A Microfluidic Coulter Counting Device for Metal Wear

A MICROFLUIDIC COULTER COUNTING DEVICE FOR METAL WEAR

DETECTION IN LUBRICATION OIL

A Thesis

Presented to

The Graduate Faculty of The University of Akron

In Partial Fulfillment

of the Requirements for the Degree

Master of Science

Srinidhi Veeravalli Murali

December, 2008 A MICROFLUIDIC COULTER COUNTING DEVICE FOR METAL WEAR

DETECTION IN LUBRICATION OIL

Srinidhi Veeravalli Murali

Thesis

Approved: Accepted:

______Advisor Department Chair Dr. Jiang John Zhe Dr. Celal Batur

______Faculty Reader Dean of the College Dr. Joan Carletta Dr. George K. Haritos

______Faculty Reader Dean of the Graduate School Dr. Dane Quinn Dr. George R. Newkome

______Date

ABSTRACT

Real time monitoring of lubrication oil quality has become an important issue in today’s military, transportation and manufacturing industries. Accurate condition monitoring methodologies are needed to effectively schedule maintenance downtime as well as to ensure the accomplishment of long-range military and commercial operations.

The degradation of lubrication oil is typically from two sources: 1) the accumulation of metal wear particles and 2) degradation in the physical properties of the lubrication oil. During normal machine operation, small wear debris particles with sizes in the range of 1 to 10 microns are usually generated. When abnormal wear begins, larger particles in the range of 10 to 150 microns are generated. The particle population and size will increase gradually with time and this trend of growing particle population and size will increase until machine failure. Thus, continuous monitoring of wear debris in lubrication oil is critical to avoid catastrophic system failure of machines.

This thesis demonstrates that the capacitance Coulter counting principle can be used to detect and count metal wear particles generated in lubrication oil. The Coulter counting principle is an established technique to count biological cells in an electrolyte solution. A Coulter counter consists of two reservoirs connected by a microchannel.

When a particle is present in microchannel, it causes a change in resistance of the liquid- filled microchannel. As lubrication oil is non-conductive, the resistance changes due

iii

to the passage of a particle is difficult to measure. To overcome this, we monitor the

change in capacitance formed between two electrodes in a microchannel. When a metal particle passes through the microchannel, a change in the capacitance can be detected owing to the difference in permittivity between the lubrication oil and the metal particle.

To demonstrate the capacitive Coulter counting principle for metal particle detection in lubrication oil, a meso-sized device consisting of two parallel electrode plates immersed in SAE-5W30 motor oil was used to simulate a fluidic channel. Metal particles were dropped from the top of the channel and allowed to travel to the bottom. It was found that the passage of the metal particle did cause a capacitance pulse, monitored using a capacitive readout IC chip. It was found that the capacitance change increases as the particle size is increased. The demonstration using the meso-sized device indicates the possibility of using a microfluidic device for detection and counting metal wear particles in lubrication oil based on the Coulter counting principle. Furthermore, to validate the dynamic response of the measurement circuitry in a fluid environment for the microfluidic device using co-planar electrodes, we first test the microfluidic device for detection of Juniper scopulorum pollen and polystyrene particles in de-ionized water.

Next, we present the use of a microfluidic sensor for electronic monitoring of wear debris in lubrication oil based on the capacitance measurement setup demonstrated earlier.

Aluminum particles (size ranges from 15 to 25 µm in diameter) suspended in SAE-5W30 motor oil were used for device testing. When oil with aluminum particles was loaded, capacitive pulses due to passage of these particles were observed. The magnitude of the pulses is in the range of 2 to 7 femto-farads. The test results show the microfluidic device to be highly promising for use in online debris monitoring in lubrication oil.

ACKNOWLEDGEMENTS

Grateful acknowledgement is made to my Advisor, Dr. Jiang Zhe, for his constant guidance and mentoring through the project. I would like to thank my co-advisor Dr. Joan

Carletta with all sincerity for her valuable suggestions in this project. My special thanks to Ashish V. Jagtiani for his help all through.Li Du, Hui Ouyang, Abhay Vasudhev and

Xinggao Xia have been of great support in this project. I would also like to extend my heartfelt thanks to my fiancé Anup Viswanathan and my family for their constant support and prayers all through my study away from home.

TABLE OF CONTENTS

Page

LIST OF TABLES...... ix

LIST OF FIGURES ...... x

CHAPTER

I. INTRODUCTION...... 1

1.1 In situ monitoring of lubrication oil quality...... 1

1.2 Coulter counting principle ...... 3

1.3 Microfluidic sensor technology ...... 4

1.4 Research objectives...... 4

II. BACKGROUND WORK ...... 7

2.1 Rotary machine maintenance techniques...... 7

2.1.1 Condition monitoring...... 8

2.1.2 Wear debris monitoring ...... 12

2.1.2.1. Spectrographic analysis ...... 12

2.1.2.2. Ferrography...... 13

2.1.2.3. Acoustic and ultrasonic sensors...... 14

2.1.2.4. Chip detectors / magnetic plugs...... 15

2.1.2.5. Inductive sensors...... 16

2.1.2.6. Particle counting ...... 17

2.1.2.7. Mesh / filter blockage ...... 18

2.1.2.8. Gravimetric analysis ...... 19

2.1.2.9. Coulter counters...... 19

2.1.3 Visual inspection monitoring...... 20

2.1.4. Vibration monitoring ...... 20

2.1.5. Performance monitoring ...... 21

2.2 Summary...... 22

III. CHARACTERIZATION OF THE MEASUREMENT IC MS3110...... 24

3.1 Working principle of MS3110 IC ...... 25

3.2 Static characterization of MS3110...... 27

3.3 Dynamic characterization of MS3110 ...... 33

3.3.1 Response time of MS3110...... 33

3.4 Summary...... 40

IV. MESO-SCALE SENSOR WITH PARALLEL PLATE ELECTRODES...... 41

4.1 Meso-sized device and experimental setup...... 42

4.2 Finite element model...... 44

4.3 Results and discussion ...... 46

4.3.1 Instrumentation validation ...... 46

4.3.2 Detection of conducting particles ...... 47

4.3.3 Comparison of experimental and simulation results

for pulse height of particles...... 47

4.3.4 Comparison of experimental and theoretical analysis

for pulse width of particles ...... 50

vii

4.4 Summary...... 54

V. DEVICE DESIGN, FABRICATION AND PRELIMINARY TESTING

OF A MICROMACHINED SINGLE CHANNEL DEVICE...... 55

5.1 Device design...... 55

5.2 Device fabrication ...... 58

5.3 Material used for device testing...... 62

5.4 Measurement setup ...... 62

5.4.1 Detection of Juniper pollen...... 63

5.4.2 Detection of polystyrene...... 65

5.4.3 Detection of Juniper tree pollen and polystyrene mixture...... 68

5.5 Summary...... 69

VI. MICROMACHINED SINGLE CHANNEL DEVICE FOR WEAR

DEBRIS DETECTION ...... 70

6.1 Experimental setup and testing ...... 70

6.2 Results and discussion ...... 72

6.3 Summary...... 77

VII. CONCLUSIONS...... 78

REFERENCES ...... 82

viii

LIST OF TABLES

Table Page

3.1 Comparison of slopes for CF ranging from 5.13 pF to 0.204 pF 34

4.1 Comparison of theoretical and experimental values for the time

taken by the particle to travel a distance of d=2.5 cm ...... 50

4.2 Comparison of experiment and simulation results with

(a) lower bound (b) upper bound ...... 54

LIST OF FIGURES

Figure Page

2.1 The Bath tub curve -The three stages in the life of a system

1. Running in 2. Normal wear 3. Disaster...... 9

2.2 Schematic outline of the various condition monitoring techniques ...... 11

3.1 Block diagram of MS3110 circuitry ...... 25

3.2 MS3110 output response showing the static characteristic curve CF = 5.13 pF;

CS2 =0.798 pF ...... 29

3.3 Static characteristic curve of MS3110 IC for CF = 1.197 pF; CS2 =0.798 pF ...... 29

3.4 Static characteristic curve of MS3110 IC for CF = 0.494 pF; CS2 =0.798 pF...... 31

3.5 MS3110 IC response to CF = 0.209 pF; CS2 =0.798 pF ...... 31

3.6 MS3110 IC response to CF = 0.209 pF and varying

values of CS1 for a given experiment showing constant gain...... 33

3.7 Schematic of the measurement setup connected to MS3110 IC...... 35

3.8 (a) Dynamic characterization of the MS3110 IC to a switching response,

the upward peak represents the response when the switch is closed and the

downward peak represents the response when the switch is open...... 37

3.8 (b) A magnification of the dynamic response of the MS3110 IC when switch is

closed (connected to LabView) ...... 37

3.9 Schematic of the measurement setup with the mechanical switch and external

resistor...... 38

3.10 (a) Plot showing the response time measurement of the switch connected

to resistor when the switch is closed and open...... 39

3.10 (b) A plot magnifying the first transition region. The response time is 1μs...... 39

4.1 Schematic of the modified mesoscale device, d =1 cm ...... 43

4.2 Mesoscale sensor with parallel plate electrodes filled with lubrication oil ...... 431

4.3 Steel spherical particles used to test the mesoscale device. From left to right

3.5 mm, 4.5 mm and 6 mm steel particles ...... 44

4.4 (a) Finite element simulation on parallel plate electrodes device model geometry.....46

4.4(b) Finite element simulation on parallel plate electrodes meshing obtained

by using 52500 elements...... 46

4.5 Measured capacitance change in response to metal particles of varied size

(a) Dp= 3.5mm (b) Dp= 4.5 mm (c) Dp= 6.0 mm...... 49

4.6 Comparison of the experimental and FEM simulated capacitance change for

the steel particles used in the mesoscale device ...... 50

4.7 Particle traveling through the fluid under the action of three forces ...... 51

4.8 Time taken for steel particles to travel through the fluid (lubrication oil)...... 53

5.1 Schematic of the microfluidic device for preliminary testing with bio-particles ...... 57

5.2 (a) Microscopic image of the microchannel and co-planar electrodes used for

preliminary testing ...... 61

5.2(b) Fabricated microfluidic device ...... 61

5.3(a) Microscopic images of Juniper scopulorum used for preliminary testing ...... 62

5.3(b) Microscopic images of polystyrene particles used for preliminary testing...... 62

5.4(a) Voltage and capacitance change due to 20 μm Juniper scopulorum particles ...... 64

5.4(b) Capacitance change generated by 20 μm Juniper scopulorum ...... 65

5.4(c) Magnified capacitive pulses generated by Juniper scopulorum tree pollen...... 65

5.5(a) Typical capacitive pulse of a 20 μm polystyrene in DI water...... 67

5.5(b) Typical magnified capacitive pulse generated by polystyrene...... 67

5.6 Detection of bio-particles in DI water – each upward pulse represents

a pollen particle and a downward pulse represents a polystyrene particle ...... 68

6.1(a) Schematic of the testing setup of the microfluidic device for wear detection in

lubrication oil...... 71

6.1(b) Experimental setup for testing the microfluidic sensor...... 71

6.2 Optical image of aluminum particles of varying sizes...... 72

6.3(a) Measured capacitance change of the microfluidic sensor

without aluminum particles in oil at 60 kHz sampling frequency...... 74

6.3(b) Measured capacitance change of the microfluidic sensor when oil

was loaded with aluminum particles at 60 kHz sampling frequency...... 74

6.4(a) Typical capacitive pulses when aluminum particle flows through the

microchannel at 200 kHz sampling frequency...... 75

6.4(b) Typical magnified capacitive pulse when aluminum

particle flows through the microchannel...... 75

xii

CHAPTER I

INTRODUCTION

1.1 In situ monitoring of lubrication oil quality

In situ monitoring of lubrication oil quality has become an important issue in today’s

military, transportation and manufacturing industries [1-7]. Maintenance costs, for a

variety of rotating machinery [1, 2], can represent a large percentage of the overall costs

of the manufactured products. As an example, in some heavy manufacturing industries,

maintenance can be as much as 40% of the total cost of the operations [3, 4]. During

machine operation, small wear debris particles are usually generated. During abnormal

wear conditions larger particles start to be generated, these conditions eventually cause

machine failure. Accurate condition monitoring methodologies are sought to effectively

schedule maintenance downtime as well as to ensure the accomplishment of long-range

military and commercial operations. Currently, scheduled off-line oil sampling for

laboratory analysis is the dominating technique for oil condition monitoring [5]. Typical

laboratory analysis includes ferrography, spectroscopy, and filtration. Although

laboratory analysis procedures are comprehensive and detailed, they do not provide the timely information about machinery health critical for event management. A few oil condition monitoring methods have been developed to be used on-line: optical methods,

magnetic inductive methods, acoustic emission, and bulk electric capacitance methods.

