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Complementary metal oxide semiconductor-compatible silicon nanowire biofield-effect transistors as affinity biosensors

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Affinity biosensors use biorecognition elements and transducers to convert a biochemical event into a recordable signal. They provides the molecule binding information, which includes the dynamics of biomolecular association and dissociation, and the equilibrium association constant. Complementary metal oxide semiconductor-compatible silicon (Si) nanowires configured as a field-effect transistor (NW FET) have shown significant advantages for real-time, label-free and highly sensitive detection of a wide range of biomolecules. Most research has focused on reducing the detection limit of Si-NW FETs but has provided less information about the real binding parameters of the biomolecular interactions. Recently, Si-NW FETs have been demonstrated as affinity biosensors to quantify biomolecular binding affinities and kinetics. They open new applications for NW FETs in the nanomedicine field and will bring such sensor technology a step closer to commercial point-of-care applications. This article summarizes the recent advances in bioaffinity measurement using Si-NW FETs, with an emphasis on the different approaches used to address the issues of sensor calibration, regeneration, binding kinetic measurements, limit of detection, sensor surface modification, biomolecule charge screening, reference electrode integration and nonspecific molecular binding.
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IS SN 174 3- 58 8910.2217/NNM.13.156 © 2013 Future M edic ine Ltd Nanomedici ne (2 013) 8(11), 1839 –1851
PersPective
Complementary metal oxide
semiconductor-compatible silicon nanowire
biofield-effect transistors as affinity biosensors
Affinity biosensors
An affinity biosensor is an integrated device
providing analytical information (qualitative or
semiquantitative/quantitative) by using a bio-
molecular recognition approach (affinity-based
recognition) interfaced to a signal transducer
that converts the biochemical event into a record-
able signal ( Figur e 1) [1]. Two components of an
affinity biosensor, namely, the biorecognition
element (referred to as a receptor) and the trans-
ducer play important roles in the construction of
a sensitive and specific device for the analyte of
interest (referred to as the target). Affinity-based
sensors are very sensitive, selective and versatile
since affinity-based recognition elements can be
generated for a wide range of targets. Common
biorecognition elements include DNA hybridi-
zation, antigen–antibody and protein–ligand
interactions, as well as other receptor–target
interactions with or without labels. A number
of different types of transducers have been used
in biosensors, including electrochemical [2 ,3],
optical [4,5], piezoelectric [6–8] and thermal [9,10]
transducers. In all cases, they share the same fun-
damental basis, relying on the specificity of the
biomolecular recognition and interaction lead-
ing to the binding of an analyte to form a stable
complex either in solution or on the solid-state
interface, thus, changing the physical/chemical
properties of the sensor. Affinity biosensors are
interesting not only because of their use in bio-
molecule detection, but also because they could
serve as a powerful tool for disease diagnosis,
genetic screening and drug discovery [11,12]. Real-
time recorded sensor signals can be used to moni-
tor the dynamics of biomolecular binding and
unbinding, thereby allowing one to determine
the on/off rate constants and equilibrium associa-
tion constants for biomolecular interactions. The
kinetic data provide information on how strongly
the analyte binds to a target, which is central to
clinical diagnostics and drug development, where
this type of analysis is used to identify promising
therapeutic candidates [13 –15].
The present standard methods for biomolecule
affinity or kinetic analysis include fluorescent
labeling, isothermal titration, surface plasmon
resonance (SPR) and quartz crystal microbalance
(QCM). However, these methods suffer from
extra labeling steps, high cost, low throughput,
low sensitivities and limited dynamic ranges. As
the state-of-the-art advances, demand for accu-
rate, sensitive, specific, high-throughput and rapid
methods for the determination of molecular iden-
tities and reaction details places increasing pressure
on the evolution of analytical methods [16].
Affinity biosensors use biorecognition elements and transducers to convert a biochemical event into a
recordable signal. They provides the molecule binding information, which includes the dynamics of
biomolecular association and dissociation, and the equilibrium association constant. Complementary metal
oxide semiconductor-compatible silicon (Si) nanowires configured as a field-effect transistor (NW FET)
have shown significant advantages for real-time, label-free and highly sensitive detection of a wide range
of biomolecules. Most research has focused on reducing the detection limit of Si-NW FETs but has provided
less information about the real binding parameters of the biomolecular interactions. Recently, Si-NW FETs
have been demonstrated as affinity biosensors to quantify biomolecular binding affinities and kinetics.
They open new applications for NW FETs in the nanomedicine field and will bring such sensor technology
a step closer to commercial point-of-care applications. This article summarizes the recent advances in
bioaffinity measurement using Si-NW FETs, with an emphasis on the different approaches used to address
the issues of sensor calibration, regeneration, binding kinetic measurements, limit of detection, sensor
surface modification, biomolecule charge screening, reference electrode integration and nonspecific
molecular binding.
