Biosensor Regeneration

Invited Instructional Review
pubs.acs.org/Langmuir
Biosensor Regeneration: A Review of Common Techniques and

Outcomes
J. A. Goode,*,, J. V. H. Rushworth,, and P. A. Millner
School of Biomedical Sciences, Faculty of Biological Sciences, University of Leeds, Leeds, United Kingdom
AbCam Plc, Cambridge, United Kingdom
School of Allied Health Sciences, Faculty of Health and Life Sciences, De Montfort University, Leicester, United Kingdom
ABSTRACT: Biosensors are ideally portable, low-cost tools for the rapid detection of
pathogens, proteins, and other analytes. The global biosensor market is currently worth
over 10 billion dollars annually and is a burgeoning eld of interdisciplinary research that
is hailed as a potential revolution in consumer, healthcare, and industrial testing. A key
barrier to the widespread adoption of biosensors, however, is their cost. Although many
systems have been validated in the laboratory setting and biosensors for a range of
analytes are proven at the concept level, many have yet to make a strong commercial case
for their acceptance. Though it is true with the development of cheaper electrodes,
circuits, and components that there is a downward pressure on costs, there is also an
emerging trend toward the development of multianalyte biosensors that is pushing in the
other direction. One way to reduce the cost that is suitable for certain systems is to enable
their reuse, thus reducing the cost per test. Regenerating biosensors is a technique that
can often be used in conjunction with existing systems in order to reduce costs and
accelerate the commercialization process. This article discusses the merits and drawbacks of regeneration schemes that have been
proven in various biosensor systems and indicates parameters for successful regeneration based on a systematic review of the
literature. It also outlines some of the diculties encountered when considering the role of regeneration at the point of use. A
brief meta-analysis has been included in this review to develop a working denition for biosensor regeneration, and using this
analysis only 60% of the reported studies analyzed were deemed a success. This highlights the variation within the eld and the
need to normalize regeneration as a standard process across the eld by establishing a consensus term.
1. INTRODUCTION
Biosensors are often described as a three-element system
consisting of a bioreceptor, a transducer, and a signal-processing
unit;1 when the analyte interacts with the bioreceptor, a
quantiable signal is generated. Sensors have been developed
for a variety of analytes spanning the elds of medicine,2 food
testing,3 and environmental sensing4 as well as process control
monitoring for research and industry.5 These sensors have been
developed to replace traditional testing procedures that are often
technical in nature, requiring specic expertise and time,
therefore representing a signicant cost in their respective
industries.68
Although some more expensive sensors are being used in
research environments,9 cheaper sensors have the potential to
penetrate wider markets. The current high costs are typically
attributed to the specialized nature of instrumentation required
as well as the reliance on high-grade analytical reagents and
materials; a standard system may require many thousands of
dollars of upfront capital investment, additionally each sensor
transducer assembly can cost up to $80.6
There are a number of strategies currently being pursued to
bring down the costs of biosensors. On the one hand, the
development of low-cost disposable transducers and biosensor
assemblies is using techniques such as advanced printing using
conductive polymer inks.10 This has had some success in
XXXX American Chemical Society
displacing more expensive components and bringing down

system costs. These disposable biosensors may be useful,
particularly in medical application where cross contamination
and hygiene may present a concern.11 For some systems,
however, disposable sensors are unsuitable; if, for example, time
course measurements need to be taken, then chip-to-chip
variance may become a source of error. Equally, for some
applications very accurate high-grade transducers are required,
and the associated costs cannot be avoided. In such situations,
regeneration may be a key technique in lowering the cost per test.
Cost reduction is particularly important when considering the
development of biosensors that address the needs of the
developing world.12,13 Proven examples of biosensor catering,
particularly toward these needs, include biosensors to assess food
safety,14 water sanitation,15,16 and environmental testing.1719
Another key need in the developing world is in healthcare and
diagnostic tools for preventable diseases that currently cause high
rates of mortality and morbidity.12
More recently, there has been an emerging trend toward the
development of multianalyte arrays of biosensors.20 The analysis
of multiple biomarkers can, in principle, provide a higher
Received: September 4, 2014
Revised: November 13, 2014
dx.doi.org/10.1021/la503533g | Langmuir XXXX, XXX, XXXXXX
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certainty in diagnosis. However, one problem presented by

multianalyte arrays is the inherent need for more complex
transducer systems and data analysis, making cost a signicant
barrier to their commercialization. These multianalyte arrays may
present a particular diculty when regenerating because each
receptor/analyte pair will have its own discrete binding physics20
and buer systems that are optimized for one receptor/analyte
pair may be a poor choice for others.
By enabling biosensor regeneration, an accessible method for
multiple sampling is permitted. In doing so, sensor-to-sensor
variance is removed, which is particularly useful when measuring
over a time course or interrogating similar levels of analyte. One
area in which the chip-to-chip variance still represents an
important barrier is in the development of impedimetric
immunosensors.21 By enabling regeneration, this issue may be
eliminated entirely.
While appraising the literature, it became apparent that there
was no easy method of comparing the success of dierent
regeneration schemes. This was primarily due to the varying
denitions of regeneration across the literature. In our
conclusions, we propose a number of criteria to determine
biosensor regeneration to develop an accepted denition within
the eld and ensure that this is applicable across all areas of
biosensor research.
Figure 1. Schematic of biosensor operation (left) and regeneration

