Biosensors and Instrumentation - Fireworks PHP … · U-Tokyo Special Lectures Biosensors and Instrumentation Stewart Smith Biosensors and Instrumentation Stewart Smith Institute

Stewart SmithBiosensors and InstrumentationU-Tokyo Special Lectures

Biosensors and Instrumentation

Stewart Smith Institute for BioengineeringThe University of Edinburgh

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• Course Aims:

‣ Look at how biosensors interface with electronics to build complete systems

‣ Cover many different transduction methods for biosensing applications

‣ See some real examples of biomedical sensor and transducer systems

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• Learning Outcomes:‣ Understanding of transducers and methods

to extract information from sensors

‣ Use analogue circuit concepts to analyse sensor outputs

‣ Understand a wide range of biosensor instrumentation implementations

‣ Gain knowledge of the state of the art

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Lecture 1:Introducing Biosensors and Transducer Basics

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Measurement

“I often say that when you can measure what you are speaking about and express it in numbers you know something about it; but when you cannot measure it, when you cannot express it in numbers, your knowledge is of a meagre and unsatisfactory kind”

- William Thompson, Lord Kelvin

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Definition of a Biosensor

•A device that uses specific biochemical reactions mediated by isolated enzymes, immunosystems, tissues, organelles or whole cells to detect chemical compounds usually by electrical, thermal or optical signals.

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Biosensors7

• Biosensors produce an output (often electrical) which is proportional to the concentration of biological analytes.

• Two-stage sensing ‣ Biological molecular sensor → Traditional sensor/transducer

• Example is glucose sensor‣ Enzyme - glucose oxidase → Electrochemical sensor (O2/H2O2)

Molecular Sensing Element

Physical/Chemical

Transducer

ElectricalSignal

Analyte


Biosensing Principles8

Type Examples Electrochemical:

Potentiometric Amperometric FET based Conductometric

Glucose Sensor Neurochemical Sensor for Dopamine, Nitric Oxide, etc.

Optical (Spectroscopy, Fluorescence, Polarimetry)

Environmental Monitoring

Mechanical or Piezoelectric (QCM, SAW, MEMS)

DNA sequencing

Thermal (Thermistor, pn junction, thermopile)

Sensing by Enzymatic Catalysis


Electrochemical Sensors• Potentiometric‣ Measurement of the emf of an electrochemical cell at

zero current. ‣ The emf is proportional to the log. concentration of the

analyte.

• Amperometric‣ Apply a specific potential to an electrode to oxidise or

reduce the analyte of interest‣ Current will be proportional to the analyte concentration

• Conductometric‣ Sensing reaction leads to a change in the composition of

the solution‣ This is measured as a change in the conductivity

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Optical Biosensors• Changes in light intensity are linked to changes in mass

or concentration. Hence, fluorescent or colorimetric molecules must be present.

• Methods include: optical fibres, surface plasmon resonance, absorbance, luminescence.

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LED

Photodetector

Finger

IR light


Glucose Sensor (Electrochemical)

• Principle: Glucose diffuses through to enzyme GOx and reacts to give H2O2 and glucono-lactone

• Oxygen consumption or H2O2 production can be measured amperometrically

• Reaction also changes the local pH which can be measured potentiometrically

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Pt Anode (+)

Ag Cathode (-)

Immobilized Glucose Oxidase (GOx)

Polyurethane Membrane

glucose + O2GOx��! glucono-lactone + H2O2


Glucose Sensor (Optical)

• Based on the competitive binding of glucose to ConA on dialysis fibre

• It replaces fluorescently labelled molecules (Dextran)

• These are then exposed to excitation and emission increases

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3 mm

0.3

mm

Hollow Dialysis Fibre Immobilised Con A

Excitation

Emission

Optical Fibre

Glucose


Transducers

• What is a transducer?

‣ A device to convert energy from one form to another.

‣ Sensors

‣ Actuators

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Sensors

• Convert physical/chemical parameters into an electrical signal

‣ Biosensors use a biological element to transduce what they measure

‣ Often this needs a subsequent transducer to obtain an electrical signal

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Actuators

• Transducers that convert electrical signals into a physical output

‣ Anything from a loud speaker to a flat panel display.

