (2021)  
Unsteady DPM  1.5  Restaurant  SGS  SARS-CoV-2transmission  Wu (2020)  
No. Model Droplet size (μm) Domain Turbulence model Target Reference
Unsteady DPM  1–50  Fever clinic  RNG k–ε  Breathing simulations  Zhou and Ji (2021)  
Unsteady DPM  1–50  Classroom  RNG k–ε  SARS-CoV-2transmission  Abuhegazy (2020)  
Unsteady DPM  2.5  School buses  RNG k–ε  Particle flows  Li (2015)  
Unsteady DPM  1–100  Air-conditioned room  Wale  Breathing simulations  Zhang (2019)  
Unsteady DPM  1–50  A large dental clinic  Realizable k–ε  Aerosol delivery  Komperda (2021)  
Unsteady DPM  1.5  Restaurant  SGS  SARS-CoV-2transmission  Wu (2020)  

Komperda (2021) attempted to carry out CFD numerical simulations of droplet distribution during ultrasonic scaling in an actual dental clinic, focusing on the residence time of the droplets (particle size <50 μm), droplet concentration, and the number of droplets escaping through the vent. However, the angle of the DPM jet source and the particle velocity settings in the simulation were relatively homogeneous and did not consider the differences in droplet flow induced by the ultrasonic scaler in actual dental surgery when treating different parts of the teeth under other working conditions. The difference in the initial exhalation velocity had a more significant impact on the flow of particles (Chen and Zhao, 2010). The analysis of the spatial flow characteristics of large droplets (particle size >100 μm) is missing in the study. Large droplet particles (particle size >100 μm) generated during dental procedures settle rapidly. They are one of the most important factors causing a significant risk of infection to the surrounding healthcare workers. If adsorption is not carried out promptly, large droplet particles evaporate and become aerosol particles suspended in the air, posing an infection risk that continues to affect the healthcare workers and patients who enter the clinic next. In addition, the accuracy of the numerical modeling in this study has not been experimentally verified in detail.

Li (2021) attempted to analyze the flow field and velocity of large droplet particles (>100 μm in size) induced by ultrasonic dental scaling procedures in the XY two-dimensional plane using the particle image velocity measurement in an attempt to provide data to support the analysis of the spatial distribution of the droplets using CFD numerical simulation. However, only the mandibular mesial incisors were analyzed in this study, and the experimental variables were too homogeneous. Moreover, the ultrasonic scaler was fixed on the tooth surface with improved power and was not operated by a professional doctor. Therefore, the droplet flow induced during the experiment lacked realism compared to the flow generated during the surgical procedure.

Therefore, the current study of droplet flow caused by power machinery in oral diagnosis and treatment must be revised to provide data support for developing droplet adsorption and elimination equipment for oral diagnosis and treatment. In this study, the PIV particle velocimetry system was used to study the ultrasonic scaler used in the dental scaling procedure and the flow of droplets in the XY plane triggered by the ultrasonic scaler using different vibration frequencies to treat other tooth areas in the human oral cavity model was fully recorded in the clinic. Then, a numerical model was established for the experimental scenario, and the droplets' representative initial velocity and diffusion angle were selected as the initial set values of the spray source in combination with the experimental data. The boundary conditions of the numerical model are consistent with the experimental conditions. CFD is used to analyze the spatial flow characteristics of droplets with different particle sizes (1–1000 μm) under different treatment modes. Finally, the model's accuracy was verified using the droplet flow data obtained in the experiment.

The conclusions reveal the similarities and differences in the spatial flow characteristics of droplets caused by different modes of operation during ultrasonic scaling, which will provide more robust guidance for developing microbial adsorption and elimination equipment for dental clinics and formulating new preventive and control opinions for dental clinics. The conclusions of this paper are also helpful for the study of droplet flow in other types of dental procedures.

Accurate prediction of airflow organization within a dental office is a prerequisite for investigating droplet flow characteristics, and the k–ε two-equation model in the Reynolds averaging vortex viscosity model is more commonly used in simulations for predicting the human boundary layer and surrounding flow field. Among them, for the simulation of airflow organization in confined spaces (Liu , 2013), the RNG k–ε model has higher accuracy. The RNG k–ε turbulence model is similar to the standard k–ε model. Still, the RNG k–ε turbulence model predicts better than the standard k–εmodel turbulence model in the case of complex shear flows, flows containing high shear rates, and cyclonic and separated flows. Also, Chan , (2002) stated that the RNG k–ε model performs slightly better in predicting the strength of the central eddies compared to the standard k–ε model, the turbulence models of the realizable k–ε model, the Reynolds Stress Model (RSM), the k–ω SST model, and the large eddy simulation (LES). The RNG k–ε model has good accuracy in predicting the flow in turbulent core regions with high Reynolds numbers, and the RNG k–ε model has the advantages of simplicity, reliability, and accuracy. The enhanced wall treatment is used to predict the solid phase flow field (Al Assaad , 2019). The improved wall treatment ensures the validity of the model's wall shear stresses due to velocity and temperature gradients (Belhoucine , 2004). A study by Ramponi and Blocken (2012) found that the RNG k–ε turbulence model is also well suited for flow field simulations in the current environment.

Therefore, this paper solves the RANS equations using ANSYS Fluent 2022R1 (ANSYS, 2022) commercial software package. The SIMPLE algorithm is used to couple pressure velocity and first-order pressure interpolation. The convective and viscous terms of the control equations are discretized using the second-order discrete format. The RNG k–ε turbulence model was selected to calculate the flow field in the dental consulting room, and the expression of the control equations is
t ( ρ k ) + x i ( ρ k u i ) = x j ( α k μ i k x j ) + G k + G b ρ ε Y m + S k ,
(1)
( ρ ε ) t + x i ( ρ ε u i ) = x j [ ( α ε μ t ) ε x j ] + C 1 ε ε k [ ( G k + C 3 ε G b ) ] C 2 ε ρ ε 2 k R ε + S ε ,
(2)
where C 1 ε = 1.42; C 2 ε = 1.68; C 3 ε = tanh | v c u c |; and α k = α ε = 1.39.
The wall has a noticeable influence on the turbulent flow along the average direction of the wall; the turbulence is divided into the near-wall zone, the fully developed core zone, and the RNG k–ε model for the fully developed turbulence for the near-wall area; this paper selects the standard wall function method; at this time, it is necessary to introduce the dimensionless distance y +; and the expression is
y + = Δ y v τ w ρ .
(3)