Bulk capacitance sensing [5] uses a simple sensing structure, but its measured

capacitance often reflects not only the presence of particles but also changes in total acid number and oil viscosity in the working fluid; this, in turn, creates difficulties in detecting debris. Acoustic emission detection methods, based on the amplitude change of reflected acoustic waves, are sensitive to interference caused by background acoustic emission and lubrication oil temperature variation [8]. Magnetic inductive debris sensors have met some success but are limited to ferromagnetic debris larger than about 100 µm [15].

Optical methods such as scattering counters are capable of detecting particles in oil.

However, the accuracy of the optical approach is affected by particle properties

(refractive index, shape, etc) and the existence of air bubbles [19]. Generally speaking,

today’s real-time debris monitoring methods can provide only limited advanced warning

prior to any catastrophic failure.

In this work, a novel microfluidic device is demonstrated for detecting and

counting metal wear particles in lubrication oil. Because of its simple micromachined

structure, it is highly possible that the device can be used for in-situ oil quality

monitoring. In this paper, we propose to utilize the principal microfluidic device to detect

and count metal wear particles generated in lubrication oil. We choose to monitor the

change in capacitance across a pair of micro electrodes in a microchannel. When a metal

particle passes through the microchannel, a change in the capacitance can be detected

owing to the difference in permittivity between the lubrication oil and the metal particle.

1.2 Coulter Counting principle

One method of detecting particles known as the Coulter counter method [9] has been in

use since 1953 in biological applications of particle counting and detection. A typical

Coulter Counter sensor consists of two chambers, namely, the inlet and the outlet

reservoirs, separated by a single microchannel. As a particle flows through the

microchannel, it causes a change in the electrical parameters such as resistance or capacitance of the microchannel. The change can be measured as a voltage or current

pulse; this pulse contains information about the particle measured. From these voltage/

current pulses, information about the size, shape, mobility, surface charge and

concentration of the particles may be derived [10].

Coulter counting is predominantly used in the detection of bioparticles such as

pollen [11], virus, bacteria [12] and even nanoparticles like DNA [13], and antigen-

antibody [14] in aqueous solutions. The counter detects the change in resistance of the

medium when a particle flows through the channel. A resistive-based Coulter counting

method cannot be used for highly resistive liquids such as lubrication oil. Since

lubrication oil is low-conductive, resistance change due to the flow of particle is difficult to measure making this method inappropriate for the required purpose [15]. As an alternative to this, it is possible to measure the capacitance change in the microchannel when a particle passes through it. This is because of the difference in permittivity of the particle and the oil in the microchannel, and is measurable, unlike the resistive change.

The capacitance-based Coulter counting principle, also called capacitance cytometry [16], has been used for detection of biological cells. A polydimethylsiloaxane

(PDMS) microfluidic channel with a pair of gold microelectrodes has been used for

detecting and quantifying the polarization response of a DNA within the nucleus of a cell.

It has been reported that there is a linear relationship between the DNA content in the cell

and the change in capacitance when a particle flows through the microchannel between

the gold electrodes. This change in capacitance caused by the passage of particles varies

from 3 fF to 27 fF. Inspired by this work, the capacitance Coulter counting principle was

utilized in a microfluidic device for detection of metal particles in lubrication oil. The

challenge lies in the measurement of the small base capacitance and the change in

capacitance when the particle in the lubrication oil passes through the microchannel.

1.3 Microfluidic sensor technology

There is considerable interest in using micro-fluidic technology in commercial, health

monitoring, defense and other varied applications. Microfluidic devices, called lab-on-a-

chip devices, have emerged as very powerful technology over the years. Microfluidic devices, evolved from Micro-Electro-Mechanical-Systems (MEMS), can be batch fabricated, which reduces manufacturing and assembly costs. Microfluidic devices are typically cost-effective, easy to operate and have low power consumption [17]. In this thesis, we will use microfluidic technology for wear detection in lubrication oil.

1.4 Research Objectives

The main objective of this work is to develop a new microfluidic sensor that can be used for wear particle detection in lubrication oil. The wear particles in oil flowing through the channel or a narrow orifice modulate the capacitance from the base. This change in capacitance is detected as voltage pulses using the measurement setup. The main

emphasis is to develop a portable, on-line device that allow reliable and accurate health

monitoring of rotary machines or machine parts, thereby making it a cost-effective

method for future use. Design concepts and constraints for non-conductive fluids led to

the development of a sensor that could help to better understand the phenomena of

tribological fluids, the interaction between fluids and electrical fields in a microscale

channel. To date, no microfluidic sensor for the detection of wear abrasives/debris has

been published in the literature. To validate the design concept of the microscale device,

testing with a meso-scale device consisting of two parallel plate electrodes was performed. Consequently, we design, fabricate and test the microscale devices. .

Specifically, the objectives are listed below:

• Demonstrate the use of the Coulter counting principle for detecting and counting

metal particles with a meso-scale device

• Develop a capacitance measurement setup for small, dynamic capacitance change

• Design, fabricate and test a microfluidic device for wear debris detection in low-

conductive highly viscous lubrication oil such as motor oil.

The layout of this thesis is as follows: In Chapter 1, a brief introduction to technologies for in-situ monitoring of oil, the Coulter counting principle and microfluidic technology have been described. Chapters II describe the background and literature review of oil monitoring technology in detail. In Chapter III, the measurement circuitry is characterized and validated. Chapter IV describes the design and fabrication of a microfluidic sensor for particle detection. Chapter V outlines the preliminary testing of

5 the microfluidic sensor for detection of particles in water; Juniper pollen and polystyrene particles are used. Finally in Chapter VI detection of wear debris particles in oil using the microfluidic device is demonstrated and Chapter VII has conclusions.

CHAPTER II

BACKGROUND WORK

2.1 Rotary machine maintenance techniques

Machine maintenance could be preventive, corrective, proactive, default type, discard type, offline, and online. The type of maintenance needed for a system is chosen based on efficiency and appropriateness of the maintenance technique [18-20]. The two most commonly used maintenance techniques discussed here are preventive and corrective maintenance. Preventive maintenance is aimed at the prevention of breakdown and failures of systems before they actually occur. Hence this is an extremely important and cost-effective tool. Corrective maintenance, on the other hand, refers to the repair and replacement of a component / system during or after a catastrophic failure. Preventive maintenance has long-term benefits and savings when compared to corrective maintenance. The long-term benefits include improved system reliability, decreased cost of replacement, decreased system downtime and effective system service life. The limitations of a preventive maintenance program, however, are that it can be used only if there is an increasing failure rate rather than an exponential or a constant failure rate. If the failure rate is constant, then preventive maintenance might prove to be more expensive than estimated [21].

2.1.1 Condition monitoring

Condition monitoring is a preventive maintenance tool which has been in use for over 50 years. British Standards (BS 3811) defines condition monitoring as: “The continuous or periodic measurement and interpretation of an item to determine the need for maintenance” [8]. This is therefore a measurement technology where a measurable parameter is chosen and this parameter is continuously monitored for any changes from the normal operation. When a change is detected, a more detailed diagnosis for the problem is performed [8, 22]. We discuss here the degradation and wear of highly viscous fluids and the science of its detection. This science of detection, known as condition monitoring, helps us not just in observation but also helps to warn us of any upcoming failure in the system. This is particularly important as it is an extremely cost effective tool. The degradation of fluids in engines and other hydraulic machinery is a topic of utmost importance. The dark appearance of the oil in engines after a period of time indicates the need to change the oil and also causes concern about the condition of the engine [8, 23, 24].

Figure 2.1 shows a general wear pattern occurring in any system during its period of operation. In the initial life of any system, it undergoes a ‘running-in’ process where the rate of wear away of some spots is rapid. This period usually does not last long. The second stage is the period of normal wear. During this stage not much wear occurs in the system and the system is in ‘normal operation’ condition. Anything beyond the second stage means that the parts and the components have worn off leading to a ‘disaster’. It has exceeded the acceptable wear rate and a change of the concerned component is required

[8].

1 3 2 Wear Rate

Life

Figure 2.1: The Bath tub curve -The three stages in the life of a system

1. Running in 2. Normal wear 3. Disaster [8]

Wear debris monitoring is a common technique of condition monitoring where the lubrication oils is analyzed using various methods. The samples are first taken from the machine to a laboratory and the tests are performed. But in recent times, the disadvantage of off-line monitoring has been overcome as lubrication oil samples may be analyzed by continuous online monitoring methods. The advantage of the wear debris monitoring techniques is that it can detect the root cause of the problem, rather than the onset of the problem itself. For example, if the lubrication oil that is analyzed has been contaminated by wear of metals/chemicals/water or other dust particles, it may be removed even before those particles cause any kind of damage to the components in the system. These contaminants in oil may be solid, liquid or gaseous and are known as the debris particles.

Metal wear due to abrasives, fatigue, adhesives and other tribochemical effects need to be continuously monitored and thereby detected in order for the system to run efficiently.

The various techniques used in the analysis of lubrication have been discussed with more emphasis below. The measurable parameters may include vibration, sample detection, viscosity changes and so on. Condition monitoring techniques are broadly classified into four main types shown in Figure 2.2.

Figure 2.2 Schematic outline of the various condition monitoring techniques [8]

2.1.2 Wear debris monitoring

2.1.2.1. Spectrographic analysis

This is one of the most prevalent optical methods used to study the spectra of chemical

elements in debris particles. The analysis could be a chemical or a quantitative analysis

where the constituents of the debris are directly analyzed by decomposition. Hence it is a multi-elemental analysis of wear debris in lubricating oil. The commonly present chemicals in the debris of lubrication oil include Al, Fe, Ca, Pb, Ni, Cu, Na, Zn, Sn, Cr,

Ba, Bo, Mg, P, Mo, Ag, V, and Si Some of these elements are formed by wear in gears, bearings, cylinders, pistons but others, like zinc, might be present in the lubrication oil as

an element in the additives. Hence while performing the analysis it is necessary to have a

complete knowledge about the additives, dispersants and all related information of the

lubrication oil that is being analyzed for effective monitoring. The serious disadvantage

of the method is that it cannot detect particles greater than eight microns. The sensitivity

reduces while the time lag for detection increases as the size of the particle increases [25 -

30].

Atomic Emission Spectroscopy is a spectroscopic method where the atoms in a

solid, liquid or gas are decomposed/vaporized using flame, plasma or discharge.

Depending on the type of source used to ignite the particles in the debris, spectroscopic

methods are classified as Inductively Coupled Plasma (ICP), Laser Induced Breakdown

Spectroscopy (LIBS), Spark or arc OES (Optical Emission Spectroscopy), XRF

Spectrometry (X-ray fluorescence), and SIMS (Secondary Ion Mass Spectrometry) among others. Among others X-Ray Florescence technology has been used in military

applications to detect iron and copper particles [25]. LIBS accounts for the most

12 commonly used detection technique for ferrous metals as debris particles in lubrication oil [46]. Chandrasekharan et al used combined methods such as LA-ICP-TOFMS as a debris detection method by combining direct laser ablation (LA) of the oils with inductively coupled plasma time-of-flight mass spectrometry (ICP-TOFMS) [31]. The wear interactions of steel and carbide tools were studied. This method helps in the multielemental determination of Na, Mg, Al, Ti, Cr, Fe, Ni, Co, Cu, Ag and Pb in lubricating oil samples as well.

2.1.2.2. Ferrography

Ferrography is a condition monitoring technique where a microscopic examination and analysis of the ferrous wear particles in lubrication oil is done [18, 32, and 33]. It was developed in the mid 1970’s by the precipitation of the ferrous wear particles to separate them from the lubricating fluid. The test procedure for a complete ferrographic analysis is lengthy and the analyst has to be well-trained. Ferrography (Jones and Scott 1982, Scott et al 1974) is a method of recovering particles from a fluid and depositing them on a substrate according to size and magnetic susceptibility for analysis.

The direct reading ferrograph is used to determine the amount and size distribution of wear particles in a sample of lubrication oil from a machine. Analysis of the data gives significant information about the wear debris. The particles are subjected to a powerful, magnetic gradient field and are separated by order of decreasing size [33].

Use of a simple equation provides a single figure for the severity of wear or other index.

For more detailed information, full ferrographic analysis using the bichromatic microscope (Ferrescope), electron microscopy, heating techniques and Quantimet can be

13 used [34, 35]. When direct ferrograph readings indicate abnormal wear, analytical ferrographic techniques can be used to study the wear pattern [33]. When lubrication oil contains a large number of ferrous particles, a ferrogram may be used to detect the particles. Here, the direction of magnetic force is opposite to the direction of gravitation and deposition of the wear debris particles onto the substrates is decreased by gravitation

[36]. Optical ferroanalyzer in addition to ferrograph allows us to estimate total contamination of oil, increasing the reliability of condition monitoring [37].