‣ Can provide forcing in a control system

‣ Examples of “bio-actuators”?

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Sensors and Actuators16

TRANSDUCERS

SENSORS ACTUATORS

PhysicalParameter

↓ElectricalOutput

ElectricalSignal↓

PhysicalOutput

e.g. Piezoelectric:

Force → Signal

Signal → Force

ULTRASOUND!


Traditional Sensors

• Advantages: Simple, cheap, complicated processing can be separated from signal acquisition

• Disadvantages: Weak signal prone to noise, sensor itself may be inflexible and lacks intelligence

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Sensing Element

Transmission Link

Signal Processing

PhysicalParameter

Display,Storage,Further

Processing


Smart Sensors

• Amplification, signal processing, digitisation can be integrated with sensing elements

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Sensing Element & Signal Processing

PhysicalParameter

Transmission Link

Signal Processing

Display,Storage,Further

Processing


Low Power Considerations

• Power consumption depends on the precision and the bandwidth

• Processing high resolution data quickly uses more energy

• Complex processing tasks will also require more power

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Mixed Signal Architecture20

ANALOG

ADC DSPSensorInput

Meaningful, low

bandwidthoutput

information

High bandwidth,high output SNR

High speed, high

precision

High speed, high precision

Meaningless, high bandwidth numbers


Low-powerprogrammableanalog stage

Mixed Signal Architecture21

Low-Power ADC

Low-Power DSP

SensorInput

Meaningful, low

bandwidthoutput

information

Low bandwidth,Low output SNR Low speed, low precision

ADC and digital processing


Types of Sensor22

RLLight in

Photovoltaic Cell

Electrical SignalOut

Self generating or Active transducer:• Output signal is generated from the input energy

of whatever is being measured


Types of Sensor23

Light in

Photoconductive Cell

Electrical SignalOut

Modulating or Passive transducer:• Sensor modulates the flow of electrical energy

from a power source to the load

+ −

RL


Biosensor Characteristics

• Microfabricated glucose sensor: 1. Working Electrode (Pt), 2. Counter Electrode (Pt), 3. Reference Electrode (Ag/AgCl), 4. Connecting Wires, 5. Contact Pads.

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Sensors 2002, 2 129

served as adhesion promoters to the silicon dioxide and the following layer of silicon nitride. Metallic layers were evaporated by an electron gun at a substrate temperature of 25°C. Electrode geometry was patterned using the lift-off technique. Silicon nitride was chosen as the top insulator because it is known to be less permeable to water vapor and alkali ions than silicon dioxide. Reactive ion etching was used to etch away the silicon nitride in the areas of WE, CE, RE and their pads (Fig. 1). The wafer was immersed in a titanium etchant (H2SO4/H2O = 1/1, 80°C) to ensure that there was no titanium left on the free surfaces. Partial chlorination of the Ag layer was performed at wafer level in 0.25 mol/L FeCl3 solution.

Figure 1. Schematic of the chip: 1) WE, 2) CE, 3) Ag/AgCl RE, 4) Conductive lines, 5) Pads.

After dicing the wafer, the individual chips were mounted on printed circuit boards (PCB) and wire bonding was done. An epoxy resin was used to encapsulate the sensor, leaving only the active area exposed.

PB layer electropolymerization

PB layer was prepared by cyclic voltammetric method. Before electrodeposition PB film, Pt disk electrode was chemically cleaned and rinsed in order of 2 mol/L of KOH, sulfuric acid, ethanol and deioned water. Then PB was electrodeposited on the Pt electrode by cyclic scan within the limits of 0 to 0.5 V in a fresh solution containing 1.5 mM K3Fe(CN)6 and 2 mM FeCl3 · 6H2O in 0.1 M KCl and 2 mM HCl (pH 2.0). Before being used, the PB/Pt electrode was processed in 1 M KCl solution (pH4) by cyclic scan in a rate of 50 mV/s from -0.2 to 0.5 V until stable response was established.

