Fabrication and Analysis of Polydimethylsiloxane (PDMS) Microchannels for Biomedical Application

: In this research work, Polydimethylsiloxane (PDMS) has been used for the fabrication of microchannels for biomedical application. Under the internet of things (IoT)-based controlled environment, the authors have simulated and fabricated bio-endurable, biocompatible and bioengineered PDMS-based microchannels for varicose veins implantation exclusively to avoid tissue damaging. Five curved ascending curvilinear micro-channel (5CACMC) and ﬁve curved descending curvilinear micro-channels (5CDCMC) are simulated by MATLAB (The Math-Works, Natick, MA, USA) and ANSYS (ANSYS, The University of Lahore, Pakistan) with actual environments and conﬁrmed experimentally. The total length of each channel is 1.6 cm. The diameter of both channels is 400 µ m. In the ascending channel, the ﬁrst to ﬁfth curve cycles have the radii of 2.5 mm, 5 mm, 7.5 mm, 10 mm, and 2.5 mm respectively. In the descending channel, the ﬁrst and second curve cycles have the radii of 12.5 mm and 10 mm respectively. The third to ﬁfth cycles have the radii of 7.5 mm, 5 mm, and 2.5 mm respectively. For 5CACMC, at Reynolds number of 185, the values of the ﬂow rates, velocities and pressure drops are 19.7 µ Ls − 1 , 0.105 mm/s and 1.18 Pa for Fuzzy simulation, 19.3 µ Ls − 1 , 0.1543 mm/s and 1.6 Pa for ANSYS simulation and 18.23 µ Ls − 1 , 0.1332 mm/s and 1.5 Pa in the experiment. For 5CDCMC, at Reynolds number 143, the values of the ﬂow rates, velocities and pressure drops are 15.4 µ Ls − 1 , 0.1032 mm/s and 1.15 Pa for Fuzzy simulation, 15.0 µ Ls − 1 , 0.120 mm/s and 1.22 Pa for ANSYS simulation and 14.08 µ Ls − 1 , 0.105 mm/s and 1.18 Pa in the experiment. Both channels have three inputs and one output. In order to observe Dean Flow, Dean numbers are also calculated. Therefore, both PDMS channels can be implanted in place of varicose veins to have natural blood ﬂow.


Introduction
Internet of things (IoT) has been potentially used to develop miniaturized devices. Various parameters have been controlled using IoT during the process level fabrication. These miniaturized devices are used in different fields. IoT plays a significant role in advancement of microfluidic devices. Diverse parameters of microfluidic devices like fluid flow time, temperature, flow rate, flow velocity, pressure drop, density and viscosity can be monitored and examined using IoT devices. These devices can be used potentially for analysis and detection for specific applications [1][2][3][4]. The schematic of IoT controlled microfluidic system is shown in Figure 1.