An old and simple method for ferrous wear debris analysis is the magnetic plug sensor. The particles can be detected by a change in the magnetic flux fitted in a cyclonic chamber. They are still in use because of their design and ease in portability. A magnetic chip collector is usually located in a gearbox or reservoir drain plug location, where the collector captures and retains particles so that they can easily be removed and analyzed off-line. Quantitative Debris Monitor (QDM) and Rotary Particle Depositor (RPD) are other examples of methods similar to ferrography [8].

When direct ferrograph readings indicate abnormal wear, analytical ferrographic techniques can be used to study the wear pattern. The purpose is to pinpoint the difficulty and identify the nature of potential machine problem. However, the method is time consuming, expensive with bulk equipments making it suitable for offline detection only.

2.1.2.3. Acoustic and ultrasonic sensors

Acoustic sensors work on the principle that particles, air bubbles, and lubrication oil reflect high frequency acoustic waves differently. The reduction in the amplitude of the reflected acoustic signal is proportional to the dimensions of the scattering contaminant

14 and debris particles. During normal wear, in engines, gear box and other machinery small particles starting from 3 µm are generated and gradually increase in size (100 µm). Hence detection of this particle range (3 µm ~ 100 µm) is of paramount importance. An acoustic based sensor for detecting this wide range of debris particles has been developed [65].

Acoustic sensors consist of both ultrasonic and infrasonic waves. In yet another method, the sensors detect stress waves transmitted though a machine. The amplitude of the stress waves is proportional to the debris particles present. These waves are converted to electrical signals using piezo-electric crystals mounted in the sensor. Ultrasonic monitoring devices [38, 39] are also based on the principle of Doppler Effect. An emitter in the sensor emits a high frequency ultrasonic signal through a flow stream. The signal is reflected back to the receiver from the contaminants, debris particles and turbulence in the flow. This change in energy of the reflected or the scattered signal is determined from the Doppler shift in frequency. The frequency is proportional to the flow velocity and can be converted into voltage or current. The technique is very sensitive to the positioning of the transducer as it will be influenced by the acoustic emissions from background sources and the signal attenuation varies with the temperature of lubrication oil. However, they can measure particle sizes as small as 3 µm and are simple, cost-effective and robust in use [40].

2.1.2.4. Chip detectors/ magnetic plugs

Chip detectors maybe magnetic, electric, pulsed electric (used in helicopter gear boxes), and are a commonly used technique for wear debris detection in oil of aircraft engines and gear boxes [2, 64].

In the magnetic method, a pair of permanently magnetized electrodes is separated by a small distance from each other, thus forming a magnetic field. One of the electrodes is grounded and the other is connected to an electrical warning system using a capacitive circuit. The capture of the metal debris is achieved using the magnetic field. The magnetic plugs have to be removed and visually inspected to avoid the agglomeration of debris particles disturbing the integrity of the system leading to catastrophic failures [55].

However, this disadvantage has been overcome by using chip detectors that currently work on the principle of electrical conductance (electric chip detectors).

Conductive debris particles are detected in a non-conducting fluid when the particles bridge the electrodes between them indicating a decrease in the resistance of the chip detector and increase in the current flow (short circuit) [46]. However, the particles must have certain geometry to pass through the distance between the electrodes and must be capable of causing a change in the impedance of the output signal indicating their presence. Chip detectors usually detect bigger particles (~ >75 microns) and since magnetic approach is used for capture the particles must be ferromagnetic. The detectors provide several false alarm signals due to the shorting of the electric gap. This has led to serious considerations of reverting once again to the visual inspection of the magnets for detection purposes [47].

2.1.2.5. Inductive sensors

Inductive sensors have the advantage of detecting both ferrous metal and non- ferrous metal particles [42] when compared to chip detectors that is can detect only ferromagnetic wear particles in lubrication oil. It also has the advantage of an on-line

monitoring capability with an inductive coil as the sensing element.

This method was developed by the Smith’s Industries and is called the Smiths

Inductive Debris monitor for detecting particles greater than 100 microns in oil. Ferrous

debris was differentiated from steel debris in lubrication oil. The passage of a particle

causes a change in flux. The debris may also be removed from the carrier fluid and

placed between the sensing areas of the inductive coils causing an imbalance in the

measurement. The electronics along with a microprocessor are a part of the on-line

sensing. The disadvantage of this method is that if a ferrous and a non-ferrous particle

appear at the same time, the effect nullifies each other and no particle is detected [8, 42].

The changes in inductance monitored are small and hence the measurement circuitry

must be extremely sensitive to very small variations. This makes differential bridge

methods unsuitable leading to other complex circuitry. Due to the high sensitivity of the coils designed, the output is sensitive to changes not only in eddy current flow but also to the dielectric constant of the fluid employed. Hence, this gives a false indication in the measurement [46]. The sensor enables the system to detect, count and classify wear metal

particles by size and type (ferromagnetic or non-ferromagnetic) with a detection

efficiency of close to 100% above the minimum particle size threshold of approximately

175 microns [41] only.

2.1.2.6. Particle counting

Spectrographic analysis, ferrography, X-Ray and Infra-red analysis are particle

characterization techniques whereas Image Analyzing Computer (IAC), Automatic

particle counter, Distribution Analyzers, and Mesh Blockage are some of the particle

17 counting methods. The above methods may also be classified under visual inspection monitoring as they are based on the principle of scattering light and using visual images for analysis. IACs use computers to automatically count the particles based on video counting of particles by the computer based on the size, color, fluid used, etc. In this method, magnification based on the particle size is a problem, as the larger particles maybe out of focus. If the color of the particle and fluid is of same color, the particle may not be detected. Distribution analyzers are based on the principle of light scattering due to diffraction. A beam of light is focused on the sensing path of the particle. Based on the angle of diffraction, the size of the particle maybe estimated. The method can be used only when the particulate concentration is high and hence is not suitable for online monitoring. Thus a range of particle detection techniques have been in practice over the years [8].

2.1.2.7. Mesh / filter blockage

The technique is based on determining the pressure / flow characteristics when the fluid containing debris particles are allowed to flow through an orifice of a filter. The orifices in the filter are usually of the same size. Hence large particles block the orifice reducing the flow rate of the fluid. This increases the pressure drop across the filter. The flow rate of the fluid maybe monitored keeping pressure constant, or constant supply of fluid flow maybe maintained and the change in pressure monitored. The method, however, does not differentiate between metallic and non-metallic particles and this method does not trap smaller particles [8, 54-55].

2.1.2.8. Gravimetric analysis

In this method, the weight of the solid wear debris in a specific volume of fluid is

determined. The fluid (100 ml) is passed through a fixed size (0.8 μm) analysis

membrane by vacuum filtration [8]. As the debris particles in the filter increases, there is

a corresponding increase in the weight of the filter. Low weights (0.0002 g) of wear

debris particles are captured to avoid weighing errors. For reduction of errors, larger

volumes of the fluid have to be passed through the filter (l liter or more), the method is

suitable for offline monitoring only [55].

2.1.2.9. Coulter counters

W.H Coulter in 1953 developed the Coulter Counter method for counting and particle

detection using the method of resistive pulse sensing. Coulter Counters consists of two reservoirs with particles laden solution separated by a single channel. Liquid Resistor or the Coulter Counter method has been in use to detect wear debris in oil. The counter detects the change in resistance of the medium when a particle flows through it by modulating the cross-section of the channel. This method cannot be used for highly resistive liquids such as lubricating oil. To overcome this problem as a resistive sensor,

Coulter counter based microfluidic device where the particle flowing through a narrow orifice or channel modulates the capacitance has been detected. The change in capacitance of the electrical signal causes a capacitive pulse. Thus wear debris metal particles as they flow through oil in the channel cause a capacitive change.

The sensors have the advantage of being portable and extremely small making it

easy to handle for online monitoring. The sensor recognizes the degradation or wear in

19 lubrication oil used which is an indication to change the oil in the system. These sensors have been tested for repeatable, accurate and reliable results. It is anticipated that the sensor maybe used for real time monitoring in aircrafts, military and other commercial applications.

2.1.3. Visual inspection monitoring

Some of the monitoring techniques categorized as visual inspection include using boroscopes & fiberscopes [43-45, 55], stroboscopes, dye penetrates, radiography, optical counting using microscopes and laser systems methods. Boroscopic inspections are performed using a video camera to constantly observe the components under failure.

However, the method is used offline and skilled, well trained personnel are necessary for effective monitoring and interpretation of the video. Optical methods maybe observed using microscopes and Scanning Electron Microscope (SEM). The methods are more efficient when combined with other condition monitoring methods. The above methods do not give very accurate results since the method is dependent on the particle concentration, size, fluid under examination (cannot operate on opaque fluids). The methods discussed above are simpler approaches compared to vibration analysis and have merits of their own. The diagnostic techniques are varied and it is very exhaustive to explain each of the methods in detail.

2.1.4. Vibration monitoring

This is an extensively used measurement technology where the vibration of a system is continuously monitored. When there is a change in the vibration level of a component in

20 the system, it signals the need for a detailed analysis and immediate diagnosis [56]. The vibration parameters that are most commonly observed include displacement, velocity, frequency [57, 58], peak value [59], power spectral density, etc. Sensors such as Linear

Variable Differential Transformer (LVDT) [60], Rotary Variable Differential

Transformer (RVDT), strain gauges maybe used as displacement sensors, tacho- generators [60] as velocity sensors, and accelerometers for determining acceleration.

Piezoelectric accelerometers [61 - 63] are preferred in comparison to displacement and velocity sensors.

The most widely used method in vibration analysis is to measure the overall vibration level over a broad range of frequencies. The data obtained are complex sinusoidal signals of different amplitude, frequency, phase information and are usually plotted as a function of time against the working condition of the machine. The time domain signals are often peak values and are not a reliable indicator of the fault in the machine [64]. For a detailed analysis of the signal, it is essential to convert the time domain into frequency domain signals performed with Fast Fourier transform which is complex and tedious. Minor faults that do not cause a significant increase in the vibration level cannot be detected. Vibration monitoring cannot be performed on machines with a varying speed as the vibrations become non-stationary [8].

2.1.5. Performance monitoring

This type of monitoring technique is performed in systems that are stable in normal operating conditions and is used extensively in process control systems. The output is monitored with respect to the input as absolute value of the system output. Performance

21 monitoring refers to observing and measuring a number of different physical parameters such as temperature, flow rate, pressure, etc. The measured parameters obtained are either manual or automatic in nature [8]. This is a less well-known condition monitoring method when compared to the other more widely used methods mentioned above. Unless the system is stable under operating conditions, performance monitoring is not preferred.

2.2 Summary

Wear debris sensors are used to detect solid contaminants that occur in lubrication oil over a period of time. In this chapter some of the basic methods prevalent in wear debris detection and their advantages and disadvantages are discussed briefly. All the techniques that fall into this category are further divided into on-line and offline sensing depending on the component that is to be monitored. For example, oil filters and magnetic plugs are often contaminated by wear debris which is analyzed by off-line techniques. Extensive work has been done to detect wear debris particles in oil using inductive sensors, spectrographic analysis, and vibration analysis as has been discussed.

A judicious selection of the type of monitoring technique is dependent on the type of problem that that system would encounter or the part in the system that is to be monitored. For example, if a system undergoes rotational imbalance, it is best to monitor it by vibration analysis. If a wear debris detection monitoring is performed, the fault is identified only after a period of time when the stress on the bearings increases which thereby increases the wear rate of the lubricating oil. On the other hand, if the engine or any other component is exposed to continuous abrasion, then an oil analysis may be performed. Vibration analysis may indicate the amount of wear in this case only after

22 some significant amount of time leading to catastrophic system failure before any preventive measure can be taken. A properly chosen method would help in not just identifying a failure but also help in predicting and controlling the system with any impending failures [23].

In general, available debris monitoring methods either are unable to provide real time information or can provide only limited advanced warning prior to any catastrophic failure. In this thesis, a microfluidic device utilizing the capacitance Coulter counting principle to detect and count metal wear particles generated in lubrication oil. The device is in microscale with a sensing channel of 40 X 100 X 300 microns. Using a pair of microelectrodes in this microchannel, the device is expected to be able to scan each individual particle one by one. The small size of sensing channels enables the sensor to detect metal particles of size ranging from 15 microns to 25 microns.

CHAPTER III

CHARACTERIZATION OF THE MEASUREMENT CHIP: MS3110 IC

The measurement circuitry used for testing of the capacitive sensors includes the MS3110

IC with the MS3110BDPC Evaluation Board read-out circuitry purchased from Irvine

Sensors Corporation. The MS3110 SOIC is placed in the socket of the evaluation board and it is interfaced to the computer via parallel port. Programming of the MS3110 IC is enabled using the evaluation board connected to the computer. The MS3110 is a switched-capacitor integrator type differential open-loop capacitive readout circuit [66,

67]. It outputs a voltage that is proportional to the change in capacitance (Equation 3.1).