(right). After analyte binding and interrogation, regeneration is executed
in order to return the sensor and bioreceptor to their original
conguration.
and an entropic contribution must be considered. These forces

are subject to the solvent environment and can therefore be
altered using a regeneration buer.
3.1. Enthalpic Interactions. Enthalpy is dened as the total
energy of a thermodynamic system.25 This energy can be
distributed in a number of ways, including heat (kinetic energy)
and potential energy that can take on many forms such as
chemical bonds or as ionic or polar charges. According to the rst
law of thermodynamics, a system will equilibrate to reduce the
total potential energy.
When considering interactions involved in biosensor operation, the potential energy dierences are often a major force in
bioreceptor/analyte binding. The interactions are commonly
mediated by chargecharge interactions. At a given solution pH,
various amino acids may be either positively or negatively
charged, depending on the isoelectric point (pI) of the amino
acid residue. Taking the physiological example of blood (around
pH 7.4), in this environment there are acidic, positively charged
amino acids such as asparagine and glutamine, as well as
corresponding basic or negatively charged residues such as lysine,
arginine, and histidine. These charged side groups are integral in
forming the tertiary structure of the bioreceptor binding region.
The interaction of charges is spontaneous and tends toward the
minimum potential energy of the system.
Because charge is dependent on the solvent environment,
factors such as the ionic strength, pH, and presence of competitor
ions within the solvent can alter the relative strength of the
charge interactions to screen enthalpic interactions eectively
between the analyte and the bioreceptor assisting in biosensor
regeneration.26 Typical changes in enthalpy upon antibody/
antigen binding range from changes as low as 26 kJ mol1 down
to more enthalpically driven interactions where the change may
be 130 -kJ mol1 in the most extreme examples.27 This is a
considerable change in enthalpy when compared to typical values
for covalent bonds that range from 200 to 400 kJ mol1.25 It is
important to note that at very low ionic strengths the binding of
an antibody can be promiscuous as any charge dierential
mediates less specic binding that may lower the overall
stringency of the binding species. Conversely, high-ionic-
2. BIOSENSOR CLASSIFICATION
Biosensors may be classied in two ways: according to the signal
transduction method (optical, mechanical, or electrochemical)
or according to the bioreceptor type. Classifying the uby
bioreceptor generates two broad categories: catalytic sensors that
use enzymes22 and anity sensors that use binding proteins or
nucleotides, a category that includes immunosensors. Immunosensors are anity sensors that use antibodies or their derivatives
to detect the target analyte. When considering the regeneration
of biosensors, it is important to consider the molecular
interaction of the bioreceptor and the analyte by mediating a
particular reaction. Catalytic sensors use enzymes as the
bioreceptor and process an analyte in order to generate a signal.
In the context of regeneration, some of these enzymatic sensors
need not be actively regenerated because the analyte is consumed
and the baseline signal is eventually restored. Though some
studies have reported the reuse of these biosensors,23,24 this is
not active regeneration, which is an important distinction to
make within the eld. Sometimes this process is referred to as
passive regeneration. Another important distinction to make
between biosensors is whether assays measure an analyte directly
or in an assay such as a competitive assay. Competitive-assaybased sensors do not directly generate data from the analyte itself
but work on the competitive binding or inhibition of a secondary
process. When considering regeneration, one must consider the
inhibition of analyte detection alongside the inhibition of other
steps in the assay technique, which may also aect the signal.
3. MECHANISMS OF BIOSENSOR REGENERATION
Regeneration has been demonstrated in a number of systems,
though the techniques, reagents, and conditions employed vary
signicantly throughout the literature. In what follows, the
various mechanisms of regeneration are discussed, and the most
successful regeneration agents are evaluated. In all cases,
regeneration is achieved by overcoming the attractive forces
between the bioreceptor and analyte. If these forces are
considered in terms of thermodynamics, then both an enthalpic
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strength environments may screen the antigen antibody

interaction and reduce binding.28
3.2. Entropic Interactions. Entropy is dened as the
inherent chaos or disorder of a system.25 The second law of
thermodynamics states that the entropy of a system will always
increase, creating a more disordered system. This acts to lower
the potential energy of the system overall. According to Gibbs
law, a process will be spontaneous if the Gibbs free energy is
negative. The Gibbs free energy is the change in enthalpy minus
the change in entropy.25 Though analyte binding may be
assumed to cause a decrease in the entropy of a system, there is
also entropic compensation by processes such as solvent
displacement. To explain this, we must consider the role of
solvent molecules in the system. In most systems, the unbound
state is the high-entropy system because the free analyte is highly
disordered; although there is a decrease in entropy when the
analyte binds, this is outweighed by the change in enthalpy and
overall there is a negative Gibbs energy change that explains why
this is a spontaneous process. Though less frequent, there are
certain systems in which the entropy is increased upon binding,
particularly when dealing with hydrophobic analytes. This is due
to the fact that hydrophobic analytes lead to ordered water caging
at the solvent interface. Upon binding, these interactions are
interrupted, and the solvent molecules are then free in solution,
thus leading to a rise in entropy overall.
Certain amino acids are known for their hydrophobic
properties, including tryptophan, valine, leucine, methionine,
phenylalanine, cysteine, and isoleucine. In some systems,
hydrophobic interactions are key to the antibody/antigen
interaction, and it has been identied that apolar surfaces are
often buried at the binding interface, which may be critical to
analyte binding and the subsequent regeneration of a
biosensor.27 At the protein level, biosensors have been developed
for the detection of hydrophobic analytes such as brin, which
have been subsequently regenerated.29
If an analyte in solution has any hydrophobic regions, for
instance, due to clusters of hydrophobic amino acids, then
hydration shells must be formed. Hydration shells are arrangements of water molecules around a solute.25 In hydrophobic
systems, the water is arranged to minimize the surface contact
between the hydrophobic areas and the polar water, often by the
lateral mediation of charge between water molecules across the
interface and the coalescence of the hydrophobic species.30 The
formation of hydration shells leads to a highly ordered lowentropy system with respect to the solvent, in particular, at the
interface where any less order would be energetically unfavored.
In these cases, the minimal reduction in entropy with respect to
the analyte and receptor is outweighed by the increase in entropy
from liberated water molecules. To reverse these interactions,
entropically driven binding must be minimized by negating the
eects of hydrophobic regions; consequently, aliphatic detergents are often used. In aqueous solution, this allows the
interruption of water caging and the minimization of the
hydrophobic eect31 at the interface of the analyte and
bioreceptor to enable regeneration.
3.3. Chemical Regeneration. As discussed above, the
solvent environment at a sensor interface is a key parameter that
determines analyte/bioreceptor binding. The most widely used
approach for regenerating biosensors is therefore to alter the
solvent environment chemically. This can be accomplished
readily by removing the transducer from any assembly and
submerging it in a regeneration buer. Because such regeneration
solutions are often composed of common reagents, this
Figure 2. Schematic of biosensor regeneration showing relevant forces