Biomaterial
Usage Synthetic polymers [23] Encompasses water-repelling materials Naturally occurring polymers [24] Blood purification, dental-related problems Polyvinylchloride [25] The bag containing blood and solutions Polyethylene [26] Bone related implants Nowadays, polymer-based implantable devices like bioengineered veins, plates of bone, dental parts, muscles, discs of vertebral, valves of heart, and pacemakers are used in biomedical engineering to reinstate the damaged organs [5,6]. Polymer-based biomaterials can provide assistance for the healing process in the human body. The polymer materials are utilized mostly due to adaptability, structure, properties, and treatment methods. These biomaterials can be utilized in various compositions like solids, films, gels, and fibers for treatment and analysis in several biomedical applications [7][8][9][10][11][12]. Every implant in the human body must be done accurately for the proper functions of cells, tissues, and fluids. The biomaterial used for surgical implants must be biocompatible [13][14][15]. The core purpose of the present work is to engineer and evaluate the use of PDMS microchannels as a potential replacement for varicose veins. These bioengineered veins must promote a better blood flow and avoid tissue-damaging [16].
PDMS can be considered as the functional material for implantations in various biomedical applications like joints, nasal bones, other bones, teeth, sutures, blood tubes, spine and hearts [17,18]. All biomaterials used in implantations have fundamental properties for long term usage in the body [19]. Metals, composites, polymers, and ceramics can be used separately and in combination for the development of these implantations. These implantations should be developed and implanted very carefully, so that these may not result into issues like cancer, immunogenicity, teratogenicity and toxicity. These materials must have high mechanical properties and corrosion resistance. The common biomaterials are polymeric biomaterials, synthetic polymers, naturally occurring polymers, polyvinylchloride, polyethylene, polypropylene, polymethylmetacrylate, polyurethane, polyethersulfone, polyetherimide, polyamide, polystyrene, polytetrafluoroethylene, polyethylenterephthalate, composite biomaterials and ceramic biomaterials [20][21][22]. Their usage has been given in Table 1.
Three-dimensional (3D) printing technology has been used to design a wearable electronic system based on IoT to determine the health monitoring system [38]. Qualitative research has been done to investigate the various designs of microchannels that have been reported with the application of IoT. In this decade, IoT devices have been widely used for 3D printing [39,40]. IoT has also been reported for the development of automatic microfluidic system [41].
In our previous papers, the authors fabricated silver bio-engineered veins for varicose vein implantation. The previous studies have some limitations like biocompatibility, elasticity and friction [42,43]. Here, in this research, the authors have tried to overcome the limitations of biocompatibility, flexibility and resistance. Therefore, the PDMS has been used to fabricate the microchannels for varicose vein implantation. This polymer is naturally flexible, biocompatible and exhibits less friction with the flow speed [16,44,45]. These polymer channels can be implanted to avoid tissue-damaging effect after the removal of varicose veins. There is a wide use of microchannels in biomedical engineering and other fields [46][47][48][49][50][51].

Sterilization and Toxicity of Polymeric Implants
The sterilization can be explained as a procedure by which organisms, germs, bacteria and other contaminated cells are eliminated from the implanted device. Aseptic processing and terminal sterilization can be used for contamination removal [52,53]. The most common and economical method is the terminal sterilization to reduce the threat of infection. It is essential to sterilize these bio-medical transplants and surgical implant before use to decrease the danger of contaminations and related impediments. The standard sterilization methods are applying radioactive rays, steam spray, heat or a mixture of these techniques and γ-radiations [54]. Possible toxic impurities can be transferred in these devices through handling, cleansing, storing, packing and transportation. Allergy, haemolysis, toxicity, thrombosis, carcinogenicity, eye-tissue irritation, and pyrogenicity can be caused due to contaminated polymeric devices [55]. PDMS-based devices are prone to less contamination [56,57]. The authors have proposed a PDMS-based bioengineered vein to implant as varicose vein. Human blood has been used for extensive investigations in this research. The viscosity of human blood is averagely taken as 0.0032 Kg/ms [71]. Viscosity and density of blood can exhibit a strong effect on the blood flow rate in veins. Blood volume and its mass have been used as the traditional method for blood density measurement. It is related to the hematocrit of blood. Blood density is different in young, old, males, and females. Blood density of young females is less than adult females while blood density of young males is less than adults. The density of blood in females is less than males. This is because the red cells of young males and females have a higher negative charge than the adults. Therefore, the mobility of blood cells is higher in young people [52,53,55,56]. Females have more blood flow than the male because of increased rate of pumping of their heart [72]. The human blood density differs from person to person due to blood count changes and other factors. The venous blood and arterial blood differ in density. The venous blood density seems to change even with the change in the posture of the body. In this study, blood (plasma) density is considered 1025 kg/m 3 and blood (cells) density is taken as 1125 kgm −3 [44,57]. Typically, the blood density for humans (female and male) is taken as 1060 kgm −3 [16,45]. Some possible PDMS-based implants in humans depending on the requirement have been shown in Figure 2.
The authors have simulated two types of microchannels using MATLAB and ANSYS. First one is five curved ascending curvilinear microchannels (5CACMC) and the second one is five curved descending curvilinear microchannels (5CDCMC). After the simulation, the fabrication of the channels has been performed. Then these channels have been tested using actual human blood. This research work may provide new way of PDMS-based varicose vein implantation. The human blood density differs from person to person due to blood count changes and other factors. The venous blood and arterial blood differ in density. The venous blood density seems to change even with the change in the posture of the body. In this study, blood (plasma) density is considered 1025 kg/m 3 and blood (cells) density is taken as 1125 kgm −3 [44,57]. Typically, the blood density for humans (female and male) is taken as 1060 kgm −3 [16,45]. Some possible PDMS-based implants in humans depending on the requirement have been shown in Figure 2.