KEYWORDS: affinity biosensor n binding kinetics n charge screening n field-effect
transistor n limit of detection n reference electrode n silicon nanowire n surface
functionalization
Xuexin Duan1,2,
Nin K Rajan3,
Mohammad Hadi Izadi2,4
& Mark A Reed*2,3
1State Key Labora tory of Preci sion
Measuring Techn ology & Inst ruments,
Tianjin University, Tianjin 300072,
China
2Department of Electrical Engineering,
Yale University, New Haven, CT, USA
3Department of Appli ed Physics, Yale
Universit y, New Haven, C T 06520, USA
4RIKEN Quan tave Biolo gy Center,
Kobe, Japan
*Author for correspondence:
mark.reed@yale.edu
part of
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Nanomedicine (2 013 ) 8( 11)
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Through utilizing benefits from the develop-
ment of nanotechnology and bioelectronics, it is
becoming possible to more accurately measure
specific biomolecular interactions in combination
with miniaturization to optimize existing micro-
scale transducers at the nanoscale. The higher
surface-to-volume ratio of nano-objects makes
their physical properties increasingly sensitive
to external influences, especially as these struc-
tures continue to shrink toward the atomic limit.
Additionally, sensitivity may also increase owing
to higher capture efficiency associated with a
high density of nanostructures [17,18]. Nanowires
(NWs), carbon nanotubes, nano particles and
nanorods are merely some of the familiar nano-
objects that are emerging as candidates to become
crucial elements of future affinity biosensors
[19–25]. Furthermore, the nanometer dimensions
of these objects are comparable with the size of
the target biomolecules (e.g., proteins and DNA),
and such nanoscale bioelectronic devices are able
to measure single molecule events that can help
to elucidate the fundamentals of biomolecular
interactions [26,27].
Silicon NW biofield-effect transistors
A field-effect transistor (FET) is a solid-state
device in which the conductance of the semicon-
ductor between the source and drain terminals is
controlled by a third gate electrode via an insula-
tor, utilizing the effects of the electric field [28].
Biomolecules are usually charged in solution and
adsorption of biomolecules to the FET gate die-
lectric can function as an extra virtual gate bias,
which changes the threshold voltage accordingly.
This interaction between the adsorbate and chan-
nel surface is analogous to applying a voltage
using a gate electrode. A highly sensitive platform
can, therefore, be designed to exploit the depend-
ence of the conductance on the adsorbed species
as a result of the field effect. Such devices have
been generally labeled as biofield-effect transistor
(bioFET) devices or simply bioFETs (Figure 2) [29].
The first realization of a biosensor utilizing
these principles occurred in the 1970s with the
concept of an ion sensitive field-effect transistor
(ISFET) [30]. However, owing to the large size
of ISFETs (on the microscale), they tended to
exhibit a low signal-to-noise ratio when used to
directly detect charged biomolecules and, there-
fore, most of the research on ISFETs focused on
the detection of pH changes [31]. Using silicon (Si)
NWs as the transducing element in bioFETs was
first introduced by Cui and Lieber in 2001 [32].
As immediately suggested by their name, NWs
have diameters in the nanometer range. As a wire
decreases in diameter to the nanometer regime,
the ratio of surface atoms compared with interior
atoms (i.e., the surface-to-volume ratio) drasti-
cally increases. Therefore, external influences of
charged biological species increasingly influence
the conduction both on the wire surface and in
the wire interior. Chemically synthesized Si-NWs
(grown using chemical vapor deposition) can gen-
erate NWs approximately 10–20 nm in diam-
eter and such bioFETs have shown tremendous
increases in sensitivity to detect protein and DNA
binding, and viral and cancer biomarkers [22,33,34].
Despite the advantages of the low cost of
chemical vapor-grown NW FETs, the primary
Interfacial
chemistry
Biorecognition
element
DNA
Ligand–protein
Antigen–antibody
Receptor–target
Electrical
signal
Sensor materials
Transducer:
Electrochemical
Optical
Piezoelectric
Thermal
Figure 1. Affinity biosensor.
PersPective Duan, Rajan, Izadi & Reed
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Silicon nanowire biofield-effect transistor as affinity biosensors PersPective
drawback is the poorly controlled placement of
prefabricated Si-NWs onto device substrates,
which leads to a large amount of randomness in
fabrication, and does not seem to be easily scal-
able for mass fabrication and multiplexing a large
number of sensors. This challenge was confronted
with a Si-NW patterned directly on a device sub-
strate using an ultrathin Si on insulator (SOI)
wafer to define the channel height and anisotropic
etching to define the channel width [35]. N Ws
were fabricated on the SOI using e-beam and
optical lithography followed by plasma etching.
Their widths were varied using different doses
in the e-beam lithography process, thus, creating
openings in the resist of different sizes. This pro-
cess creates NWs with uniform dimensions and
does not require the difficult task of positioning
prefabricated NWs onto the substrate. The result
is a Si-NW fabrication process that is fully com-
patible with standard complementary metal oxide
semiconductor (CMOS) technology.
To solve the disadvantages (i.e., high costs and
a slow rate of production) of using e-beam litho-
graphy to define the 1D-NWs, recently, there has
also been interest in using wider sensors (larger
than 1 um) with nanometer thicknesses, referred
to as Si nanoribbons or nanoplates. The top Si
layer of the SOI wafer is thinned to the desired
nanometer thickness using oxidation and wet
etching techniques and the nano ribbon widths
are defined using optical lithography, which sig-
nificantly reduces the time and cost of device
fabrication. The sensing resulting from these
wider devices showed sensitivity comparable
with 1D-NW systems, which indicates that the
thickness of the Si channel is the critical para-
meter in determining device sensitivity [36,37].
Polycrystalline Si has also been used as an alter-
native to the more expensive SOI wafer to further
reduce the cost of fabrication [38,39].
Si-NW FETs as affinity biosensors
CMOS-compatible Si-NW FETs are ideally
suited for affinity-based detection and can be
readily downsized and integrated by using tra-
ditional semiconductor processing technology
[40–42]. Compared with current methodologies
(e.g., SPR [43,44] or QCM [45]), which use lasers
and detectors, alignment and integration to sur-
faces, FET biosensors can instantly transform
the intrinsic charges of molecules trapped on the
gate surface into electrical signals, providing a
fast, simple and label-free biosensing platform
[14]. Owing to their low cost, low power and
ease of miniaturization, Si-NW FETs hold great
promise for applications where minimizing size,
detection time and cost is crucial, such as point-
of-care diagnostics, a measurement and diagnosis
at a bedside, in an ambulance or during a visit
to the clinic [46,47]. Furthermore, a large body of
evidence already exists on the doping and sur-
face modification effects of bulk Si, which can
be readily applied to Si-NWs. For example, by
controlling the dopant type and concentration,
Reference gate
Flow inlet
Front OX
Outlet
SD
[A]
[AB] [B]
Si-NW
Buried OX
Si
Back gate
Figure 2. Biofield-effect transistors. (A) The biofield-effect transistor setup. (B) Optical and scanning electron microscope images of
the Si-NW field-effect transistors.
[A]: Analyte; [AB]: Analy te–receptor complex formed on the sensor surface; [B] : Receptor; NW: Nanowire; OX: Oxide; Si: Silicon.
Reproduced with permission from [63] .
PersPective Duan, Rajan, Izadi & Reed
Nanomedicine (2 013 ) 8( 11)
1842 future science group
Si-NW sensitivity can be tuned for the detection
of various chemical and biological species [24,25].
Calibrations, quantification
& regeneration
Despite an overwhelming number of papers is this
field over the last 10 years, most of the research
has been focused on reducing the detection limit
of Si-NW FETs, while few have studied the bind-
ing affinities and kinetics of bio molecules, which
are central to clinical diagnostics and drug devel-
opment. Although a few theoretical studies have
appeared in the literature on quantifying signal
changes in Si-NW FETs with respect to the
biomolecular interaction affinities and kinetics
[48 –51], definitive experimental proofs are quite
limited [52,53]. The lack of experimental results is
partially owing to the variations in device char-
acteristics from one device to another, which
is noticed by the distribution of the threshold
voltages for the Si-NW FET array [54]. The dis-
tribution of threshold voltages is dominated by
any nonuniform thickness of the active Si layer
or variations in the thickness of the dry oxide
used for thinning of the active region. Thus, it
would appear every device needs to be calibrated
separately before their deployment in a real meas-
urement. The most commonly used calibration
in the literature is the relative changes of the
sensor signal (I/initial current [I0]); however,
this method only normalizes the sensor response
without reflecting or giving further insight into
the real binding parameters of the biomolecular
interactions [55].
Si-NW FETs often operate in the linear
response regime where the drain source current
(Ids) increases linearly with the gate voltage (Vg).
When analytes bind to the surface of the tran-
sistor channel (functionalized with the recep-
tor), the changes in surface charge density (q),
induced by the adsorbent, produce variations in
the surface potential (∆ψ) and, in turn, shift the
threshold voltage (VT), which can be detected
as changes in the drain source current (Ids). Th is
can be described by equation 1:
g
IVC
q
m
ds T
0
DDD
== (equat ion 1)
here, C0 is the capacitive coupling between
the analyte molecules and Si channel. Device
transconductance (gm) can be easily estimated
through Ids – Vg measurements for each device
without performing actual sensing experiments
using equation 2:
gV
I
m
g
d
d
d
=
cm
(equat ion 2)
Thus, Ids/gm is no longer a function of the
device characteristics, and it only depends on the
equivalent gate potential induced by the absor-
bent (VT). This provides a method to calibrate
different devices, and the calibrated responses
significantly improve the consistency of the
measurements [55,56].
To quantify biomolecular interactions using
Si-NW FETs, we use the calibrated responses
combined with a Langmuir adsorption model
(equation 3) [56–59].
[] []
[]
[]
[]
g
IVC
qBAK
AZ
MAK
AZ
m
ds T
A
max
D
D
0
#
#
DD
== ++
=++
(equat ion 3)
here, [A], [B]ma x, and [AB] represent the concen-
tration of analyte in bulk solution, the maximum
surface density of binding sites on the Si-NW
surface and the surface density of adsorbed ana-
lyte molecules, respectively. KD is the equilibrium
(dissociation) constant. The physical meaning of
KD is important because its value indicates the
strength of the binding energy between analytes
and receptor ligands and the higher the KD the
weaker the inter actions. The amount of surface
charges (q) owing to the adsorbents is propor-
tional to the surface density of adsorbed analytes
as qA[AB], where qA is the electric charge contrib-
uted by the unit surface density of the adsorbed
analytes to the Si-NW. M = (qA/C0)[B]ma x rep-
resents the maximum sensor response (sensor
saturation when all binding sites are occupied),
whereas Z is an overall offset to account for the
response to any background (e.g., nonspecific
binding).
It should be mentioned that the Langmuir
isotherm assumes that the analyte is both mono-
valent and homogenous, that the ligand is homo-
geneous and that all binding events are inde-
pendent. For more complex cooperative binding,
the Hill isotherms may be used to describe the
binding process [38,60]. In the literature, it has also
been shown that the device response amplitude
depends logarithmically on [A] until saturation
[52,61]. It is partially owing to using I/I0 as the
normalization instead of calibrated responses with
the transconductance. The surface site heteroge-
neity (e.g., polyclonal antibody probes or distribu-
tion in binding site availability) may also violate
the Langmuir isotherm. The Temkin isotherm is a
better model in this case, since it predicts that the
amount of molecular adsorption is proportional
to a logarithm of the analyte concentration [62].
Other issues that may inf luence sen-
sor reproducibility include the biosurface
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Silicon nanowire biofield-effect transistor as affinity biosensors PersPective
functionalization. From equat io n 3, Ids/gm
offers a simple way to calibrate each device that
minimizes the variance owing to fluctuations in
device characteristics. The final response is also
strongly determined by the density of immo-
bilized ligands ([B]max). The best way to solve
this issue is to design a regenerable surface so
that different concentrations of the analytes can
be measured from a single device, thus, largely
reducing the errors owing to surface function-
alization and device variations [63]. An analysis
cycle can be designed to follow the molecular
interactions (adsorption, desorption and surface
regeneration) on the Si-NW surface (F igur e 3a ).
The entire binding cycle can be repeated several
times at varying concentrations of analyte to
generate a robust data set for affinity and kinet-
ics analysis [64]. An alternative method is the
kinetic titration in which the signal from the
bound material is continuously accumulated
by applying a step-wise increase in analyte con-
centration, followed by a single dissociation
phase. This has the advantage of not requiring
a regeneration step for each analyte concentra-
tion and largely reduces the time of the meas-
urement [38,65,66]. Although Si-NW FETs have
been demonstrated to have femtomolar sensi-
tivities, for affinities or kinetic measurements,
it is advised to use higher analyte concentrations
since at very low concentrations the bioFET
Association (k1)
Kinetics
Dissociation (k-1)
Regeneration
kM
k-1
k1
[A]0
[A]S
[AB]
[B]
Current (A)
(Ids – I0)/gm (V)
(Ids – I0)/gm (V)
(k1[DNA] + k-1 (s-1)
k1 = 1.55 × 105 M-1s-1 k-1 = 1.59 × 10-3 s-1
´ = 1.20 ± 0.03 × 10-3
= 3.10 ± 0.07 × 10-5
y = y0 + A1* exp(-k´(x – x0)) + A2* exp(-k´´(x – x0))
[DNA] (nM)
100
0
0
0.000
0.002
0.004
0.006
0.008
0.010
1000
200 400
2000 4000 5000 0.0
0.15 Competed desorption
Data t
0.20
0.25
0.30
0.35
0.6 1. 11.7 2.2 2.8 3.3 3.9 4.4
3000
0.00
0.05
0.10
0.15
0.20
0.25
500 nM
400 nM
300 nM
200 nM
150 nM
100 nM
50 nM
30 nM
3 nM
200 300
Time (s)
Time (s) Time (h)
400 500
Data t
Figure 3. Kinetics of biofield-effect transistors. (A) A typical binding cycle observed with a silicon nanowire field-effect transistor.
(B) A two-compartment model. (C) Real-time sensor responses of HMGB1–DNA binding. Each curve represents measurement of a
different DNA concentration from the same device and sensor responses were plotted by using (Ids – I0)/gm (solid line) and fitted with
first-order kinetics (dashed line). (D) The competitive dissociation processes of streptavidin from the biotin-functionalized surface.
Recorded sensor response of streptavidin competitive dissociation with d-biotin (solid line) and fitted with a biexponential decay function
(dashed line).
[A]0: Concentration of analytes in the bulk solution; [A]s: Concentration of analytes in the surface reaction compartment;
[AB]: Analyte–receptor complex formed on the sensor surface; [B]: Receptor; gm: Device transconductance; I0: initial current; Ids: Drain
source current; k1: Association rate constant; k-1: Dissociation rate constant; kM: Diffusion-limiting rate constant.
Reproduced with permission from [6 4] .
PersPective Duan, Rajan, Izadi & Reed
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requires very long association times to reach
equilibrium [67].
To solve the issue of long association times at
low analyte concentrations, kinetic parameters
can be used to calibrate the sensor responses.
This involves using the initial current of the
sensor response to calibrate the device-to-device
variations [54,68]. At low analyte concentrations,
the sensor response is mainly limited by the ana-
lyte diffusion rates. A linear sensor response is
normally observed and the initial rate is propor-
tional to the analyte concentration at the sen-
sor surface. Therefore, by measuring the initial
rate of increase of current, one can quantify the
amount of analyte in the solution. Moreover, the
ratios of current rates corresponding to different
analyte concentrations should be equal to the
ratios of concentrations. Details of the kinetic
analysis will be discussed in the next section.
Kinetics
As mentioned above, Si-NW FETs are able to
monitor the molecular binding in real time,
and, thus, measure the values of the rate con-
stants that describe the association and dissocia-
tion of the biomolecular interactions (Fig ure 3a).
However, in actual measurements, the kinetics
are limited by the sample delivery method, size
of the sensors and analyte concentration [49,67].
Since the analyte and the surface ligands are
initially physically located at different points, this
brings about the necessity to transport the ana-
lyte to the surface in the association phase, and to
transport it away from the surface in the dissocia-
tion phase. In the association phase a depletion
zone will be created, where the local concentra-
tion of the analyte is lower than in the bulk solu-
tion, whereas in the dissociation phase a retention
zone is present close to the surface sites that will
induce dissociated analyte molecules to rebind to
empty surface sites before they can escape into
the bulk. These concentration gradients (rela-
tive from surface to bulk) diminish continuously
with time as steady state is attained. Unless the
lifetime of these gradients is much shorter than
the timescale of the chemical kinetics, they will
have a profound influence on the observed bind-
ing kinetics. We developed a two-compartment
model [69] to investigate the binding kinetics on
the Si-NW FETs [64] (Figure 3B). [A], concentration
of analytes in the surface reaction compartment
([A]s), [B]max and [AB] represent the concentra-
tion of analytes in bulk solution, in the surface
reaction compartment, the maximum surface
density of binding sites on the Si-NW and the
surface density of adsorbed analyte molecules.
[A] is assumed to be constant, while the ligand
concentration in the surface compartment, [A]s,
is depleted owing to the binding reaction, but is
replenished by both diffusion of the bulk ligand
and dissociation of surface-bound complexes.
This two-compartment reaction can be described
as follows:
[] [] [] ,
[]
AkAB
kk AB
M
s
**
+-11 (equation 4 )
kM is a diffusion-limiting rate constant, and
k1 and k-1 are the association and dissociation
rate constants. The net reaction rates of [A]s
and [AB] can be defined by the following set
of equations:
[] (([] []
)[
]
([ ][]) [])
vdt
dA sk AA kA
BABkAB
s
Ms
s
max
1
1
=--
-+
-(equat ion 5a)
[] []([ ][])
[]
dt
dABkA BABkAB
smax
11
=--
-
(equat ion 5B)
As indicated by equation 5 , fast mixing (high-
flow rate) is preferred for real binding kinetic
measurements. In the case of fast mixing, the
replenishing of the analyte from the bulk is
always faster than its consumption on the sensor
surface, thus, the analyte surface concentration
[A]s can be regarded as equal to the bulk con-
centration [A]. In this case, the sensor response
is only limited by the chemical reaction at the
sensor surface and equation 5 can be simplified as
a first-order Langmuir adsorption kinetics equa-
tion. Thus, k1 and k-1 can be determined by fit-
ting the association and dissociation phases with
a monoexponential curve (Figu re 3C). In the case
of diffusion limitations, as no analytical solutions
are known for equation 5, such differential equa-
tions have to be solved by numerical integration.
Consistency of the fits is improved by fitting the
binding curves recorded from different concen-
trations of analytes with the same kinetic param-
eters (kM, k1 and k-1). In real cases, it is nontrivial
to ascertain whether the observed time trace of
the binding signal is limited by reaction or dif-
fusion. A rule of thumb is to compare the sensor
responses by varying the flow rate. If a change in
the analyte or buffer flow rate results in differ-
ences in the observed surface binding kinetics, the
sensor response is most likely limited by diffusion.
For more complex cooperative binding, multiple
exponential curves may be applied to better fit
the data. For example, streptavidin dissociated
from the biotin surface was fitted with a biexpo-
nential decay function and two dissociation rate
constants were obtained (k´ and k´´) (Figure 3D).
PersPective Duan, Rajan, Izadi & Reed
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Silicon nanowire biofield-effect transistor as affinity biosensors PersPective
Limit of detection & dynamic range
One of the key directions of research into affin-
ity biosensors includes strategies to reduce the
limit of detection (LOD). LOD can be deter-
mined by measuring the sensor response to a
dilution series and determining the smallest tar-
get concentration at which the sensor response
is clearly distinguishable from the response to
a blank solution. From our affinity model, we
can predict the LOD of our Si-NW FETs. The
Si-NW FET measures the drain source current
(Ids) changes resulting from molecule binding or
unbinding, in which case the sensor response is
limited by the drain source current noise level
(Ids,N) or the noise level of the measured surface
potential change (V,N). Then from equation 3 ,
the minimum meaningful sensor response:
,[
]
[]
[]
g
I
g
IVN C
qB
AK
Az
,
min
min
m
ds
m
DS NA
max
D
0
#
,
DD
==
++
(equation 6 )
where [Amin] represents the detection limit, the
minimum concentration of the target analyte
that can be detected by the sensor. To identify
[Amin], we can use the following equation:
[]
,
[]
A
VN z
C
qB
K
1
min
A
max
D
0
D
=
--
fp
(equat ion 7)
equat ion 7 clearly shows that the barrier to
reducing the LOD is the thermodynamics of
the affinity reaction, since [Amin] is linearly pro-
portional to the binding affinity KD. For a fixed
binding system, LOD can be further reduced
by increasing surface receptor densities [B]max,
and electrical coupling efficiency of the ana-
lyte s (qA/C0; e.g., reducing the gate oxide thick-
ness and the ionic concentration of the buffer).
Reducing the nonspecific binding (z) will also
improve the detection limit. In complex sam-
ples, nonspecific binding is expected to give
a response unrelated to target concentration,
which is an issue in any label-free biosensor. One
strategy to minimize nonspecific binding is to
block binding sites on the biosensor (e.g., PEG
coating the sensor surface) [54 ].
Other studies have focused on reducing
V,N to improve the LOD [64,65]. V,N can be
modeled as:
,VN WLCf
kTqN
ox
ot
2
2
m
D= (equation 8 )
where λ is the tunnel attenuation distance, Not is
the oxide trap density, W and L are the effective
channel width and length, and Cox is the oxide
capacitance per unit area. Measurements of Ids,N
and extraction of the gate-referred V,N a l l o ws
one to directly probe the LOD of a bioFET sensor,
as well as compare different process parameters,
biasing schemes and device structures.
If the sensor is to be used to quantify the ana-
lyte concentration and not just detect its pres-
ence, the range of measurable concentrations is
important. The concentration range is termed
the dynamic range (DR) of the sensor, which
is defined as the ratio of the largest nonsaturat-
ing input signal to the LOD. The upper limit
is almost invariably set by the saturation of the
probe with target molecules and, thus, is deter-
mined by the affinity step. For a fixed affinity
system, lowering the LOD improves the DR.
Surface functionalization
The performance of affinity biosensors, spe-
cifically the sensitivity, specificity, reusability,
chemical stability and reproducibility, are criti-
cally dependent on the biofunctionalization of
the sensor platform. The type of linkers used
for the immobilization of the capture probes
and the exact immobilization protocols play
a vital role in the overall performance of the
sensors. Currently, the commonly used strat-
egy is to attach the receptor molecules to the
NW surface via a covalent approach through
amino silanization of the Si/Si dioxide (SiO2)
surface, followed by amine coupling. Given its
reactivity toward aldehyde, carboxylic acid and
epoxy functionalities, and rather short length,
3-aminopropyltriethoxysilane (APTES) has
become the most frequently used silanization
compound. However, crosslinking between the
alkoxysilane units may yield oligomerized silane
structures on the surface, resulting in random
orientations and rough layers that are thicker
than a monolayer. To overcome the problem of
the disordered monolayers, recent work demon-
strated post-treatment of APTES-functionalized
devices using high electric fields to align the
internal dipoles of the APTES molecules, thus,
decreasing the disorder in the monolayer [70].
Other methods to improve the APTES layer
quality include using gas-phase deposition
techniques [64 ]. Beside the covalent approach,
supramolecular interactions have been of inter-
est as an alternative strategy for bio molecular
attachment on NW surfaces owing to their high
specificity, controllable affinity and reversibil-
it y. β-cyclodextrin and polyelectrolyte assembly
have been used to functionalize the Si-NWs in
order to obtain a fully regenerable surface [63,71].
An interesting candidate to replace silanization
PersPective Duan, Rajan, Izadi & Reed
Nanomedicine (2 013 ) 8( 11)
1846 future science group
of the Si/SiO2 surface can be found in alk-
ene (or alkynes) prepared organic monolayers
directly on oxide-free Si-NWs [52]. The organic
layer formed by such hydrosilylation is a true
monolayer with high chemical stability.
Screening limitations
One of the limitations of FET-based biomole-
cular detection is Debye screening [31,72]. Most
biological molecules are charged in an aque-
ous environment; however, the charge of the
molecules can be screened by dissolved ions in
electrolyte solutions. A negative species such
as the DNA molecules will be surrounded by
positively charged ions owing to electrostatic
interactions. Beyond a certain distance, termed
the Debye length (λD), Coulomb interactions
can be ignored because the positively charged
cloud of ions will cancel out the negative charge
inherent to the DNA molecule, rendering the
species effectively charge neutral. Thus, for
Si-NW FETs, only changes in charge density
that occur on the surface or within the order of
the λD from the surface can be detected [72]. For
aqueous solutions at room temperature, the λD
can be estimated by λD (nm) = 0.32 × [I], where
[I] is the ionic strength of the buffer solution [73].
In physiological conditions ([I] 100 mM), the
calculated λD is approximately 1 nm. The detec-
tion of small and highly charged molecules, such
as DNA oligonucleotides (2 nm), is relatively
straightfor ward [52,61]. To detect molecules such
as antigens, which require the immobilization of
large antibodies (10 nm), the ionic concentra-
tion has to be reduced to ensure that the bound
analyte is within the λD [48,72,74].
Lowering the ionic concentration of the buffer
will facilitate the sensing measurement; how-
ever, proteins tend to lose their stability or bio-
logical activity at very low ionic concentrations.
Therefore, the buffer ionic concentration has to
be carefully optimized. Another way to reduce
the screening effects is to reduce the height of
the immobilized receptor layer so that the bound
analyte can be brought closer to the Si surface.
This is the motivation behind the use of antibody
fragments or aptamers as receptors to overcome
the limitations owing to Debye screening [75–78].
Both antibody fragments and aptamers are small
recognition molecules that show similar affini-
ties to the larger antibodies. However, the main
limitation is the availability of such molecules
since they require extra chemical synthesis and
purification steps. Perhaps a better solution may
be to adopt a competitive binding (or displace-
ment binding) method [79– 81]. In this method,
the Si-NW FETs are initially functionalized
with an analog of the target analyte that has a
lower affinity (but is smaller and highly charged)
for binding to the biological recognition mole-
cule than the actual analyte does; this is fol-
lowed by binding to the biological recognition
element, such as a big antibody. Upon addition
of the sample analyte to the device, the analyte
competes with the analog for the bioreceptor
and displaces it from the Si-NW FET surface,
leading to a change in the sensor conductance.
Additionally, such a method can be applied to
detect uncharged molecules [82,83].
Conversely, the Si-NW FET is an ideal plat-
form to study small molecules and their affinity
binding, which is important in drug discovery
and the food safety industry [53, 59,84]. Si-NW
FET detects changes in the surface charge den-
sity by the binding of charged molecules, which
has great advantages over mass detection tech-
niques, such as SPR or QCM, which are limited
by the molecular weight of the analytes (typi-
cally required to be >2000 g/mol when covering
the surface in a monomolecular fashion) [69,85].