Figure 3.1 shows the circuit diagram of the MS3110 IC chip, and is taken from the

MS3110 datasheet provided by the manufacturers [66]. CS1 and CS2 are the internal trimming capacitances that can be adjusted to balance the external capacitances CS IN1

and CS IN2 . The total capacitances CS1T and CS2T are given as the sum of the internal

and external capacitances as shown in Equations (3.1) and (3.2). If Vref is the reference

voltage which is set to 2.256V, then the output voltage Vo is:

CS1IN CS1 CS1T CS1 += CS1IN (3.1)

CS2IN CS2 CS2T CS2 += CS2IN (3.2)

− CSCS )12( o = PVV Gain **14.1*252 + Vref (3.3) C F

Gain = 2 or 4 V/V nominal; PV 252 = 2.25 VDC nominal; 1.14 is a constant provided by the manufacturer [66].

Figure 3.1 Block diagram of MS3110 circuitry [66]

3.1 Working principle of MS3110 IC

The MS3110 may be operated in either differential input mode or single-ended mode. In the single ended input mode that has been employed for our measurements, the external device or sensor is connected asCS IN2 , and the internal capacitance CS1 is used to balance the circuit, that is to eliminate any offset in the baseline of the voltage output.

During the experiment, a particle passing through the sensor causes a change inCS IN2 ; this is reflected as the output voltage, as the bridge is no longer balanced [68]. This is because a capacitance mismatch is created when external changes are deliberately 25

made to the sensor. This mismatch in capacitance is converted into voltage with the help

of the switch capacitor-integrator [66]. It is important to confirm that the changes in the

output voltage are not due to undesirable noise but due to the actual changes in the sensor. The MS3110 circuitry consists of a charge amplifier, low-pass filter, and a buffer for amplification, as shown in Figure 3.1.

Charge amplifiers convert the input charge into output voltage. They are commonly used in read-out circuitry as they have the advantage of measuring very small charges thus enabling small capacitance measurement. A charge amplifier consists of an

operational amplifier with a feedback capacitor (CF ). The feedback capacitor has a wide range of values for selection and the value selected determines the sensitivity of the measurement. The charge amplifier is followed by a low pass filter (LPF) which filters out the high frequency components of the signal; noise tends to be high frequency. The maximum frequency response of the MS3110 is limited by the LPF, the break-frequency of which is adjustable from 500 Hz to 8 KHz. For our work, the break frequency is set to

8 kHz; this means that the IC has a -3dB (decibel) roll off at 8 kHz. The final stage is the

amplification of the signal using buffering components and amplifiers. The signal from

the output buffer is converted to digital mode using an A/D converter on a National

Instruments data acquisition board controlled via LabView software. Besides, the

MS3110BDPC Evaluation Board consists of a × "5.4"5.4 PCB in conjunction with

WINDOWS-based software for programming the MS3110 SOIC. The evaluation board

also consists of signal and test points, jumpers, programming specifications using

EEPROM, methods for trimming oscillator frequency, voltage reference value, and

current reference value.

3.2 Static characterization of MS3110

A set of experiments were done in order to test the lower range of capacitance that the

chip can measure, as well as the chip’s repeatability of the measurement technique. For

these experiments, the internal capacitancesCS1, CS2 and the feedback capacitance CF

were varied. For a particular experiment, the internal trim capacitance CS2 was set to a

fixed value, and CS1 and CF were varied over a range of values. This resembles a single variable-mode type of measurement where CS2IN was fixed by an external device and

CS1 was the internal trim capacitance for balancing the bridge. Other parameters like the

current, voltage and oscillator trim, gain and bias settings were adjusted as specified in

the data sheet of the MS3110 Universal Capacitive Readout IC [66]. The theoretical

output Vo (shown asV ) is determined from the transfer function as follows: (Here

Gain = 2 ; Vref = 256.2 V)

− CSCS )12( = VV Gain **14.1* +V ref C ref F (3.4)

Δ − ΔCSCS )12( =Δ VV ref Gain **14.1* CF (3.5)

In the experiments, CS2 was set to 0.798 pF and CS1 was increased in steps of

0.019 pF. The feedback capacitance CF was first set to 5.13 pF and then decreased to

1.197 pF. The capacitance change was monitored in terms of voltage change using the

LabView and DAQ. The measurement results for CF = 5.13 pF and 1.197 pF are shown

in Figure 3.2 and Figure 3.3 respectively. When CS2 is equal toCS1, the bridge was

balanced and there was no change in voltage. However, an offset between CS1

and CS2 values produces a change in voltage. No saturation was observed for the given

range of ∆CS1; the output voltage change was linear and is quite stable and repeatable.

The experimental and theoretical curves match well for CF = 5.13 pF (for ∆CS1 varying

between ±1 pF); the slope of the theoretical curve for CF = 5.13 pF is determined to be

1.001 V / pF and the slope of the experimental curve is 1.0 V / pF. Since we are

interested in measuring capacitance changes in the order of femto-farad level, the voltage

change that would be needed to produce a 1fF change is 1 mV. Similarly, when CF =

1.197 pF (for ∆CS1 varying between ±0.6 pF), the slope of the theoretical curve is 4.285

V / pF matches well with the slope of the experimental curve (i.e.) 4.261 V / pF. From this it is observed that, a change of 1 fF would cause an output voltage change of 4.261

mV. Thus ideally, the sensitivity is increased 4.285 times by varying CF from 5.13 pF to

1.197 pF.

Figure 3.2: MS3110 output response showing the static characteristic curve for CF = 5.13 pF; CS2 =0.798 pF

Figure 3.3: Static characteristic curve of MS3110 IC for CF = 1.197 pF; CS2 =0.798 pF

Next, the value of CF was decreased to 0.494 pF and 0.209 pF respectively to increase

the sensitivity of the measurement setup. CS2 was maintained at 0.798 pF and CS2 was

again varied in steps of 0.019 pF. For CF = 0.494 pF, saturation was observed at about ±

0.19 pF change in capacitance (Figure 3.4). The slope of the theoretical curve is observed

to be 10.431 V / pF while the slope of the experimental curve is 10.32 V / pF (10.32 mV/

fF). At CF = 0.209 pF, for a small capacitance change ΔCS1 = 0.019 pF, the output voltage change is -0.4592 V (theoretical) and -0.3652 (experimental) as shown in Figure

3.5. For CF = 0.209 pF, saturation was observed between ΔCS1 values ranging between -

0.133 pF and 0.114 (Figure 3.5). Any ΔCS1 value higher than the specified (ΔCS1) value leads to its saturation. This is because the working range of voltage for the IC is

between +/- 5V. A low value of feedback capacitance (CF ) leads to saturation. The

theoretical curve between the region of ΔCS1 values ranging between -0.133 pF 29 and 0.114 pF has a slope of 24.5 V / pF and the experimental curve is 20.6 V / pF (20.6 mV / fF). The sensitivity (ideally) is improved from 10.5 times to 24.5 times by varying

CF from 0.494 pF to 0.209 pF respectively. Thus, we can set a lower value of CF for measurement of very small capacitance changes because of the high sensitivity.

Figure 3.4: Static characteristic curve of MS3110 IC for CF = 0.494 pF; CS2 =0.798 pF

Figure 3.5: MS3110 IC response to CF = 0.209 pF;CS2 =0.798 pF

However, we found that for very small feedback capacitance values, the gain of

the MS3110 chip (the slope of the ΔV-ΔCS1 curve) was degraded. As shown in Figure

3.5, when CF = 0.209 pF, the slope of the ΔV-ΔCS1 curve was reduced to approximately

20.6 V / pF from the predicted value 24.1 V / pF while when CF = 5.13 pF, 1.197 pF or

0.498 pF, the slope of the ΔV-ΔCS1 curve exactly followed the theoretical prediction as

shown in Figure 3.2, 3.3 and 3.4. Experiments were also conducted to determine if the

gain was dependent on the values of adjustable internal capacitance,CS1 when CF =

0.209 pF. The characteristic output curve is shown in Figure 3.6. The slope of each curve

(gain of the MS3110) was calculated and compared with the slope of the theoretical

prediction in Figure 3.6. The experiment showed that as CS1 values were varied for a

given set of experiments, the gain remained fairly constant (approximately 20.5 V/ pF).

This indicated that a very small value of CF can be used to obtain much higher

sensitivity, but a proper calibration may be needed to determine the real gain value. The

static characterization results with different CF values are summarized in Table 3.1. It

was noticed that a high sensitivity at small CF values also makes the output signal more

susceptible by environmental noise. Hence a tradeoff between the noise level and the

sensitivity has to be made by choosing an appropriate CF value. In the testing, CF value

is chosen between 5.13 pF to 1.197 pF for the meso-scale and microfluidic chip testing.

From Table 3.1, it is observed that when CF = 1.197 pF, a change of 0.1 fF can generate

an output voltage change of 0.4261 mV; this change in voltage is detected using the

LabView connected to a 16 digit NI-DAQ. The resolution for the 16-digit DAQ is in the

order 153 μV for a voltage range of -5 to + 5 V. Hence, when CF=1.197pF the MS3110

IC is able to measure a small capacitance change of 0.036 fF in theory. However, when

the MS3110 IC was connected to the external devices, the environmental noise level we

measured under proper shielding is 5 ~ 8 mV; thus it can be used to measure small

capacitance as low as 1 fF ~ 2 fF.

Figure 3.6: MS3110 IC response to CF = 0.209 pF and varying values of CS1 for a given experiment showing constant gain 32

The use of small CF value is particularly useful for sensor application where the

change in capacitance is extremely small, e.g., less than 10-15 F. The measurement of a

small capacitance in sub femto-farad level still remains a challenge. The testing presented herein implied a possible solution for solving low-level capacitances changes in the order

of femto-farad level. From Table 3.1, it is observed that when CF was reduced to smaller

values (0.494 pF and 0.204 pF), a capacitance change of 0.1 fF produces 1.032 mV and

2.06 mV change in output voltages respectively. Thus, we can use low CF values for

measuring smaller capacitances, i.e. 0.01 fF to 0.02 fF. However, we should note here

that, a high sensitivity for low CF values also makes the output signal more subjected to

environmental noise. It is critical to use good shielding of the device to reduce external /

environmental noise for very small capacitance measurement.

Table 3.1: Comparison of slopes for ranging from 5.13 pF to 0.204 pF CF Theoretical Experimental Error Experimental CF (pF) slope slope (%) slope (mV/ fF) (V / pF) (V / pF) 5.13 1.001 1.00 0.09 1.00

1.197 4.285 4.261 0.56 4.261

0.494 10.431 10.32 1.06 10.32

0.204 24.5 20.6 15.9 20.6

4.3. Dynamic characterization of MS3110

In this section, the dynamic characterization of the MS3110 IC was determined by

determining the response time of the IC and how fast it could respond to an external

change.

4.3.1 Response time of MS3110

Response time is defined as ‘The elapsed time between the end of an inquiry or demand on the system to the beginning of the response to it.’ It is an analog parameter of fundamental importance in high speed electronics, since it is a measure of the ability of a circuit to respond to fast input signals [69]. In this thesis, the MS3110 chip was used to measure the dynamic capacitance pulses due to passage of metal particles. The pulse width typically ranges from 0.1 ms to 1 ms. Thus the capacitance measurement must have a response time less than 0.1 ms to capture all pulses.

Experiments were conducted to determine the response time of the MS3110

IC. For this, an external capacitor (CS2IN) was connected to CS2 using a mechanical switch. Internal trimming capacitance CS1 was used for balancing the circuit when the switch was closed. By turning the switch on (closed) and off (open), we connected or disconnected the external capacitor to the MS 3110 chip and circuitry. When the switch was off, the external capacitor was disconnected from the MS3110 circuitry; when the switch was suddenly turned on, the external capacitor was connected to the circuit, causing a dynamic change in the output voltage. The dynamic change in voltage was converted into a capacitance change using Equation 3.1. From the transitional capacitive curve, we can determine the response time of the MS3110 IC and its circuitry. The detailed description of the experiment is given below.

Figure 3.7: Schematic of the measurement setup connected to MS3110 IC

First we connected a 1pF external capacitor in parallel with the internal capacitance CS2 of the MS3110 circuitry. A mechanical switch was used to connect/disconnect the external capacitor. The schematic of this measurement setup is

shown in Figure 3.7. The mechanical switch was turned on/off quickly, and the dynamic

response of the MS3110 chip was recorded using LabView. From the recorded data, the

response time was measured. The measured response time consists of two parts: 1) how

fast the MS3110 IC circuitry responds to a change in the capacitance caused by the

external sensor/device connected to CS2 and 2) the switching time of the mechanical

switch indicating how fast the switch is capable of connecting / disconnecting the 1pF

external capacitor with the IC circuit. Figure 3.8 (a) shows the dynamic response of the

MS3110 output when the switch was turned on and off. The first peak occurs when

capacitance CS2 was changed from a low value to a high value when the 1 pF external

capacitor was connected in parallel. The second peak occurs when the external capacitor

was disconnected fromCS2 . Figure 3.8 (b) is the magnified response of the chip when

35 the switch is turned on. It can be seen that the response time for the IC was determined to be 70μs.