The grid is refined near surfaces to maintain a wall 15< y +<30 (Liu and Niu, 2019; Sheikholeslami , 2018; and Ai and Mak, 2013) during the simulations. It has good performance and saves computation time. The definitions of the above parameters are listed in Table II. In order to verify whether the value of y + in the grid meets the calculation requirements, in this paper, two kinds of wall boundaries that have great influence on the calculation results are selected in the YZ plane of the 3D model for analysis, which are wall 1 and wall 2. As shown in Fig. 2, wall 1 is the wall inside the clinic and wall 2 is the surface of the human body. As shown in Fig. 3, the height of the first layer of wall 1 is in the range of 8.51–8.09 mm, the fluid is air, and the range of y + is 28–30. The height of the first layer of wall 2 is in the range of 5.96–5.68 mm; the fluid is air; and the range of y + is 20–21. Therefore, the value of y + in the model meets the requirements of the calculation.

TABLE II.

The definition of parameters.

Parameters Meanings
k  Turbulent kinetic energy 
u i  Mean velocity 
μ i  Eddy viscosity 
ρ  Air density 
Time 
x i x j  Coordinate direction 
G k  Velocity gradient 
G b  Turbulent kinetic energy-induced by buoyancy 
Y m  Diffusion-induced fluctuations 
v c  The velocity component-parallel to the gravity vector 
u c  The velocity component-perpendicular to the gravity vector 
S k S ε  User-defined source items 
Δ y  Distance from wall 
τ w  Wall shear stress 
v  Kinematic viscosity 
Parameters Meanings
k  Turbulent kinetic energy 
u i  Mean velocity 
μ i  Eddy viscosity 
ρ  Air density 
Time 
x i x j  Coordinate direction 
G k  Velocity gradient 
G b  Turbulent kinetic energy-induced by buoyancy 
Y m  Diffusion-induced fluctuations 
v c  The velocity component-parallel to the gravity vector 
u c  The velocity component-perpendicular to the gravity vector 
S k S ε  User-defined source items 
Δ y  Distance from wall 
τ w  Wall shear stress 
v  Kinematic viscosity 
FIG. 2.

Cross-sectional view of the flow field grid in a dental office.

FIG. 2.

Cross-sectional view of the flow field grid in a dental office.

Close modal
FIG. 3.

Wall boundary value of y +.

FIG. 3.

Wall boundary value of y +.

Close modal

The DPM model employs the Euler–Lagrange method, solving the continuous phase using the Navier–Stokes equations. In contrast, the discrete phase is solved by tracking many droplet streams through a mean velocity flow field (Ansys, 2019). As shown in Table I, the DPM model in Fluent has been used in several recent prominent numerical-numerical studies of virus-carrying droplet dispersion and evaporation (Zhao , 2021; Talaat , 2021; Zhang , 2019; and Heck, 2021). The DPM model was highly accurate and economical in predicting human-generated droplet flow compared to other Eulerian–Lagrangian models.

Therefore, in this paper, the indoor air is used as a continuous medium, the particles are used as a discrete medium, and a discrete phase model (DPM) model using ANSYS Fluent 2021R1 is adopted to track the propagation trajectory of droplets during ultrasonic dental cleaning. The force equilibrium analysis of the discrete-phase particles was carried out, and according to Newton's second law, the inertial force of the particles is equal to other forces acting on the particles, which leads to the equation of motion of the mass point in the x-direction in the Carter coordinates as
d u p d t = F D ( u a u p ) + g ( ρ p ρ a ) ρ p + F x .
(4)
F D ( u a u p ) is the drag force due to the relative sloshing of air and particles, F D described as
F D = 18 μ C D R e p ( u u p ) 24 ρ p d p 2 .
(5)
R e p is the particle Reynolds number, described as
R e p = ( u u p ) d p / μ ,
(6)
where g is the acceleration of gravity and ρ p and ρ a are the densities of the particles and air, respectively. u p is the particle velocity; t is the time; u is the air flow rate; F x is the additional force, which mainly consists of Saffmanlift and thermoswelling force on the particles; μ is the dynamic viscosity of air; d p is the particle diameter; C D is the traction factor; considering the effect of turbulent pulsation on particle diffusion; and the random orbit model was chosen to describe the motion of the particulate matter (Chao and Wan, 2006).
To simplify the calculations, the evaporation model was simplified to a single component by disregarding the effect of the content of nonvolatile components on the molar concentration of vapor at the droplet surface. The evaporation rate of a droplet is determined by gradient diffusion, and the flux of droplet water vapor into the gas phase is related to the water vapor concentration gradient between the droplet surface and the native gas (Chen , 2010),
d N d t = c ( C s C ) ,
(7)
where
C s = P sat ( T p ) R T p ,
(8)
C = X i P op R T ,
(9)
where c is the mass transfer coefficient, estimated from the correlation at the air–water interface (Aissa , 2015), and are the molar concentrations of water vapor on the surface of the droplet aerosol and in the carrier phase, gmol/ m 3.

The dental surgery was modeled in CATIA based on the physical dimensions of the actual dental surgery. The dimensions of the office are 7.0 × 5.0 × 3.0 m3 (X × Y × Z) with a total area of 35  m 2, and the office is equipped with an integrated dental treatment bed. The integrated dental unit is the main equipment section for dental procedures and consists mainly of a dental chair, a lamp, spittoon table, high and low volume evacuation systems, and a water supply system. The integrated dental treatment unit is equipped with surgical instruments such as high-speed hand-pieces, low-speed hand-pieces, and ultrasonic scalers. The patient is reclined in a treatment chair, and the dentist sits behind the patient to perform the appropriate examination, during which droplets are expelled from the patient's mouth and dispersed into the air. The geometric model has been set up to correspond to the experimental environment as a confined space where heat and contaminants would escape on contact with the walls and floor.