Fuzzy Logic Simulation for 5CACMC and 5CDCMC
Fuzzy simulation is extensively used in almost every field of research [73][74][75][76][77]. In this simulation, two fuzzy systems for 5CACMC and 5CDCMC have been developed with four inputs and three outputs. The inputs are Reynolds Number, applied pressure, viscosity and density. The outputs are flow rate, velocity and pressure drop. All have three membership functions. The ranges are 0 to 200 for Reynolds number, 1 to 1.5 Kilo Pa for applied pressure, 0.003 to 0.004 Kgm −1 s −1 for viscosity, 925 to 1125 Kgm −3 for blood density, 15 to 25 µLs −1 for flow rate, 0.01 to 0.2 mms −1 velocity and 0.2 to 2 Pa for pressure drop. For simulation, eighty-one rules were prepared with If-AND-THEN statements. The rule viewers in MATLAB are shown below in Figure 3a,b for both 5CACMC and 5CDCMC. The authors have simulated two types of microchannels using MATLAB and ANSYS. First one is five curved ascending curvilinear microchannels (5CACMC) and the second one is five curved descending curvilinear microchannels (5CDCMC). After the simulation, the fabrication of the channels has been performed. Then these channels have been tested using actual human blood. This research work may provide new way of PDMS-based varicose vein implantation.

Fuzzy Logic Simulation for 5CACMC and 5CDCMC
Fuzzy simulation is extensively used in almost every field of research [73][74][75][76][77]. In this simulation, two fuzzy systems for 5CACMC and 5CDCMC have been developed with four inputs and three outputs. The inputs are Reynolds Number, applied pressure, viscosity and density. The outputs are flow rate, velocity and pressure drop. All have three membership functions. The ranges are 0 to 200 for Reynolds number, 1 to 1.5 Kilo Pa for applied pressure, 0.003 to 0.004 Kgm −1 s −1 for viscosity, 925 to 1125 Kgm −3 for blood density, 15 to 25 µLs −1 for flow rate, 0.01 to 0.2 mms −1 velocity and 0.2 to 2 Pa for pressure drop. For simulation, eighty-one rules were prepared with If-AND-THEN statements. The rule viewers in MATLAB are shown below in Figure 3a,b for both 5CACMC and 5CDCMC.  After the simulation, the three-dimensional graphs obtained for 5CACMC from Fuzzy logic simulation with flow rate as output are shown below in Figure 4. After the simulation, the three-dimensional graphs obtained for 5CACMC from Fuzzy logic simulation with flow rate as output are shown below in Figure 4.  The three-dimensional graphs obtained for 5CACMC from Fuzzy logic simulation with velocity as output are shown below in Figure 5. The three-dimensional graphs obtained for 5CACMC from Fuzzy logic simulation with velocity as output are shown below in Figure 5. The three-dimensional graphs obtained for 5CACMC from Fuzzy logic simulation with pressure drop as output are shown below in Figure 6. The three-dimensional graphs obtained for 5CACMC from Fuzzy logic simulation with pressure drop as output are shown below in Figure 6. The three-dimensional graphs obtained for 5CDCMC from Fuzzy logic simulation with flow rate as output are shown below in Figure 7. The three-dimensional graphs obtained for 5CDCMC from Fuzzy logic simulation with flow rate as output are shown below in Figure 7. The three-dimensional graphs obtained for 5CDCMC from Fuzzy logic simulation with velocity as output are shown below in Figure 8. The three-dimensional graphs obtained for 5CDCMC from Fuzzy logic simulation with velocity as output are shown below in Figure 8. The three-dimensional graphs obtained for 5CDCMC from Fuzzy logic simulation with pressure drop as output are shown below in Figure 9. The three-dimensional graphs obtained for 5CDCMC from Fuzzy logic simulation with pressure drop as output are shown below in Figure 9. The results assessment for simulation and using Mamdani model is shown in Table 3.  The results assessment for simulation and using Mamdani model is shown in Table 3.  [78]. The percentage errors in results are ignorable. The results are in agreement at Reynolds numbers 185 (for 5CACMC) and 143 (for 5CDCMC). Reynolds number 185 is higher than 143 because of more flow rate in 5CACMC. After fuzzy simulation, ANSYS Fluent simulation has been performed for the confirmation of obtained results.