(a)

Figure 3.8 (a): Dynamic characterization of the MS3110 IC to a switching response, the upward peak represents the response when the switch is closed and the downward peak represents the response when the switch is open.

Figure 3.8 (b): A magnification of the dynamic response of the MS3110 chip when switch is open (connected to LabView) 36

The measured response time of 70 µs of the MS3110 IC obtained from the

experiment includes the response times of 1) the mechanical switch and 2) the MS3110

IC chip and its circuitry. Next, we experimentally determined the response time of the

mechanical switch only, without connecting the MS3110 IC to the switch. The schematic

of the measurement setup is shown in Figure 3.9. A resistor (100 Ω) was connected to a

battery (3 V) via the same mechanical switch we used in Figure 3.7. In the experiment,

the switch was quickly turned to on / off positions so that the resistor was connected /

disconnected to the battery (Figure 3.9). When the switch was off, there is no voltage

across the resistor. When the switch was on, there was a voltage across the resistor

measured with an Agilent 54642D Mixed Signal Oscilloscope. The measured voltage

when the switch was first turned on and then turned off is shown Figure 3.10 (a). Figure

3.10 (b) magnifies the first transition region of Figure 3.10(a). It can be seen from the

Figure 3.10(b) that the response time was approximately 1 µs, which is much faster than the 70 µs combined response time seen in the first experiment. Thus, we are confident that the 70 µs response time found in the first experiment is an accurate measurement of the MS3110 device itself.

R OSCILLOSCOPE MECHANICAL SWITCH

Figure 3.9: Schematic of the measurement setup with the mechanical switch and external resistor

Figure 3.10 (a): Plot showing the response time measurement of the switch connected to resistor when the switch is closed and open.

Figure 3.10(b): A plot magnifying the first transition region. The response time is 1μs.

3.4 Summary

The static and dynamic characterizations show the MS3110 chip and circuitry was capable of dynamic measurement of small capacitance change (> 1 femto-farad). The

static characterization shows that the change in output voltage is linear to the capacitance

change. With the differential measurement scheme, the parasitic capacitance was

eliminated. The experiments were repeatable and stable for various trials. The gain

remained constant for varying values of CS1 indicating that the change in voltage is not

dependent on the value of CS1 chosen. The dynamic characterization indicated that the response time of the MS3110 chip and circuitry was about 70 μs. Both tests demonstrated that the MS3110 can be used for dynamic measurement in microsensors due to the dual advantages: 1) high sensitivity; 2) fast dynamic response.

CHAPTER IV

MESO-SCALE SENSOR WITH PARALLEL PLATE ELECTRODES

In this chapter, the capacitance Coulter counting principle for detecting and counting metal wear particles generated in lubrication oil is described. The Coulter Counter is a well-established technique for the counting and detection of bio-particles in electrolytic solution. A Coulter Counter consists of two reservoirs connected by a microchannel.

When a particle is present in the microchannel, it causes a change in resistance of the fluid-filled channel. Because lubrication oils are typically low-conductive, the resistance change due to the passage of a particle is difficult to measure. Therefore, we chose to monitor the change in capacitance formed between the two electrodes in the microchannel as explained previously. When a metal particle passes through the microchannel, a change in the capacitance can be detected owing to the difference in permittivity of the lubrication oil and metal particle. In this chapter, the capacitive

Coulter counting principle using a meso-sized device is demonstrated. The capacitance change was detected by the MS3110 circuitry described in Chapter III. The concept and working principles can be extended to a micro-scale device.

4.1 Mesosized device and experimental setup

The mesosized device consists of two parallel plates (cross section 2.5 cm (H) x 4 cm

(W)) immersed in SAE-5W30 motor oil forming a channel (Figure 4.1). The distance d

between the two plates was 1 cm. Figure 4.2 is a picture of meso-scale device with

parallel plates and the medium between the two plates being oil. This setup mimics a

fluidic channel through which particles pass for Coulter counting.

Metal particles (diameters Dp = 3.5 mm, 4.5 mm and 6 mm McMaster-Carr, USA)

were dropped one at a time from the top of the channel and allowed to travel to the

bottom. Figure 4.3 shows the spherical steel particles of sizes 3.5 mm, 4.5 mm and 6 mm

used to test the mesoscale capacitive device. A capacitive readout MS3110 IC chip

(Irvine Sensors, USA) and a NI-6220 DAQ (National Instruments, USA) were used to

record the capacitance change as the particle passes through the channel. The MS3110 IC

chip is an off-the-shelf differential capacitance measurement chip (Chapter III). The chip

senses the change in the differential capacitance between two capacitors and provides an output voltage proportional to that difference; differential measurements eliminate the effects of large parasitic capacitances, which can make a direct measurement of capacitance difficult. The change in capacitance in terms of the measured voltage is given as

ΔCS2 KV ⋅=Δ T (4.1) CF

where K is a constant proportional to the gain setting and the reference voltage of the

MS3110 chip, CS2T is the sensing capacitance (denoted as ΔC in the Figures) and CF is the feedback capacitance. For these experiments, K was 5.14 and feedback capacitance

CF was 1.197 pF.

Figure 4.1: Schematic of the modified mesoscale device, d =1 cm

Faraday cage

Figure 4.2: Mesoscale sensor with parallel plate electrodes filled with lubrication oil [6]

Figure 4.3: Steel spherical particles used to test the mesoscale device. From left to right 3.5 mm, 4.5 mm and 6 mm steel particles

4.2 Finite element model

Finite element simulation of the device was performed by another student Xinggao Xia to predict the capacitance change of the mesoscale device for each of the particles [70]. The simulation was conducted under electrostatic mode in the AC/DC application module of

COMSOL Multiphysics 3.4. The governing equation for electrostatics simulation is given by Poisson‘s equation,

ρ −∇= ε 0ε r ∇V )( , (4.2) where ρ is space charge density and V is electrical potential.

A mesh independent result was obtained by using 52500 elements. The simulation was validated by simulating self capacitance of a conducting sphere, in which the simulation results match the theoretical prediction well. The boundary conditions for the two electrodes were set as port and ground, forcing the boundary potentials to one and

43 zero, respectively. Aluminum particle was modeled as a sphere with floating potential boundary condition.

Q = ρ , (4.3) ∫ s Ω∂

where ρs is the surface charge on sphere boundaryΩ . This setting kept the surface potential as a constant value such that the total surface charge was equal to Q. Here Q was set to zero, assuming no complex interface phenomenon. Other boundaries were defined as Zero Charge / Symmetry condition.

nD = 0 (4.4)

For subdomain setting, the device contained two subdomains: the oil and the particle subdomain. Oil was assumed to be filled up to the edge of the parallel plates.

Considering linear material constitutive relationship, relative permittivity in governing

equation was set asε r = 4.2~1.2 for general engine oil [74] andε r = 1 for air. For the particle subdomain, a floating potential boundary condition already keeps the entire spherical subdomain equipotential, regardless of the value of what relative permittivity specified in this subdomain.

Meshing was normal globally with maximum element size of 1mm specified on electrode boundaries. A weak boundary condition was activated on all boundaries for accurate flux calculation. As a result, an incomplete LU pre-conditioner and Generalized

Minimal Residual (GMRES) algorithm linear system solver were both selected.

Figure 4.4: Finite element simulation on parallel plate electrodes (a) device model geometry (b) meshing obtained by using 52500 elements [70]

4.3 Results and discussion:

In this section, the experimental results are compared with theoretical results to validate the pulse width and with simulation results to validate the pulse height of the particles.

4.3.1 Instrumentation validation

The characterization of MS3110 in Chapter IV showed the response time of the capacitance measurement circuitry was approximately 70 μs and can measure a capacitance change as small as 0.1fF. The meso-scale device was connected to the

MS3110 IC and the evaluation board using short co-axial cables, which were well shielded to reduce external noise. Note here that the objective was to measure the capacitance change before and after the particle was dropped. Our testing indicated that the parasitic capacitance caused by these co-axial cables was constant and did not affect the capacitance change (ΔC) measurement.

4.3.2 Detection of conducting particles

Metal particles with diameters of 3.5 mm, 4.5 mm and 6 mm were dropped one at a time between the two plates and capacitance was measured before and after insertion. The two plates were immersed in SAE5W-30 motor oil thus forming a channel. Each time a particle was dropped, it was allowed to travel to the bottom of the plate and settle down between the two plates. Figure 4.5 shows the measured capacitance change in response to metal particles of varied sizes when dropped between the two plates. A capacitive pulse was generated as each metal particle passed through the fluidic channel. The magnitude of the capacitance pulse increases with increased particle size; the pulse width decreases with increased particle size. The validation of the magnitude (capacitance change) and the pulse width (time) generated by the particles of different sizes are discussed in the next sections (4.3.3 and 4.3.4).

4.3.3 Comparison of experimental and simulation results for pulse height of particles

We compare the magnitude of the change in capacitance with the particle size. It can be seen from Figure 4.6 that as the particle size increases, the change in capacitance also increases. Table 4.1 lists the data point values of Figure 4.6. The capacitance change depends on the position of the particle as it passes through the fluidic channel; a particle traveling along the centerline midway between the electrodes causes the least change, and a particle traveling close to (but not touching) one of the electrodes causes the most change. A particle near an electrode causes a bigger capacitive change because it distorts the distribution of charge on that electrode. Thus, simulation of a centered particle is used to determine a lower band on the capacitance change, and simulation of a particle near

46 the electrode is used to determine an upper band. Figure 4.6 shows simulation results for the range of capacitance changes, with lower and upper band as a function of particle size. Each band is for a range of oil permittivity 2.1 ~ 2.4. The upper band represents a particle near the electrode; a gap of 50 μm between the outer shell of the particle and the electrode was used for the upper band because of the numerical difficulties in simulation of smaller gaps. The figure also shows the capacitance changes seen with the experimental device, with error bars to show the range of capacitance changes seen over the course of ten experiments. The simulation and experimental results agree fairly well.

Because it is difficult to control an experimental particle’s trajectory, we expect only that the measured capacitance change fall between the lower and upper band simulation points for a particle of that size. These results on a mesoscale device demonstrate the feasibility of using a capacitance-based Coulter counting principle for metal wear detection. Next, we present a microscale device for detection of wear debris in lubrication oil based on the capacitive Coulter counting principle.

Figure 4.5: Measured capacitance change in response to metal particles of varied size (a) Dp=3.5mm (b) Dp=4.5 mm (c) Dp=6.0 mm

Table 4.1: Comparison of experiment and simulation results with (a) lower bound and (b) upper bound

Simulation Simulation Experiment Diameter ΔC ΔC ΔC (mm) (pF) along one Measurement (pF) middle (pF) electrode plate Uncertainty 3.5 0.014 0.022 0.022 -6.5332e-4

4.5 0.031 0.047 0.030 ±1.1316e-3

6.0 0.080 0.120 0.100 ±6.5332e-3

Figure 4.6: Comparison of the experimental and FEM simulated capacitance change for the steel particles used in the mesoscale device

4.3.4 Comparison of experimental and theoretical analysis for pulse width of particles

It was observed that as the size of the particle increased, the pulse width decreased. This is because a heavy particle travels through the fluid channel with a higher velocity. The time taken for the particle to reach the bottom may be theoretically calculated from the

Newton’s Second Law as shown in Figure 4.7.

Fd Fb

Steel Particle

Lubrication oil

Figure 4.7: Particle traveling through the fluid under the action of three forces

Consider a particle of mass m moving through a fluid under the action of an external force Fg. The velocity of the particle is u and the density of the particle is ρ (7.95 g/cm3). There are three forces acting on a particle moving through a fluid:

1. The external force, gravitational or centrifugal (Fg):

g = ρvgF (4.5) where v is the volume of the particle and g is the acceleration due to gravity (980 cm/s2).

2. The buoyant force, a force acting parallel with the external force but in opposite direction (Fb) given by Archimedes principle:

= ρoilb vgF (4.6)

3 where ρ oil is the density of the fluid (0.9 g/cm ) through which the particle is moving

(lubrication oil).

3. The drag force due to the relative motion between the particle and the fluid (Fd) is given by Stokes law applied for particles Reynolds number less than 1:

Fd = μuD3 pπ (4.7)

where μ is the dynamic viscosity of the fluid (lubrication oil) , here μ = 3.96 cm/s [73], Dp is the diameter of the particle and u is the velocity of the particle in oil.