Due to the complexity of the geometric model, in order to ensure the accuracy of the simulation results, the computational domain of the consultation room was Discretized using ANSYS Fluent Meshing (ANSYS, 2022) to generate a polyhedron unstructured mesh of the computational domain. The physical model of the consultation room was simplified, and the basin was created to improve mesh quality and computational economy without compromising computational accuracy, as shown in Fig. 4. The numerical model was divided into a total of 5.9 × 10 6 mesh cells with a minimum cell size of 0.5 cm and a maximum cell size of 10 cm with a gradual transition. The maximum slope is 0.756 (mean 0.456), and the maximum aspect ratio is 3.36 (mean 1.35).

FIG. 4.

Basin setting for dental office. (Patient reclining in treatment chair in consulting room, doctor sitting behind patient, dashed area is area at high risk of infection, office dimensions 7.0 × 5.0 × 3.0 m3, office is closed environment, no mechanical ventilation.).

FIG. 4.

Basin setting for dental office. (Patient reclining in treatment chair in consulting room, doctor sitting behind patient, dashed area is area at high risk of infection, office dimensions 7.0 × 5.0 × 3.0 m3, office is closed environment, no mechanical ventilation.).

Close modal

Considering the accuracy of the simulation calculation of microbial aerosol concentration around the human body, the number of grids should be increased appropriately. The gridding is sparser in the region where the velocity gradient changes less due to the smooth change of the associated physical information. The grid independence study is carried out in the absence of droplet motion. Three different grid quantities of 8.5 × 10 6, 5.9 × 10 6, and 2.9 × 10 6 were established, respectively. Grid independence analysis was assessed by comparing the magnitude of airflow velocity above the patient's mouth (data lines A–B shown in Fig. 2). As shown in Fig. 5, a grid number of 5.9 × 10 6 was used in this paper to balance the computational cost and the accuracy of the results due to the slight difference between the fine and medium grids.

FIG. 5.

Grid independence analysis.

FIG. 5.

Grid independence analysis.

Close modal

The accuracy of the CFD numerical simulation results depends to a large extent on the setting of the boundary conditions in the consultation room. The room was set to be clean prior to the start of the ultrasonic scaling treatment, with a room temperature of 300 K and a relative humidity of 50  %. The air flow induced by the ultrasonic scaling was 289 K (Morawska , 2009), and the relative humidity was 90  % (Graham and James, 1996). The initial jet source was set to be a droplet aerosol particle consisting of 98.2  % water and 1.8  % solids. Taking evaporation into account, the mean diameter of the particles after complete evaporation, dep, satisfies dep = 0.26  d 0, where d 0 is the initial diameter of the spray source particles (Chao and Wan, 2006). The particle size range of aerosol droplets produced by a single treatment is 1–1000 μm, with particles larger than 50–100 μm settling rapidly under the influence of gravity, and particles with a particle size of 1–50 μm remaining suspended in air for a longer period of time. The particle diameter that can be captured by filming experiments is 100 μm or larger. To simulate the flow trajectory of particles of different sizes, the initial particle size range of the jet source was set to 1–1000 μm. The total number of droplet aerosol particles of different particle sizes produced by the jet source during a single treatment was set to 4000 (Lindsley , 2012).

The mean values of the droplet flow data triggered by the treatment of the six major dental regions in the oral cavity using the three powers of the ultrasonic scaler, respectively, are shown in Figs. 6 and 7. According to the experimental data, as shown in Fig. 8, three types of initial spray diffusion angles were set, namely, 30°, 45°, and 90°. In this paper, the initial spray velocities of the droplets induced by the ultrasonic scaler working in low, medium, and high gears were 2, 5, and 8 m/s, respectively. As shown in Table III, cases 1–9 were used to simulate the flow trajectory and diffusion of droplets induced by different treatment situations by setting different jet source parameters. The geometric model was set up to be consistent with the experimental environment without mechanical ventilation, as the droplets escaped upon contact with the walls and the human body, considering that different ventilation methods in the consulting room would affect the droplet flow. The finite volume method and SIMPLE algorithm were employed using Ansys Fluent software. In order to improve the accuracy of the calculations, the second-order windward format was chosen for the momentum, turbulent dissipation rate, and convective terms of the turbulent kinetic and energy equations, and the default settings were selected for the iterative relaxation factors. The control equations are solved iteratively until the residuals of the continuity, momentum, and energy equations reach 10 5 , 10 5, and 10 6, respectively, then the computation converges. The model parameters are listed in Table IV, the specific boundary condition settings are listed in Table III, and the solver settings are listed in Table V.

FIG. 6.

Mean values of droplet flow velocities induced by treatment of six dental regions in the oral cavity using three powers of an ultrasonic scaler.

FIG. 6.

Mean values of droplet flow velocities induced by treatment of six dental regions in the oral cavity using three powers of an ultrasonic scaler.

Close modal
FIG. 7.

Mean values of the initial spreading angle of droplets triggered by treatment of six dental regions in the oral cavity using three powers of an ultrasonic scaler.

FIG. 7.

Mean values of the initial spreading angle of droplets triggered by treatment of six dental regions in the oral cavity using three powers of an ultrasonic scaler.

Close modal
FIG. 8.

Dental office droplet spray source angle settings (front view). (a)–(c) are three typical initial spreading angles of droplets in the experiment. (d)–(f) are the initial droplet dispersion angles of 30°, 45°, and 90° respectively. (h)–(j) are the three typical initial droplet flow velocities in the experiment.

FIG. 8.

Dental office droplet spray source angle settings (front view). (a)–(c) are three typical initial spreading angles of droplets in the experiment. (d)–(f) are the initial droplet dispersion angles of 30°, 45°, and 90° respectively. (h)–(j) are the three typical initial droplet flow velocities in the experiment.

Close modal
TABLE III.

Simulation cases.