ANSYS Simulation of 5CACMC and 5CDCMC
ANSYS has been extensively used in research, especially in the field of biomedical engineering [42,43,79]. With the help of the design modeler tool of ANSYS Fluent, the geometries of the channels (5CACMC and 5CDCMC) have been created. After creating the geometries, mesh was performed with the mesh analysis successfully. In the setup tool, K-epsilon model was selected for the simulation because, in these channels, the flow is laminar with some turbulent shape at the curves of the channel. This model was also used because of the low Reynolds Numbers. There were four blood samples (young, old male and female with different blood densities, i.e., 950 Kg/m 3 , 1000 Kg/m 3 , 1060 Kg/m 3 and 1125 Kg/m 3 . For simulation, the velocities were taken as 0.02 to 0.07 m per second and the pressure values were taken from 1 to 1.5 kilo Pascal [80]. Therefore, different velocities of the blood in veins were obtained in the result section of the ANSYS Fluent. The simulation results are shown below in Figure 10. Mamdani's numerical value was calculated for both channels by using mathematical relation [78]. The percentage errors in results are ignorable. The results are in agreement at Reynolds numbers 185 (for 5CACMC) and 143 (for 5CDCMC). Reynolds number 185 is higher than 143 because of more flow rate in 5CACMC. After fuzzy simulation, ANSYS Fluent simulation has been performed for the confirmation of obtained results.