in binding and debinding. The role of hydrophobicity and the formation
of hydration cages can be seen (left). The screening of these by
detergent molecules can be observed (right), as well as enthalpic
interaction screening mediated by the increasing ionic strength of the
solvent environment.
represent an inexpensive method for sensor regeneration.

Though it is a crude technique, it can be rened by the use of
a uidic control system or a computerized control module,
something that has limited demonstration currently. This
approach may prove vital in the development of a eld-use
regenerable biosensor. Below is an evaluation of the common
chemical approaches that have been demonstrated for biosensor
regeneration
3.3.1. Acid/Base-Mediated Regeneration. In many reports,
regeneration has been achieved by the application of high-32,33 or
low-pH3437 buers to the system. Typically, a low-pH buer
will go no lower than pH 2 in order to prevent irreversible
damage of the bioreceptor. Conversely, a high-pH buer will
often be limited to a pH of around 11 for the same reason.38 This
has a twofold eect on the system.
First, a change in pH alters the enthalpic state of the system by
changing the relative charges between the analyte and the
bioreceptor. As the side groups become ionized,39,40 the charge
distributions that maintain the tertiary structure of the
bioreceptor are also altered. This structural denaturing aids the
decoupling of the analyte from the bioreceptor.41
Second, the change in pH contributes to a change in the ionic
strength of the environment and the screening of receptor/
analyte interactions.39,40 Another way of altering the ionic
strength is to use strong electrolytes such as Ca2+39 and NaCl.42 If
a system is particularly sensitive to a change in pH, then this may
oer a preferable alternative to prevent irreversibly denaturing
components of the sensor such as the bioreceptor or altering the
electronic state of the transducer.
Though the use of acidic/basic regeneration has been widely
reported, one disadvantage is that it can be used only in systems
where the altered pH will not interfere with the sensor signal.
This makes it particularly dicult to use pH regeneration in
electrochemical systems where charge may aect the baseline
signal of the sensor itself. Another key area that is unsuitable for
pH regeneration is where particularly fragile bioreceptor proteins
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atures give molecules increased kinetic energy, which may allow

