modeling and analysis of biosensors for evaluation of its ...considered to build up biosensors based...

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

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.

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


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


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


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


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


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




2 Kf 3e+06 1e+03 - 1e+03

3 Kr 1 0.01 – 10



6 Diffusion parameters Diffusion

coefficient

Diffusion coefficient/ DNA diffusion

model


Ambient Conditions


2 Temperature in (0K) 300 K 200 K - 350 K

Sl no Simulation settings Values Range










4 Steps 100 100 -1000

Microfluidic channel parameter


1 Fluid flow 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


4 Buffer Ion Concentration (M) 1e-05 1e-07 – 10

Selectivity

Molecule Parameters







Other Parameters






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


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



Figure 19-Conductance modulation

vs Analyte Concentration



Figure 21- Conductance

modulation

vs pH of Buffer Simulation for Selectivity

Figure 22- SNR of biosensor in


Type of Analyte – Protein Simulation for settling time


time vs Analyte




Figure 25-Conductance modulation

vs Analyte Concentration




modulation vs pH of Buffer


Figure 28- SNR of biosensor in presence of parasitic

molecules

Figure 29- Nanosphere

Sl

No


Device parameters

1 Radius(cm) 3e-06cm 5e-7 – 1000e-7



2 Kf 3e+06 1e+03 - 1e+03

3 Kr 1 0.01 – 10



6 Diffusion parameters Diffusion coefficient Diffusion coefficient/ DNA diffusion

model


Ambient Conditions


2 Temperature in (0K) 300 K 200 K- 350 K

Sl

no

Simulation settings Values Range



1 Lower value of analyte concentration (molar

units)

1e-15 1e-20 -1


units)

1e-06 1e-15 -10







4 Steps 100 100 -1000



Selectivity

Molecule Parameters







Other Parameters






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




Diffusion Parameter DNA Diffusion Model, Simulation for Settling time, Output – NO Output,




Type of Analyte is Protein, Simulation for

Settling time

Figure 34-Simulation for Settling time vs

Analyte Concentration


molecules


Figure 36 -DGFET Design Parameters

Sl

No.


Device parameters

1 Device width (um) 1um 0.1 – 10 (um)

2 Device length (um) 1um 0.25 – 10 (um)


4 Back Oxide thickness (cm) 1.5e-05 cm ------

5 Silicon Body thickness (cm) 8e-06cm ------




2 Kf 3e+06 1e+03 - 1e+03

3 Kr 1 0.01 – 10




coefficient


model


Ambient Conditions



DGFET Bio sensor setting

Sl

no











4 Steps 100 100 -1000



pH Parameters



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



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


4 Buffer Ion Concentration (M) 1e-05 1e-07 – 10

5 Vfg 1.0 ------

6 Vbg 0.0 ------

Selectivity

Molecule Parameters







Other Parameters






Type of Analyte is DNA, Diffusion Parameter – Diffusion Co-efficient, Simulation for settling time


time vs Analyte



Simulation for sensitivity

Figure 39-Conductance modulation vs

Analyte Concentration


modulation vs Buffer ion

Concentration


modulation vs pH of Buffer




Diffusion Parameter is DNA Diffusion Model, Simulation for Settling Time, Output – NO Output, Simulation

for Sensitivity




Concentration



Concentration

Figure 45- Conductance modulation vs

pH of Buffer



presence of parasitic molecules,

Analyte type is Protein

Simulation for Settling Time

Figure 47-Simulation for

Settling time vs Analyte


molecules Concentration




Concentration



Concentration


vs pH of Buffer


Figure 52- SNR of biosensor in presence of parasitic

molecules

Figure 53 - Extended gate FET

Sl No. Parameters Value Range

Device parameters







7 Sensor Area Fixed Fixed/Variable

8 Area of Sensing Layer (Asen/Aox) 1 ------

9 Area of Interconnect (Asen/Aox) 100 ------



2 Kf 3e+06 1e+03 - 1e+03

3 Kr 1 0.01 – 10





coefficient


model


Ambient Conditions



Sl

no




units)

1e-15 1e-20 -1


units)

1e-06 1e-15 -10







4 Steps 100 100 -1000



Transfer Characteristics

1 pH value 2 ------

2 Electrolyte concentration(M) 0.1 ------

3 DNA surface Density (/cm2) 1e13 ------



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







Other Parameters






Type of Analyte – DNA, Diffusion Parameter– Diffusion Co-Efficient, Simulation for Sensitivity , Selectivity and

Settling Time


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.


Device parameters

1 Radius in cm 1um 0.1 – 10 (um)

2 Density 1e-15 1e+15 – 1e+21



2 Kf 3e+06 1e+03 - 1e+03

3 Kr 1 0.01 – 10




coefficient

Diffusion coefficient/ DNA diffusion model


Ambient Conditions

1 Incubation time (mins) 60 min 5min -100min


Sl

no












4 Steps 100 100 -1000



Molecule Parameters







Other Parameters






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.



Device parameters








2 Kf 3e+06 1e+03 - 1e+03

3 Kr 1 0.01 – 10



6 Diffusion parameters Diffusion coefficient Diffusion coefficient/ DNA diffusion

model


Ambient Conditions



Sl no Simulation settings Values Range

Sensitivity

pH parameters


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


Figure 67- Threshold voltage as a function of pH in front gate peration

Figure 68 - EGFET pH

Sl

No.


Device parameters







7 Sensor Area Fixed Fixed/Variable

8 Area of Sensing Layer (Asen/Aox) 1 ------

9 Area of Interconnect (Asen/Aox) 100 ------



2 Kf 3e+06 1e+03 - 1e+03

3 Kr 1 0.01 – 10



6 Diffusion parameters Diffusion coefficient Diffusion coefficient/ DNA diffusion model


Ambient Conditions


2 Temperature in (0K) 300 K 200 K – 350 K Sl

no Simulation settings Values Range



units)

1e-15 1e-20 -1


units)

1e-06 1e-15 -10



Time-dependent Capture of target

Molecules




4 Steps 100 100 -1000



Sensitivity

EGFET Transfer Characteristic

1 pH Starting Point 4 ------

2 pH Ending Point 8 ------

3 pH Step size 1 ------


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







Other Parameters






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


Device parameters

1 Width (m) 1e-06 ------

2 Length (m) 4e-06 ------

3 Air gap (m) 1e-07m ------


4 Thickness (m) 4e-08 m ------

5 Dielectric thickness (cm) 5e-09 m ------



2 Kf 3e+06 1e+03 - 1e+03

3 Kr 1 0.01 – 10




coefficient


model


Ambient Conditions



Sl

no




units)

1e-15 1e-20 -1


units)

1e-06 1e-15 -10



Time-dependent Capture of target

Molecules




4 Steps 100 100 -1000



Selectivity

Molecule Parameters







Other Parameters






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


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


Figure 78- Sensitivity with

respect to NS at pull in




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




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


Figure 84- Sensitivity with

respect to NS at pull in




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.


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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