Case No Diffusion angle (°) Injection velocity (m/s) Temperature (K) Injection duration (s)
Case 1  30  284 
Case 2  30  284 
Case 3  30  284 
Case 4  45  284 
Case 5  45  284 
Case 6  45  284 
Case 7  90  284 
Case 8  90  284 
Case 9  90  284 
Case No Diffusion angle (°) Injection velocity (m/s) Temperature (K) Injection duration (s)
Case 1  30  284 
Case 2  30  284 
Case 3  30  284 
Case 4  45  284 
Case 5  45  284 
Case 6  45  284 
Case 7  90  284 
Case 8  90  284 
Case 9  90  284 
TABLE IV.

Indoor related parameters.

Name Number Size (m) Temperature (K) Boundary condition Setting point
Fever clinic  7.0 × 5.0 × 3.0  300  Escape  Convective heat transfer coefficient: 5 W / ( m 2 K ) 
Indoor occupant  0.3 × 0.4 × 1.7  310  Escape  Heat flux density: 20 W / m 2 
Dental chair  0.3 × 0.45 × 1.76  285  Escape  ⋯ 
Name Number Size (m) Temperature (K) Boundary condition Setting point
Fever clinic  7.0 × 5.0 × 3.0  300  Escape  Convective heat transfer coefficient: 5 W / ( m 2 K ) 
Indoor occupant  0.3 × 0.4 × 1.7  310  Escape  Heat flux density: 20 W / m 2 
Dental chair  0.3 × 0.45 × 1.76  285  Escape  ⋯ 
TABLE V.

Solver settings.

Setting items Parameter setting
Turbulent flow model  RNGk–ε 
Wall functions  Standard law of wall 
Spatial discretization  Secondorder upwind scheme 
Computing method  SIMPLE 
Setting items Parameter setting
Turbulent flow model  RNGk–ε 
Wall functions  Standard law of wall 
Spatial discretization  Secondorder upwind scheme 
Computing method  SIMPLE 

In this paper, experiments were carried out in a simulated dental clinic, the dimensions of the laboratory are 7.0 × 5.0 × 3.0 m3 (X × Y×Z), and the Woodpecker USD-E ultrasonic scaler was selected to analyze the dynamics of droplet flow triggered by the ultrasonic scaler in the periodontal diagnosis and treatment. A high-frequency oscillation circuit generates the ultrasonic scaler. It acts on the ultrasonic transducer, the use of inverse piezoelectric effect (or magnetostrictive effect) to cause ultrasonic vibration (vibration frequency of 28–30 kHz), the resonance stimulates the tip of the work, the use of ultrasonic waves generated by a variety of effects will be the surface of the tooth plaque, calculus, or periodontal surface of the bacteria and other removals. At the same time, the cleaning solution is released through a small hole in the bend of the scaler's tip (water flow rate is about 55 ml/min) to clean the tooth surface. The high vibration energy of the information atomizes the mixture of cleaning solution and saliva. Many tiny droplets and aerosols will be discharged from the mouth and mixed with the patient's dental calculus, blood, saliva, etc. The ultrasonic scaler comprises the central unit, handle, working tip, foot switch, and power adapter.

The experiments were conducted in a simulated dental office with laboratory dimensions of 7.0 × 5.0 × 3.0 m3 (X × Y×Z). A USD-E ultrasonic scaler was used in the experiments. The scaler tip was excited to resonate (vibration frequency of 28 kHz) to remove plaque, calculus, or bacteria from the tooth surface using different effects of ultrasound, while the cleaning solution was released through a small hole in the bend of the scaler tip (water flow rate of ∼55 ml/min) to clean the toot-h surface. The high vibrational energy of the tip atomizes the mixture of cleaning solution and saliva. A large number of small droplets and aerosols are expelled from the mouth and mix with the patient's tartar, blood, saliva, etc.

The droplet flow induced by the ultrasonic scaler during periodontal treatment is swift. Therefore, a high-speed camera was used to record the droplet flow. The camera's performance must be high to ensure that a clear droplet flow is captured for data analysis. Cameras according to the chip type, industrial cameras can be divided into CCD and CMOS cameras. CMOS image sensors have the advantages of low cost and low power consumption. Still, CCD image sensors have better performance in terms of image quality and higher sensitivity than CMOS (Itonaga , 2011). A high-speed camera (IDT Y4-S2) was selected considering the actual demand and subsequent use needs. To capture the high-speed flow of droplets, the choice of light source usually needs to be based on the specific requirements of the shooting field of view, the site environment, and other circumstances for careful consideration. Due to the fast flow of droplets, the exposure time of the camera is set short, so choosing a specific type of light source is necessary to make the shooting field of view obtain enough light and ultimately select a high-brightness white 120 W LED light source. The main parameters of the experimental equipment are set as shown in Table VI.

TABLE VI.

Main parameter settings of the experimental equipment.

Equipment name Main parameters Parameter values
Experimental light sources  Type of light source  LED white 
Light source power  120W 
Color temperature  6600 K 
Camera  Sensor type  CMOS—Polaris II 
Sensor size  13.9 × 13.9 mm2 
Maximum resolution  1016 × 1016 
Maximum frame size  13.68 × 13.68 μm2 
Minimum exposure time  1 μ
Lens  Output tip main vibration offset  F2.8 
Focal length range  24–85 mm 
Angular range of view  28°–84° 
Ultrasonic scaler  Output tip main vibration excursion  1–100 μm2 
Output tip vibration frequency  28–30kHz 
Output half offset force  0.1–2 N 
Output power  3–20 W 
Equipment name Main parameters Parameter values
Experimental light sources  Type of light source  LED white 
Light source power  120W 
Color temperature  6600 K 
Camera  Sensor type  CMOS—Polaris II 
Sensor size  13.9 × 13.9 mm2 
Maximum resolution  1016 × 1016 
Maximum frame size  13.68 × 13.68 μm2 
Minimum exposure time  1 μ
Lens  Output tip main vibration offset  F2.8 
Focal length range  24–85 mm 
Angular range of view  28°–84° 
Ultrasonic scaler  Output tip main vibration excursion  1–100 μm2 
Output tip vibration frequency  28–30kHz 
Output half offset force  0.1–2 N 
Output power  3–20 W 