GOD immobilization

One gram of chitosan was added into 4 ml of acetic acid and stirred for 20 minutes until complete dissolution. Transfer one microliter of chitosan solution on the WE and naturally dried. Then it was rinsed several times with deionized water to remove residual acetic acid in the chitosan membrane and dried. Next it was immersed into 2.5% glutaradehyde solution for 5 minutes. One microliter of 250

J. Zhu et al, “Planar Amperometric Glucose Sensor Based on Glucose Oxidase Immobilized by Chitosan Film on Prussian Blue Layer”, Sensors, 2, 127-136, 2002.


Sensor Characteristics

• Zhu et al. glucose sensor

• Selection of operational pH

• Enzymes are very sensitive to environment

• Similar to selectivity issue we will return to

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Sensors 2002, 2 132

Selection of pH value

Actually, the effect of pH on the sensor involves two factors: (1) the effect of pH on the electrocatalystic property of the PB layer (Fig.4 (a)); (2) the effect of pH on the activity of GOD (Fig. 4(b)). Fig. 4(c) shows the combined relation between the response current of the sensor and the pH values of the glucose solution. The sensor exhibits that larger response current can be obtained within the range of pH 6.0 to 7.0. However, the higher value of pH in the glucose solution, the lower stability of the PB. Thus, pH6.8 phosphate buffer solution was chosen as bulk solution.

5.0 5.5 6.0 6.5 7.0 7.5 8.0

0.7

0.8

0.9

1.0

Rel

ativ

e re

spon

se

pH

5.0 5.5 6.0 6.5 7.0 7.5 8.0

0.6

0.7

0.8

0.9

1.0

Rel

ativ

e re

spon

se

pH

5.0 5.5 6.0 6.5 7.0 7.5 8.00.4

0.5

0.6

0.7

0.8

0.9

1.0

Rel

ativ

e re

spon

se

pH

(a) (b) (c)

Figure 4. Effect of pH on the responses for (a) PB/Pt electrode, (b) GOD/Pt electrode and (c) GOD/PB/Pt electrode.

Effect of temperature on response

Fig. 5 shows the effect of temperature on the response current of the sensor (concentration of glucose, 3.0mM). The current at certain concentration increased with temperature until 40oC and then decreased. The lower temperature, the lower activity of GOD is. When the temperature is higher than 40oC, the GOD on the WE will partially lost its activity. Hence, it is necessary to control seriously environmental temperature during measurement or to make compensation for temperature.

10 20 30 40 50

160

200

240

280

320

Res

pons

e/nA

Temperature/ oC

Figure 5. Effect of temperature on response current of the sensor.



• Transfer Function

‣ Relates sensor input to electrical output

‣ Often presented as a graph of response

‣ Calibration curve

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• Sensitivity

‣ Very important parameter

‣ Derivative of transfer function

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δinput

δoutput

S =�output

�input



• Dynamic Range

‣ Range of input levels for specified accuracy

‣ Linear region of transfer function?

• Nonlinearity/Linearity

‣ Maximum error over the dynamic range

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LinearNon-Linear?



• Hysteresis

‣ Memory or lag in a sensor system

‣ Output depends on history of input

‣ Can be a positive attribute for a sensor

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Output

Input

1 3

2



• Resolution

‣ Minimum detectable signal fluctuation

‣ Related to the time scale of the change

‣ May be dominated by digitisation conditions

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-4.8 -4 -3.2 -2.4 -1.6 -0.8 0 0.8 1.6 2.4 3.2 4 4.8

-1.5

-1

-0.5

0.5

1

1.5

Input

Output

-4.8 -4 -3.2 -2.4 -1.6 -0.8 0 0.8 1.6 2.4 3.2 4 4.8

-1.5

-1

-0.5

0.5

1

1.5



• Resolution




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-4.8 -4 -3.2 -2.4 -1.6 -0.8 0 0.8 1.6 2.4 3.2 4 4.8

-1.5

-1

-0.5

0.5

1

1.5

Input

Output

-4.8 -4 -3.2 -2.4 -1.6 -0.8 0 0.8 1.6 2.4 3.2 4 4.8

-1.5

-1

-0.5

0.5

1

1.5

-4.8 -4 -3.2 -2.4 -1.6 -0.8 0 0.8 1.6 2.4 3.2 4 4.8

-1.5

-1

-0.5

0.5

1

1.5

-4.8 -4 -3.2 -2.4 -1.6 -0.8 0 0.8 1.6 2.4 3.2 4 4.8

-1.5

-1

-0.5

0.5

1

1.5



• Resolution




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-4.8 -4 -3.2 -2.4 -1.6 -0.8 0 0.8 1.6 2.4 3.2 4 4.8