Alternating current detection
To date, most of the sensing experiments are
based upon directly measuring the change in
direct current conductance. Recently, a few
studies have demonstrated that some fundamen-
tal limitations of field-effect detections can be
overcome by applying alternating current (AC)
signals at the solution gate or with another metal
electrode. A simulation and experimental study
demonstrates that the screening of the bio logical
charge can be significantly suppressed under
quasiequilibrium conditions, which are induced
by the step-pulse voltage [86]. The assumption
behind this is that the mobility of the ions in an
electrolyte solution is lower than that of electrons
and holes in a semiconductor, thus, the resis-
tor–capacitor time constant of the sensor sys-
tem, the time required to reach the steady state
after disturbance, is large enough to perform the
transient electrical measurements. Such electri-
cal measurement in a transient state can avoid
the charge screening effect, thereby, significantly
improving both the LOD and DR. Such an AC
detection system may also break the kinetic bar-
rier of molecule transportation by using electro-
kinetic effects [87]. In this case, by inducing the
AC signal, dielectrophoresis increases the ana-
lytes concentration at the local sensor surface and
the induced electrostatic interaction enhances
biomolecule association after the electrokinetic
application. However, AC detection may not be
PersPective Duan, Rajan, Izadi & Reed
www.futuremedicine.com 1847
future science group
Silicon nanowire biofield-effect transistor as affinity biosensors PersPective
favorable for affinity measurements, since the
sensor responses do not reflect the affinity and
kinetics of the biomolecular interactions under
electrokinetic conditions.
Reference electrode
As shown in Fig ure 2a , the Si-NW FET measures
surface potential changes on the Si surface owing
to biomolecular adsorptions and it requires a
reference gate electrode (Ref-GE) to apply a
solution potential that can tune the conduct-
ance of the device. An important requirement of
the Ref-GE is that it must maintain a constant
potential throughout, which is insensitive to
any changes occurring in the electrolyte solu-
tion [88]. Any change in VT or Ids can then be
assumed to originate from the variation of the
inter facial potential at the Si surface. Normally
this is achieved by using a reference electrode
made from an Ag/Ag chloride (AgCl) wire sealed
in a 3.4 M potassium chloride solution and sepa-
rated from the test solution via a membrane or
salt bridge. The Ref-GE is put inside the sensing
compartment that is connected to the sample by
a more or less open channel, the liquid junction.
However, conventional Ag/AgCl reference elec-
trodes are rather big and hard to integrate into
the sensing chamber of Si-NW FETs. Besides,
there is a continuous drive for miniaturization
of the biosensors, higher speed, performance and
portability sensing, which means miniaturiza-
tion of not just the sensing element, but also
the reference electrode. Attempts to preserve the
general structure of the reference electrode, while
making it smaller have been made, including in
on-chip microfabricated reference electrodes [89],
but the penalty that one pays is a reduction in
the stability and lifetime of the smaller refer-
ence electrodes. This is owing to a loss in the
amount of Ag and AgCl present at the electrode,
or evaporation of the inner filling solutions.
Alternative Ref-GEs involve using on-
chip fabricated pseudoreference electrodes
(e.g., Ag/AgCl without the inner filling solution
or noble metal electrodes – platinum and gold)
[64,90]. Such electrodes can be easily integrated
into a standard CMOS fabrication processes.
The major problems associated with these elec-
trodes are their sensitivity to hydrogen ions or
other salts. In other words, the test solution pH
and ion concentrations determine the operating
potential of these pseudoreference electrodes.
For bioFET charge sensing, which occurs mostly
in buffer solutions, pseudoreference electrodes
may be used. Extra care is needed to make sure
that the analytes do not have strong adsorptions
on the gate electrode and control experiments
need to be performed to validate the experiment
results [64,91]. Other studies have reported the use
of capacitive coupling using a back gate configu-
ration or with an on-chip metallic gate electrode
covered by a thin SiO2 layer [68,92]. However,
such configurations need a higher potential to
operate the FET device and tend to result in
increased gate current leakage.
The best way to overcome the reference elec-
trode dilemma is probably to utilize a differential
measurement scheme that can eliminate varia-
tions in the sensor output caused by disturbances
unrelated to the analyte being detected (Figu re 4).
It requires using a reference FET (REFET) that
has the same electrical characteristics as the
working bioFET but where the surface is pas-
sivated with inert chemical groups that would
be ‘insensitive’ to analytes. The real signal then
consists of the difference between the working
and reference sensor responses. Such a differen-
tial measurement can overcome the instability
of the pseudoreference electrode, sensor thermal
fluctuations and drifts, because the unwanted
signals are similar for both the bioFET and
REFET [93,94]. In particular, if working and
reference sensors react similarly to nonspecific
binding, differential schemes can enhance both
selectivity and sensitivity of the overall system.
However, the differential response may contain
Differential
amplier
Amplier Amplier
Pseudo (quasi)
reference electrode
REFET (inert passivation)NW FET
Signal
V
Vg
Figure 4. Reference field-effect transistor measurement setup.
NW FET: Nanowire field-effect transistor; REFET: Reference field effect transistor;
V: Surface potential measured from the NW FET; V’: Surface potential measured
from the REFET; Vg: Gate voltage.
PersPective Duan, Rajan, Izadi & Reed
Nanomedicine (2 013 ) 8( 11)
1848 future science group
Executive summary
Affinity biosensors
Affinity biosensors use a biorecognition molecule, such as an antibody or DNA, which selectively binds to an analyte to form a complex. A
signal transducer determines the extent of the binding reaction and outputs this information to the end user, which includes the dynamics
of biomolecular association and dissociation, and the equilibrium association constant.
Affinity biosensors require a integrating biorecognition element and transducers to convert the biochemical event into a recordable signal.
Nanowires (NWs), carbon nanotubes, nanoparticles and nanorods are emerging as candidates to become crucial elements of future
affinity biosensors.
Silicon NW biofield-effect transistors
Biofield-effect transistors are based on the classical electrical behavior of field-effect transistors (FETs), which exhibit a conductivity
change in response to charged biomolecule binding at the semiconductor surface.
Studies using NW FET sensors have demonstrated significant advantages for real-time, label-free and highly sensitive detection of a wide
range of species. The major advantage of using a NW comes from the increase in sensitivity owing to the high surface-to-volume ratio.
Complementary metal oxide semiconductor-compatible silicon (Si)-NWs configured as FET fabrication can produce NWs in high yields, in
a predetermined orientation and position on the substrate, making them easy to integrate into functional devices.
Si-NW FETs as affinity biosensors
The responses of different Si-NW FETs can be calibrated using device solution transconductance, thus, decreasing variability from
eliminating the dependence of response on the variable threshold voltage of NW FETs.
Surface regeneration of Si-NW FETs gives the possibility of measuring different samples from a single device without calibrations, thus,
allowing improvement in the measurement reproducibility.
Si-NW FETs are able to quantify the biomolecule binding affinities and kinetics.
To obtain a full kinetic analysis, higher concentrations of analytes are preferred (from 0.1 × KD to 10 × KD), so that the whole binding cycle
can be monitored in a reasonable time. To break the diffusion limitations, fast mixing is necessary to obtain the real binding kinetics. For
a high-affinity binding system, competitive binding can be used to get the dissociation rate constant.
Advances in organic surface modifications of Si- NW FETs is critical in sensor performance, and will also contribute significantly to diverse,
highly usable sensor applications in a variety of fields.
Screening limitations are the major considerations of Si- NW FETs. Optimizing the buffer ionic concentrations, using small receptors,
applying a competitive binding scheme and measuring under alternating current signals can overcome the screening effects.
A reference electrode is another issue that prevents miniaturization of Si-NW FETs. Differential measurement with reference FETs will
facilitate using an on-chip fabricated metal pseudoreference electrode to substitute the bulk reference electrode and eliminate variations
in the sensor output caused by disturbances unrelated to the analyte being detected.
contributions from common-mode changes
owing to imperfect matching of bioFET/REFET.
Careful selection of a reference probe coat-
ing on the REFET is required to ensure total
cancellation of other effects.
Conclusion & future perspective
In the past few years, Si-NW FETs have shown
the ability to detect very low concentrations of a
variety of biomolecules through affinity binding,
which is rather promising for applications in clin-
ical diagnostics or medicine. However, the real
commercialization of such devices is still limited.
The reasons are, in part, unresolved questions
relating to charge screening, reference electrode
integration, measurement reproducibility and
nonspecific molecular binding. In this article,
different approaches and advances to overcome
these issues have been summarized. In addition,
another consideration is which clinical applica-
tions may benefit most from Si-NW FETs in the
routine medical laboratory [95–97]. Only if there is
consensus on the clinical utility of this new tech-
nique can the gap between the high expectations
of the developer and reality be closed. Most pub-
lished works use target analytes of real-world
interest but in highly purified conditions; appli-
cations dealing with ‘real’ clinical samples are still
rare [58,68]; efforts, including sample handling,
prepurification, multiplexing and system inte-
gration, are critical practical considerations for
robust analysis of clinical samples [54,68].
Financial & competing interests disclosure
This work was supported, in part, by the Defense Threat
Reduction Agency under Grants HDTRA1-10-1-0037 and
HDTRA-1-12-1-0042, and by the US Army Research
Laboratory and the US Army Research Office under
Contract/Grant number MUR IW911NF-11-1-0024. The
facilities used were supported by the Yale Institute for
Nanoscience and Quantum Engineering and NSF MRSEC
DMR1119826. The authors have no other relevant affili-
ations or financial involvement with any organization or
entity with a financial interest in or financial conflict with
the subject matter or materials discussed in the manuscript
apart from those disclosed.
No writing assistance was utilized in the production of
this manuscript.
PersPective Duan, Rajan, Izadi & Reed
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Silicon nanowire biofield-effect transistor as affinity biosensors PersPective
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Nanowire field-effect sensors have recently been developed for label-free detection of biomolecules. In this work, we introduce a computational technique based on Bayesian estimation to determine the physical parameters of the sensor and, more importantly, the properties of the analyte molecules. To that end, we first propose a PDE based model to simulate the device charge transport and electrochemical behavior. Then, the adaptive Metropolis algorithm with delayed rejection (DRAM) is applied to estimate the posterior distribution of unknown parameters, namely molecule charge density, molecule density, doping concentration, and electron and hole mobilities. We determine the device and molecules properties simultaneously, and we also calculate the molecule density as the only parameter after having determined the device parameters. This approach makes it possible not only to determine unknown parameters, but it also shows how well each parameter can be determined by yielding the probability density function (pdf).
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