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International Journal of Nanotechnology and Applications
ISSN 0973-631X Volume 11, Number 3 (2017), pp. 213-235
© Research India Publications
http://www.ripublication.com
Modeling and Analysis of Biosensors for Evaluation
of Its Mechanical and Electrical Properties
Santhosh Kumar D.R
Research Scholar, Department of ECE
Jain University, Bengaluru-5600024, India.
Dr. P.V Rao
Professor, Department of ECE
Vignana Bharati Institute of Technology, Hyderabad, India.
Abstract
A biosensor detects specific biological analytes and converts it into some
electrical, optical or other signal for analysis. The effective recognition of
charged biomolecules in biosensor by suitable semiconducting nanomaterial
and with ideal device geometry is the area of research interest. In this work,
the response of diverse label-free electronic biosensors to identify
biomolecules is detected by using biosensor lab a numerical simulation tool in
nanohub. Planar biosensor, cylindrical nanowire, nanoshpere, DGFET,
Extended gate, Magnetic particle, DGFET pH, EGFET pH and Flexure FET
sensors with different device parameters are reported here. The reaction of a
sensor is noted in conditions of selectivity, sensitivity and settling time.
Keywords: biosensor, nano material, simulation.
I. INTRODUCTION
Signals are detected by sensors that react to chemical or physical stimulus. Sensors
require target condition and signal transduction. Any chemical or biological entity like
small organic molecules, proteins, peptides, carbohydrates can be target recognition
[1]. According to International Union of Pure and Applied Chemistry, biosensor is “a
device that uses biochemical reactions mediated by isolated enzymes, organelles or
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214 Santhosh Kumar D.R and Dr. P.V Rao
whole cells to detect the effects of chemical compounds by electrical, thermal or
optical signals”. Based on the origin of transducer type, biosensors are of four classes:
(i) optical biosensors - colorimetric, fluorescent, luminescent, and interferometric, (ii)
electrochemical biosensors - amperometric, potentiometric and conductometric, (iii)
mass-based piezoelectric and acoustic wave biosensors and (iv) calorimetric biosensor
[2]. Amperometry is operated at a specified practical potential linking the effective
electrode with the reference electrode, moreover the generated signal is allied with
target compounds concentration. The current signal is generated as a function of the
reduction or oxidation of an electro-active product on the outside of a effective
electrode, in the amperometric detection.[3] The biosensor contains component
receptor to detain a target ligand and quantifiable signals like colorimetric,
electrochemical, fluorescence, magnetic response, and chemiluminescence are
generated by signal transduction [4]. The diverse biorecognition elements are tissues,
organisms, organcells, cell receptors, enzymes, antibodies, nucleic acids and synthetic
receptors. It is a biologically resulting substance with the aim of interacting (bind or
recognize) with the analyte under investigation [5]. Whole cells use full cell systems
microorganisms like bacteria, algae with yeast, or more multifaceted eukaryotic cells.
It helps in discovering the wide toxicity of the sample. The main constraint of whole
cell biosensors is the reaction time that could vary among minutes, with hours plus
survival of the cell in active in addition to responsive status [6]. Nanomaterials
present great material property such as tunable conductivity via doping moreover
synthesis process, plus high carrier mobility to grasp real-time sensing in 0- or 1-
dimensional structure. So far, these rewards of nanomaterials have been keenly
considered to build up biosensors based on inorganic or organic nanomaterials.
Inorganic nanomaterials such as CNT and Si nanowires have been made-up during
diverse methods with developed for the application of biosensors, chemical sensors
and electrical devices [7].The expanding candidates like nanorods, nanoparticles,
carbon nanotubes and nanowires have become the vital element of bioelectronic
devices and biosensors. Nanowires or carbon nanotubes combined with FET
technology [8]. The present work reported in this paper deal with the study of label-
free, electronic biosensors response, evaluation of its mechanical and electrical
properties. Modeling and simulation is carried out using Biosensor lab available in
nanohub for nine different biosensors. Based on the hypothesis on self consistent
solution of the diffusion capture model and Poisson Boltzmann equation, illustrating
the magnitude of screening limited kinetic reaction of nano biosensors, biosensor lab
provides simulation tool. The different biosensor responses in terms of graph are
reported in the result section after simulation setting. The conclusion section notes
down the observation done during the simulation. This research work attempts to give
a summary of the different electronic nano biosensors response for interested
researchers.
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Modeling and Analysis of Biosensors for Evaluation of Its Mechanical… 215
II. MODELING AND SIMULATION
Biosensor Lab is a statistical simulator to envisage the performance metrics meant for
diverse type of label-free, electronic biosensors. At present, the BioSensorLab focuses
only on those sensors that can detect the presence of charged biomolecules near the
sensor surface by electrostatic interaction. To shun parasitic reaction, the electronic
biosensors surfaces of the planar Insulated Gate FET or ISFET, are primary
functionalized by receptor molecules of recognized identity. Unknown target
molecules disseminate all over the sensor volume once introduced to the sensor
volume. The unknown molecules will be ‘captured’ by the receptors merely if the
target is a precise and select complement to the receptor by lock and key principle.
Bio-molecules similar to DNA hold negative charge below normal physiological
circumstances, as the net charge of a protein molecule depends on the pH of the
solution. The surplus charge of the receptor-bound target biomolecule modulate the
conductivity of FET channel electrons via coulomb interaction plus this change in
conductivity signal the existence of balancing target molecules in the solution [9]. The
chemical sensors use fluorescent labeling with parallel optical finding method.
Straight label free electronic sensing of biomolecules by nanoscale biosensors purpose
is vital. Silicon nanowires (Si-NWs) biosensors promise extremely responsive
dynamic label-free electrical detection of biomolecules. Si-NW and CNT’s
characteristics such as small size plus large surface to volume ratio, a small amount of
charged biological molecules on surface are capable to alter the carrier allocation
ensuing in an augmented sensitivity. Si-NW sensors detect DNA, proteins, pH levels,
etc. NO2 and interaction with protein can be detected by CNT sensors. Improved
sensitivity can be obtained by NWs with lesser doping intensity plus lesser diameter.