-1.5

-1

-0.5

0.5

1

1.5

Input

Output

-4.8 -4 -3.2 -2.4 -1.6 -0.8 0 0.8 1.6 2.4 3.2 4 4.8

-1.5

-1

-0.5

0.5

1

1.5

-4.8 -4 -3.2 -2.4 -1.6 -0.8 0 0.8 1.6 2.4 3.2 4 4.8

-1.5

-1

-0.5

0.5

1

1.5

-4.8 -4 -3.2 -2.4 -1.6 -0.8 0 0.8 1.6 2.4 3.2 4 4.8

-1.5

-1

-0.5

0.5

1

1.5

-4.8 -4 -3.2 -2.4 -1.6 -0.8 0 0.8 1.6 2.4 3.2 4 4.8

-1.5

-1

-0.5

0.5

1

1.5

-4.8 -4 -3.2 -2.4 -1.6 -0.8 0 0.8 1.6 2.4 3.2 4 4.8

-1.5

-1

-0.5

0.5

1

1.5




• Response time to changes in glucose

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Sensors 2002, 2 133

Performances of the sensor

Response time

The response times of the glucose sensor were 42s for concentration change from 0 to 6 mmol/L and 60 s for reversion, respectively. They were estimated from the response-time curve using a potentiostat set at +50 mV (Fig. 6) indicating the establishment of stable mass transport within 60 s.

Figure 6. The response time curves of the sensor.

Precision

Good precision was confirmed by 11 continuous measurements in pH 6.86 phosphate buffer solutions containing 3.0 mM of glucose. The experiment values were listed in Table 1.

Table 1. The precision of the sensors.

No. 1 2 3 4 5 6 7 8 9 10 11

I/nA 291 294 298 292 297 295 290 289 299 295 294

AV 294

RSD 3.0%

AV: Average value, RSD: Relative standard deviation.

Calibration curve

The calibration curve of the glucose sensor with experimental condition chosen above is shown in Fig. 7. Linear range, detection limit, sensitivity and response time are 0.1 – 6.0 mM, 0.06 mM (signal-to-noise ratio, 3), 98 nA/M and 60 sec, respectively. Measurement range may be expended up to 20 mM. The apparent Michanelis – Mentent constant of the sensor is 21 mM calculated according to Michanelis – Mentent equation.



• Bandwidth

‣ Response time to change in signal

‣ Decay of response after change

‣ Upper and lower cutoff frequencies

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• Selectivity

‣ Sensors respond to undesired parameters

‣ In biosensors this can be chemical or physical

‣ Desired sensitivity divided by the unwanted sensitivity

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Sensors 2002, 2 132

Selection of pH value

Actually, the effect of pH on the sensor involves two factors: (1) the effect of pH on the electrocatalystic property of the PB layer (Fig.4 (a)); (2) the effect of pH on the activity of GOD (Fig. 4(b)). Fig. 4(c) shows the combined relation between the response current of the sensor and the pH values of the glucose solution. The sensor exhibits that larger response current can be obtained within the range of pH 6.0 to 7.0. However, the higher value of pH in the glucose solution, the lower stability of the PB. Thus, pH6.8 phosphate buffer solution was chosen as bulk solution.

5.0 5.5 6.0 6.5 7.0 7.5 8.0

0.7

0.8

0.9

1.0

Rel

ativ

e re

spon

se

pH

5.0 5.5 6.0 6.5 7.0 7.5 8.0

0.6

0.7

0.8

0.9

1.0

Rel

ativ

e re

spon

se

pH

5.0 5.5 6.0 6.5 7.0 7.5 8.00.4

0.5

0.6

0.7

0.8

0.9

1.0

Rel

ativ

e re

spon

se

pH

(a) (b) (c)

Figure 4. Effect of pH on the responses for (a) PB/Pt electrode, (b) GOD/Pt electrode and (c) GOD/PB/Pt electrode.