The experiments were filmed with a camera resolution of 1016 × 1016 pixels2, a frame rate set to 1000 frames per second (fps), an exposure time of 575 μs, and a single shot time of 3 s to record high-speed video of the droplets after they left the mouth. However, at the ideal exposure time of 1000 fps, if the camera is perpendicular to the light source, the intensity of a single light source is not sufficient to capture the flow of droplets due to the Mie scattering properties of droplets (Bahl , 2020). To overcome this problem, the camera is placed at an angle different from the direction of illumination of the light source. In addition, due to the high speed movement of the droplets, recording the video with the camera at a fixed angle would result in perspective distortion. Therefore, the camera was fixed on the left side of the head mold during the experimental shooting, at 75° to the center axis of the head mold, at a balance between light scattering and perspective distortion levels. The height of the lens center from the ground is 950 mm, and the lens center is 1450 mm from the center of the oral cavity of the head model; after many tests, this arrangement is the optimal shooting angle, and the real experimental scene experiments are shown in Fig. 8(a). In order to obtain a larger field of view, record the more complete flow trajectory of the droplets, the camera is equipped with a Nikon 24–85 mm, the maximum aperture of F2.8 zoom lens, the shooting lens parameters are: focal length of 35 mm due to the limited energy of a single light source, in order to ensure that sufficient light intake to clearly capture the droplets, so this paper uses the lens of the maximum aperture of F 2.8. the LED lamp is fixed in the head model. The LED light is fixed in front of the head model, irradiating the oral position of the head model from high to low to ensure sufficient light illumination in the area of droplet flow.

There are several reasons why photographing the treatment of actual patients affects the capture of droplet flow compared to that of mannequins. (1) The high light intensity and long duration are intolerable to patients. (2) The position of the light source needs to be adjusted with the doctor's position, which affects the camera focusing effect. (3) Due to the lack of light intensity during treatment, a 100 mm fixed-focus macro lens was used for the experimental photography, which could capture the flow of droplets but had a small field of view and could not record the trajectory of droplet flow. A larger field of view can be obtained using a 24 mm wide-angle lens for filming. Still, due to the variable position of the light source, the limited intensity of the light source, and the position of the doctor's operating hand changing at any time, it is difficult for the camera to capture the flow of droplets. Compared with filming the treatment process of actual patients, using a dummy as the filming subject can solve the problems encountered in filming actual patients, so this study chose to use a dummy as the filming subject.

The Particle image velocity measurement was used to analyze the droplet flow velocity and flow field, and the experimental principle is shown in Fig. 9. The experiment divided the teeth in the mouth into six large zones, as shown in Fig. 10(b). It also split its palatal, buccal, and occlusal sides separately, in which the occlusal surfaces of the first and fifth regions were too small, so they were not targeted at the occlusal side, for a total of 16 areas, as shown in Fig. 10(c). The teeth in the 16 regions of the mouth were treated using the ultrasonic scaler's high, medium, and low speeds. A high-speed camera was used during the ultrasonic cleaning process to record the flow of the droplets generated during cleaning in the XY plane, as in Fig. 11(a). To observe the trajectory of the droplets more intuitively, the data were stacked using the KymographClear 2.0 module of the Image J software to synthesize a map of the continuous flow of the recorded droplets, as shown in Fig. 11(b). The droplet flow field was analyzed using the Matlab PIVlab toolkit. First, the background noise was removed by background subtraction. Then, the droplet flow field information and velocity vectors were obtained using the adaptive PIV method, as in Fig. 11(c). The systematic and statistical errors of the PIV measurements in the velocity field were less than 3.5  % (Cao , 2014).

FIG. 9.

Schematic diagram of PIV experimental equipment arrangement and principle.

FIG. 9.

Schematic diagram of PIV experimental equipment arrangement and principle.

Close modal
FIG. 10.

(a) A diagram of an experimental scenario containing particle velocimetry equipment, a manikin and an ultrasonic scaler; (b) A diagram of the dental partitions of the mouth, in which the teeth in the mouth are divided into six major zones; and (c) A diagram of the partitions of a single tooth, in which each tooth is divided into three surfaces, the palatal, buccal and occlusal sides.

FIG. 10.

(a) A diagram of an experimental scenario containing particle velocimetry equipment, a manikin and an ultrasonic scaler; (b) A diagram of the dental partitions of the mouth, in which the teeth in the mouth are divided into six major zones; and (c) A diagram of the partitions of a single tooth, in which each tooth is divided into three surfaces, the palatal, buccal and occlusal sides.

Close modal
FIG. 11.

(a) Droplet flow; (b) Droplet flow trajectory diagram; and (c) Droplet flow field calculation.

FIG. 11.

(a) Droplet flow; (b) Droplet flow trajectory diagram; and (c) Droplet flow field calculation.

Close modal

To verify the accuracy of the CFD model, this paper uses a high-speed video camera to record the droplet flow triggered by cleaning teeth on the occlusal side of a zone using an ultrasonic scaler (medium gear). It compares the numerical simulation results with the experimental data. The boundary conditions of the flow field and the settings of the DPM model are consistent with the practical environment and parameter settings. The initial velocity of the spray source was set to 5 m/s, the spray angle was 45°, and the particle temperature was 284 K for 3 s. The flow field was validated to ensure that the continuous phase flow was not constant but an ongoing phase flow.

Flow field validation ensures the accuracy of numerical simulation results for continuous-phase fluids. In this paper, the flow velocity and splash height of the highest point of the flow trajectory of large droplet particles (particle size > 100 μm) at eight points (0.05, 0.15, 0.25, 0.35, 0.45, 0.5, 0.65, and 0.75 m from the horizontal displacement distance of the human oral cavity) were measured using the PIV particle velocimetry system and the image j software, respectively. As shown in Fig. 12, the simulated data at each point are consistent with the experimental data. The natural and fake flow traces of droplets (particle size > 100 μm) are compatible, and the average error between the actual results of flow velocity and splash height at the highest point of the flow trajectory and the simulated data are within 8    %. This shows that the numerical prediction is highly agreed with the experimental data, indicating that the dental office numerical model in this paper can reasonably predict the flow characteristics of the droplet aerosol triggered by ultrasonic tooth cleaning.