Abridged doping, lesser diameter, and shorter length of nanosensor enhance
sensitivity. Sensors should be operated in depletion mode in air to get maximum
sensitivity. Presence of water also affects sensitivity. Ion concentration, fluidic
conditions and sensor geometry are the functions used to optimize sensor reaction
[10]. More sensitivity and less incubation time are the characteristics of modern label-
free biosensors. Selectivity is the capacity to parallel plus distinctively identify several
target biomolecules in the existence of intrusive species [11].The different values are
selected as shown in the tool for carrying out the simulation. The same is reported in
the simulation setting tables.
III. HYPOTHESIS
The space connecting vacant notional perceptive plus the reported experiment is
chiefly apparent from the next unsolved interpretations: (i) Irregular logarithmic
reliance on sensor reply on target biomolecule concentration (ii) linear reliance of
sensitivity of pH, which is vital to detect protein (iii) non linear reliance of sensitivity
on electrolyte concentration and (iv) logarithmic time reliance of sensor reaction. The
hypothesis is based on self consistent solution of the diffusion capture model and
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216 Santhosh Kumar D.R and Dr. P.V Rao
Poisson Boltzmann equation, illustrating the magnitude of screening limited kinetic
reaction of nano biosensors. The diffusion-capture model relating the kinetics of
biomolecule adsorption on nanosensors is specified by 𝑑𝜌
𝑑𝑡= 𝐷∇2𝜌 (1)
𝑑𝑁
𝑑𝑡= [𝑘𝐹(𝑁0 − 𝑁)𝜌𝑠 − 𝑘𝑅 − 𝑁] (2)
Equation (1) represents the circulation of target molecules to the sensor exterior where
F is the concentration and D is the diffusion coefficient of target biomolecules
(analyte) in solution, respectively. Equation (2) represent the capture of biomolecules
by the receptors functionalized on the sensor surface, where N0 is the density of
receptors on the sensor surface, N is the density of conjugated receptors, kR and kF are
the dissociation and capture constants, and 𝜌𝑠 is the concentration of analyte particles
at the sensor surface [12].
Electrical reaction of Biosensor: The ensuing of a simple capacitor model is the
conductance of a NW sensor. Charge safety of the system specify that
𝜎𝑇= −(𝜎𝐷𝐿 + 𝜎𝑁𝑊 ) (3)
where 𝜎𝑇= sensor exterior charge density caused by analyte molecules, σNW =charge
induced inside sensor with σDL = sensor exterior contain net charge in the electrical
double layer. σT = σSN(t) specifies the charge density owing to analyte biomolecules.
σS=the charge of a biomolecule. σT estimates charge because of captured
biomolecules. These estimates apply toward high sensitivity, little analyte density
sensors significant in favor of present biosensing applications.
𝑆(𝑡) = 𝑐1[ln (𝜌0) −ln (𝐼0)
2+ 𝑐2] (4)
Equation (4) illustrate that nanoscale sensors validate logarithmic reliance on the
target molecule concentration 𝜌0 plus the electrolyte concentration 𝐼0 owing to the
intrinsic nonlinear viewing by the electrolyte of the method. These theoretical details
determine the problem of log reliance of electrical reaction on object biomolecule.
Equation (4) specify that, used for a recognized analyte density,𝜌0, sensitivity reduced
logarithmically by the ion concentration, 𝐼0.
S (t) = c1 [2.303α×l|pH-pKa|- ln(𝐼0)
2 +c3] (5)
where α plus c3 are constants to facilitate the reaction of sensors differ linearly with
pH of solution (0 ≤ α ≤ 0.5) [12].
IV. SCREENING-LIMITED TRANSIENT RESPONSE OF BIOSENSOR.
𝑆(𝑡) = 𝑐1[ln(𝜌0) +ln (𝑡)
𝐷𝐹−
ln (𝐼0)
2+ 𝑐4 (6)
the pure power-law kinetic response of biosensors to a logarithmic dependence in
time, scaled by sensor-specific fractal dimension is converted by electrostatic
screening as indicated in Equation (6). Increasing the average incubation times for
achieving the same sensor response is done by screening due to ions. The hypothesis
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Modeling and Analysis of Biosensors for Evaluation of Its Mechanical… 217
of nanoscale biosensor reply, based on analytic solutions of Poisson–Boltzmann with
reaction-diffusion equations, the model predicts that the sensor response vary (i)
logarithmically with target biomolecule concentration, (ii) linearly with pH, (iii)
logarithmically with the electrolyte concentration, also (iv) the transient response
varies logarithmically through time [12]. According to electrostatics considerations, it
is recognized that two dimensional 2D cylindrical nanowires are extra sensitive to
adsorbed charges e.g., DNA, protein, etc. compare to one dimensional 1D planar ion-
sensitive field-effect transistor ISFET or chemical field-effect transistor CHEMFET.
The nanosensor reply misses the kinetic part of the recognition procedure. There exist
fundamental restrictions in the concentration of biomolecules which know how to be
detected by any sensor under realistic settling times in a diffusion restricted given by
ρ0tsMD~kD (7)
where MD and kD are sensor-dimensionality dependent constants. Reducing the sensor
diameter (a0 ≤10 nm), lessening the smallest number of analytes essential for
noticeable signals (NS~1) and rising the efficient diffusion coefficient ‘D’ by rising the
ambient solution temperature excluding beyond the melting point of the target-analyte
conjugate sensitive nonchemical sensors or nanobiosensors are obtained. Since
(NS~a0) and (ρ0 ~ a20/D), scaling the radius of the sensor provide mainly promise way
to femtomolar revealing edge with sub-1000-s detection time. Though, one should
recognize that even though average detection time can be reduced to < 100 s for small
diameter sensors, statistical inconsistency can still be important and must be
accounted for through sensor array design [13].
Figure-1 Planar Biosensor
Planar Design parameters
Sl No Parameters Value Range
Device parameters
1 Device width (um) 3e-06 05e-7 – 1000e-7
2 Device length (um) 3e-06 05e-7 – 1000e-7
3 Top Oxide thickness (cm) 1e-07 cm ------
4 Doping density 1e+20/cC 1e+15 – 1e+21
Biological parameters
1 Type of Analyte DNA DNA/Protein
2 Kf 3e+06 1e+03 - 1e+03
3 Kr 1 0.01 – 10
4 Receptor density 1e+12 1e+10 - 1e+15
5 DNA strand length (base pair) 12 1-100
6 Diffusion parameters Diffusion coefficient Diffusion coefficient/ DNA diffusion model
7 Diffusion coefficient 1e-06 1e-09 - 1e-03
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218 Santhosh Kumar D.R and Dr. P.V Rao
Ambient Conditions
1 Incubation time (mins) 60 min 5 min – 100 min
2 Temperature in (0K) 300 K 200 K – 350 K
Bio sensor setting
Sl No Simulation settings Values Range
Settling Time vs Analyte Concentration
1 Lower value of analyte concentration (molar units) 1e-15 1e-20 -1
2 Lower value of analyte concentration (molar units) 1e-06 1e-15 -10
3 Number of intermediate concentration steps 30 10-100
4 Minimum number of molecules 10 ------
Time-dependent Capture of target Molecules
1 Analyte Concentration 1e-09 1e-15 - 1e-03
2 Start time for transient response (s) 1e-06S 1e-07 – 1e+05
3 Final time for transient response (s) 10000s 1e+02 - 1e+06
4 Steps 100 100 -1000
Numerical Simulation
1 Numerical Simulation No Yes/No
Sensitivity
EGFET Transfer characteristics
1 Electrolyte Concentration 0.1 ------
2 Surface Density (/cm2) 5e14 ------
3 Protonation constant (kPa) -2 ------
4 De-protonation constant (kPb) 6 ------
5 Vds (v) 0.1 ------
6 Vfg Strating point (v) 0.4 ------
7 Veg Ending point (v) 1 ------
8 Vfg Step length 0.01 -----
Selectivity
Molecule Parameters
1 Size of receptor molecules 2e-07 cm 0.1e-07 - 3e07
2 Size of parasitic molecules 1e-07 cm 0.1e-07 - 3e07
3 Concentration of Target molecules (Molar) 1e-12 1e-15 - 1e-06
4 Concentration of Parasitic molecules (Molar) 1e-06 1e-09 – 1e-03
5 Charge of individual Target Molecules (eu) 10 ------
6 Charge of Parasitic Molecules (eu) 1 ------
Other Parameters
1 Maximum surface coverage 0.54 0.54-1
2 Lower value of Receptor density 1e+11/cm2 1e+09 – 1e+12
3 Upper value of Receptor density 5e+12/c m2 1e+12 – 5e+13
4 Number of Steps 50 10-100
5 Rate Constant 0.1 1e-03 – 1e+02
Simulation result for settling time
Figure 2- Settling Time Vs Analyte
Concentration Simulation result for
sensitivity
Figure 3- Transient Capture of Target
Molecules-Analytical
Simulation result for selectivity
Figure 4-Transfer Characteristics for
Hybrid And Unhybrid Parameters
DIFFUSION PARAMETERS – DNA Diffusion Model, SIMULATION FOR
SETTLING TIME
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Modeling and Analysis of Biosensors for Evaluation of Its Mechanical… 219
Figure 5-SNR of bio sensor in presence
of Parasitic Molecules Output – no output, Simulation for
sensitivity
Figure 6-Transient Capture of Target
Molecules-Analytical Simlulation
Simulation for selectivity
Figure 7- SNR of bio sensor in presence of
Parasitic Molecules. Type of analyze protein Simulation for
settling time
Figure 8-Settling Time Vs Analyte Concentration Simulation for sensitivity
Figure 9- Transient Capture of Target
Molecules- Analytical Simlulation
Simulation for selectivity
Figure 10-Transfer Characteristics for
Hybrid And Unhybrid Parameters
Figure 11- SNR of bio sensor in
presence of Parasitic Molecules.
Figure 12- Cylindrical Nano wire
Sl
No..
Parameters Value Range
Device parameters
1 Radius (cm) 3e-06 cm 5e-7 – 1000e-7
2 Length (cm) 0.0005 cm 1e-5 - 1e-2
3 Oxide thickness (cm) 1e-07 cm 1e-7 - 10e-7
4 Doping density 1e+19/cC 1e+15 – 1e+21
Biological parameters
1 Type of Analyte DNA DNA/Protein
2 Kf 3e+06 1e+03 - 1e+03
3 Kr 1 0.01 – 10
4 Receptor density 1e+12 1e+10 - 1e+15
5 DNA strand length (base pair) 12 1-100
6 Diffusion parameters Diffusion
coefficient
Diffusion coefficient/ DNA diffusion
model
7 Diffusion coefficient 1e-06 1e-09 - 1e-03
Ambient Conditions
1 Incubation time (mins) 60 min 5 min – 100 min
2 Temperature in (0K) 300 K 200 K - 350 K
Sl no Simulation settings Values Range
Settling Time vs Analyte Concentration
1 Lower value of analyte concentration (molar units) 1e-15 1e-20 -1
2 Lower value of analyte concentration (molar units) 1e-06 1e-15 -10
3 Number of intermediate concentration steps 30 10-100
4 Minimum number of molecules 10 ------
Time-dependent Capture of target Molecules
1 Analyte Concentration 1e-09 1e-15 - 1e-03
2 Start time for transient response (s) 1e-06S 1e-07 – 1e+05
3 Final time for transient response (s) 10000s 1e+02 - 1e+06
4 Steps 100 100 -1000
Microfluidic channel parameter
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220 Santhosh Kumar D.R and Dr. P.V Rao
1 Fluid flow NO YES/NO
Numerical Simulation
1 Numerical Simulation No Yes/No
pH Parameters
1 Surface density (/cm2) 5e14 ------
2 Protonation constant (pKa) -2 ------
3 De-protonation constant (pKb) 6 ------
Conductance modulation vs Analyte Concentration
1 Lower value of analyte concentration (Molar) 1e-15 1e-20 - 1e-12
2 Upper value of analyte concentration (Molar) 1e-06 1e-09 – 1e-03
3 Number of steps 30 10-100
4 Buffer Ion Concentration (M) 1e-05 1e-07 -10
Conductance modulation vs Buffer ion Concentration
1 Lower value of electrolyte concentration (Molar) 1e-05 1e-07 -10
2 Highest value of electrolyte concentration (Molar) 2 1e-07 -10
3 Step number 20 1-100
4 Vbg(V) 0v ------
5 Analyte concentration (Molar) 1e-09 1e-20 – 1e-03
Conductance modulation vs pH
1 Lower value for the pH 1 1-14
2 Upper value for the pH 10 1-14
3 Number of steps 20 1-30
4 Buffer Ion Concentration (M) 1e-05 1e-07 – 10
Selectivity
Molecule Parameters
1 Size of receptor molecules 2e-07 cm 0.1e-07 - 3e07
2 Size of parasitic molecules 1e-07 cm 0.1e-07 - 3e07
3 Concentration of Target molecules (Molar) 1e-12 1e-15 - 1e-06
4 Concentration of Parasitic molecules (Molar) 1e-06 1e-09 – 1e-03
5 Charge of individual Target Molecules (eu) 10 ------
6 Charge of Parasitic Molecules (eu) 1 ------
Other Parameters
1 Maximum surface coverage 0.54 0.54-1
2 Lower value of Receptor density 1e+11/cm2 1e+09 – 1e+12
3 Upper value of Receptor density 5e+12/c m2 1e+12 – 5e+13
4 Number of Steps 50 10-100
5 Rate Constant 0.1 1e-03 – 1e+02
Type of analyte is DNA, Diffusion Parameter – Diffusion Coefficient, Simulation Result for settling
time
Figure 13-Simulation for Settling
time vs Analyte,
Figure 14-Transient Capture of
target molecules Concentration
Simulation for Sensitivity
Figure 15-Conductance
modulation vs Analyte
Concentration
Figure 16- Conductance modulation
vs Buffer ion Concentration
Figure 17- Conductance modulation
vs pH of Buffer
Simulation for Selectivity
Figure 18-SNR of biosensor in the
presence of parasitic molecules
Diffusion Parameter – DNA Diffusion Model, Simulation for settling time, Output – NO output
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Modeling and Analysis of Biosensors for Evaluation of Its Mechanical… 221
Simulation for Sensitivity
Figure 19-Conductance modulation
vs Analyte Concentration
Figure 20- Conductance modulation
vs Buffer ion Concentration
Figure 21- Conductance
modulation
vs pH of Buffer Simulation for Selectivity
Figure 22- SNR of biosensor in
presence of parasitic molecules
Type of Analyte – Protein Simulation for settling time
Figure 23-Simulation for Settling
time vs Analyte
Figure 24-Transient Capture of
target molecules Concentration
Simulation for Sensitivity
Figure 25-Conductance modulation
vs Analyte Concentration
Figure 26- Conductance modulation
vs Buffer ion Concentration
Figure 27- Conductance
modulation vs pH of Buffer
Simulation for Selectivity
Figure 28- SNR of biosensor in presence of parasitic
molecules
Figure 29- Nanosphere
Sl
No
Parameters Value Range
Device parameters
1 Radius(cm) 3e-06cm 5e-7 – 1000e-7
Biological parameters
1 Type of Analyte DNA DNA/Protein
2 Kf 3e+06 1e+03 - 1e+03
3 Kr 1 0.01 – 10
4 Receptor density 1e+12 1e+10 - 1e+15
5 DNA strand length (base pair) 12 1-100
6 Diffusion parameters Diffusion coefficient Diffusion coefficient/ DNA diffusion
model
7 Diffusion coefficient 1e-06 1e-09 - 1e-03
Ambient Conditions
1 Incubation time (mins) 60 min 5 min – 100 min
2 Temperature in (0K) 300 K 200 K- 350 K
Sl
no
Simulation settings Values Range
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222 Santhosh Kumar D.R and Dr. P.V Rao
Settling Time vs Analyte Concentration
1 Lower value of analyte concentration (molar
units)
1e-15 1e-20 -1
2 Lower value of analyte concentration (molar
units)
1e-06 1e-15 -10
3 Number of intermediate concentration steps 30 10-100
4 Minimum number of molecules 10 ------
Time-dependent Capture of target Molecules
1 Analyte Concentration 1e-09 1e-15 - 1e-03
2 Start time for transient response (s) 1e-06S 1e-07 – 1e+05
3 Final time for transient response (s) 10000s 1e+02 - 1e+06
4 Steps 100 100 -1000
Numerical Simulation
1 Numerical Simulation No Yes/No
Selectivity
Molecule Parameters
1 Size of receptor molecules 2e-07 cm 0.1e-07 - 3e07
2 Size of parasitic molecules 1e-07 cm 0.1e-07 - 3e07
3 Concentration of Target molecules (Molar) 1e-12 1e-15 - 1e-06
4 Concentration of Parasitic molecules (Molar) 1e-06 1e-09 – 1e-03
5 Charge of individual Target Molecules (eu) 10 ------
6 Charge of Parasitic Molecules (eu) 1 ------
Other Parameters
1 Maximum surface coverage 0.54 0.54-1
2 Lower value of Receptor density 1e+11/cm2 1e+09 – 1e+12
3 Upper value of Receptor density 5e+12/c m2 1e+12 – 5e+13
4 Number of Steps 50 10-100
5 Rate Constant 0.1 1e-03 – 1e+02
Type of Analyte is DNA, Diffusion Parameter – Diffusion Co efficient, Simulation for Settling Time
Figure 30-Simulation for Settling time
vs Analyte
Figure 31-Transient Capture of target
molecules Concentration
Simulation for Selectivity
Figure 32- SNR of biosensor in
presence of parasitic molecules
Diffusion Parameter DNA Diffusion Model, Simulation for Settling time, Output – NO Output,
Simulation for Selectivity
Figure 33- SNR of biosensor in
presence of parasitic molecules
Type of Analyte is Protein, Simulation for
Settling time
Figure 34-Simulation for Settling time vs
Analyte Concentration
Figure 35-Transient Capture of target
molecules
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Modeling and Analysis of Biosensors for Evaluation of Its Mechanical… 223
Figure 36 -DGFET Design Parameters
Sl
No.
Parameters Value Range
Device parameters
1 Device width (um) 1um 0.1 – 10 (um)
2 Device length (um) 1um 0.25 – 10 (um)
3 Top Oxide thickness (cm) 4e-07 cm ------
4 Back Oxide thickness (cm) 1.5e-05 cm ------
5 Silicon Body thickness (cm) 8e-06cm ------
6 Doping density 1e+19/cC 1e+15 – 1e+21
Biological parameters
1 Type of Analyte DNA DNA/Protein
2 Kf 3e+06 1e+03 - 1e+03
3 Kr 1 0.01 – 10
4 Receptor density 1e+12 1e+10 - 1e+15
5 DNA strand length (base pair) 12 1-100
6 Diffusion parameters Diffusion
coefficient
Diffusion coefficient/ DNA diffusion
model
7 Diffusion coefficient 1e-06 1e-09 - 1e-03
Ambient Conditions
1 Incubation time (mins) 60 min 5 min – 100 min
2 Temperature in (0K) 300 K 200 K – 350 K
DGFET Bio sensor setting
Sl
no
Simulation settings Values Range
Settling Time vs Analyte Concentration
1 Lower value of analyte concentration (molar units) 1e-15 1e-20 -1
2 Lower value of analyte concentration (molar units) 1e-06 1e-15 -10
3 Number of intermediate concentration steps 30 10-100
4 Minimum number of molecules 10 ------
Time-dependent Capture of target Molecules
1 Analyte Concentration 1e-09 1e-15 - 1e-03
2 Start time for transient response (s) 1e-06S 1e-07 – 1e+05
3 Final time for transient response (s) 10000s 1e+02 - 1e+06
4 Steps 100 100 -1000
Numerical Simulation
1 Numerical Simulation No Yes/No
pH Parameters
1 Surface density (/cm2) 5e14 ------
2 Protonation constant (pKa) -2 ------
3 De-protonation constant (pKb) 6 ------
Conductance modulation vs Analyte
Concentration
1 Lower value of analyte concentration (Molar) 1e-15 1e-20 - 1e-12
2 Upper value of analyte concentration (Molar) 1e-06 1e-09 – 1e-03
3 Number of steps 30 10-100
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224 Santhosh Kumar D.R and Dr. P.V Rao
4 Buffer Ion Concentration (M) 1e-05 1e-07 -10
5 Vfg(V) 1.0 ------
6 Vbg(V) 0.0 ------
7 pH 4 ------
Conductance modulation vs Buffer ion Concentration
1 Lower value of electrolyte concentration (Molar) 1e-05 1e-07 -10
2 Highest value of electrolyte concentration (Molar) 2 1e-07 -10
3 Step number 20 1-100
4 Vbg(V) 0v ------
5 Analyte concentration (Molar) 1e-09 1e-20 – 1e-03
6 Vfg(V) 1.0 ------
7 Vbg(V) 0.0 ------
8 pH 4 ------
Conductance modulation vs pH
1 Lower value for the pH 1 1-14
2 Upper value for the pH 10 1-14
3 Number of steps 20 1-30
4 Buffer Ion Concentration (M) 1e-05 1e-07 – 10
5 Vfg 1.0 ------
6 Vbg 0.0 ------
Selectivity
Molecule Parameters
1 Size of receptor molecules 2e-07 cm 0.1e-07 - 3e07
2 Size of parasitic molecules 1e-07 cm 0.1e-07 - 3e07
3 Concentration of Target molecules (Molar) 1e-12 1e-15 - 1e-06
4 Concentration of Parasitic molecules (Molar) 1e-06 1e-09 – 1e-03
5 Charge of individual Target Molecules (eu) 10 ------
6 Charge of Parasitic Molecules (eu) 1 ------
Other Parameters
1 Maximum surface coverage 0.54 0.54-1
2 Lower value of Receptor density 1e+11/cm2 1e+09 – 1e+12
3 Upper value of Receptor density 5e+12/c m2 1e+12 – 5e+13
4 Number of Steps 50 10-100
5 Rate Constant 0.1 1e-03 – 1e+02
Type of Analyte is DNA, Diffusion Parameter – Diffusion Co-efficient, Simulation for settling time
Figure 37-Simulation for Settling
time vs Analyte
Figure 38-Transient Capture of
target molecules Concentration
Simulation for sensitivity
Figure 39-Conductance modulation vs
Analyte Concentration
Figure 40- Conductance
modulation vs Buffer ion
Concentration
Figure 41- Conductance
modulation vs pH of Buffer
Simulation for Selectivity
Figure 42- SNR of biosensor in
presence of parasitic molecules
Diffusion Parameter is DNA Diffusion Model, Simulation for Settling Time, Output – NO Output, Simulation
for Sensitivity
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Modeling and Analysis of Biosensors for Evaluation of Its Mechanical… 225
Figure 43-Conductance
modulation vs Analyte
Concentration
Figure 44- Conductance
modulation vs Buffer ion
Concentration
Figure 45- Conductance modulation vs
pH of Buffer
Simulation for Selectivity
Figure 46- SNR of biosensor in
presence of parasitic molecules,
Analyte type is Protein
Simulation for Settling Time
Figure 47-Simulation for
Settling time vs Analyte
Figure 48-Transient Capture of target
molecules Concentration
Simulation for Sensitivity
Figure 49-Conductance
modulation vs Analyte
Concentration
Figure 50- Conductance
modulation vs Buffer ion
Concentration
Figure 51- Conductance modulation
vs pH of Buffer
Simulation for Selectivity
Figure 52- SNR of biosensor in presence of parasitic
molecules
Figure 53 - Extended gate FET
Sl No. Parameters Value Range
Device parameters
1 Device width (um) 1um 0.1 – 10 (um)
2 Device length (um) 1um 0.25 – 10 (um)
3 Top Oxide thickness (cm) 4e-07 cm ------
4 Back Oxide thickness (cm) 1.5e-05 cm ------
5 Silicon Body thickness (cm) 8e-06cm ------
6 Doping density 1e+19/cC 1e+15 – 1e+21
7 Sensor Area Fixed Fixed/Variable
8 Area of Sensing Layer (Asen/Aox) 1 ------
9 Area of Interconnect (Asen/Aox) 100 ------
Biological parameters
1 Type of Analyte DNA DNA/Protein
2 Kf 3e+06 1e+03 - 1e+03
3 Kr 1 0.01 – 10
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226 Santhosh Kumar D.R and Dr. P.V Rao
4 Receptor density 1e+12 1e+10 - 1e+15
5 DNA strand length (base pair) 12 1-100
6 Diffusion parameters Diffusion
coefficient
Diffusion coefficient/ DNA diffusion
model
7 Diffusion coefficient 1e-06 1e-09 - 1e-03
Ambient Conditions
1 Incubation time (mins) 60 min 5 min – 100 min
2 Temperature in (0K) 300 K 200 K – 350 K
Sl
no
Simulation settings Values Range
Settling Time vs Analyte Concentration
1 Lower value of analyte concentration (molar
units)
1e-15 1e-20 -1
2 Lower value of analyte concentration (molar
units)
1e-06 1e-15 -10
3 Number of intermediate concentration steps 30 10-100
4 Minimum number of molecules 10 ------
Time-dependent Capture of target Molecules
1 Analyte Concentration 1e-09 1e-15 - 1e-03
2 Start time for transient response (s) 1e-06S 1e-07 – 1e+05
3 Final time for transient response (s) 10000s 1e+02 - 1e+06
4 Steps 100 100 -1000
Numerical Simulation
1 Numerical Simulation No Yes/No
Transfer Characteristics
1 pH value 2 ------
2 Electrolyte concentration(M) 0.1 ------
3 DNA surface Density (/cm2) 1e13 ------
4 Surface density (/cm2) 5e14 ------
5 Protonation constant (pKa) -2 ------
6 De-Protonation constant (pKb) 6 ------
7 Vds (v) 0.1 ------
8 Vfg starting point (v) 0.4 ------
9 Vfg ending point (v) 1 ------
10 Vfg step size 0.01 ------
Molecule Parameters
1 Size of receptor molecules 2e-07 cm 0.1e-07 - 3e07
2 Size of parasitic molecules 1e-07 cm 0.1e-07 - 3e07
3 Concentration of Target molecules (Molar) 1e-12 1e-15 - 1e-06
4 Concentration of Parasitic molecules (Molar) 1e-06 1e-09 – 1e-03
5 Charge of individual Target Molecules (eu) 10 ------
6 Charge of Parasitic Molecules (eu) 1 ------
Other Parameters
1 Maximum surface coverage 0.54 0.54-1
2 Lower value of Receptor density 1e+11/cm2 1e+09 – 1e+12
3 Upper value of Receptor density 5e+12/c m2 1e+12 – 5e+13
4 Number of Steps 50 10-100
5 Rate Constant 0.1 1e-03 – 1e+02
Type of Analyte – DNA, Diffusion Parameter– Diffusion Co-Efficient, Simulation for Sensitivity , Selectivity and
Settling Time
Page 15
Modeling and Analysis of Biosensors for Evaluation of Its Mechanical… 227
Figure 54-Transient Capture of Target
Molecules-Analytical Simlulation
Figure 55- SNR of bio sensor
in presence of Parasitic
Molecules
Figure 56-Transient Capture of Target Molecules-
Analytical Simlulatin
Diffusion Parameter is DNA Diffusion Model, Simulation for
Sensitivity, Selectivity and Settling Time, Output – NO Output,
Type of Analyte is Protein, Simulation for Sensitivity, Selectivity
and Settling Time
Figure 57- SNR of bio sensor in presence of Parasitic Molecules
Figure 58-Magnetic particle Device
Parameters
Sl
No.
Parameters Value Range
Device parameters
1 Radius in cm 1um 0.1 – 10 (um)
2 Density 1e-15 1e+15 – 1e+21
Biological parameters
1 Type of Analyte DNA DNA/Protein
2 Kf 3e+06 1e+03 - 1e+03
3 Kr 1 0.01 – 10
4 Receptor density 1e+12 1e+10 - 1e+15
5 DNA strand length (base pair) 12 1-100
6 Diffusion parameters Diffusion
coefficient
Diffusion coefficient/ DNA diffusion model
7 Diffusion coefficient 1e-06 1e-09 - 1e-03
Ambient Conditions
1 Incubation time (mins) 60 min 5min -100min
2 Temperature in (0K) 300 K 200 K – 350 K
Sl
no
Simulation settings Values Range
Settling Time vs Analyte Concentration
1 Lower value of analyte concentration (molar units) 1e-15 1e-20 -1
2 Lower value of analyte concentration (molar units) 1e-06 1e-15 -10
3 Number of intermediate concentration steps 30 10-100
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228 Santhosh Kumar D.R and Dr. P.V Rao
4 Minimum number of molecules 10 ------
Time-dependent Capture of target Molecules
1 Analyte Concentration 1e-09 1e-15 - 1e-03
2 Start time for transient response (s) 1e-06S 1e-07 – 1e+05
3 Final time for transient response (s) 10000s 1e+02 - 1e+06
4 Steps 100 100 -1000
Numerical Simulation
1 Numerical Simulation No Yes/No
Molecule Parameters
1 Size of receptor molecules 2e-07 cm 0.1e-07 - 3e07
2 Size of parasitic molecules 1e-07 cm 0.1e-07 - 3e07
3 Concentration of Target molecules (Molar) 1e-12 1e-15 - 1e-06
4 Concentration of Parasitic molecules (Molar) 1e-06 1e-09 – 1e-03
5 Charge of individual Target Molecules (eu) 10 ------
6 Charge of Parasitic Molecules (eu) 1 ------
Other Parameters
1 Maximum surface coverage 0.54 0.54-1
2 Lower value of Receptor density 1e+11/cm2 1e+09 – 1e+12
3 Upper value of Receptor density 5e+12/c m2 1e+12 – 5e+13
4 Number of Steps 50 10-100
5 Rate Constant 0.1 1e-03 – 1e+02
Type of Analyte – DNA, Diffusion Parameter – Diffusion Co-Efficient, Simulation for Settling Time , Selectivity and
Sensitivity
Figure 59 – MP Settling time
Figure 60 - SNR of bio sensor in
presence of Parasitic Molecules
Figure 61– MP Settling time
Diffusion Parameter – DNA Diffusion Model Simulation for Settling Time ,
Selectivity and Sensitivity, Output- NO Output, Type of Analyte is – Protein,
Simulation for Settling Time , Selectivity and Sensitivity
Figure 62 - SNR of bio sensor in presence of Parasitic Molecules
Figure 63-DGFET pH Device parameters
Sl
No.
Parameters Value Range
Page 17
Modeling and Analysis of Biosensors for Evaluation of Its Mechanical… 229
Device parameters
1 Device width (um) 1um 0.1 – 10 (um)
2 Device length (um) 1um 0.25 – 10 (um)
3 Top Oxide thickness (cm) 4e-07 cm ------
4 Back Oxide thickness (cm) 1.5e-05 cm ------
5 Silicon Body thickness (cm) 8e-06cm ------
Biological parameters
1 Type of Analyte DNA DNA/Protein
2 Kf 3e+06 1e+03 - 1e+03
3 Kr 1 0.01 – 10
4 Receptor density 1e+12 1e+10 - 1e+15
5 DNA strand length (base pair) 12 1-100
6 Diffusion parameters Diffusion coefficient Diffusion coefficient/ DNA diffusion
model
7 Diffusion coefficient 1e-06 1e-09 - 1e-03
Ambient Conditions
1 Incubation time (mins) 60 min 5 min – 100 min
2 Temperature in (0K) 300 K 200 K – 350 K
Sl no Simulation settings Values Range
Sensitivity
pH parameters
1 Surface density (/cm2) 5e14 ------
2 Protonation constant (kPa) -2 ------
3 Deprotonation constant (kPb) 6 ------
4 pH starting point 4 ------
5 pH ending point 7 ------
6 pH step length 1 ------
7 Electrolyte concentration
(IO)(M)
150e-3 ------
DGFET Transfer
Characteristic
1 Mode of operation Front gate Operation Front gate operation or back gate
operation
2 Vds(v) 0.1 ------
3 Vbg (v) (fixed) 0.0 ------
4 Vfg starting point (v) 0.3 ------
5 Vfg ending point (v) 1 ------
6 Vfg step length (v) 0.01 ------
Type of Analyte – DNA, Diffusion Parameter – Diffusion Co-Efficient, Simulation for Settling Time and Sensitivity
Figure 64- Transfer Characteristic (Ids vs
Vfg) in front gate operation
Figure 65- Threshold voltage as a
function of pH in front gate operation
Figure 66- Transfer Characteristic (Ids vs
Vfg)in front gate operation
Diffusion Parameter is – DNA Diffusion Model, Simulation for Settling Time
and Sensitivity, Output – NO Output, Type of Analyte is Protein, Simulation
for Settling Time and Sensitivity
Page 18
230 Santhosh Kumar D.R and Dr. P.V Rao
Figure 67- Threshold voltage as a function of pH in front gate peration
Figure 68 - EGFET pH
Sl
No.
Parameters Value Range
Device parameters
1 Device width (um) 1um 0.1 – 10 (um)
2 Device length (um) 1um 0.25 – 10 (um)
3 Top Oxide thickness (cm) 4e-07 cm ------
4 Back Oxide thickness (cm) 1.5e-05 cm ------
5 Silicon Body thickness (cm) 8e-06cm ------
6 Doping density 1e+15/cC 1e+15 – 1e+21
7 Sensor Area Fixed Fixed/Variable
8 Area of Sensing Layer (Asen/Aox) 1 ------
9 Area of Interconnect (Asen/Aox) 100 ------
Biological parameters
1 Type of Analyte DNA DNA/Protein
2 Kf 3e+06 1e+03 - 1e+03
3 Kr 1 0.01 – 10
4 Receptor density 1e+12 1e+10 - 1e+15
5 DNA strand length (base pair) 12 1-100
6 Diffusion parameters Diffusion coefficient Diffusion coefficient/ DNA diffusion model
7 Diffusion coefficient 1e-06 1e-09 - 1e-03
Ambient Conditions
1 Incubation time (mins) 60 min 5 min – 100 min
2 Temperature in (0K) 300 K 200 K – 350 K Sl
no Simulation settings Values Range
Settling Time vs Analyte Concentration
1 Lower value of analyte concentration (molar
units)
1e-15 1e-20 -1
2 Lower value of analyte concentration (molar
units)
1e-06 1e-15 -10
3 Number of intermediate concentration steps 30 10-100
4 Minimum number of molecules 10 ------
Time-dependent Capture of target
Molecules
1 Analyte Concentration 1e-09 1e-15 - 1e-03
2 Start time for transient response (s) 1e-06S 1e-07 – 1e+05
3 Final time for transient response (s) 10000s 1e+02 - 1e+06
4 Steps 100 100 -1000
Numerical Simulation
1 Numerical Simulation No Yes/No
Sensitivity
EGFET Transfer Characteristic
1 pH Starting Point 4 ------
2 pH Ending Point 8 ------
3 pH Step size 1 ------
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Modeling and Analysis of Biosensors for Evaluation of Its Mechanical… 231
4 Electrolyte Concentration(M) 0.1 ------
5 Surface Density (/cm2) 5e14 ------
6 Protonation Constant (kPa) -2 ------
7 De-Protonation Constant(kPb) 6 ------
8 Vds(v) 0.1 ------
9 Vfg Starting Point(v) 0.4 ------
10 Vfg Ending point(v) 1 ------
11 Vfg Step length(v) 0.01 ------
Selectivity
Molecule Parameters
1 Size of receptor molecules 2e-07 cm 0.1e-07 - 3e07
2 Size of parasitic molecules 1e-07 cm 0.1e-07 - 3e07
3 Concentration of Target molecules (Molar) 1e-12 1e-15 - 1e-06
4 Concentration of Parasitic molecules (Molar) 1e-06 1e-09 – 1e-03
5 Charge of individual Target Molecules (eu) 10 ------
6 Charge of Parasitic Molecules (eu) 1 ------
Other Parameters
1 Maximum surface coverage 0.54 0.54-1
2 Lower value of Receptor density 1e+11/cm2 1e+09 – 1e+12
3 Upper value of Receptor density 5e+12/c m2 1e+12 – 5e+13
4 Number of Steps 50 10-100
5 Rate Constant 0.1 1e-03 – 1e+02
Type of Analyte – DNA, Diffusion Parameter – Diffusion Co-efficient, Simulation for Settling time and
Sensitivity
Figure 69- Transfer
Characteristics
(Ids vs Vfg for different pH)
Figure 70-Threshold voltage as a
function of pH