ANSYS Simulation of 5CACMC and 5CDCMC
ANSYS has been extensively used in research, especially in the field of biomedical engineering [42,43,79]. With the help of the design modeler tool of ANSYS Fluent, the geometries of the channels (5CACMC and 5CDCMC) have been created. After creating the geometries, mesh was performed with the mesh analysis successfully. In the setup tool, K-epsilon model was selected for the simulation because, in these channels, the flow is laminar with some turbulent shape at the curves of the channel. This model was also used because of the low Reynolds Numbers. There were four blood samples (young, old male and female with different blood densities, i.e., 950 Kg/m 3 , 1000 Kg/m 3 , 1060 Kg/m 3 and 1125 Kg/m 3 . For simulation, the velocities were taken as 0.02 to 0.07 m per second and the pressure values were taken from 1 to 1.5 kilo Pascal [80]. Therefore, different velocities of the blood in veins were obtained in the result section of the ANSYS Fluent. The simulation results are shown below in Figure 10. The simulation results for 5CDCMC are shown below in Figure 11. The simulation results for 5CDCMC are shown below in Figure 11. The simulation results for 5CDCMC are shown below in Figure 11. The graphical results of ANSYS Fluent are described below. For 5CACMC, the graph between Reynolds number and pressure drop is shown in Figure 12a and the graph between the Reynolds number and the flow rate is shown in Figure 12b. These graphs have four different lines in different colors. The red, blue, yellow and green lines are shown with blood density 950, 1000, 1060, and 1125 kgm −3 , respectively. Data in Figure 12a shows that with the increase in Reynolds number, there is an increase in pressure drop. The maximum simulated pressure drop is 1.6 Pa. The pressure drop was calculated by the following equation.
Here, ∆ is the pressure drop, is the viscosity, = 〈 〉 is the flow rate, is the diameter of the channel. Reynolds numbers were calculated by the equation.
The graphical results are shown below for ANSYS simulation in Figure 12. The graphical results of ANSYS Fluent are described below. For 5CACMC, the graph between Reynolds number and pressure drop is shown in Figure 12a and the graph between the Reynolds number and the flow rate is shown in Figure 12b. These graphs have four different lines in different colors. The red, blue, yellow and green lines are shown with blood density 950, 1000, 1060, and 1125 kgm −3 , respectively. Data in Figure 12a shows that with the increase in Reynolds number, there is an increase in pressure drop. The maximum simulated pressure drop is 1.6 Pa. The pressure drop was calculated by the following equation. ∆p Here, ∆p is the pressure drop, µ is the viscosity, Q = v π 4 D 2 c is the flow rate, D c is the diameter of the channel. Reynolds numbers were calculated by the equation.
The graphical results are shown below for ANSYS simulation in Figure 12. Figure 12b shows that there is an increase in flow rate with the Reynolds number. At the first point, flow rate is increased up to 3.75%, at the 2nd point, flow rate is increased up to 5.58%, at the 3rd point, flow rate is increased up to 7.38%, at the 4th point, flow rate is increased up to 9.23%, at the 5th point, flow rate is increased up to 11.12%, at the 6th point, flow rate is increased up to 12.83%, at the 7th point, flow rate is increased up to 14.83%, at the 8th point, flow rate is increased up to 16.59% and finally at the last point, flow rate is increased up to a maximum of 18.68%. The flow rate has increased gradually. The maximum simulated flow rate is 19.3 µLs −1 , with an average human blood density of 1060 kgm −3 .
For 5CDCMC, the graph between Reynolds number and pressure drop is shown in Figure 13a, and the graph between the Reynolds number and the flow rate is shown in Figure 13b. These graphs have four different lines in different colors. Data in Figure 13a shows that with the increase in Reynolds number, there is an increase in pressure drop. The maximum simulated pressure drop is 1.  Figure 12b shows that there is an increase in flow rate with the Reynolds number. At the first point, flow rate is increased up to 3.75%, at the 2nd point, flow rate is increased up to 5.58%, at the 3rd point, flow rate is increased up to 7.38%, at the 4th point, flow rate is increased up to 9.23%, at the 5th point, flow rate is increased up to 11.12%, at the 6th point, flow rate is increased up to 12.83%, at the 7th point, flow rate is increased up to 14.83%, at the 8th point, flow rate is increased up to 16.59% and finally at the last point, flow rate is increased up to a maximum of 18.68%. The flow rate For 5CDCMC, the graph between Reynolds number and pressure drop is shown in Figure 13a, and the graph between the Reynolds number and the flow rate is shown in Figure 13b. These graphs have four different lines in different colors. Data in Figure 13a shows that with the increase in Reynolds number, there is an increase in pressure drop. The maximum simulated pressure drop is 1.22 Pa.   Figure 13b shows that there is an increase in flow rate with the Reynolds number. At the first point flow rate is increased up to 3.7%, at the 2nd point flow rate is increased up to 5.83%, at the 3rd point flow rate is increased up to 7.16%, at the 4th point flow rate is increased up to 9.15%, at the 5th point flow rate is increased up to 11.27%, at the 6th point flow rate is increased up to 12.74%, at the 7th point flow rate is increased up to 14.86%, at the 8th point flow rate is increased up to 16.7% and finally at the last point flow rate is increased up to a maximum of 18.58%. The flow rate has increased gradually. The maximum simulated flow rate is 15 µLs −1 , with an average human blood density of 1060 kgm −3 .
The ANSYS simulation results (at the same input conditions that were used in Fuzzy MATLAB simulation) are shown in Table 4. After these simulations, the authors have fabricated and tested the channels experimentally to optimize the final results.