FIG. 12.

Experimental and simulation results of the flow of large splash particles triggered during tooth cleaning on the occlusal side of a zone. (a) Flow velocity at the highest point of the flow trajectory and (b) splash height at the highest point of the flow trajectory.

FIG. 12.

Experimental and simulation results of the flow of large splash particles triggered during tooth cleaning on the occlusal side of a zone. (a) Flow velocity at the highest point of the flow trajectory and (b) splash height at the highest point of the flow trajectory.

Close modal

In dental surgery, the doctor uses high-frequency vibrations (28–32 kHz) to clean the calculus and plaque from the top of the teeth. The doctor needs to adjust the vibration frequency of the working tip of the ultrasonic scaler according to the physiological condition of the patient's teeth and the thickness of the calculus. Among the nine cases simulated, cases 7–9 affect the droplet flow triggered by using low, medium, and high power at a large initial spray angle. Since the initial spray angle is set to the maximum in cases 7–9, the droplet flow is analyzed in these three cases as an example. The results of the visualization of the droplet movement during ultrasonic scaling are shown in Fig. 13. The droplets generated by the ultrasonic scaler from the spray can be a dangerous source of microbial infections for health-care workers and patients, with particle sizes ranging from 1 μm to more than 100 μm. As shown in Figs. 13(a)–13(c), large droplet particles (>100 μm) can be splashed out of the oral cavity and settle on the surface of objects or organisms nearby for tens of seconds before evaporating water. Organisms near the droplets are at high risk of infection during the droplet-settling process (Kohn , 2003). To avoid potential disease transmission, surfaces around dental patients should be sterilized even if they are not. The particle size distribution of droplets initiated by the ultrasonic scaler is shown in Figs. 13(d)–13(f). Initial particle size greater than 100 μm droplets by gravity is more substantial, after leaving the oral cavity, began to move upward to the highest point of the trajectory after the fall; the flow trajectory is mostly parabolic form, which is very different from the other initial diameter of the droplet flow. Tiny droplets with an initial diameter of less than 50 μm splashed out of the oral cavity; due to the weak influence of gravity, droplets will continue to move upward until they touch the ceiling and then spread in other directions, and the flow trajectory in the clinic is not regular. The droplet concentration tends to change with time and splash distance from dense to thin and tends to gradually spread to the whole dental clinic with the flow of tiny droplets. When the doctor is sitting directly behind the patient, the settlement of large droplets is mainly concentrated on the doctor's arm and the patient's face and body. However, some operations during the surgical procedure will bring the doctor and the patient's face closer together, the spreading of the large droplets will be blocked, and the splashing will be concentrated on the doctor's protective mask and gown (Veena 2015). As shown in Figs. 13(g)–13(i) and 14. As the power of the ultrasonic scaler used gradually increases, the initial flow velocity, splash height, and splash distance of the large droplet particles also gradually increase, but the flow of the small droplet particles is less affected. As the instrument's power grew, the large droplets' spreading area gradually expanded from the surface of the patient's body to the surrounding medical devices. Except for case 7, the large droplet flow area caused by the ultrasonic scaler under medium and high power conditions has wholly covered the respiratory range of the medical personnel. In all cases, the doctor's breathing zone was fully exposed to the small droplet flow area, thus confirming that the droplet flow caused by dental procedures is highly susceptible to infection by health-care workers.

FIG. 13.

Cases 7–9 simulate the droplet flow triggered by using low, medium and high power at a large initial spray angle, respectively. (a)–(c) Residence time of droplet particles of different particle sizes in air; (d)–(f) Velocity distribution of droplet particles of different particle sizes; (g)–(i) Particle size distribution of droplet particles.

FIG. 13.

Cases 7–9 simulate the droplet flow triggered by using low, medium and high power at a large initial spray angle, respectively. (a)–(c) Residence time of droplet particles of different particle sizes in air; (d)–(f) Velocity distribution of droplet particles of different particle sizes; (g)–(i) Particle size distribution of droplet particles.

Close modal
FIG. 14.

Cases 7–9 simulate the droplet flow triggered by using low, medium and high power at a large initial spray angle, respectively. (a) Initial spray velocity of droplet 2 m/s, initial spray angle of droplet 90°; (b) initial spray velocity of droplet 5 m/s, initial spray angle of droplet 90°; and (c) initial spray velocity of droplet 8 m/s, initial spray angle of droplet 90°.

FIG. 14.

Cases 7–9 simulate the droplet flow triggered by using low, medium and high power at a large initial spray angle, respectively. (a) Initial spray velocity of droplet 2 m/s, initial spray angle of droplet 90°; (b) initial spray velocity of droplet 5 m/s, initial spray angle of droplet 90°; and (c) initial spray velocity of droplet 8 m/s, initial spray angle of droplet 90°.

Close modal

Figure 15 shows the spatial distribution of large droplet particles in the consultation room with changes in the operating conditions of the ultrasonic scaler when the initial spreading angle was fixed at 30°, 45°, and 90°, respectively. As shown in Fig. 15(a), the hazardous range for the propagation of large droplet particles (particle size >100 μm) induced by the ultrasonic scaler when it was operated at a low speed was within 0.6 m of the patient. When treatment is carried out in the medium range, this risk range extends to 1.6 m around the patient. Early epidemiological and modeling studies of specific diseases have shown that the risk range for droplet transmission in everyday interpersonal interactions is within 3 ft (0.9144 m) of the patient (Dick 1987; Feigin 1982), and even during the SARS outbreak in 2003, the droplet transmission distance for SARS patients was only 6 ft (1.8288 m) (Wong 2004). However, the risk of transmission of large droplet particles (particle size >100 μm) triggered by an ultrasonic scaler operating in high gear can extend to 2.75 m around the patient.

FIG. 15.

Effects of different combinations of three working conditions and three initial diffusion angles of an ultrasonic scaler on the spatial flow characteristics of large droplet particles during surgery. (a) Variation of splash distance (b) Variation of contaminated space (c) Variation of splash deposition area (d) Variation of splash height.

FIG. 15.