When the particle is at rest, the velocity and the distance travelled by the particle is zero.

dx (i.e) Initial conditions: ut === 0,0 and x = 0; dt

We apply Newton’s 2nd law on the particle and obtain:

2 xd =−− ρvFFF (4.8) bdg dt 2

Substituting (5.5), (5.6) and (5.7) in (5.8):

dx 2 xd vg −− 3)( D = ρπμρρ v (4.9) oil p dt dt 2

By solving the above Equation (5.9), the solution is obtained as follows:

⎛ ⎞ ⎜ − 3πμD ⎟ ⎜ p ⎟ ⎜ ⎟t ⎜ ⎟ 2 ρv 2 ⎛ gv Δρρ ⎞ ⎜ ⎟ ⎛ vgΔρ ⎞ ⎛ Δρρ gv ⎞ x = ⎜ ⎟ × e⎝ ⎠ + ⎜ ⎟ t −× ⎜ ⎟ (4.10) ⎜ 2 ⎟ ⎜ ⎟ ⎜ 2 ⎟ ⎝ πμD p )3( ⎠ ⎝ 3πμD p ⎠ ⎝ πμD p )3( ⎠

From Equation (4.10), the time taken for the particle to reach the bottom may be determined (Figure 4.6). Terminal velocity may be determined as the particle reaches a constant velocity which is the maximum attainable. The maximum settling velocity

51 is terminal velocity. Figure 4.8 plots the theoretical relationship between the distance travelled by the particle, and the time taken by the particle to travel through the fluid for the three particles used in the experiments.

ga 2 − ρρ )(2 Terminal velocity u = Lp (4.11) 9μ

Figure 4.8: Time taken for steel particles to travel through the fluid (lubrication oil)

The time taken for the particle to travel through the plates increases exponentially for short distances. However, for large distances, it maybe observed that the time taken increases linearly with the distance travelled (Figure 4.8). Table 4.2 lists the data points for the time taken for the particle to travel through a distance of 2.5 cm (height of the plates in the setup). Differences between the experimental and theoretical values maybe attributed to the actual viscosity of oil being dynamic and different from the assumed value. 52

Table 4.2: Comparison of theoretical and experimental values for the time taken by the particle to travel a distance of 2.5 cm (height of the plates in the setup).

Theoretical Experimental Diameter (mm) Time taken (s) Time taken (s)

3.5 0.23 0.4

4.5 0.15 0.3

6.0 0.1 0.2

5.4 Summary

The experimental results and comparisons to simulation and theoretical predictions indicated that a metal particle can be detected when it passes through a fluidic channel and the size of particle could be accurately measured. It is expected that the use of a microfluidic channel by reducing the size of and spacing between the electrode plates would enable detections of smaller metal particles.

Experiments conducted with the meso-scale sensor also demonstrate the dynamic measurement capability of the MS3110 IC chip. With proper shielding of the device and by using co-axial cables for external connection, the dynamic, small capacitance pulses were observed. The experimental results on the mesoscale device demonstrated the feasibility of using a capacitance-based Coulter counting principle for metal wear detection. Further investigation will be conducted using microfluidic devices with integrated micro electrodes fabricated inside a microchannel.

CHAPTER V

DEVICE DESIGN, FABRICATION AND PRELIMINARY TESTING OF A

MICROMACHINED SINGLE CHANNEL DEVICE

The sensitivity of Coulter counting devices is dependent on the size of the microchannel.

To enable detection of micro-particles, there is a need to scale down the size of the electrodes and the distance between them. The meso-scale device used in Chapter V cannot be used for detecting micro scale metal debris. This chapter presents the design and fabrication of a microfluidic device that allows detection of microparticles. This device is an improvement from meso-scale sensor because it is small, portable and requires only a small amount of sample to analyze. The micro-sensor may be used in real time application. To demonstrate the capability of using the microfluidic device for detecting micro scale particle and to validate the dynamic response of the MS3110 measurement circuitry, Juniper scopulorum and polystyrene particles suspended in de- ionized water have been used.

5.1 Device design

The design concept for the micromachined devices is illustrated in Figure 5.1. The single channel sensor consisted of an inlet reservoir, an outlet reservoir, a single channel with

54 dimensions of 40 μm (H) × 100 μm (W) ×300 μm (L). A pair of co-planar electrodes was used for detecting microparticles. The use of co-planar electrodes allowed us to simplify the micromachining of the device. The microchannels and reservoirs were fabricated on

PDMS using soft lithography, and bonded to a glass substrate with patterned Au/Ti electrodes. The gap between the pair of electrode was 20 µm.

The single channel sensor counts the particles as they flow through it using the

Coulter counting principle. The sensor was connected to the measurement setup as described in Chapter III. The particle-laden fluid was forced to pass through the microchannel. The fluid used in the experiment was de-ionized water. When a particle passes through a microchannel, it causes a change in the capacitance of the fluid, thereby resulting in a voltage pulse across the electrodes. Figure 5.1 shows the schematic of the microfluidic device for preliminary testing with bio-particles.

Figure 5.1: Schematic of the microfluidic device for preliminary testing with bio-particles

5.2 Device fabrication

The fabrication of the sensor firstly, involves micromachining of the electrodes and microchannel. This was followed by bonding the two with each other. The PDMS

(Polydimethylsiloxane) was fabricated using the following method [71]: i) Preparation of SU-8 mold:

1) The glass slide was first cleaned with acetone and isopropyl alcohol. It was then

washed with DI water and rinsed dry. A negative-tone photoresist such as SU8-

2025 (MicroChem Inc., USA) was spun on a glass slide for the required thickness.

For a channel thickness 50 µm, the SU-8 mold was spun at a speed of 1750 µm

for 35 sec. It was pre-baked at 65ºC for 3 min and soft-baked at 95ºC for 6 min.

2) After being exposed to air naturally for 10 min, the SU-8 spun on the glass slide

was exposed to near ultra-violet (UV) light (365 nm). The UV light was processed

using a mask aligner OAI-200 (OAI, CA, USA) and the exposure dose was 380

mJ/cm2. A photolithographic mask was used to pattern i.e. this mask was opaque

at certain regions and this region remains intact after exposing them to UV light.

3) The glass slide was then post-baked at 95ºC for 6 min. To develop the photoresist

the exposed regions are immersed in a developer solution (SU 8 Developer,

MicroChem, MA) for 3 min. The required pattern of the photo-resist was thus

obtained. The glass slide was rinsed with isopropyl alcohol and then hard-baked

at 150ºC for 1 hour. ii) Fabrication of PDMS microchannel:

1) To form the PDMS (Sylgard 184, Dow Corning, USA) substrate, first a mixture of

the base and curing agent were mixed in a ratio 10:1. The mixture was poured into

the SU 8 mould to form a PDMS substrate thickness of about 5 mm and placed in

a dessicator for degassing it for about 1 hour until all the air bubbles were

removed. This was then followed by curing the prepolymer mixture at a

temperature of 80ºC for 4-6 hours.

2) The next step was peeling the PDMS off from the glass substrate after it was

brought to room temperature. The formed PDMS microchannel was rinsed with

DI water and blow dried with dry clean air. iii) Fabrication of electrodes

1) For the fabrication of electrodes, titanium (10 nm) followed by gold (100 nm)

(Evaporated Metal Films, NY, USA) sputtered on a glass substrate were used.

Here, titanium was used as an adhesion layer between the gold and glass

substrate. Photoresist AZP 4620 (AZ Electronic Materials, USA) was spun on the

gold sputtered glass substrate. The required mask and glass substrate were aligned

for photolithography and exposed to UV light for 14 sec. The glass substrate with

the required pattern was immersed in a developer solution for about 180 sec. Once

the required pattern of photoresist was developed on the glass substrate, it was

rinsed with DI water and blown dry with compressed air. The glass substrate was

then hard baked at 100ºC for 1 hour on a hot plate.

2) For removing the unpatterned gold from the glass substrate, the substrate was first

agitated in a solution of KI: I2 complex (Transcene Chemicals). It was then rinsed

with DI water. To etch the titanium adhesion layer, the substrate was immersed in

a 30 % solution of hydrogen peroxide (H2O: HF: H2O2 in a ratio of 20:1:1).

3) An AZP stripper solution was used to remove the photoresist on top of the gold

layer. iv) Bonding of the PDMS microchannel with electrodes using Plasma cleaner

Before bonding, it was ensured that holes for the inlet and outlet reservoirs were

created by punching 3 mm through holes on the PDMS layer. It was important to

keep the device extremely clean and the surfaces smooth for bonding to happen. The

device with electrode and channel were placed inside the Plasma cleaner PDC-32G

(Harrick PDC-32G, USA) for bonding. The surfaces to be bonded was kept facing up

and exposed to oxygen plasma generated by the plasma cleaner for 35 seconds. They

were then removed from the plasma cleaner and brought into contact within 60

seconds for aligning and sealing the channel with the electrode. This created a strong

bond between the channel and electrodes, thus forming the complete device. The

device was placed on the hot plate at 60ºC for 2 hours to ensure no leakage and a

complete sealing between the PDMS channel and electrode was obtained. Figure 5.2

(a) shows the optical microscopic image of the microchannel and electrodes used.

The image of the fabricated microfluidic device is shown in Figure 5.2 (b).

Figure 5.2: (a) Microscopic image of the microchannel and co-planar electrodes used for preliminary testing

500 μm

Figure 5.2: (b) Fabricated microfluidic device

5.3 Material used for device testing

Rocky mountain Juniper (Juniper scopulorum) tree pollen (Sigma Aldrich Inc.) and polystyrene (Sigma Aldrich Inc.) were used in the experiment for preliminary testing of the device. The density of pollen was estimated around 1.1g/cm3. The average diameter of the Juniper pollen particles was estimated from 17.5 μm to 22.5 μm. For the polystyrene, the certificate of analysis from Sigma Aldrich confirmed the particle diameter to be 20 μm with a standard deviation of 0.5 μm [10]. Figure 5.3 (a) and (b) show the microscopic images of the Juniper scopulorum and polystyrene used for testing.

50 μm

Figure 5.3: Microscopic images of (a) Juniper scopulorum (b) polystyrene particles used for preliminary testing [10]

5.4 Measurement setup

The measurement setup has been described in Chapter IV. The microfluidic device was connected to the MS3110IC and measurements were thereby made. An input DC voltage of 5V was applied across the MS3110 IC circuitry. The output was observed in terms of voltage using a Data acquisition board (National Instruments, NI-6220) and LabView

(National Instruments). The sampling frequency used was 60 kHz. The feedback

61 capacitance CF was maintained at 5.13 pF. When a particle passes through the microchannel, there was a change in the capacitance. The expected change in capacitance was in the order of 10-15 F. At this low-level of capacitance measurement, external noise and parasitics are prevalent. The MS3110 IC was expected to detect this small and fast dynamic capacitance change. Using LabView, the capacitance change was obtained in terms of voltage. From this, the capacitance change may be back calculated as explained in Equation 4.2.

5.4.1. Detection of Juniper pollen

The sample was prepared by diluting 12 ~ 15 mg (approximately) of Juniper pollen in 10 ml of DI water. This sample was injected into the inlet reservoir by using a syringe, which created a pressure difference that pumped the fluid flow through the microchannel.

The Juniper pollen particle with solution was loaded into the inlet reservoir until it reached the outlet reservoir.

The voltage obtained due to the flow of the particles was continuously monitored as shown in Figure 5.4(a). This voltage response from the sensor was converted into change in capacitance (Equation 4.1) and is plotted in Figure 5.4 (a) on the

Y-axis (From right). It was found that there was an increase in the capacitance every time the particle passed through the pair of electrodes in the microchannel. This change in capacitance was observed to be varied from 20 ~ 40 fF for Juniper pollen in DI water.

This variation in capacitance was primarily because 1) the particle size varied between

17.5 μm to 22.5 μm, 2) particles have different surface charges. The high capacitance spike, i.e., > 50 fF (Figure 5.4(a)) was most likely because of the presence of more than

one particle in the microchannel. Capacitance change in majority of the spikes was found

to be in the range of 20 fF ~30 fF. While the mechanism of evoking a large capacitance

change is still not clear, the high sensitivity of capacitance Coulter counter allows

detection of smaller particles using relatively large microchannel. Figure 5.4(a), 5.4(b)

and 5.4(c) shown below illustrates the capacitance change due to the flow of the Juniper

pollen particles through the microchannel.

Figure 5.4 (a): Voltage and capacitance change due to 20 μm Juniper scopulorum particles

Figure 5.4 (b): Capacitance change generated by 20 μm Juniper scopulorum

Figure 5.4 (c): Magnified capacitive pulses generated by Juniper scopulorum tree pollen

5.4.2 Detection of polystyrene

About 12 mg of polystyrene particles of ~12mg were diluted in 15 ml of DI water. The average diameter of the polystyrene particle was 20 μm. The prepared solution was injected into the inlet reservoir using a syringe. The flow created was pressure driven and the particle laden solution was thus pushed to the outlet reservoir. As the polystyrene laden sample was loaded and forced to pass through the microchannel, voltage pulses were generated (Figure 5.5 (a)). The voltage pulses were then converted to capacitive pulses (Figure 5.5 (b)). It was found that there was a decrease in the capacitance every time the particle passed through the pair of electrodes in the microchannel. The change in capacitance was observed to be varying from -10fF to -30fF for polystyrene particles in

DI water. This was because of the relative permittivity of the polystyrene particles (εr)

~2.3, while water has a relative permittivity (εr) of 78. The decrease in the magnitude of the capacitive pulses could be due to surface charge and insulating properties of polystyrene.