Effect of temperature on response

Fig. 5 shows the effect of temperature on the response current of the sensor (concentration of glucose, 3.0mM). The current at certain concentration increased with temperature until 40oC and then decreased. The lower temperature, the lower activity of GOD is. When the temperature is higher than 40oC, the GOD on the WE will partially lost its activity. Hence, it is necessary to control seriously environmental temperature during measurement or to make compensation for temperature.

10 20 30 40 50

160

200

240

280

320

Res

pons

e/nA

Temperature/ oC

Figure 5. Effect of temperature on response current of the sensor.




• Testing anti-interference

• Responses to ascorbic and uric acid

• Prussian Blue reduces H2O2 over-potential

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Sensors 2002, 2 134

0 5 10 15 20

0

200

400

600

800

1000

1200

Res

pons

e/nA

Cglucose

/mM

Figure 7. Calibration curve of the sensor.

Testing of anti-interference

Fig. 8 shows the effect of ascorbic and uric acids on the glucose sensor response. The arrows indicate the peaks for different solution and their concentrations. The peaks for 0.06 mM of ascorbic acid, 0.3 mM of uric acids and their mixture are 3,1 and 4 nA higher than the average of background current, respectively while the peak for 3.0 mM of glucose with two interferent species is only 4 nA higher than one for pure glucose solution. The difference is within 3.0 % of RSD.

Figure 8. Response of the sensor in the presence of ascorbic and uric acids.

Long-term stability

The GOD-PB/Pt sensor response for 4.0mM glucose was measured 10 times a day (storage in refrigerator during nonuse). The average value for the 10 measurements is plotted against the number of days after the preparation of the sensor. No apparent change in the response of the sensors was




• Drift and long term stability of sensor

‣ Change in output with no change in input

‣ Long term drift can indicate degradation of the sensor itself

35Sensors 2002, 2 135

observed during the 30 days from Fig.9. Good long-term stability obtained can be attributed to the use of double functional chitosan membrane, which is permeable for H2O2 but impermeable for larger molecules such as Prussian Blue resulting in no loss of it from the electrode surface (9).

Figure 9. Long-term stability of the sensor.

Accuracy

Measurement of accuracy of the glucose sensor was taken in a standard serum with composition similar to that of normal human blood serum. The measured result of glucose was 6.1 mM, which are almost identical to the recommended values of 6.0.

Results obtained from the glucose sensor directly in human blood serum were compared with ones obtained by spectrophotometry, as shown in Fig. 10. On testing 100 samples of serum, the correlation coefficient reaches 0.996.

Figure 10. Correlation and regression line for measurements of 100 human serum samples between spectrophotometry and our sensors.



• Accuracy: expected maximum systematic bias

• Precision: reproducibility of measurements

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Probabilitydensity

Accuracy

PrecisionValue

Reference value




• Correlation with other standards

• Demonstrates accuracy of sensor

• Comparison with spectrophotometry for 100 human samples

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Sensors 2002, 2 135

observed during the 30 days from Fig.9. Good long-term stability obtained can be attributed to the use of double functional chitosan membrane, which is permeable for H2O2 but impermeable for larger molecules such as Prussian Blue resulting in no loss of it from the electrode surface (9).

Figure 9. Long-term stability of the sensor.

Accuracy

Measurement of accuracy of the glucose sensor was taken in a standard serum with composition similar to that of normal human blood serum. The measured result of glucose was 6.1 mM, which are almost identical to the recommended values of 6.0.

Results obtained from the glucose sensor directly in human blood serum were compared with ones obtained by spectrophotometry, as shown in Fig. 10. On testing 100 samples of serum, the correlation coefficient reaches 0.996.

Figure 10. Correlation and regression line for measurements of 100 human serum samples between spectrophotometry and our sensors.


Transducer Basics

• Learning Outcomes

‣ Define a transducer, describe the difference between sensors and actuators

‣ Understand sensor instrumentation at a high level - Smart sensors, power issues

‣ Describe the basic characteristics of a sensor

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Documents

Biosensors and Instrumentation - Fireworks PHP … · U-Tokyo Special Lectures Biosensors and Instrumentation Stewart Smith Biosensors and Instrumentation Stewart Smith Institute