Fabrication
The production of the five curved ascending and descending curvilinear micro-channel is completed with the method of photolithography. IoT devices were used to control the environment. PDMS has been sued for microchannels fabrication. The benefits of PDMS are replica molding, flexible, instinctively durable, exceptional properties of the polymer, translucent, suitable for microscopy, and high gas permeable. Firstly, the mask was designed on by using software and then these patterns were transfer on mask using silicon wafer with area 6 × 6 mm 2 . Then silicone mold were fabricated. A Silicon wafer was washed through the solution of piranha (H2SO4+H2O2). The coating was done with SiO 2 . It was baked nearly 210 • C for approximately 1 min and 40 s for the dehydration of wafer. In the method of photolithography, the coating was done with photoresist PFI27C9 on the silicon wafer, which is spin-coated in the range of 800 to 3500 rpm by using IoT controlled environment. Then the second phase was initiated, namely the exposure of PFI27C9 to ultraviolet light for a half minute. The mask was placed on a glass slide and finally accurately associated with PFI27C9 for the chemical reaction. Now the substrate was again baked at 100 • C for one minute. It was cooled then at room temperature. After the exposure, the required pattern of the channel of the mask on the substrate was obtained. Etching of silicon dioxide was completed by using deep reactive ion etching (DRIE) procedure in half an hour with oxide etcher machine. Etching of substrate was completed in 60 min. After etching, the silicon mold was kept inside the stripping device for 60 min. After all these phases, the PDMS elastomer was arranged. This mixture is then decanted with a prepared mold. For more toughening of PDMS, the substrate is baked at 75 • C for up to 60 min. Sterilization of PDMS material is an important step after fabrication process. It was sterilized by autoclaving. Fabricated material was exposed to high pressure (110 kPa) steam at 120 • C for 15 min. All microorganisms were eliminated by this procedure. Lastly, the toughened PDMS is removed from the silicon wafer, and inputs and outputs are pierced. The glass slides and the PDMS are bonded with each other by baking in the oven for an hour. After final and complete fabrication, microchannels are shown in Figure 9. The total length of each channel is 1.6 cm. The diameter of both channels is 400 µm. In the ascending channel, the first curve cycle has the radius 2.5 mm, the 2nd cycle has 5 mm, the 3rd cycle has 7.5 mm, the 4th cycle has 10 mm, and the 5th cycle has 12.5 mm. It has three inputs and one output. In the descending channel, the first curve cycle has the radius 12.5 mm, the 2nd cycle has 10 mm, the 3rd cycle has 7.5 mm, the 4th cycle has 5 mm, and the 5th cycle has 2.5 mm. Both channels have three inputs and one output as shown in Figure 14.

Experimental Verification of Simulation Results
To verify the simulated findings the experimentation for both channels was performed. For the experiment, three commercial mp6 micro-pumps with electronic circuits (mp6-EVA) were used. Heparin was mixed in the blood to avoid the clotting. With the help of a USB port, signals were given to all of the pumps. In the IoT controlled environment, three mp6 pumps were used for 5CACMC, because it has three inputs and one output. For 5CDCMC, only one pump was utilized, because it has only one input and three outputs. Three pumps generated sufficient pressure for the continuous blood flow through both channels of diameter 400 µm. The actual experimental setup is shown in Figure 15a,b. The study shows these 5CACMC and 5CDCMC bioengineered vein are suitable for implant. However, it can be investigated further for other parameters and related issues.

Results and Discussion
The result comparison of MATLAB, ANSYS and experiment for both channels is given in this section. With the rise of the Reynolds number, there is an increase in pressure drop. In this graph Figure 16a, there are two rapid upsurges; one is initially and the other in the middle. The channel has consistent curves with increasing radii. In these curvilinear channels, the fluidic parameters are greater in ascending than in descending channels [81].

Experimental Verification of Simulation Results
To verify the simulated findings the experimentation for both channels was performed. For the experiment, three commercial mp6 micro-pumps with electronic circuits (mp6-EVA) were used. Heparin was mixed in the blood to avoid the clotting. With the help of a USB port, signals were given to all of the pumps. In the IoT controlled environment, three mp6 pumps were used for 5CACMC, because it has three inputs and one output. For 5CDCMC, only one pump was utilized, because it has only one input and three outputs. Three pumps generated sufficient pressure for the continuous blood flow through both channels of diameter 400 µm. The actual experimental setup is shown in Figure 15a

Experimental Verification of Simulation Results
To verify the simulated findings the experimentation for both channels was performed. For the experiment, three commercial mp6 micro-pumps with electronic circuits (mp6-EVA) were used. Heparin was mixed in the blood to avoid the clotting. With the help of a USB port, signals were given to all of the pumps. In the IoT controlled environment, three mp6 pumps were used for 5CACMC, because it has three inputs and one output. For 5CDCMC, only one pump was utilized, because it has only one input and three outputs. Three pumps generated sufficient pressure for the continuous blood flow through both channels of diameter 400 µm. The actual experimental setup is shown in Figure 15a,b. The study shows these 5CACMC and 5CDCMC bioengineered vein are suitable for implant. However, it can be investigated further for other parameters and related issues.

Results and Discussion
The result comparison of MATLAB, ANSYS and experiment for both channels is given in this section. With the rise of the Reynolds number, there is an increase in pressure drop. In this graph Figure 16a, there are two rapid upsurges; one is initially and the other in the middle. The channel has consistent curves with increasing radii. In these curvilinear channels, the fluidic parameters are greater in ascending than in descending channels [81]. The study shows these 5CACMC and 5CDCMC bioengineered vein are suitable for implant. However, it can be investigated further for other parameters and related issues.

Results and Discussion
The result comparison of MATLAB, ANSYS and experiment for both channels is given in this section. With the rise of the Reynolds number, there is an increase in pressure drop. In this graph Figure 16a, there are two rapid upsurges; one is initially and the other in the middle. The channel has consistent curves with increasing radii. In these curvilinear channels, the fluidic parameters are greater in ascending than in descending channels [81].  In 5CACMC, the maximum experimental flow rate is 18.23 µLs −1 . The velocity in this experiment is calculated as 0.1332 mm/s. The experimental pressure drop in 5CACMC is maximum 1.5 Pa. In 5CDCMC, the maximum experimental flow rate is 14.08 µLs −1 . The velocity in this experiment is calculated as 0.105 mm/s, with a pressure drop of 1.18 Pa. All input conditions are the same as in the two simulations for both channels. Pressure drop takes place by increasing resistance inside the veins and acts on blood when it runs through the veins. The main factors of friction to blood flow are blood velocity through the veins, blood viscosity and density. If there is a small blood velocity, then the pressure drop is minimal and vice versa. The values of pressure drop in experiment are 1.5, 1.3, 1.1, 1, 0.9, 0.7, 0.5, 0.3 and 0.2 for 5CACMC and 1.18, 0.9, 0.87, 0. 76 38. ANSYS presents higher pressure for the descending channel because of heavy flow. Dean numbers were determined by using the following formulation [82].
The observed Dean numbers for 5CACMC and 5CDCMC are shown in Tables 5 and 6.  [83]. If there is Dean flow, then flow must be circular at some turning points [84]. IoT environment provides a purely error-free atmosphere, in which Reynolds Number, time, temperature, pressure and all real-time parameters can be controlled. IoT provided a significant role for biomedical devices and systems [85][86][87]. The comparison of results is shown below in Table 7. 5CACMC has a higher flow rate, velocity and pressure drop than 5CDCMC. The first reason is the curvilinear nature of the channels. The channels have consistent curves with increasing radii. The second reason is the pressure difference, which is varied inside the channels. Only the ANSYS Fluent can describe the pressure difference inside these channels. The results are in closed agreement with the previously available research [81].