Figure 5.5 (a): Typical capacitive pulse of 20 μm polystyrene in DI water

Figure 5.5 (b): Typical magnified capacitive pulse generated by polystyrene

5.4.3 Detection of Juniper pollen and polystyrene mixture

In the experiments conducted, the two samples of Juniper tree pollen and polystyrene particles in DI water were mixed vigorously using ultrasonic bath. The particles were tested in the microfluidic device with co-planar electrodes. The mixed solution was introduced into the inlet reservoir using a syringe. Voltage traces across the measurement circuitry was continuously monitored and recorded. As the sample with both Juniper pollen and polystyrene were injected into the sensor, capacitive pulses were obtained

(Figure 5.6). The upward capacitive pulses were generated by the Juniper tree pollen and the downward pulses were generated by polystyrene. This testing demonstrated that the device could differentiate particles in terms of difference in permittivity.

Figure 5.6: Detection of bio-particles in DI water – each upward pulse represents a pollen particle and a downward pulse represent of a polystyrene particle

5.5 Summary

The design and fabrication of the microfluidic device was presented. Furthermore, validation of the dynamic response of the measurement circuitry in fluid environment for the microfluidic device using co-planar electrode was discussed. The microfluidic device was used to detect Juniper scopulorum pollen and polystyrene particles in de-ionized water (εr =78). The change in capacitance caused by pollen particle was observed to be ranging often between 20fF ~ 100fF and the capacitance change caused by 20 μm polystyrene particles ranges from -10fF to -30fF. The testing demonstrated the device capability of 1) detecting and 2) differentiating micro particles based on their size and surface charge properties. Based upon these preliminary testing in this chapter, the same principles have been used for detecting and counting metal debris particles in lubrication oil.

CHAPTER VI

MICROMACHINED SINGLE CHANNEL DEVICE FOR WEAR DEBRIS

DETECTION

This chapter presents the use of the microfluidic device for detection of micro metal particles in highly viscous fluids such as lubrication oil. The design and fabrication were described in detail in Chapter V.

6.1 Experimental setup and testing

The schematic of the microfluidic device for wear detection in lubrication oil was shown in Figure 5.1 in Chapter V. Here, the same device is used for detecting metal debris in lubrication oil. Figure 6.1 (a) is the schematic of the device and 6.1 (b) is a picture of the experimental setup. The particles used in the experiment as debris wear were aluminum powder (Atlantic Equipment Engineers, Inc). The size of the particles varied from 10 μm to 30 μm. However, all particles greater than 25 μm were filtered to ensure that no particles get blocked in the channel as the height of the microchannel was designed for 40

μm only. Thus, aluminum particles of size varying from 10μm to 25μm were used to detect the response of the microfluidic sensor. 1 mg aluminum particles mixed with 20 ml

SAE-5W30 lubrication oil were loaded into the inlet reservoir and forced to flow through the microchannel under pressure. In order to prevent the agglomeration of the metal

particles with each other in oil, the solution was mixed vigorously in an ultrasound before the test. Figure 6.2 shows the optical image of aluminum metal particles.

Figure 6.1(a): Schematic of the testing setup of the microfluidic device for wear detection in lubrication oil

MS311 BDPC Evaluation Board

Co-axial cables

Microfluidic device MS3110 IC

Figure 6.1(b): Experimental setup for testing the microfluidic sensor

50 μm

Figure 6.2: Optical image of aluminum particles of varying sizes

6.2 Results and Discussion

Lubrication oil with and without aluminum particles was pumped from inlet reservoir to outlet reservoir using a syringe. The flow rate is estimated to be 70 µl/min. The electrodes were connected to the MS3110 capacitance measurement chip, and the voltage response from the MS3110 was monitored using a NI-6220 DAQ. In the measurement, the change in output voltage in terms of the capacitance change of microchannel is given by the Equation 3.1 explained in Chapter III. The feedback capacitance CF was set to

1.197 pF and K was set to 5.14. First the oil without the aluminum particles was allowed to flow through the microchannel. This data was continuously monitored for a few seconds. Next, the solution laden with the metal particles is loaded into the inlet reservoir. Response of the device was recorded. Each pulse represents the passage of one aluminum particle through the microchannel.

As before, the data is initially obtained in terms of voltage pulses and is then converted to the change in capacitance. Figure 6.3 (a) shows the capacitance/voltage change when only oil is loaded into the inlet reservoir and allowed to flow through. Fig.

6.3 (b) shows that when oil with aluminum particles was loaded, capacitive pulses were observed. Each capacitive pulse represents passage of an aluminum metal particle. We note that many of the aluminum particles settle at the bottom of the inlet reservoir, so that only a small fraction of the particle passes through the channel. The magnitude of the pulses was in the range of 2 to 7 femto-farads. According to the testing of the meso-sized device in Chapter IV, the variation of the capacitance change is primarily due to the size variation of the test particles and the off-axis passage of the particles through the microchannel. Due to the high viscosity of the oil, a high driving pressure was required to maintain flow in the channel, resulting in high particle velocity through the channel. It is worthwhile to mention here that the device response to 20 µm non-conductive polystyrene particles (from Sigma Aldrich) suspended in lubrication oil was also tested.

No capacitive pulse was observed; this is due to the similarity in the relative permeability of the polystyrene particles (εr ~ 2.56) and lubrication oil (εr 2.1~ 2.4). Thus our technique holds promise to respond to metal wear debris particles and not to other types of particles in the lubrication oil. In addition, testing results of the meso-sized device indicate that the current device is not able to differentiate ferrous and nonferrous metal particles; we note that this differentiation is needed in rotary machinery health monitoring.

In order to see the shape of an individual pulse clearly, the experiment was repeated using a higher sampling frequency of 200 kHz. The feedback capacitance used in the experiment is CF = 1.502 pF. Fig. 6.4 (a) shows the device response over one second.

Fig.6.4 (b) is a magnified view of a single capacitive pulse due to the passage of an

aluminum particle through the microfluidic channel. The pulse width observed is approximately 0.5 ms.

Figure 6.3 (a): Measured capacitance change of the microfluidic sensor without aluminum particles in oil at 60 kHz sampling frequency

Figure 6.3 (b): Measured capacitance change of the microfluidic sensor when oil was loaded with aluminum particles at 60 kHz sampling frequency 73

Figure 6.4 (a): Typical capacitive pulse when aluminum particle flows through the microchannel at 200 kHz sampling frequency

Figure 6.4 (b): Typical magnified capacitive pulse when aluminum particle flows through the microchannel

FEM simulation was conducted for the microfluidic device to predict the capacitance change. However, it was found that the predicted values were at sub femto- farad levels, much smaller than the experimental result. This is possibly because the current FEM model neglects the double layer formed on the electrode surface; this double layer may play a dominant role in capacitance change when a particle passes through a microchannel. Further effort is needed to build a reasonable simulation model to predict the behavior of microscale devices as the mechanism of evoking a large change in capacitance is still not known.

6.3 Summary

The experimental results described here demonstrated the feasibility of using the capacitance Coulter counting principle for detection and counting of metal debris particles in lubrication oil. Unlike bulk measurement methods, the developed method scans each individual particle, and is expected to be able to measure the size of individual particles. Degradation of oil over time affects the electrical conductivity of the oil; these effects, however, do not affect the counting of debris particles in our differential capacitive measurements. This microfluidic device is the first step towards online detection of debris in lubrication oil for rotary machinery health monitoring [72]. To improve its capability, a number of different microchannels with different dimensions can be used to allow detection of debris of different sizes. A particle separator between the inlet and the microchannels would steer individual particles to the detection microchannel of appropriate size. Finally, the throughput of this device can be significantly improved by using multiple microfluidic detection channels [75]. 75

CHAPTER VII

CONCLUSIONS

The experimental results described here demonstrate the feasibility of using the capacitance Coulter counting principle for detection and counting of metal debris particles in lubrication oil. Experiments done with a meso-scale device (Chapter IV) with parallel plate electrodes immersed in lubrication oil demonstrate that the size of the capacitive pulse increases as the size of the particle increases. Besides, detecting steel particles in lubrication oil, the device was also capable of detecting aluminum and lead particles (solder). However, no noticeable change was found in the magnitude of the capacitance pulses between these three different tested metals of same size. The mesoscale device was used in the detection of particles ranging from 3 mm to 6 mm in size.

This can, however, be extended to detecting smaller particles by reducing the size of the plates and the distance between them. The flexibility in the detection of various particle sizes motivated us to extend the working principle to a microfluidic device with coplanar electrodes (Chapter V).

The microfluidic device has a higher sensitivity, and can detect particles as small as a few microns. Validation experiments have been performed with the microfluidic device, (Chapter V) first to detect bio-particles in de-ionized water. So far, Coulter

counters are used for detection of bio-particles in electrolytic solutions only with low selectivity [10]. From this study, we determined that the microfluidic device was capable of detecting as well as differentiating particles based on their permittivity. Final testing results using aluminum particles in lubrication oil (Chapter VI) proved that the device is efficient in its performance as a typical Coulter counter. The coplanar electrode design of the microfluidic sensor simplified its fabrication. Differential capacitance measurement has been used which further improved the sensitivity and eliminated the parasitic effects caused by external connections. However, in our experiments the noise level is about 5 to

8 mV when the microfluidic devices with external cables are connected to the measurement setup. There is a need to reduce this level of noise considerably in order to identify the peaks caused by the particles more clearly. Original concentration and measured concentration of the particles in solution may be compared to determine the efficiency of the sensor. Peak detection algorithms may also be used to confirm that the peaks are caused by the passage of the particle. We also plan to study on how to differentiate ferrous and non-ferrous metal particles in microfluidic channels based on a combination of inductive and capacitive measurements. The dimension of microchannels can be modified to allow detection of debris of different sizes. Particle sorting based on size can be used to separate particles and then be detected by appropriate sized capacitance sensing microchannels. Finally, the throughput of this device can be significantly improved by using multiple microfluidic detection channels [75]. Thus, this microfluidic device is the first step for online detection of debris in lubrication oil for rotary machinery condition monitoring. The microfluidic sensor can also be extended to detect liquid and other contaminants [72, 76, 77]. Viscosity [78, 79], TAN [76], TBN

[76], oxidation [80], moisture [81] and soot content [82, 83] are prognostic indicators of the engine and lubrication oil condition. The oil properties have been monitored using

EIS and cyclic voltammetric methods. These methods prove to be both sensitive and versatile for continuous monitoring. It is possible to extend the application of the microfluidic sensor to detect the above mentioned properties in lubrication oils using the above methods also if required. Further detailed analysis on the shape of the pulse, its height and width should give more information on the surface properties of the particle in addition to just detecting the presence of particles in the fluid. This will allow us to obtain more information. High sampling frequency, proper electrical shielding and the proper measurement circuitry can be used. In the experiments the particles in oil were forced to pass through the microchannel at a high velocity; this led to a small pulse widths and create difficulty in conducting shape analysis. This problem can be improved by applying smaller pressure gradients but with a more controlled flow system [10]. In the experiments, fewer particles were detected because many of them settled at the bottom of the inlet reservoir due to the high density of the particles. This can be overcome considerably, by using a vertical flow channel and a fluid system with sufficient stirring.

Condition monitoring can be an efficient tool for preventive maintenance when diagnostic devices used in on-line monitoring systems are combined, thus providing improved service efficiency and reduced cost for maintenance. In order to overcome the current difficulties in wear debris detection, various methods of condition monitoring maybe combined such that reasonable levels of sophistication are achieved. The microfluidic sensor explained here, is promising to be used as one component in real time, on-line detection as they are portable requiring small samples for analysis.

REFERENCES

1. Dempsey P.J., 2001, Gear Damage Detection Using Oil Debris Analysis, NASA TM- 210936

2. Dempsey P.J., 2000, A comparison of vibration and oil debris gear damage detection methods applied to pitting damage, NASA, TM-210371.

3. Peng Z., and Kessissoglou N., 2003, An integrated approach to fault diagnosis of machinery using wear debris and vibration analysis, Wear, 255, p.1221-1232.

4. Barraclough T.G., Lucas M., and Anderson D.P., 2001, Comparison of wear and contaminant particle analysis techniques in an engine test cell run to failure, Application white papers of Spectro Inc.

5. Raadnui S., and Kleesuwan S., 2005, Low cost condition monitoring sensor for used oil analysis, Wear, 259 (7-12), p.1502-1506.