Simulation for Settling time and
Selectivity
Figure 71 –SNR of biosensor in presence of
Parasitic molecules For Diffusion Parameter –
DNA Diffusion Model Simulation for Settling Time Sensitivity, Output – NO Output, Simulation for Settling Time and Selectivity
Output – NO Output, Type of Analyte – Protein, Simulation for Settling Time Sensitivity
Figure 72- Transfer
Characteristics (Ids vs Vfg) for
different pH
Figure 73-Threshold voltage as a
function of pH
Simulation for Settling Time and
Selectivity
Figure 74 –SNR of biosensor in
presence of Parasitic molecules
Figure 75- Flexure FET
Sl
No
Parameters Value Range
Device parameters
1 Width (m) 1e-06 ------
2 Length (m) 4e-06 ------
3 Air gap (m) 1e-07m ------
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232 Santhosh Kumar D.R and Dr. P.V Rao
4 Thickness (m) 4e-08 m ------
5 Dielectric thickness (cm) 5e-09 m ------
Biological parameters
1 Type of Analyte DNA DNA/Protein
2 Kf 3e+06 1e+03 - 1e+03
3 Kr 1 0.01 – 10
4 Receptor density 1e+12 1e+10 - 1e+15
5 DNA strand length (base pair) 12 1-100
6 Diffusion parameters Diffusion
coefficient
Diffusion coefficient/ DNA diffusion
model
7 Diffusion coefficient 1e-06 1e-09 - 1e-03
Ambient Conditions
1 Incubation time (mins) 60 min 5 min – 100 min
2 Temperature in (0K) 300 K 200 K – 350 K
Sl
no
Simulation settings Values Range
Settling Time vs Analyte Concentration
1 Lower value of analyte concentration (molar
units)
1e-15 1e-20 -1
2 Lower value of analyte concentration (molar
units)
1e-06 1e-15 -10
3 Number of intermediate concentration steps 30 10-100
4 Minimum number of molecules 10 ------
Time-dependent Capture of target
Molecules
1 Analyte Concentration 1e-09 1e-15 - 1e-03
2 Start time for transient response (s) 1e-06S 1e-07 – 1e+05
3 Final time for transient response (s) 10000s 1e+02 - 1e+06
4 Steps 100 100 -1000
Numerical Simulation
1 Numerical Simulation No Yes/No
Selectivity
Molecule Parameters
1 Size of receptor molecules 2e-07 cm 0.1e-07 - 3e07
2 Size of parasitic molecules 1e-07 cm 0.1e-07 - 3e07
3 Concentration of Target molecules (Molar) 1e-12 1e-15 - 1e-06
4 Concentration of Parasitic molecules (Molar) 1e-06 1e-09 – 1e-03
5 Charge of individual Target Molecules (eu) 10 ------
6 Charge of Parasitic Molecules (eu) 1 ------
Other Parameters
1 Maximum surface coverage 0.54 0.54-1
2 Lower value of Receptor density 1e+11/cm2 1e+09 – 1e+12
3 Upper value of Receptor density 5e+12/c m2 1e+12 – 5e+13
4 Number of Steps 50 10-100
5 Rate Constant 0.1 1e-03 – 1e+02
Sensitivity
1 Sensitivity or Response Sensitivity with respect to captured
molecule density
Sensitivity with respect to
captured molecule density /
Response to Bio molecule
capture
2 Dielectric constant Er 3.9 ------
3 Youngs modulus of the beam (Pa) 200e9 ------
4 Doping(1/m^3) 6e22 ------
5 Drain to source voltage (v) 0.5
6 Captured Molecule Density starting
point(cm^-2)
5e11
7 Captured Molecule Density ending point(cm^-
2)
1e13
Page 21
Modeling and Analysis of Biosensors for Evaluation of Its Mechanical… 233
8 Captured Molecule Density steps 6
Type of Analyte DNA, Diffusion Parameter – Diffusion Co-efficient, Simulation for Settling time
Figure 76- Settling time vs Analyte
Concentration
Figure 77- Transient Capture of
target molecules-Analytical
Simulation
Simulation for Sensitivity
Figure 78- Sensitivity with
respect to NS at pull in
Simulation for Selectivity
Figure 79- SNR of biosensor in
presence of Parasitic molecules
Diffusion Parameter – DNA Diffusion Model, Simulation for Settling
Time, Output – NO Output Simulation for Sensitivity
Figure 80- Sensitivity with respect to
NS at pull in
Simulation for Selectivity
Figure 81- SNR of biosensor in
presence of parasitic molecules
Type of Analyte is Protein, Simulation for Settling Time
Figure 82- Settling time vs Analyte
Concentration
Figure 83- Transient Capture of
target Molecules-Analytical
Simulation
Simulation for Sensitivity
Figure 84- Sensitivity with
respect to NS at pull in
Simulation for Selectivity
Figure 85- SNR of biosensor in
presence of Parasitic molecules
V. CONCLUSION
According to the hypothesis based on analytic solutions of Poisson–Boltzmann with
reaction-diffusion equations, the biosensor simulations results for different biosensor
were found to be as follows. Sensor reaction varies, (a) logarithmically through target
biomolecule concentration. (b) Linearly with pH. (c) Logarithmically through the
electrolyte concentration, also (d) the transient responses vary logarithmically through
time.
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234 Santhosh Kumar D.R and Dr. P.V Rao
ACKNOWLEDGEMENTS
Authors are thankful to the Nano hub for permitting to carry out the simulations and
Jain University for providing academic environment.
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