Effects of different combinations of three working conditions and three initial diffusion angles of an ultrasonic scaler on the spatial flow characteristics of large droplet particles during surgery. (a) Variation of splash distance (b) Variation of contaminated space (c) Variation of splash deposition area (d) Variation of splash height.

Close modal

As shown in Fig. 15(b), the contamination height of large droplet particles (particle size >100 μm) caused by the ultrasonic scaler working in low gear was 0.3 m above the patient's mouth. The contamination height increased to 0.8 m above the patient's mouth when the machine was switched to the medium setting during the procedure. The size of contamination caused by large droplets triggered by a high gear can rise to 1.7 m above the patient's mouth, and even if the surgeon is standing during the procedure, the surgeon's breathing area can be contaminated. As shown in Fig. 15(c), the maximum contamination space caused by large droplet particles (particle size >100 μm) when the ultrasonic scaler is operated in the low gear is 0.25  m 3, which increases to 4.8  m 3 when the scaler is switched to the medium bag for the procedure. The range of contamination space caused by large droplets in the high gear is extended to 11  m 3.

The ultrasonic scaler constantly generates many new particles during the procedure. Most of the larger droplets exceeding 100 μm are deposited on the patient's torso, head, floor, and dental chair at rates of up to 78.5%, contaminating surfaces in the dental surgical environment. Large droplet particles evaporate on the surface of an object. They are converted into smaller droplet nuclei (<5 μm), which, when suspended in the air, become aerosolized particles containing biological particles such as bacteria and viruses, which under certain circumstances, can become airborne and cause disease, with a more excellent range of transmission and a longer residence time. The risk of spreading droplets from immediate spraying during surgery is usually limited to organisms in the vicinity of the droplet source when it was created. However, the risk of disease transmission from droplet nuclei and aerosolized particles is elevated, and studies have shown that bacterial concentrations in the patient's immediate vicinity do not return to pre-operative levels until two h after the end of surgery (Maghlouth , 2007; Grenier, 1995). As shown in Fig. 15(d), large droplet particles (particle size >100 μm) triggered by the ultrasonic scaler operating at a low gear caused contamination of an area of up to 0.65  m 2 around the patient. This contamination area increased to 4.7  m 2 when the machine was switched to the medium setting during the procedure, and the contamination area caused by large droplet flow in the high environment increased to 6.55  m 2. Therefore, this result shows that as the power of the ultrasonic scaler increases in dental procedures, the contamination area caused by the droplet flow increases to 0.65  m 2.The area of contamination caused by the droplet flow increases, and the risk of infection for health-care workers increases.

Figure 9 shows the changes in the spatial distribution of large droplet particles in the consultation room with the difference in the initial diffusion angle when the working conditions of the ultrasonic scaler were fixed at low, medium, and high gears, respectively. As shown in Fig. 12(a), when the power of the ultrasonic scaler was set at one bag, there was a slight tendency to increase the splash distance of the triggered large droplet particles (particle size >100  m 3) with the increase in the initial diffusion angle. Except for the high gear condition, the contamination range increased by 0.5 m after the initial diffusion angle was increased from 30° to 45°. Still, the changes that occurred in all other conditions were small. As shown in Fig. 12(b), compared with other states, the splash height of large droplet particles increased from 1.15 to 1.65 m with the increase in the initial diffusion angle when working with the high-grade position of the ultrasonic scaler, and the trend of the rise was pronounced.

Compared with the changes in splash distance and height, the increase in the contaminated area and contaminated space caused by droplet flow with the rise in initial diffusion angle is particularly significant. As shown in Fig. 12(c), under the low-range condition, the pollution space caused by large droplet particles was stable at 0.15  m 3 after the initial diffusion angle increased from 30° to 90°. Still, under the medium-range condition, the pollution space caused by large droplet particles expanded from 1.05 to 4.85  m 3 after the initial diffusion angle increased from 45° to 90°, and the pollution space caused by large droplet particles expanded from 1.05 to 4.85  m 3 under the especially high-range condition. In the incredibly high-level state, the initial diffusion angle was increased from 30° to 90°, and the contamination space caused by large droplet particles increased from 2.35 to 10.95  m 3. As shown in Fig. 12(d), under the low-level condition, after the initial diffusion angle was increased from 30° to 90°, the contaminated area caused by large droplet particles after settling was stable within 0.75 m2. In the middle gear condition, after the initial diffusion angle increases from 30° to 45°, the pollution area caused by the settlement of large droplet particles is controlled within 1.85 m2. However, after the initial diffusion angle increased from 45° to 90°, the pollution area caused by the settlement of large droplet particles increased to 4.65 m2. In the high-grade working condition, after the initial diffusion angle increased from 30° to 45°, the pollution area caused by the settlement of large droplet particles increased from 2.35 to 6.55 m2. However, as the initial diffusion angle was increased from 45° to 90°, the pollution area caused by the settlement of large droplet particles did not change significantly.

It is well known that ultrasonic scaling produces the most aerosols and splatters compared to other dental treatments, which can travel a considerable distance from the surgical site (Scannapieco , 2009). Studies have shown that ultrasonic scalers that do not use coolants can still produce large amounts of aerosols and splatter, and most dental procedures cause some degree of mucosal damage, which is more difficult to avoid (Haas , 1999). Therefore, during the COVID-19 pandemic, dental practices were widely regarded as one of the most vulnerable healthcare providers. The characteristics of the droplet flow induced by the ultrasonic scaler during the procedure can be influenced and controlled by many factors, especially since the effect of the different manipulation methods of the surgeon during the process on the droplet flow is often difficult to analyze. The flow of large particles is less affected due to the delicate and subtle airflow variations in the surrounding area. To avoid the complexity of the simulation, factors such as ventilation and tool table placement were not considered in this paper. Recent studies have focused on the effect of patient respiratory rate on triggering droplet dispersion in dental procedures. The advantage of this study is that it focuses on the impact of changes in the power of the ultrasonic scaler and the site of tooth cleaning on the spatial distribution characteristics of large droplet particles (particle size >100  m3) during ultrasonic scaler procedures. For droplet particles (particle size >100  m3), due to their high initial momentum, they can move a long distance even when subjected to air resistance. After the droplets lose their initial momentum, gravity acts as the dominant force on the droplets, leading to their deposition near the dental treatment area. In particular, the size of the deposit of droplets increases as the working power and the initial diffusion angle of the dental cleaner increase. However, it does not show linear growth, and the details of the variations have been described in detail in the previous section (Liu , 2023b).