binding forces to be overcome.62 Although for most proteins
overheating causes irreversible denaturing and aggregation,63
certain groups of proteins and more broadly oligonucleotide base
pairs can be decoupled by raising the temperature in a process
known as melting.54 At ambient temperature, double-stranded
DNA dsDNA is held together by base pairing between
nucleotides.
The number of base pairs involved in bonding the strands
together translates directly to an associated temperature at which
the individual DNA strands gain enough kinetic energy to
overcome the base pairing and separate.64 The use of DNA
melting has been previously demonstrated to be a viable method
for the regeneration of nucleic acid biosensors.62 Doublestranded DNA and other DNA structures such as aptamers can
be transiently denatured. This has been demonstrated for sensor
regeneration using DNA/protein interaction-based biosensors.65
Though thermal regeneration has been proven to be successful
when using nucleotide-based bioreceptors, it is practically limited
to this type of sensor because heating would cause the
destruction or denaturing of the biological components of
many other sensors.
3.5. Electrochemical Regeneration. In a limited number
of studies, biosensors have been regenerated using direct
electrochemical methods. In these studies, the reductive
desorption of surface species has been achieved by applying a
negative potential to the sensor surface.29,66 Though presently
under-represented in the literature, perhaps because of its limited
applicability, this is an elegant solution for the problem of
regeneration because it provides a highly localized regeneration
environment that can be precisely controlled. One example of
the electrochemical regeneration of a biosensor demonstrated by
Liron et al.67 subjected indium tin oxide electrodes to an
electroreductive current in order to regenerate the antibodies on
the sensor surface.
are used. If they are easily denatured, then this would be a poor
method because it would irrevocably damage the bioreceptor.
The major advantage of using acidic or basic regeneration is the
low cost and general utility.
3.3.2. Use of Detergents. Detergents are often used at low
concentrations in the regeneration of biosensors.38,43,44
Structurally, detergents are conventionally heterobifunctional
molecules that comprise two distinct regions: a polar head that is
highly soluble owing to its charge and an aliphatic nonpolar tail.
Because the tail regions are hydrophobic, they coordinate with
similar regions of the bioreceptor or analyte in an entropically
driven process. The polar headgroup then extends into the
aqueous phase, minimizing repulsion and encouraging the
solubility of the analyte.45 In certain biosensor systems,
hydrophobicity may be a key force in the interaction of the
bioreceptor with the analyte such as in the detection of
hydrophobic analytes including 2-naphthol and 3-isobutyl-2methoxypyrazin.31,46 Therefore, detergents may be a key
component of a regeneration buer. Typically, mild detergents
such as Tween are used for this,38,47 although low concentrations
of harsher detergents such as SDS have been used.17,43,44
Although detergents are useful at low concentration and avoid
extremes in pH, they may interrupt systems such as selfassembled monolayers (SAMs) and so should be used only in
systems with a solid transducer interface.
3.3.3. Glycine. One regeneration agent that is widely used is
the amino acid glycine,4,4853 which is useful for a number of
reasons that aid separation and minimize damage to the
bioreceptor. Glycine is a widely available low-cost regent that
has a buering range of pH 27.54 This buering range makes it
ideal for an acidic buer that avoids localized extremes in pH.
Because it is the simplest amino acid with both positively and
negatively charged regions, glycine dissolves well in both aqueous
and more hydrophobic environments and can readily mediate
forces at a particularly hydrophobic interface, thus reducing the
entropic favorability of the bound state. In solution, glycine is
zwitterionic and acts as a mild screening agent for charges at the
interface, again helping to reduce enthalpic forces between the
bioreceptor and analyte. Glycine tends to bind to the surface of
the bioreceptor and analyte because this is thermodynamically
favored. During exposure to a regeneration buer, the
bioreceptor is therefore partially protected from damage caused
in the altered pH environment. Although useful for optical and
mechanical sensor systems, glycine may have limited application
in electrochemical sensor systems because the use of low pH may
aect the sensor signal permanently.
3.3.4. Urea. Another widely demonstrated chaotrope is
urea,5557 which is often employed in pH-sensitive environments
to maintain a neutral pH in solution. In publications by both
Yang et al. and Liu et al., urea was used to regenerate sensors that
obtained their data using cyclic voltammetry methods, which
avoided the alteration of the tethering layer and any disturbance
in signal.55,57
Other chaotropes that have been successfully used for the
regeneration of biosensors are dimethyl sulfoxide (DMSO) by
Tedeschi et al. for the regeneration of the human serum albumin
sensor;58 formidamide for an oligonucleotide sensor;59 EDTA in
the case of IgE sensors;60 and nally potassium thiocyanate by
Karaseva et al. that was used to regenerate sensors detecting
chloramphenicol.61
3.4. Thermal Regeneration. The structure and behavior of
biological molecules such as proteins and oligonucleotides are
often aected by changing the temperature. Elevated temper-
4. BIOSENSOR ARCHITECTURE AND CONSTRUCTION

4.1. Transducer Surface. When constructing a biosensor,
the transducer surface is a primary consideration because this is
the physical substrate on which the sensor is constructed and to
which the bioreceptor is attached. The choice of transducer often
has an inuence on the regeneration technique employed. Below
is a brief description of the common transducer materials used.
Silica, SiO2 (more commonly glass), is a frequently used
transducer in a range of biosensor systems.68 Silica facilitates
regeneration particularly well because it is chemically robust,
etching only usually at high pH, and though limited etching can
occur even at neutral pH, this is unlikely to be an issue over the
few hours to days that would represent normal use.
Silica is also particularly useful because it can be fabricated to
have surfaces that are at on the microscale. Its inert chemical
nature prevents the reaction of the regeneration buer with the
transducer surface, and the atness ensures that the buer can be
easily washed from the sensor surface.
Electrochemical sensors require an electrically conductive
substrate. Many current examples achieve this through the
screen-printing of carbon or metallic electrodes, which, although
economic, presents potential quality issues that are due to the
diverse array of micro- and nanotopologies generated in the
printing process.69 This local variation becomes problematic
upon attempted regeneration, particularly when rinsing the
electrodes, with rougher regions proving more dicult to
regenerate.70 Other methods for electrode fabrication have been
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undertaken in order to generate atter surfaces; these include

sputtering and vapor deposition of the conductive layer that
generate layers that are at on the nanoscale.6 Such additional
preprocessing inevitably results in additional costs.
Though some types of biosensor allow the direct conjugation
of the bioreceptor to the transducer surface, there is often an
associated loss in biological activity that is a particular problem
when dealing with metal surfaces.71 To prevent this, the
bioreceptor is usually separated from the sensor substrate. This
is commonly achieved through the use of a thin tethering layer
that provides accessible functional groups for chemical coupling;
examples of tethering layers include self-assembled monolayers
(SAM),72 polymers,73 and silanes.68
After attaching a suitable tethering layer, the next key
consideration is the conjugation method used to attach the
bioreceptor to the tethering layer: it must be robust enough to
withstand regeneration. If the bioreceptor is noncovalently
tethered to the transducer surface, for instance, via weak charge
interactions, the alteration of ionic strength or pH when
regenerating the sensor may induce the dissociation of the
bioreceptor from the sensor surface itself. Therefore, covalent
tethering of the bioreceptor to the transducer surface is desirable.
Many optical biosensors are constructed on silica ber optic
surfaces, which are often functionalized to enable the conjugation
of the bioreceptor through silinization. This is a particularly
robust covalent method for the attachment of the bioreceptor
and has been extensively proven to be resilient to the
regeneration of biosensors, particularly on silica substrates74
where the available surface silicon atoms provide an accessible
chemical route for conjugation, which makes it a viable method
for use in the quartz crystal microbalance (QCM) as
demonstrated by Bunde et al.75
QCM may also operate using gold-coated quartz wafers.
Similarly, a gold surface is also used in surface plasmon resonance
(SPR) as well as in electrochemical methods. Gold is used
because its atoms will spontaneously form a dative bond with any
sulfhydryl group,34,52,59 which is particularly resilient against
damage often caused by regeneration techniques. These dative
bonds can be exploited for the construction of SAMs and mixed
SAMs (mSAMs) that may be attached to the surface of the sensor
before being stabilized by noncovalent forces that, though
relatively weak individually, provide a substantial matrix for the
conjugation of the bioreceptor.29,32,43,76 Care must be taken
when regenerating SAM-based sensors because the molecular
components may be susceptible to reactions with certain
components in the regeneration buer itself, for example,
detergents.76,77
4.2. Bioreceptor. Biosensors are often categorized according
to the type of bioreceptor used. Catalytic biosensors use enzymes
as the bioreceptor, whereas anity-based sensors typically use
antibodies, other anity proteins such as aptamers, engineered
receptors, or nucleic acids. Enzyme-type biosensors are used
commonly for the detection of small metabolites, the most wellknown of which is the glucose biosensor often used in the
management of diabetes.78 As previously discussed, reuse has
been reported with these enzyme-based sensors.23,24 However,
there is only one example of an active regeneration approach
having been used where a regeneration step was used rather than
allowing the catalysis of the residual analyte to restore the
baseline signal. In the study by Lu et al., 8 M urea was applied to
the sensor in order to obtain the baseline signal more rapidly.55
The principal protein bioreceptor group used in anity
biosensors is antibodies. They are particularly useful for
biosensor applications because they can be raised against a vast

array of analytes. They have predictable binding physics1,45,79
that have been widely demonstrated to be reversible;27
antibodies have an isoelectric point of around pH 7,80 and by
changing the pH, dissociation may be induced. This can be a
delicate procedure because antibodies cannot be exposed to
extremes in pH for very long without causing irreversible
denaturing and a loss of function as side chains become ionized
and the specic three-dimensional structure of the binding
region of the bioreceptor is lost.40 This denaturation will lead to a
reduction in the specicity and sensitivity across a population of
antibodies and can sometimes be seen as a loss in signal of the
biosensors over regeneration cycles as some of the antibodies
become damaged.1
As an alternative to antibodies, semisynthetic routes have been
achieved for the development of articial bioreceptors for sensor
applications, and these are collectively known as non-antibodybinding proteins (NABPs). Typically these use a stable protein
motif such as a conserved loop or fold from a natural structure to
which a paratope-like complementary determining region
(CDR) can be evolved or engineered using high-throughput
techniques.20 Such bioreceptors are useful because they are often
very small and stable and have regular binding physics. Examples
include Nanobodies,81 DARPins,82 lipocalins,83 and adhirons.84
One key advantage of using these binding proteins is that they do
not require periodic animal sources and allow for continual
production from bacterial fermentation, consequently ensuring
consistent binding physics from batch to batch. In addition,
NABPs tend to be much more stable across pH, temperature, and
time ranges.84 This exceptional stability makes NABPs benecial
for regeneration applications as well as more widely applicable in
biosensor research. Though initial studies have assessed the
suitability for NABP bioreceptors in biosensor applications, there
is as yet limited work studying the regeneration of these sensors.
A notable example is the regeneration of nanobody-based
biosensors.85
Other anity biosensors operate using nucleic acid-based
bioreceptors where biorecognition is mediated either through
direct interaction by complementary base pairing of a linear
sequence (i.e., a target strand binds to a recognition strand) or
through a non-nucleic acid analyte such as a protein binding to a
3D epitope presented by a folded nucleic acid aptamer.36,65,86
These bioreceptors can be quickly evolved in bacteria using viral
display technologies. Because the nucleotide binders are held by
their charges, they have a wide variety of potential structures that
can easily be denatured, and because this structure is selfencoded, their complex structures are easily reformed after
regeneration.42
A variant to DNA-based biosensors are sensors that use
protein nucleic acid (PNA) as a bioreceptor; PNA has a similar
structure to DNA, with the phosphatesugar backbone replaced
by protein, and has been shown to demonstrate higher anities
than DNA upon base pairing and has been demonstrated to be a
strong candidate for genetic biosensors.8789
4.3. Binding Valency. An important consideration when
considering analyte/receptor binding is the role of valency. If a
multivalent analyte is to be detected, then each analyte molecule
will be bound at more than one location.20 Each of these
locations or epitopes may have a discretely dierent binding
anity, and the analyte will be bound with the collective force of
the dierent binding events. This may occur when a relatively
large analyte is bound or a mixed batch of receptors is used, as in
the case of polyclonal antibodies.1 Another instance where
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Table 1. Regeneration Studies in Optical Biosensorsa

author
ref
Bright et al.
(1990)
Hilton and
Nguyen
(2011)
Kandimalla et
al. (2004)
Tedeschi et al.
(2003)
37
Wijesuriya et
al. (1994)
91
Albrecht et al.
(2008)
Anderson et al.
(1999)
44
Choi and Chai

(2009)
Dillon et al.
(2005)
Dillon et al.
(2003)
Drake and
Klakamp
(2011)
ForteBio Inc./
Octet
(2007)
Indyk et al.
(2004)
65
51
59
47
29
92
52
biosensor system
analyte
regeneration conditions
Absorbance/Fluorescence-Based Biosensors
silane-based mSAM on ber optic probe
HSA
0.1 M PBS + 0.1 M phosphoric acid
uorescence-tagged DNA aptamer
cocaine
temperature cycled from operational 22 to 37 C
antibodies on silinated glass beads; antigen was HRP
conjugated and optical density (450 nm) was recorded
many dierent schemes of antibody tethering on ber
optic probe
ethyl parathion
HSA
GMBS cross-linked to anti-TNB antibody on ber optic TNB

probe
SPR-Based Biosensors
reectometric interference spectrometry, NHS- crossCRP
linking
Biacore SPR platform with various binding proteins
GST
conjugated using NHS/EDC
mSAM in SPR setup, mSAM desorbed before
reformation and reinterrogation
Biacore SPR sensor with NHS-EDC cross-linking to
anti-M3G antibodies
Biacore sensors to M3G
53
Biacore chip with nonspecied monoclonal antibody

using NHS/EDC cross-linking
38
octet, SPR on ber optic probe with protein A covalently

bound
93
Biacore Q Optical biosensor with CM5 sensor substrate
brinogen
M3-G
M3-G
nonspecied
antigen protein
IgG
lactoferrin
repeats
signal
loss
data
12
5%
uorescence intensity
0%
uorescence data
glycine/HCl pH 2.3 + 1%
DMSO
DMSO
14
2%
10%
100 mM glycine/HCl in 50%

ethylene glycol pH 1.75
40%
optical density in
ELISA-style assay
binding capacity (dened by uorescence)
% uorescence signal
0.5% SDS, 6 mM NaOH,

0.6% ethanol
multivariate cocktail approach to screen various
buers
reductive desorption (30 s,
0.9 V)
100 mM glycine/HCl pH 2.2
100
N/A
20
1%
15
0%
SPR angle shift (d)
15
1.5%
40 mM HCl + 40 mM
NaOH
10 mM glycine pH 2.5
51
13.5%
binding rate (RU.sec1)

response units
200
50%
response units
5% Tween-20, 10 mM
phosphoric acid pH 2.0
10
1.2%
binding, wavelength
shift (nm)
10 mM glycineHCl, pH
1.75 at 50 mL min1
>500
0.1%
response units
change in optical
thickness
cycle 1 response
cycle 20 response
Abbreviations: EDC, ethyl-dimethyl(aminopropyl)carbodiimde; GMBS, gamma-maleimidylbutyryl succinimide; GST, glutathione-S-transferase;

HRP, horseradish peroxidase; HSA, human serum albumin; M3-G, morphine 3-glucoronide; mSAM, mixed self-assembled monolayer; NHS, Nhydroxysuccinimide; TNB, trinitrobenzene; CRP, C-reactive protein.
platform.94 Another drawback of some optical devices is their

reliance on sample processing, which requires the addition of
reagents such as uorescent markers, substrates, or enzymes
requiring a certain level of technical skill. It is important to note
that some single-use planar waveguides have been developed
specically with ease of use in mind;95 however, they have not
been demonstrated to be regenerable. There are some highthroughput methods that have been widely demonstrated to be
regenerable,38 and because of this decrease in cost, they are being
adopted more widely in both research and diagnostic elds.
5.2. Acoustic. The regeneration of acoustic biosensors such
as QCM-based sensors has also been successfully demonstrated.
These sensors operate by propagating a harmonic oscillating
wave that is varied as the mass on an oscillating surface is
altered. 96 These sensors have been shown to achieve
regeneration after a similar number of cycles as optical
sensors,34,35 and a variety of dierent regeneration buers have
been used to achieve biosensor regeneration33,60,61,97100 (Table
2). These sensors include devices such as the quartz crystal
microbalance (QCM) and other piezoelectric oscillators. In such
systems, the mechanical properties of the interface such as
elasticity are key, and it is important that the regeneration does
not aect them because this would interfere with measurement.
Because the mechanical properties are relatively dicult to alter,
any loss in signal can be attributed to the gradual degradation of
the bioreceptor. This would explain the gradual loss across a large
number of regenerative cycles that is commonly seen.75 Much
like optical sensors, acoustic sensors require expensive
multivalent binding may be encountered is when binding a large

analyte with a repeating epitope motif. In this instance, the same
paratope may bind at a number of locations, once again providing
a greater overall binding energy.
Regenerating sensors in which the analyte has bound
multivalently may prove more dicult because some epitope/
paratope pairs may be dissociated more easily than others. This
could lead to uneven dissociation and damage across the sensor
surface. It has also been reported that multivalently bound
analytes may have a tendency to denature when exposed to
common regeneration buers. This may lead to the formation of
insoluble protein on the biosensor surface.90 This is another
limitation of biosensor regeneration.
5. CASE STUDIES
5.1. Optical Biosensors. Optical sensors have been
demonstrated to be particularly successful in terms of
regeneration studies because the bioreceptor is often tethered
to a chemically robust surface, such as a plastic ELISA plate,4 a
ber optic,37,51,58,65,91 or a gold SPR wafer.29,38,47,53,92 Optical
sensors have been regenerated over 500 times with a minimal loss
in signal93 (Table 1).
Although regeneration has been extensively demonstrated in
these systems, the large capital expense associated with necessary
hardware means that they tend to be restricted to research and a
few high-throughput medical applications.
There are portable low-cost alternatives emerging such as the
Texas Instruments Spreeta chip, a relatively low cost SPR
F
Langmuir
Table 2. Regeneration Studies in Acoustic Biosensorsa

author
Mannelli et al.
(2003)
Tadeschi et al.
(2005)
Lazerges et al.
(2006)
Hong et al.
(2009)
Yao et al. (2009)
ref
biosensor system
analyte
Nucleic Acid Aptamer-Based Sensors
ssDNA
100
DNA probe for GMOs
59
ssRNA on gold QCM, SMCC cross-linker
oligonucleotides
34
disulde- ssDNA on gold QCM Chip
ssDNA
48
SAM on QCM chip SAM with N- or C-terminal tethering of

IgG
DNA aptamer tethered to mSAM using biotin/avidin
interaction
E. tarda
60
Chen et al.
(2010)
99
Hao and Wang

(2009)
March et al.
(2009)
76
Michalzik et al.
(2005)
Mattos et al.
(2012)
Steegborn
(1997)
Karaseva (2012)
32
IgE
thrombin DNA aptamers with sandwich to gold

thrombin
nanoparticle secondary aptamer conjugates for signal
amplication
Protein-Based Sensors
mSAM on gold electrode cross-linked to protein A with
Bacillus Anthracis
bound anti-B. Anthracis IgG
mSAM with EDC/NHS cross-linking to antibodies
various
metabolites and
pesticides
cystamine-glutaraldehyde SAM with protein A binding of
BMP-2
IgG
gold QCM mSAM cross-linked to anti-troponin
troponin
monoclonal antibody
silinized gold chip with glutaraldehyde cross-linker to anti- atrazine
atrazine-IgG
polypyrrole base layer with glutaraldehyde activation that
chloramphenicol
was NHS/EDC cross-linked to CAP-STI binding protein
35
43
33
61
repeats
signal
loss
1 mM HCl
20
0%
1:1 formidamide/water
solution
NaOH
N/A
0%
>100
0%
0.2 M tris-glycine, 0.6 M

NaCl 1% DMSO, pH 2.3
30 mM EDTA
10
0%
10
20%
50 mM NaOH + 1 M
NaCl + tris-HCl
0%
PBS pH 1
N/A
N/A
0.1 M HCl
150
45%
0.05 M NaOH
0%
0.1 M NaOH + 1% SDS
0%
0.1 M NaOH
25
0%
0.04 M KCNS
12
5%
data
frequency shift
(Hz)
frequency shift
(Hz)
frequency shift
(Hz)
frequency shift
(Hz)
relative
frequency
response (%)
relative
frequency
response (%)
frequency shift
(Hz)
frequency shift
(Hz)
frequency shift
(Hz)
frequency shift
(Hz)
frequency shift
(Hz)
frequency shift
(Hz)
a
Abbreviations: BMP-2, bone morphogenic protein-2; CAP-STI, CAP protein soybean trypsin inhibitor; GMO, genetically modied organism;
ssDNA, single-stranded DNA; SMCC, succinimidyl-4-(N-maleimidomethyl)cyclohexane-1-carboxylate.
Table 3. Regeneration Studies in Electrochemical Biosensorsa

author
ref
Bhalla et al.
(2010)
Yang and Chang
(2009)
Liu et al. (2010)
66
Vidal et al. (2004)
23
Manso and Mena

(2008)
Lu and Liu (2011)
24
Bryan et al. (2013)
106
Xu and Luo
(2013)
Yun et al. (2013)
50
Huang and Yang

(2010)
Querios and de
Los Santos
(2013)
49
57
56
55
108
36
biosensor system
analyte
regeneration
conditions
repeats
signal
loss
N/A
N/A
1.35%
10
37%
CV peak height
(A)
N/A (substrate is
consumed)
N/A (substrate is
consumed)
8 M urea
1000
N/A
0%
sensitivity (nA
mM1)
current (A)
10%
current (A)
6 mM NaOH + 0.6%
EtOH
100 mM glycine-HCl
pH 2.0 + 1% DMSO
NaOHH3PO4 pH 12
3%
0%
sensitivity x,
sensitivity 0
Rctx/Rct0 (%)
5%
Rct
10
4.8%
Rct
0%
(Rct,1 Rct,0)/
(Ret,0)
Cyclic Voltammetry-Based Biosensors

mSAM on interdigitated sensor, capacitance
SAM formation oxidative desorption
directly measured in a drop
(1 min at +1.4 V)
gold/CHITcomposite surface with adsorbed anti- HCG
4 M urea
HCG antibodies
antibodies on Naon lm SAM
-glucans
5 M urea
Amperometric Biosensors
amperometric sensor using cholesterol oxidase on cholesterol
FAD cofactor monolayer
alcohol dehydrogenase colloidal gold/CHIT
alcohol
matrix
polycysteine on gold plus nanocompositeanti
MPO
MPO IgG
Impedimetric Biosensors
mSAM on gold linked to goat anti-CRP antibody CRP
using EDC, impedance measurements on gold
SAM w/EDC-linked anti-insulin antibodies,
insulin
impedance measurements on gold
gold electrode + MPA then EDC-NHS crossketamine
linked to antiketamine antibody
glassy carbon electrode plus IgG-coated
Campylobacter
nanoparticle
jejuni
DNA aptamer-based impedimetric
E. coli
immunosensors
10 mM glycineHCl
pH 2.8
2 M HCl
data
CV, capacitance,
SEM inspection
CV data (A)
a
Abbreviations: CHIT, chitosan; FAD, avin adenine dinucleotide; HCG, human chorionic gonadotropin; MPA, mercaptopropionic acid; MPO,
myeloperoxidase.
instrumentation, often with a dedicated uidics control system.