6. Zhe J., Choy F.K., Murali S.V., Sarangi M.A., and Wilfong R., 2007, Oil debris detection using capacitance and ultrasonic measurements, STLE/ASME International Joint Tribology Conference, San Diego, California, USA.

7. Gardner J.W., Varadan V.K., and Osama O., 2001, Microsensors, MEMS, and Smart Devices: Technology, Applications and Devices, John Wiley and Sons, USA.

8. Rao B.K.N., 1996, Handbook of Condition Monitoring, Elsevier Advanced Technology, UK.

9. Coulter W.H., 1953, Means of counting particles suspended in a fluid, US Patent No. 2656508.

10. Jagtiani A.V., 2007, Development of novel multichannel resistive pulse sensors for micro-particle detection and differentiation, Thesis, Department of Mechanical Engineering, The University of Akron, Ohio, USA.

11. Zhang Z., Zhe J., Chandra S., and Hu J., 2005, An electronic pollen detection method using coulter counter principle, Atmos. Environ, 39, p.5446-5453.

12. DeBlois R., and Wesley R.K., 1977, Sizes and concentration of several type C oncornoviruses and bacteriophage T2 by the resistive-pulse technique, J.Virol, 23, p.227- 233.

13. Kasianiwicz J.J., Brandin E., Branton D., and Dreamer D.W., 1996, Characterization of individual polynucleotide molecules using a membrane channel, Proc. Nat. Acad. Sci., 93, p.13770-13773.

14. Saleh O.A., and Sohn L.L., 2003, Direct detection of antibody-antigen binding using an on-chip artifical pore, Proc. Nat. Acad. Sci., 100, p.820-824.

15. Flanagan I.M., Jordan J.R., and Whittington H.W., 1988, Wear debris detection and analysis techniques for lubricant-based condition monitoring, J. Phys.E: Sci. Instrum., 21, p.1011-1016.

16. Sohn L.L., Saleh O.A., Facer G.R., Beavis A.J., Allan R.S., and Notterman D.A., 2001, Capacitance cytometry: measuring biological cells one by one, Proc. Nat. Acad. Sci., 97, p.10687-10690.

17. Korvink J.G., and Paul O., MEMS: A practical guide to design, analysis, and applications, William Andrew, Inc., USA

18. Roylance B. J., 2005, Ferrography—then and now, Tribol. Int., 38, p.857-862.

19. Khandakar G., Glavas E. and Jones G. R., 1993, A fibre-optic oil condition monitor based on chromatic modulation, Meas. Sci.Technol., 4, p.608-613.

20. Campbell P., 1991, On-line monitoring of ferromagnetic debris concentration, Proc. Int. Condition Monitoring Conf., p.325-335.

21. Barlow R., and Hunter L., 1960, Optimum Preventive Maintenance Policies, Oper. Res., 8, p.90-100.

22. Barron R., 1996, Engineering Condition Monitoring: Practice, Methods and Applications, Longman, USA

23. Ball P.G., 1997, Measurement of Particulate Machine Wear Analysis – A Rational Approach to Methods Integration for Maximum Benefits, STLE, World Tribology Congress, UK.

24. Thomas S.J., Dale S.R., and Joseph K.P., 2006, Tribological debris analysis system, US Patent No. 7019834

25. Charlton B., Fisher A.S., Goodall P.S., Hinds M.W., Lancaster S., and Salisbury M., 2007, Atomic spectrometry update: Industrial analysis: metals, chemicals and 80

advanced materials, J. Anal. At. Spectrom., 22, p.1517-1560.

26. Brown J.R., 1980, Particle size independent spectrometric determination of wear metals in aircraft lubricating oils, Anal. Chem., 52, p.2365-2370.

27. Saba C., 1990, Improving the wear metal detection of spectrometric oil analysis, STLE, 46, p.310-315.

28. Lukas M., and Anderson D., 1991, Techniques to improve the ability of spectroscopy to detect large wear particles in lubricating oils, Proc. of Int. Conf. Condition Monitoring Stadthalle, Germany, Europe.

29. Chandrasekaran H., Granfors A., and M’Saoubi R., 2006, Tribological aspects of tool–chip and tool–work contact in machining and the application of laser spectrometry Wear, 260, p.319–325.

30. Kauffman R., 1989, Particle size and composition analysis of wear debris using atomic emission spectroscopy, STLE, 45, p.147-153

31. Centers P.W., 1990, Oil monitoring technology, AUTOTESTCON '90; IEEE Systems Readiness Technology Conference, San Antonio, TX, United States, p.523- 528.

32. Flanagan I.M., Jordan J.R., and Whittington H.W., 1988, Wear-debris detection and analysis techniques for lubricant-based condition monitoring, J. Phys. E: Sci. Instrum., 21, p.1011-1016.

33. Nunnari J.J., and Dalley R.J., 1991, An overview of ferrography and its use in maintenance, Predict technologies, Vice President of Corporate Accounts of the Wear Particle Analysis Division of Predict, High Pointe Corporate Park, Cleveland, OH., United States

34. Holzhauuer W., and Wurray S.F., 1983, Continuous wear measurement by on-line ferrography, Wear, 90, p.11-17.

35. Scott D., 1983, Wear analysis, Phys. Technol., 14, p.133-139.

36. Qingchang T., Bojun T., Di X., and Mingjie L., 1992, A new method of preparing Ferrograms, Wear, 155, p. 277-283.

37. Myshkin N.K., Markova L.V., Semenyuk M.S., Kong H., Han H.G., and Yoon E.S., 2003, Wear monitoring based on the analysis of lubricant contamination by optical ferroanalyzer, Wear, 255, p.1270-1275

38. Cobb W., 1995, Ultrasonic fluid composition monitor, US Patent No. 5473934

39. Zhang J., and Drinkwater B.W., 2006, Monitoring of Lubricant Film Failure in a Ball 81

Bearing Using Ultrasound, J. Tribol., 128, p.612-618.

40. Toutountzakis, T., 2003, Observations of Acoustic Emission activity during gear defect diagnosis, NDT and E Int., 36, p.471-477.

41. Howe B., and Muir D., 1998, In-Line Oil Debris Monitor (ODM) for Helicopter Gearbox Condition Assessment, DTIC, AD-a, 374, p.503-508.

42. Flanagan M., Jordan J.R., and Whittington H.W., 1990, An inductive method for estimating the composition and size of metal particle, Meas. Sci. Technol., 1, p.381-384.

43. Robert T.S., 1991, Advanced technology in failure prevention, Cambridge University Press, USA.

44. Bangert B, 2003, Aircraft engine reliability business model, US Patent No.6643570.

45. Banks J.C., Brought M.S., and Byington C.S., 2002, Vibration sensor configuration optimization for the AV-8B F402-RR-408 engine, Aero. Conf. Proc., IEEE, 6, p.3043- 3050.

46. Veronesi W.A., Weise A.P., Reed R.W., and Ringermacher H.I., 1991, In-line metallic debris particle detection probe and resonant evaluation system utilizing the same, US Patent No. 5041856.

47. Bradford M.P., Electric chip detector, 1986, US Patent No.4598280.

48. Thanagasundram S., and Schlindwein F., 2006, Comparison of integrated micro- electrical-mechanical system and piezoelectric accelerometers for machine condition monitoring, Proc. IMECH E Part C J. Mech. Eng. Sci., 220, p.1135-1146.

49. Richard E.B., 1994, Handbook of lubrication and tribology: monitoring, materials, synthetic lubricants and applications, CRC Press, USA.

50. Weeks M.W., 1982, Lubrication monitoring systems, US Patent No. 4354183.

51. Wang S.S., 2001, A physical model for the engine oil condition sensor, Tribol. Trans., 44, p.411-416.

52. Williams R., 1976, Monitoring the condition of machinery, Phys. Technol., 7, p.166- 170.

53. Nakajima Y, Suzuki T, and Wada Y., 1989, Lubrication monitoring apparatus for machine, US Patent No. 4852693.

54. Bartik, I., 1986, Filtration: A complex process whose methods are constantly improving, J. Can. Lubr. , 6, p.11-19.

55. Davis.A, 1998, Handbook of condition monitoring: techniques and methodology, Chapman & Hall, UK.

56. Gribble D.L., 1994, Vibration monitoring lubrication device, US Patent No. 5350040.

57. Victor E.H.E., 1986, Vibration monitoring in rotary machines, US Patent No. 4593566.

58. Kurihara N., 1984, Method and apparatus for monitoring the shaft vibration of a rotary machine, US Patent No. 4426641.

59. Taniguti R., 1987, Vibration monitoring apparatus, US Patent No. 4683542.

60. Wang L., Wood R.J.K, Harvey T.J., Morris S., Powrie H.E.G., and Care I., 2003, Wear performance of oil lubricated silicon nitride sliding against various bearing steels, Wear, 255, p.657- 668.

61. Peng Z., Kessissoglou, N.J., and Cox, M., 2005, A study of the effect of contaminant particles in lubricants using wear debris and vibration condition monitoring techniques, Wear, 258, p.1651-1662.

62. Polla D.L., and Francis L.F., 1998, Processing and characterization of piezoelectric materials and integration into microelectromechanical systems, Annu. Rev. Mater. Sci., 28, p. 563-597.

63. Bhushan B., 2000, Fundamentals of tribology and bridging the gap between the Macro-and Micro/Nanoscales, Kluwer Academic Publishers, USA.

64. Roylance B.J., Williams J.A., and Dwyer-Joyce R., 2000, Wear debris and associated wear phenomena—fundamental research and practice, Proc. Inst. Mech. Eng.: Part J, J. Eng. Tribol., 214, p.79-105.

65. Edmonds J., Resner, M.S., and Shkarlet, K., 2000, Detection of precursor wear debris in lubrication systems, Aero. Conf. Proc. IEEE, 6, p.73-76.

66. Evaluation/Programming Board and Support Software-Operating Specifications and Users Manual, Microsensors Inc.

67. Erismis M.A., 2004, MEMS accelerometers and gyroscopes for inertial measurement units, Thesis, Middle East Technical University.

68. Palasagaram J.N., and Ramadoss R., 2006, MEMS-Capacitive Pressure Sensor Fabricated Using Printed-Circuit-Processing Techniques, J. Sen. IEEE, 6, p.1374-1375.

69. Horenstein M.N., 1990, Microelectronic circuits and devices, Prentice Hall, USA.

70. Murali S.V., Xia X., Jagtiani A.V., Carletta J, and Zhe J., 2008, A microfluidic device for wear detection in lubricants, ASME IMECE, Boston, Massachusetts, USA.

71. Mamun N.H. and Dutta P., 2006, Patterning of platinum microelectrodes in polymeric microfluidic chips, J. Microlithogr. Microfab. Microsys. 5, 039701-63.

72. Cipris D., 1988, Lubricant oil monitoring system sensor for monitoring lubricant oil quality, US Patent No.4782332.

73. Hunt T.M., 1993, Handbook of Wear Debris Analysis and Particle Detection in Liquids, Springer Publishers, USA.

74. Denis J., Briant. J, Jean-Claude Hipeaux , Lubricant properties analysis and testing, Editions Technip, UK (2000).

75. J. Zhe., A.V. Jagtiani, P. Dutta, J. Hu and J. Carletta, 2007, A micromachined high throughput Coulter counter for bioparticle detection and counting, J. Micromech. Microeng., 17, p.304-313.

76. Watson R.W., 1984, Additives - The right stuff for automotive engine oils, S.A.E. transactions, 93, p.5457-5468.

77. Phillips A.D., Eggers W.J., Rodgers R.S., and Pappas D., 2006, On-line oil condition sensor system for rotating and reciprocating machinery, US Patent No.7043402.

78. Agoston A., Ötsch C., and Jakoby B., 2005, Viscosity sensors for engine oil condition monitoring—Application and interpretation of results, Sen. Act. A: Phys., 121, p.327- 332.

79. Frederick D.M., Chung-Chiun L., Donald F.L., and Laurie D.A., 2000, Micro- viscosity sensor and lubrication analysis system employing the same, US Patent No.6023961.

80. Wurzbach R.N., 2000, Airing Out Lubricant Oxidation, Maintenance Reliability Group Inc., Noria Learning center.

81. Smiechowski M.F., and Lvovich V., 2002, Electrochemical monitoring of water– surfactant interactions in industrial lubricants, J. Electroanal. Chem., 534, p.171-180.

82. Smiechowski M.F, and Lvovich V., 2005, Characterization of non-aqueous dispersions of carbon black nanoparticles by electrochemical impedance spectroscopy, J. 84

Electroanal. Chem., 577, p.67-78.

83. Ulrich C., Peterson H., Sundgren H., Bjorefors F., and Krantz-Rulcker C., 2007, Simultaneous estimation of soot and diesel contamination in engine oil using electrochemical impedance spectroscopy, Sen. Act. B: Chem., 27, p.613-618.