Incorporation of VOC-Selective Peptides in Gas Sensing Materials

Ana Rita Oliveira

1,2 a

, Efthymia Ramou

1,2 b

, Gonçalo D. G. Teixeira

1,2 c

Susana I. C. J. Palma

1,2 d

and Ana C. A. Roque

1,2* e

Associate Laboratory i4HB- Institute for Health and Bioeconomy, School of Science and Technology,

NOVA University Lisbon, 2829-516 Caparica, Portugal

UCIBIO – Applied Molecular Biosciences Unit, Department of Chemistry, School of Science and Technology,

NOVA University Lisbon, 2829-516 Caparica, Portugal

Keywords: Ionogels, Hybrid Gels, Peptides, Gas Sensing, Electronic Nose.

Abstract: Enhancing the selectivity of gas sensing materials towards specific volatile organic compounds (VOCs) is

challenging due to the chemical simplicity of VOCs as well as the difficulty in interfacing VOC selective

biological elements with electronic components used in the transduction process. We aimed to tune the

selectivity of gas sensing materials through the incorporation of VOC-selective peptides into gel-like gas

sensing materials. Specifically, a peptide (P1) known to discriminate single carbon deviations among benzene

and derivatives, along with two modified versions (P2 and P3), were integrated with gel compositions

containing gelatin, ionic liquid and without or with a liquid crystal component (ionogels and hybrid gels

respectively). These formulations change their electrical or optical properties upon VOC exposure, and were

tested as sensors in an in-house developed e-nose. Their ability to distinct and identify VOCs was evaluated

via a supervised machine learning classifier. Enhanced discrimination of benzene and hexane was detected

for the P1-based hybrid gel. Additionally, complementarity of the electrical and optical sensors was observed

considering that a combination of both their accuracy predictions yielded the best classification results for the

tested VOCs. This indicates that a combinatorial array in a dual-mode e-nose could provide optimal

performance and enhanced selectivity.

1 INTRODUCTION

Gas sensing is currently emerging as a critically

important technology related to a broad range of

applications such as medicine (van Hooren et al.,

2016), and early diagnosis of disease (Broza et al.,

2015; Cruz et al., 2017; Fitzgerald & Fenniri, 2017;

Krilaviciute et al., 2015; Susana I.C.J. Palma et al.,

2018; