Ternary Gas Mixture Measurements Using Micromachined Thermal Conductivity Sensors

Xiang Zheng Tu

 

According to Chapman–Enskog theory elastic gases deviation from the Maxwell–Boltzmann distribution in the equilibrium is small and it can be treated as a perturbation.

So the thermal conductivity of the ternary gas mixture can be expressed as  

K_mix = k₁ N₁ / (N₁ + N₂ Φ₁₂ + N₁₃ Φ₁₃) + k₂ N₂ / (N₂ + N₃ Φ₂₃ + N₁ Φ₂₁)

+ k₃ N₃ / (N₃ + N₁ Φ₃₁ + N₂ Φ₃₂)                                                                        (1)

N₁ + N₂ + N₃ = 1                                                                                              (2)

where Φ₁₂, Φ₁₃, Φ ₂₃, Φ ₂₁, Φ ₃₁ and Φ ₃₂ are the Wasiljewa constants, k, k₂, k₃ are the conductivities of air, carbon dioxide and water vapor, and N₁, N₂ and N₃ are the molar fractions of air, carbon dioxide and water vapor.

The Wasiljewa constants can be given by

Φ_αβ = (1/8^1/2) ( 1 + M_α/M_β)^-1/2 [ 1 + (μ_α /μ_β )^1/2 (M_β / M_α )^1/4 ]²                                        (3)

Hear M_α is the molecular weight of species α and μ_α is the viscosity of pure species α. Equations (1),(2) and (3) has been shown to reproduce measured values of the thermal conductivity of mixtures within an average deviation of about 2%.

Equation (1) (2) and (3) are used to predict the thermal conductivity of a gas mixture of CO₂, O₂ and N₂. The following data of the pure CO₂, O₂ and N₂ at 1 atm and 293K can be found from a Physical Handbook.

It is assumed that molecular fractions of CO₂ (1), O₂ (2) and N₂ (3) are 0.133, 0.039 and 0.828 respectively. Using equation (3) it can be found the related values as

                                          N₁+N₂Φ₁₂+N₁₃Φ₁₃=0.763              (4)

N₂+N₃Φ₂₃ +N₁Φ₂₁ =1.057              (5)

N₃+N₁Φ₃₁ +N₂Φ₃₂ =1.049              (6)

Substitution in equation (1) gives

K_mix =(0.133)(383)(10^-7) /0.763+(0.039)(612)(10^-7) /1.057+(0.828)(627)(10^-7) /1.049

        =584(10^-7) cal/cm-s-K                                                      (7)

This is the principle of thermal conductivity sensors able to measure the concentrations of any gas mixtures such as a ternary gas mixture consisting of CO₂, O₂ and N₂. The thermal conductivity sensors manufactured by POSIFA Microsystems Company are shown in the above figure. The sensors are created in a silicon substrate and configured to have a hot plate suspending over a cavity recessed into the substrate, a resistive heater and a plural of hot junctions of a thermopile disposed on the hot plate and a plural of cold junction of the thermopile disposed the frame region of the cavity which is formed by the substrate. An interface circuit of the sensors is also shown in the above figure. The circuit comprises a microcontroller, a pre-amplifier, a measurement thermal conductivity sensor and a reference thermal conductivity sensor. The two sensors are heated by applying PWM to the sensor heaters from the microcontroller. The outputs of the sensors are sent to the pre-amplifier and then to the microcontroller for digital processing. The reference sensor is used to compensate the offset, temperature drift and noise of the measurement sensor.

The quality of air inside a building depends on the concentrations of contaminants which are difficult to measure. However, CO₂ levels, which are easy to measure, can be used in place of other measurements to indicate the indoor air quality. CO₂ is produced when people breathe. Each exhaled breath by an average adult contains 35,000 to 50,000 ppm of CO₂ – 100 times higher than 350 to 500 ppm that is typically found in the outside air.

If a thermal conductivity sensing module is installed in a building it will tell you how clean or polluted your air is, and also actuates a ventilation system to supply the building continuously with fresh air. Other applications of the thermal conductivity sensing modules include:

• 0 – 100% Hydrogen in Air
• 0 – 100% Methane in Air
• 0 – 100% Carbon Dioxide in Methane
• 0 – 100% Helium in Air

Micromachined Thermal Conductivity Sensor

with a Thermopile on a Hot-plate

Xiang Zheng Tu

  

The thermal conductivity sensor with a heater and a thermopile is manufactured by POSIFA Microsystems Company. The sensor is created in a silicon substrate and constructed with a thin membrane suspending over a cavity recessed into the substrate. A resistive heater and a plural of hot junctions of a thermopile are disposed on the membrane and a plural of cold junctions of the thermopile are disposed the top of the substrate which is surrounded the membrane. The cavity is configured to have a bottom surface parallel with the top membrane allowing heat generated by the heater transfers perpendicular through the cavity to the bottom. The path length is optimized to have a maximum heat conduction transfer efficient.

The sensors rely on the thermal conductivity of a gas mixture which affects thermal phenomenon by way of heat conduction transfer that, in turn, is converted into a varying electrical signal capturing the sensor response to its component concentration change. As shown in the top figure, the sensors are thermally isolated so only heat transfer due to thermal conductivity through a cavity. Other heat transfer pathways such as through substrate or electrical leads result in thermal losses that degrade sensor performance and have been minimized in the device design.

In sensor operation the heat P_heat generated inside the heater by a DC voltage U_DC applied to the output terminals of the thermopile sensor follows the equation

P_heat = U ² R_sensor . (1)

We can assume that the thermal contact between the periphery and the ambient is so good that the temperatures of the periphery and the ambient environment are identical. They are equal to T_amb. Since the heat P_heat is generated on the membrane, its temperature T_mem depends on the thermal conductivity λ_mem of the hot plate and the thermal conductivity λ_gas of the gas filled in the cavity as

T_mem = P_heat / (λ_mem + λ_gas) + T_amb. (2)

The generated thermopile voltage U is proportional to the temperature difference between the membrane and the periphery as

U_DC ∝ ΔT = T_mem − T_per ≈ Pheat / (λ_mem + λ_gs) . (3)

Thus,

U_DC ∝ U ² / ( λ_mem + λ_gas) . (4)

Each gas has a known thermal conductivity. The thermal conductivities of some gases can be found in the table below.

Gas	Thermal Conductivity
ACETYLENE	4.400
AMMONIA	5.135
ARGON	3.880
CARBON DIOXIDE	3.393
CARBON MONOXIDE	5.425
CHLORINE	1.829
ETHANE	4.303
ETHYLENE	4.020
HELIUM	33.60
HYDROGEN	39.60
HYDROGEN SULPHIDE	3.045
METHANE	7.200
NEON	10.87
NITRIC OXIDE	5.550
NITROGEN	5.680
NITROUS OXIDE	3.515
OXYGEN	5.700
SULPHUR DIOXIDE	1.950

The sensors can be used not only measure all the gases listed in the table but also to analyze a whole range of binary gas mixtures provided that there are only two gases present and that the two gases have significantly different thermal conductivities.  Example is nitrogen and hydrogen or a pseudo-binary mix. Air is an example of a pseudo-binary mix: it has a fixed proportion of oxygen and nitrogen, both having very similar thermal conductivities and so behaves much like a single gas.

Other examples include:

• 0 – 100% Hydrogen in Air
• 0 – 100% Methane in Air
• 0 – 100% Carbon Dioxide in Air
• 0 – 100% Carbon Dioxide in Methane
• 0 – 100% Helium in Air

Thermal conductivity sensor with a heater and a thermopile utilizes micromachining technology which is amenable to creating micro-heaters and thermal conductivity sensors with no moving parts required, thus simplifying fabrication and operational design requirements. Another reason for the large interest in thermal conductivity sensors is the advantages gained through miniaturization: low power consumption, higher sensitivity to low conductivity, fast response and ease of use with different modes of operation. 

Diaphragm Pump Controlled by Thermal Flow Sensor

Xiang Zheng Tu

  

A diaphragm pump controlled by a thermal flow sensor is shown in the above figure.

The pump assembly has a thermal flow sensor, a microcontroller, a NPN switch and a solenoid driven diaphragm pump. The microcontroller has a 10 bits ADC, due to noise and other accuracy diminishing factors, its true accuracy is less than 10 bits. This application provides a software-based oversampling technique, resulting in 16 bits resolution. When the diaphragm pump is in operation a fluid flow is driven to pass over the thermal flow sensor. The sensor measures the flow rate and output an electronic signal to the microcontroller. After ADC conversion a pulse width modulation (PWM) signal is generated by the microcontroller.  It is send to the NPN switch for applying a current to the solenoid driven diaphragm pump. The electromagnetic core of the diaphragm pump moves against a spring to slide a diaphragm into the discharge position. When current is removed, the diaphragm slides back into the suction position.

There are two ways for controlling the flow rates of the diaphragm pumps. When the used PWM frequency is in the range of 25 to 200 Hz the solenoid responds (full stroke) over the duty cycle range of control. At zero duty cycle the solenoid does not move, the pump is not opened and therefore the flow is zero. At 50% duty cycle the solenoid moves through full stroke and opens the pump to full flow. Since the pump is only allowing full flow for 50% of the time, the time averaged flow in theory will be 50% of maximum flow. This type of control is called “digital” because the pump is fully open or fully closed, “on” or “off”. Other way is the PWM frequency limited in the range of 200 to 1000 Hz which produces the time averaged current and does not allow the solenoid to fully respond as in digital control. In this case linear position control is realized and any flow rate between zero and maximum can be chosen by the user.

Theoretically diaphragm pumps can produce the same flow at a given speed (RPM) no matter what the discharge pressure. However, a slight increase in internal leakage as the pressure increases prevents a truly constant flow rate. The following figure shows the measured flow rates in one-hour intervals for an infusion pump. The X-axis reference lines showed the acceptable flow rate (5 mL/h ± 15%). In all experiments, pumps initially infused at a rate faster than their nominal flow, and then returned closer to their set rates up to the complete deflation. The percentage of the flow rate error (deviation from 5 mL/h ± 15%) was 100% in the first and second hours of infusion, 96% in the third hour, 60% in the 20th hour and zero percent in the rest of the infusion time. Flow rate error in the initial hours of infusion was due to fast pump flows, and in the 20th hour due to slow infusion rates. 

Thermal Flow Sensor Measurement Circuit with PIC Microcontroller

Xiang Zheng Tu

  

As shown in the above figure, a PIC16F1704 is used for a thermal flow sensor measurement circuit provided by POSIFA Microsystems Company. The thermal mass flow sensor consists of upstream and downstream temperature thermopiles and a heater located between the two thermopiles. If no gas flows over the sensor surface, the thermopiles measure the same rise in temperature, resulting in the same output voltage of the two thermopiles. If a non-zero gas flows, the velocity of a fully-developed laminar air flow unbalances the temperature profile around the heater and heat is transferred from upstream thermopiles to the downstream thermopiles, causing a change in the voltages of the thermopiles. Larger gas flow rates result in larger change in the temperature profile.

The sensors are thermally isolated so only heat transfer due to flow can occur. Other heat transfer pathways such as through substrate or electrical leads result in thermal losses is minimized in the device design.

To interface the microcontroller the following I/O pins of the microcontroller are setup as:

• RC0 assigned to OPA1in+, pin 9 (sensor in)
• RC1 assigned to OPA1in-, pin 8 (offset bias)
• RC2 assigned to digital out, pin7
• RC3 assigned to OPA2 out, pin 6
• RC4 assigned to OPA2 in-, pin5
• RC5 assigned to OPA2in+, pin4
• RA2 assigned to DAC1out2, pin 10 (offset setting)
• RA0 assigned to I²C data, pin 12, where I²C pull-up to be provided by host assigned to ANO when not in I²C mode (bat monitor)
• RA1 assigned to I²C CLK, pin 11, where I²C pull-up to be provided by host assigned to AN1 when not in I2C mode (temp monitor)
• RA3 assigned to /MCLR pin 3 as an input assigned to LED as an output
• RA4 assigned to AN3 pin 2 (sensor from op-amp 1)
• RA5 assigned to digital output pin 1 firmware PWM for heater current set up as open drain

The internal devices of the microcontroller are setup as:

• Set OPAMP1out to AN6
• Set DAC reference to VFR at 2.048 and V_ss
• A/D reference tied to VFR at 2.048 and V_ss 

Some concepts are adopted in operation of the microcontroller:

• Processor powered directly off battery – no regulator
• External reference for heater and thermistor
• External ref and current source is lower cost than regulator
• DAC and Analog in referenced internally to 2.048
• Offset and amps are setup to avoid amp saturation
• Calibration with no flow to obtain offset reference value
• During operation, heater is on for 15 ms at which time data is taken 

The circuit supports a battery driven power supply and is capable of time keeping. It senses the signals from the flow sensor, calculates the flow and then accumulates it. The total flow accumulated and the month wise profile of the flow are stored and updated in the memory. The user key available on the board can be used to display the flow accumulated in a month and the date on the LCD. The design also supports wireless communication with another handheld device. Thus, the device supports the AMR where

The software design matching the circuit consists mainly of the flow calculation, database, user interface, and communication modules. The software has following main modules:

• Flow Calculation Module

• Database Management Module

• User Interface Module

• Communication Module

PIC Microcontrollers - Programming in C is a Microchip site where you can browse and download free software / firmware code examples for your PIC projects. You'll find code for controlling simple timers and UARTs, low power modes, Fast Fourier Transforms, LCD displays, motor-control algorithms, and many more. These examples are better proof that program writing is neither a privilege nor a talent issue, but the ability of simple putting puzzle pieces together using directives. Design and development of devices mainly boil down to the ‘test-correct-repeat’ method. Of course, the more you are in it, the more complicated it gets since the puzzle pieces are put together by both children and first-class architects.

Example 1: Module CCP1 as PWM signal generator

Example 2: Using A/D converter

Example 3: Using EEPROM Memory

 

Example 4: Using LCD display

Increasement of Thermal Flow Sensor Resolution

by Oversampling with Lower Bit ADCs

Xiang Zheng Tu

 

As shown in the above figure, a thermal flow sensor provided by POSIFA Microsystems Company consists of a heater and two thermopiles. The sensor is heated above ambient temperature by passing a PWM output of a microcontroller through the heater and the sensor flow-dependent heat loss causes temperature changes which are converted by the thermopiles into an electrical signal. This signal is then periodically sampled and digitized by the analog-to-digital converter (ADC) of the microcontroller.

When considering the resolution required for an A/D converter (ADC) integrated in a microcontroller, embedded systems designers must balance cost and performance. Higher ADC resolution implies higher-cost microcontroller, but in some cases you can use other features in the microcontroller to enhance the ADC performance via software. That approach lets you achieve higher resolution using an inexpensive integrated ADC.

Oversampling is a process of sampling a signal with a sampling frequency significantly higher than the Nyquist rate. Theoretically a bandwidth-limited signal can be perfectly reconstructed if sampled above the Nyquist rate, which is twice the highest frequency in the signal. Oversampling improves resolution, reduces noise and help avoid aliasing and phase distortion by relaxing anti-aliasing filter performance requirements.

In our case, to implement a 12-bit converter, it is sufficient to use a 16-bit converter that can run at 256 times the target sampling rate. Combining 256 consecutive 12-bit samples can increase the signal-to-noise ratio at the voltage level by a factor of 16 (the square root of the number of samples averaged), effectively adding 4 bits to the resolution and producing a single sample with 16-bit resolution. To get the best possible representation of the analog input signal, it is necessary to oversample the signal this much, because a larger amount of samples will give a better representation of the input signal, when averaged.

Criterias for using oversampling technique are:

·       The sensor signal being measured should vary at very low frequency. Furthermore to obtain very accurate information about the dynamics of the signal, multiple harmonic components of the signal are acquired, resulting in the need to process signal bandwidths much wider than the actual signal.

·       The signal-component of interest should not vary significantly during a conversion. There should be some noise present in the signal. The amplitude of the noise should be at least 1 LSB.

Fortunately, the bandwidth of the thermal flow sensor is rather small, typically ranging from a few Hertz to a few kilohertz, high oversampling ratios can be readily employed.

Normally there are some noises present during an analog-to-digital conversion. These noises include thermal noise, noise from the CPU core, switching of I/O-ports, variation in the power supply and others, which are enough to make this method work. Another approach for satisfying the criteria is to use a method similar to a Delta-Sigma modulator, by adding a triangular wave to the input signal.

Digital Signal Processing software is required for oversampling and average. This software can be divided into five major blocks:

·       Peripheral Initialization

·       Triangular Signal Generation

·       Data Acquisition

·       Digital Filter Decimation

·       Interrupt Service Routine

A PWM output and an analog low pass filter can be used to generate a triangular signal as an additional noise signal. Reference to the above figure another PWM output of the microcontroller is used to heat the thermal flow sensor. It should be understand that the thermal inertia of the flow sensor can be modeled as a low-pass filter in the thermal domain. This may limit the response time of the flow sensor, but also remove the peak noise from the PWM output signal.

MEMS Infrared Emitters

Xiang Zheng Tu

Infrared thermal emitters can be approximated as black body radiation, which is the type of electromagnetic radiation. The radiation has a specific spectrum and intensity that depends only on the temperature of the emitters. An emitter at room temperature appears black, as most of the energy it radiates is infra-red and cannot be perceived by the human eye. When it becomes a little hotter, it appears dull red. As its temperature increases further it eventually becomes blue-white.

POSIFA Microsystems Company has developed a new generation of MEMS infrared emitters that form its hot-heater based thermal flow sensors and thermal conductivity sensors. Proprietary advanced porous silicon technology combined with silicon processing result in the highest performance MEMS infrared emitters.

The MEMS infrared emitter consists of a resistive thin film platinum based heater which is positioned on a free standing thin-film stack membrane, and allows the heater to operate continuously and reliably at a higher temperature. The freestanding thin-film stack membrane thermally and electrically isolates the heater from the silicon substrate, and reduces the power consumption of the heater. In addition, the membrane has low thermal mass so that the heater is easy to modulate. The rise time of the heater is as low as 5 ms, indicating the frequency of driving pulse voltage can be up to 100 Hz.

As shown in the above figure, when the MEMS infrared emitter is operated at 300 k, the produced infrared spectrum range can be from 2 to 20 µm. This is mid-infrared spectral region containing strong characteristic vibrational transitions of many important molecules as well as two atmospheric transmission windows of 3-5 μm and 8-13 μm, which makes it crucial for applications in spectroscopy, materials processing, chemical and biomolecular sensing, security and industry.

The MEMS infrared emitters have many industrial applications including:

• Medical (CO₂ / other gases monitoring, breath /vapor analyzing),
• Military / Law Enforcement,
• Automotive / Transportation (breath alcohol testing / exhaust monitoring)
• Aerospace (calibration systems, image sensing),
• HVAC (demand controlled ventilation, refrigerant monitoring), and
• Safety / Industrial / Environment Control (combustion gas analyzers, gas detection, air pollution). 

In the above figure the key components of an infrared greenhouse gases measurement system are infrared emitter, measurement chamber, interference filter, and infrared detector. Infrared radiation is directed from the infrared emitter through the measured greenhouse gases to the infrared detector. An interference filter located in front of the detector prevents wavelengths other than that specific to the measured gases from passing through to the detector. POSIFA Microsystems Company can provide not only infrared emitters, but also interference filters and infrared detectors. The interference filter is made of multi porous silicon layers and the infrared detectors are thermopile type.

Precise Water Delivery System for Coffee Machines

Xiang Zheng Tu

 

The above figure shows a precise water delivery system for coffee machines, which is provided by POSIFA Microsystems Company. The system comprises a cold water inlet, a water filter, a solenoid valve, a thermal flow sensor module, a one-way valve and a water heater. The cold water may be supplied by running water or a water container in which water flow is driven by water self weight. The cold water enters the inlet and then successively passes through the solenoid valve, the thermal flow sensor module, the one-way valve. Finally the cold water is heated in the water heater and the hot water is ready to flow into a spray head, and onto a ground coffee, which is contained in a brew basket mounted below the spray head.

On or off of the cold water flow is realized by the solenoid valve. The valve is controlled by an electric current through a solenoid. If the valve is open when the solenoid is not energized, then the valve is termed normally open (N.O.). Similarly, if the valve is closed when the solenoid is not energized, then the valve is termed normally closed. The solenoid is controlled by a switched circuit. A digital output is connected to the base of a transistor which controls the current to a normally open relay. When the relay coil is energized, it closes the contacts, which allows current from the DC supply to flow through the solenoid. When the solenoid coil is energized, the valve opens, allowing cold water to flow through the valve.

The digital output is send by the thermal flow sensor module. The thermal flow senor module measures the flow rate of the cold water and calculates an amount of cold water flowing through in a certain time by integration, which should equal to a desired amount of cups of finished coffee to be made. When the corresponding digital output matches the number of cups, the control circuit is open to the solenoid valve thus closing the valve and stopping the flow of cold water. As seen in the above figure, there is another similar circuit for controlling the water heater. Likewise, at the same time, another digital output is sent to the contactor of the circuit for shutting down the power supply to the water heater.

The thermal flow sensors of the module rely on the ability of fluid flows to affect thermal phenomenon by way of heat transfer that, in turn, is transduced into a varying electrical signal capturing the sensor response to flow change. The sensors are thermally isolated so only heat transfer due to flow can occur. Other heat transfer pathways such as through substrate or electrical leads result in thermal losses that degrade sensor performance and are minimized in the device design. The thermal flow sensors measure mass flow rate and response is independent upon a constant fluid temperature. 

Humidity Compensated MEMS Air Mass Flow Meters

Xiang Zheng Tu

Mass flow rate of air entering a fuel-injected internal combustion engine is necessary for the engine control unit (ECU) to balance and deliver the correct fuel mass to the engine. Air mass flow rate varies with the ambient absolute humidity, which means that a mass flow sensor should be in injunction with a humidity sensor for determining the quantity of intake air in each cylinder. That is hwy POSIFA Microsystems Company provides humidity compensated MEMS air mass flow meters.

The humidity compensated MEMS air mass flow meter is located ahead of a throttle body. After an air filter, the meter utilizes a MEMS thermal conductivity sensor measures the absolute humidity of air entering the throttle body. Then the entered air passes through a MEMS thermal mass flow sensor, which is incorporated in the same body and is used to measures the air mass flow rate. A microcontroller of the meter processes the data collected by the two sensors and provides a humidity compensated air (or dry air flow rate) to ECU.  

The combustion of gasoline or octane in pure oxygen follows this reaction:

2 C₈H₁₈ + 25 O₂ → 16 CO₂ + 18 H₂O                                                 (1)

This is so-called “on ratio” or “stoiechiometric” combustion. Molecular weights of the above reagents are C₈H₁₈ = 114, O₂ = 32, CO₂ = 44, H₂O = 18. The ratio of mass of oxygen to mass of octane is 25 x 32 / mol / 2 x 114 /mol = 3.51, which means that 1 kg of octane reacts with 3.51 kg of oxygen to produce 3.09 kg of carbon dioxide and 1.42 kg of water.

By volume, dry air contains 78.09% nitrogen, 20.95% oxygen, 0.93% argon, 0.04% carbon dioxide, and small amounts of other gases. Air also contains a variable amount of water vapor, on average around 1% at sea level, and 0.4% over the entire atmosphere.

So in dry air the reaction is expressed as:

2 C₈H₁₈ + 25 (O₂ + 3.7 N₂) → 16 CO₂ + 18 H₂O                                (2)

The ratio of mass of air to mass of octane is 12.99. Therefore for octane, the dry air–octane mixture is 12.99 i.e. for every one gram of octane 12.99 grams of air is required.

Combustion process never runs stoichiometric. It always incorporates a modest amount of excess air - 10 to 20% more than needed to burn the gasoline completely.

If insufficient amount of air is supplied to engine, unburned fuel, soot, smoke, and carbon monoxide are exhausted from the engine. The results are heat transfer surface fouling, pollution, lower combustion efficiency, flame instability and a potential for explosion.

Like all thermal mass flow meters, humidity affects their output. Sine water vapor is added to the dry air, the total mass is increased and both the overall thermal conductivity and overall viscosity change. To correct the mass flow readings of the meters what percentage of the water vapor should be know.

The thermal flow sensor of the humidity Compensated MEMS air mass flow meter consists of a thermal insulating base, a resistive heater and two thermopiles. The heater is structured as a long stripe extending from one side of the base to the opposite side and the hot junctions of the two thermopiles are arranged along the two opposite sides of the heater respectively. The cold junctions of the two thermopiles are arranged along the two opposite edges of the base. As air flows by the sensor, molecules of the flowing air transport heat away from the sensor, the sensor cools, and energy is lost, which is governed by the equation as

Qt = ΔT [ k + 2 (k V_v ρ π d V_avg)^1/2 ]                                                  (3)

Where:

qt = rate of heat loss per unit time

ΔT = mean temperature elevation of the thermal insulating base

d = width of the resistive heater

k = thermal conductivity of the air passing through the sensor

C_v = specific heat of the air passing through the sensoe

Ρ = density of the air passing through the sensor

V_avg = average velocity of the air passing through the sensor

In this equation, ρ, V_avg, qt, and ΔT are the unknowns, because they change with time while the other variables are known. However, q_t and ΔT can be obtained through measuring devices, leaving in the product of ρ and V_avg and cross section area of the pipe.

The thermal conductivity sensor the humidity Compensated MEMS air mass flow meter is the same as the thermal flow sensor except that the thermal insulating base is replaced by a plate suspending over a cavity. The cavity is filled with a measured humidity air and by conduction the humidity air transports heat away from the sensor.

According to the Wassiljewa Equation the thermal conductivity of a humidity air can be expressed as:

k_h = x_d k_d / (x_d A_d + x_w A_w) + x_w k_w / (x_d A_d + x_w A_w)                          (4)

Where:

x_d  = mole concentration of dry air

x_w = mole concentration of water vapor

k_h = thermal conductivity of humidity air

k_d = thermal conductivity of dry air

k_w = thermal conductivity of water vapor

A_d, A_w = constants to be specified

It could be convenient to use linear least squares method for anglicizing the measurement data of the thermal conductivity sensor. The regression model can be expressed as:

V_out = β x_d + β_w x_w                                                                                (5)

x_d + x_w = 1                                                                                             (6)

Where V_out is the output of the thermal conductivity sensor, β and β_w are constants to be specified by experiments. 

Assuming:

m_h = mass flow rate of humidity air which is measured by the thermal flow sensor

m_w = mass flow rate of water vapor which is calculated using absolute humidity measured by the thermal conductivity sensor

The following expression can be established:

m_d = m_h – m_w                                                                                                         (7)

Where m_d = mass flow rate of dry air which is required by all combustion engines.

In conclusion, POSIFA Microsystems Company provides humidity compensated MEMS air mass flow meters which combine air flow rate and absolute air humidity measurements and directly output dry air flow rate without adding temperature and pressure measurements.

Natural Gas Calorific Meter with Two MEMS Sensors

 Xiang Zheng Tu

As shown in the above figure, a MEMS natural gas calorific meter mainly comprises a MEMS thermal flow sensor and a MEMS thermal conductivity sensor. The thermal flow sensor measures natural gas mass flow rate. Natural gas is a naturally occurring gas mixture, consisting of methane, ethane, propane and nitrogen. The thermal conductivity sensor measures the mole percentage of methane, ethane, propane and nitrogen in the natural gas flow. With the measured mass flow rate and each composition mole percentage the calorific flow rate and total calorific value displayed on the smart phone in the above figure can be calculated.

With the state-of-the-art electronics for the signal process, MEMS natural gas calorific meters have extended dynamic range, enhanced data safety and are easy for network and remote data transmission. They have automatic temperature and pressure compensation and directly provide calorific value. As the MEMS sensor is miniature, the sensor assembly including the electronic control board can be designed into a compact form that is substantially smaller than the mechanical counterpart. This could benefit for the reduction of the cost not only in manufacture but for overall gas distribution management.

The composition of natural gas can be determined based on the fact that the temperature curve of the thermal conductivity coefficient is unique for each natural gas mixture, but highly correlated. A multiple linear regression can be used to model the relationship between two or more explanatory variables and a response variable by fitting a linear equation to observed data. The model can be expressed as:

Y = β₀ + β₁ x_Ethane + β₂ x_Propone +β₃ x_Nitrigen                                                               (1)

x_Methane + x_Ethane + x_Propone  + x_Nitrigen= 1                                                                  (2)

Where Y is the sensor signal output, β₀, β₁, β₂, β₃ are the parameters of the regression equation, and x_Methane,  x_Ethane,  x_Propone ,  x_Nitrigen are each component mole fraction of natural gas.

These equations describe how the mean response Y changes with the explanatory variables. The observed values for Y vary about their means y and are assumed to have the same standard deviation σ. The fitted values estimate the parameters β₀, β₁, β₂, β₃ of the regression equations. Since the observed values for y vary about their means Y, the multiple regression models include a term for this variation. In words, the model is expressed as DATA = FIT + RESIDUAL, where the "FIT" term represents the expression β₀ + β₁ x_Ethane + β₂ x_Propone +β₃ x_Nitrigen . The "RESIDUAL" term represents the deviations of the observed values y from their means Y, which are normally distributed with mean 0 and variance σ. The notation for the model deviations is ɛ.

The thermal conductivity sensor is excited using three voltage steps V₁, V₂, V_3, resulting in three different operation temperatures. Each operation temperature or driving voltage results a multiple linear regression as follows:

Y_v1 = β_0v1 + β_1v1 x_Ethane + β_2v1 x_Propone +β_3v1 x_Nitrigen                                                  (3)

Y_v2 = β₀v₂ + β_1v2 x_Ethane + β_2v2 x_Propone +β_3v2 x_Nitrigen                                                 (4)

Y_v3 = β_0v3 + β_1v3 x_Ethane + β_2v3 x_Propone +β_3v3 x_Nitrigen                                                  (5)

With measured Y_v1, Y_v2, Y_v3, and estimated parameters β_0v1……β_3v3, the composition mole percentage of a natural gas coming from different sources can be determined in this way: First the sensor is excited by the three voltage steps V₁, V₂, V₃ and each results a sensor signal outputs;

Then the three equations can be obtained for each exciting voltage;

Finally the equations are solved to find each mole fraction of the measured natural gas.

The calorific value of natural gas can be further calculated using the above measured data as flows:

Calorific flow rate =

(ṁ G_Methane) (HV_Methane) + (ṁG _ethane2) (HV_Ethane) + (ṁ G_Propone) (HV_Propone)          (6)                                  

G_Methane = MW_Methne  x_Methane / (MW_Methne  x_Methane +MW_Ethane  x_Ethane

+MW_Propone  x_Propone +MW_Nitrigen  x_Ntrigen  )                                                                 (7)

G_Methane = MW_Ethane  x_Ethane/ (MW_Methne  x_Methane +MW_Ethane  x_Ethane

+MW_Propone  x_Propone +MW_Nitrigen  x_Ntrigen  )                                                                (8)

G_Methane = MW_Methne  x_Methane / (MW_Methne  x_Methane +MW_Ethane  x_Ethane

+MW_Propone  x_Propone +MW_Nitrigen  x_Ntrigen  )                                                                (9)

Where: ṁ = mass flow rate measured by the thermal mass flow sensor, in b/min

HV_n = heating value of gas component n, in BTU/SCF
x_n = mole fraction of gas component n. The table below contains the list of individual component LHV & HHV.

MW = Molecular weight of gas component n.

     

The higher heating value (HHV) refers to a condition in which the water is condensed out of the combustion products. The higher heating value includes the sensible heat and latent heat of vaporization especially for water. In other words, HHV assumes all the water component is in liquid state at the end of combustion.

The lower heating value (LHV), on the other hand refers to the condition in which water in the final combustion products remains as vapor (or steam); i.e. the steam is not condensed into liquid water and thus the latent heat is not accounted for.  The LHV assumes that the latent heat of vaporization of water in the fuel and the reaction products is not recovered. 

Porous Silicon Multilayer Infrared Bandpass Filters

Xiang Zheng Tu

 

Nowadays many people with diabetes need to measure their blood glucose levels by pricking their fingers, squeezing drops of blood onto test strips, and processing the results with portable glucometers. The process can be uncomfortable, messy and often has to be repeated several times every day.

In order to help improve the lives of millions of people by enabling them to constantly monitor their glucose levels without the need for an implant, non-invasive measurement approaches of blood glucose concentration based on absorption measurements in the infrared region have been explored many years. Among them is a micro-optical-mechanical-electro-system (MOMES)-based non-invasive blood glucose monitor designed by the present author ten years ago, as shown in the above figure.

The monitor comprises a micromachined infrared optical filter array, a micromachined infrared mechanical modulator array, at least one micromachined infrared tunable filter, and at least one infrared detector. Each optical filter is aligned with a mechanical modulator along its optical axis direction. The optical filter continuously divides a monochromatic infrared light in a wavelength range within 0.8 to 25 micron from an infrared light. The aligned mechanical modulator turns the monochromatic infrared light into an alternating monochromatic infrared light. The tunable filter is aligned with the infrared detector along its optical axis direction. The tunable filter selects the back-diffused alternating monochromatic infrared light emitted from a measured blood subject that is illuminated by the alternating monochromatic infrared light. The infrared detector converts the back-diffused alternating monochromatic infrared light into an alternating electronic signal. Then a photo-integrated circuit (IC) combines with the infrared detector for synchronous detection and amplification of the electronic signal generated by the synchronous detection.

The micromachined infrared optical filter could be a porous silicon multilayer infrared pass band filter, reference to the above figure. The basis of the porous silicon filter is the same as in a common interference filter. The main difference is that in the porous silicon filter the difference in refractive indices is caused by different porosities of porous silicon layers, not different layers of different materials as in common interference filters. The porous silicon multilayers are produced by changing one of the etching parameters periodically. Etching parameters that affect the morphology and pores’ depth are current density, electrolyte composition, sample’s doping, etc. Once a porous silicon layer is formed anodization stops in this layer and only proceeds in pore tips. The porosity of layers depends only on current density when other etching parameters are kept fixed so that changing the current density results in layers with different porosities in depth of sample.

The porous silicon multilayer shown in the above figure can be expressed as:

(LH )^mLL (HL)^m-1 H                                                                                (1)

where L denotes a layer with low refractive index and H denotes a layer with high refractive index, m is the numbers of repeating periods. Optical thickness of L and H Layers in bandpass interference filter should be equal to one fourth of peak wavelength: 

n_Ld_L = n_Hd_H = λ_p/4                                                                                   (2)                                                               

where n_L is  refractive index and d_L physical thickness of L layers, similarly n_H and d_H correspond to H layers, and λp is a peak wavelength. 

The effective refractive index of porous silicon layer, n, depends on its porosity. The refractive index is almost a linear function of porosity. Bruggeman approximation is used to determine the effective refractive index of porous silicon layer:  

n = (1 - p) ( ɛ_si - ɛ_psi ) / (ɛ_si + 2 ɛ_psi ) + p (ɛ_air – 2 ɛ_psi ) / (ɛ_air + 2 ɛ_psi )         (3)

where p is the porosity, and ɛ_air, ɛ_si, ɛ_psi are the dielectric constants of air, silicon, and porous silicon, respectively.

The infrared light source shown in the above figure could comprise a resistive heater positioned on the top of a membrane suspending over a cavity. All theses elements of the device are constructed as a microstructure and integrated with the porous silicon multilayer infrared bandpass filter in a same silicon substrate. As can be seen in the figure the collimator is also formed in the silicon substrate and positioned along the extending direction of the light source and the porous bandpass filter. In this way the micro-optical-mechanical-electro-system (MOMES)-based non-invasive blood glucose monitor can be small in size, light in weight, compact in structure and low in power consumption. 

MEMS Optical Fabry–Pérot Switches

Xiang Zheng Tu

 

Ten years ago, the present author designed a MEMS optical switch array for DNA synthesis and detection, as shown in the figure 1.  A MEMS optical Fabry-Perot switch consists of a silicon substrate, a cavity and a driving circuit. The cavity is formed by a deflectable plate and a fixed plate which are separated by an air gap. The plates are constructed by dielectric thin films coated with a metal film on their opposite surfaces. The dielectric plates are transparent in the wavelength ranges 350 nm – 14000 nm. The metal films are used as both optical reflecting mirrors and electrodes connecting to the driving circuit. The air gap can be changed by applying the voltage between the two plates resulting in an electrostatic force which pulls the plates closer.

 

The principle of operation of the optical switch is illustrated in the figure 2. The input signal is incident on the left surface of the cavity. After one pass through the cavity a part of the light leaves the cavity through the right facet and a part is reflected. A part of the reflected wave is again reflected by the left facet to the right facet. If the air gap is equal to half an even multiple of the wavelength in the cavity a round trip through the cavity will be an integral multiple of the wavelength. In this case all the light waves will transmit through the right facet add in phase. Such wavelengths are called the resonant wavelengths of the cavity and the optical switch is in “on” state. Similarly, if the air gap is equal to half an odd multiple of the wavelength all the light waves will reflect by the cavity and the optical switch is in “off” state.

Optical Fabry-Perot cavities based on micro electro-mechanical systems (MEMS) are an enabling technology for hyper spectral images and micro spectrometer. MEMS optical switches are high pass filters that block the visible light and pass ultraviolet light. They are characterized by their bandwidth at which maximum transmission is 50%. A MEMS optical switch array consists of a Cartesian grid of switches. This can be used chiefly to map or "encode" the coordinate of each switch to its function. Switches in these arrays typically use a universal signal ling technique (e.g. fluorescence), thus making coordinates their only identifying feature.

Additional features of the MEMS optical switch array for DNA synthesis and detection are combination of DNA synthesis and detection, high probe density and low fabrication cost. Such DNA probes with a MEMS optical switch array can help to dramatically accelerate the identification of the estimated 80,000 genes in human DNA, an ongoing world-wide research collaboration known as the Human Genome Project. The DNA probes can quickly sequence DNA. In addition to genetic applications, the DNA probes can be used in toxicological, protein, and biochemical research. The DNA probes can also be used to rapidly detect chemical agents used in biological warfare so that defensive measures can be taken.

Thermopile Natural Gas Mass Flow Meters

Xiang Zheng Tu

  

Natural gas flow meters are used at residential, commercial, and industrial buildings that consume gas supplied by a gas utility. Several different types of gas flow meters are in common use, depending on the flow rate of gas to be measured, the range of flows anticipated, the type of gas being measured and other factors.

Diaphragm gas flow meters are one type of the most common and oldest gas meters. The advantage of these gas meters is simplicity of construction and, therefore, low cost but their limits are as follows:

(i)             presence of moving parts subject to wear;

(ii)           high pressure losses;

(iii)         mechanical output and

(iv)          inability to indicate an instantaneous flow rate value.

Nowadays new safety-related and consumption-control-related functions have brought about the development of better performing flow meters, with features such as:

(i)             distribution network and user-connection/disconnection blockage valves remote control;

(ii)           remote consumption reading; (iii) overflow and minimum level flow rate alarms;

(iii)         self-control and diagnostics;

(iv)          metrological performance improvement (accuracy, rangeability, stability and thermodynamic condition compensation); (vi) size reduction; and

(v)           advanced computing functions (prepayment and time bands).

Ultrasonic flow meters could represent a good solution in such better performing flow meters. There are two leading types, the transit-time and Doppler style meter. In the transit-time ultrasonic flow meter, the transducers are upstream and downstream of each other and each act as a transmitter and receiver. One transducer, of course, emits the ultrasound signal with the flow, while the other emits it against the flow. The meter measures the difference in transit time between the two transducers, and the velocity difference is used to calculate volume flow.

Ultrasonic flow meters are affected by the acoustic properties of the fluid and can be impacted by temperature, density, viscosity and suspended particulates depending on the exact flow meter. They vary greatly in purchase price but are often inexpensive to use and maintain because they do not use moving parts, unlike mechanical flow meters.

Actuary, gases are more difficult to measure than liquids, as measured volumes are highly affected by temperature and pressure. Gas meters measure a defined volume, regardless of the pressurized quantity or quality of the gas flowing through the meter. Temperature, pressure and heating value compensation must be made to measure actual amount and value of gas moving through a meter.

To solve these problems thermal mass flow sensors could be the best choice. As shown in the above figure, thermal flow meters measure mass flow, not volumetric flow, and use heat disperse to compute the measurement. The primary reason thermal mass flow meters are popular in many applications is their particular features including no moving parts, nearly unobstructed straight through flow path, require no temperature or pressure corrections and retain accuracy over a wide range of flow rates.

The thermal natural gas mass sensors provided by POSIFA Microsystems are manufactured using an US patented technology. The sensor comprises a porous silicon wall with numerous vacuum-pores which is created in a silicon substrate, a porous silicon membrane with numerous vacuum-pores which is surrounded and supported by the porous silicon wall, and a cavity with a vacuum-space which is disposed beneath the porous silicon membrane and surrounded by the porous silicon wall.

Compared to the other thermal natural gas flow sensors, the vacuum-cavity-insulation flow sensor presents superior properties in many aspects.  Among them are easiness of fabrication, perfection of thermal isolation, strength of membrane structure, and lower cost of manufacturing. They have additional performance such as auto-diagnostic, data-recording, block and other functions that can be integrated in an electric output sensor. In the near future they could be an excellent replacement for the ultrasonic flow meters.