This means that they are often unsuitable for eld deployment
and require considerable eort to realize a portable, aordable
device for biosensing. In spite of the above drawbacks, acoustic

sensors have been used in precision-critical applications, notably,
HIV detection.101
G
Langmuir
5.3. Electrochemical. Although electrochemical biosensors

are frequently stated as having a signicant potential impact in a
range of analytical elds,6 as the success of the glucose biosensor
has demonstrated, many electrochemical sensors have yet to
achieve widespread success. Regeneration may assist in
improving the commercial viability of these sensors, yet
investigations to date have been limited. Despite limited
research, there have been some successes, with amperometric
and potentiometric sensors shown to be regenerated successfully.57,66 In the case of the amperometric sensor, this has been
reported to be reused 1000 times with minimal signal loss,
although as previously mentioned this is not regeneration per se.
In potentiometric sensors, the current102 or potential103 is
altered in the presence of the analyte, therefore allowing a
calibration. In the most successful regeneration study by Liu et
al., urea, a strong chaotrope, was used to regenerate the sensor
through 10 cycles with minimal signal loss.56 Further examples of
regeneration in potentiometric biosensors are given in Table 3.
Urea has been employed in many examples56,57 in order to avoid
the eect of harsh acids or bases that could alter the
electrochemical properties of the sensor irreversibly. However,
the use of urea may have aected the signal over time by subtly
changing the charge characteristics of the biosensor surface. This
has led to limited success in the regeneration of electrochemical
biosensors.56
Electrochemical immunosensors can be developed for a much
wider array of analytes because of their reliance on binding
proteins that have a much wider repertoire than enzymes.104,105
Often they are interrogated impedimetrically,21 a method that is
very sensitive and depends on a combination of the capacitive
and resistive properties of the transducer surface.21 These
sensors can either directly look at the change in these properties
upon analyte binding (reagentless sensors) or use reagents such
as HRP-tagged secondary antibodies or nanoparticles to enhance
the signal observed on analyte binding. Either way, the chargetransport properties are crucial in this technique, and any
regeneration buers used may alter these charge-related aspects.
There have, however, been a few reported examples of successful
regeneration that has avoided irreversible alteration of the
biosensor, using mildly acidic glycine49,50 or mildly alkali
regeneration buer106108 before neutralizing to restore a stable
baseline signal.
provides valuable insight into how other types of biosensors may

be regenerated.
One issue encountered across the literature is that there are
currently no established criteria for determining successful
regeneration. Dierent biosensor techniques have led to data sets
that cannot be easily compared. This makes it dicult to assess
the validity of dierent regeneration procedures across studies.
Table 4 outlines a set of criteria that the authors propose as
Table 4. Criteria for the Successful Regeneration of a
Biosensor
attribute
criteria
signal loss between

interrogation cycles
number of continual cycles
achieved
restoration of baseline signal
biosensor/transducer
reconstruction
signal loss prole
<5%
>10
<5%
avoided
linear in order to allow accurate calibration
explicitly listed with time of incubation and full
buer components
parameters for determining whether successful regeneration of a

biosensor has been achieved. Although not exhaustive, it can be
unilaterally implemented across all types of biosensors. The
authors invite discussion surrounding these criteria, with the aim
of developing a robust working denition in the eld.
Using these criteria to critically appraise the studies considered
in this review, overall only 60% of the reported regeneration
successfully meet these criteria. This is particularly noticeable
within the electrochemical biosensors where only a third of
regeneration studies meet the listed criteria. We must therefore
make an eort to report regeneration as a standardized process
across the eld.
AUTHOR INFORMATION
Corresponding Author
*E-mail: sm07jag@leeds.ac.uk. Tel: (+44)11323437717.

Notes
The authors declare no competing nancial interest.

Biographies
6. CONCLUSIONS
Although many dierent biosensors have demonstrated
regeneration successfully, optical sensors have achieved the
greatest success. Optical sensors are most successful because it
has proven relatively simple to devise regeneration techniques
that do not aect the optical properties of the sensor. Though
this is also true of acoustic biosensors, regeneration has been
successful on a lesser scale, both in the number of studies and the
extent to which sensors can be reused. It can be seen that the use
of pH is the most widely used example for the regeneration of
protein/protein-interaction-based sensors and low-pH glycine
buer is the most widely used agent. Low/high pH pulses are
strong candidates for regenerating optical and acoustic sensors.
Other regeneration protocols such as altering the temperature
and ionic strength and the use of strong detergents have been
shown to regenerate some sensors. These must be chosen only in
systems where the particular biophysics of recognition is a
driving force such as in the cases of hydrophobic or highly ionic
analytes. A comparison of dierent regeneration techniques
Jack Goode received his Bs.C. in Nanotechnology from University of

Leeds in 2010 and is currently working toward his Ph.D. on the
biophysics of receptor analyte interaction, in particular, focusing on
enhancing the operation of electrochemical biosensors. His research is
sponsored by Abcam Plc, and the collaboration has led to the
investigation of novel bioreceptor constructs. He also has previous
H
Langmuir
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research experience in biomaterials engineering and has worked in the

public engagement and dissemination of research.
Jo Rushworth received her Ph.D. in structural molecular biology at

Leeds University, during which she studied the interaction between the
prion protein and A oligomers. She worked in a postdoctoral position
in the Leeds Bionanotechnology Group on the development of
biosensors for amyloid proteins. She has taught chemistry for several
years and is now a lecturer in biomedical science and biochemistry at De
Montfort University, Leicester, U.K.
Paul Millner directs the Bionanotechnology group at the University of

Leeds. The groups interests can be divided broadly into two main
themes: biosensors and nanotechnology. He initially trained as a
biochemist and obtained his Ph.D. in plant sciences from the University
of Leeds. After around 12 years of working on plant cell signaling, his
research shifted direction and he is now a leader in biosensors and
bionanotechnology. Paul is also Director of the School of Biomedical
Sciences and delivers lectures on a range of topics.
ACKNOWLEDGMENTS
We were funded by BBSRC and AbCam Plc. Particular thanks go
to C. Jackson and E. Klakus for editorial assistance.
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