Several limitations of the present study should be acknowledged. The present study considered the differences in droplet flow triggered by treating different areas of the teeth. However, there are more options for the angle of contact between the working tip of the scaler and the same tooth, which may also affect the droplet flow and distribution. In addition, this study analyzed the effect of scaler power on droplet flow by dividing it into three commonly used gears, namely, high, medium, and low gears, and more detailed power divisions could be considered in the future. Therefore, further efforts should be made on the case setup for future experiments and numerical simulations that simulate real-life situations (Liu , 2023a).

In addition, the current adsorption equipment cannot achieve complete adsorption of aerosol particles due to the limitation of system flow rate and power, and a small amount of large-diameter particles still escapes, which is a latent danger. The next step of research should be to optimize the internal flow field of the existing equipment to reduce the flow loss and achieve the complete adsorption of microbial aerosols as far as possible. Moreover, the current mainstream elimination method mainly relies on plasma discharge, and the equipment is selected in the design without an in-depth study of the principle of plasma discharge elimination and without considering the coupling of the number of different plasma generators. The following research step should study the plasma generator elimination principle to explore the influence of varying discharge methods on the elimination performance.

In conclusion, the fear and uncertainty associated with the possible airborne transmission of diseases in dental procedures cause dental patients to postpone their treatment plans, seriously affecting human health and well-being. The use of air purification devices in the office has a vital role in reducing the concentration of indoor particulate matter and the risk of viral transmission. It is one of the most recognized methods of improving indoor air quality. Using adsorption and disinfection equipment to disinfect droplets promptly after they have been released from the mouth can effectively interrupt disease transmission through droplets, droplet nuclei, and aerosol particles.

However, there needs to be more guidelines for the design of adsorption and elimination equipment for dental offices. Therefore, using the droplet transmission mode in the oral environment, further development of medical protective equipment with solid adsorption and elimination capacity and ergonomics is of great significance in preventing the spread of epidemic diseases and guaranteeing the safety of patients with oral diseases and medical personnel.

Spraying droplets carrying pathogenic microorganisms during dental scaling procedures poses a high risk of cross-infection to patients and doctors. This paper takes a dental clinic as the research object; first, we design an experiment to use a scaler to perform dental scaling surgery on different parts of the teeth under other working conditions and then use a particle velocimetry system to record and analyze the droplets generated in the surgical process. Due to the limited field of view of the high-speed camera in the particle image velocity measurement, the system can only record the flow of large droplet particles in the XY plane. Then, using some experimental data to establish a geometric model of the dental office, the following conclusions were obtained from numerical simulations of nine cases with different initial splash velocities and initial spreading ranges of droplets (1–1000 μm), combined with experimental validation:

  1. After the start of surgery, the droplet particles of different particle sizes are mixed and splashed outward. The particles are more concentrated in the patient's oral cavity within 0.75 m. The distance between the face of the health-care personnel and the patient's oral cavity is close in the surgical process. The risk of infection with disease-causing microorganisms is very high. It is recommended to strengthen the isolation of health-care personnel's wear, especially the need to protect the entrance of mucosal tissues that are exposed in the head and face to cut off the channels for pathogens to enter the human body.

  2. Droplet particles (particle size >100 μm) will settle rapidly after splashing, and pathogenic microorganisms will contaminate 2.5 m around the patient. It is recommended to rationally arrange the dental chairs in the clinic and set up physical partitions to help separate the splash range in the room during operation and reduce the risk of transmission.

  3. The splash height of droplet particles (particle size 1–100 μm) can reach the clinic's ceiling, and the contamination range can be extended to the whole clinic after a long stay in the air. Before surgery, patients should gargle antibacterial agents. During the operation, increase the amount of ventilation in the clinic, use the adsorption and elimination equipment for the dental clinic, and wear the ventilation protective mask during the operation. After the surgery, the use of ultraviolet light elimination equipment for timely elimination of the clinic. Combining the above methods can effectively reduce the risk of infection for health-care workers in the clinic.

  4. According to the simulation results, the spreading range of droplets gradually expands after splashing out of the patient's mouth, and the concentration of particles in the unit space decreases. The attention of droplets is more concentrated at this distance after splashing to 0.75 m directly in front of the patient's mouth. Considering that the adsorption device can not affect the doctor's operation in the surgery, it is suggested that the adsorption port can be set in the area of 0.25–0.75 m directly in front of the patient's mouth.

  5. After the initial splash velocity of the droplets was limited, the droplets were splashed at 30°, 45°, and 90° diffusion ranges, respectively. The simulation showed a slight difference in the contaminated area caused by different initial diffusion angles when the initial particle splashing velocity was the same. However, after the initial diffusion angle is limited when the droplets are sprayed at 2m/s, 5m/s, and 8m/s, respectively, the larger the particle splashing speed is, the larger the contaminated area is. It represents that the different operating power of the scaler has a more significant influence on the spatial flow characteristics of the droplets.

This project was supported by the grant from the Department of Science and Technology of Guizhou Province, China (No. [2022]196) and talent project (No. GCC[2023]016). The authors thank the Guiyang Dental Hospital, Guizhou Province, for providing the manikin and treatment equipment. The authors also thank Professor Jiadui Chen's team at the State Key Laboratory of Advanced Manufacturing Technology, Guizhou University for providing the high-speed image acquisition equipment.

The authors have no conflicts to disclose.

Fan Zhang: Formal analysis (equal); Methodology (equal); Writing – original draft (equal). Jin Zhao: Funding acquisition (equal); Writing – original draft (equal). Wei Yang: Funding acquisition (equal); Methodology (equal). Xiaoyan Yu: Validation (equal). Junjie He: Validation (equal). Haiyin Shu: Validation (equal). Xiankun Zhu: Software (equal).

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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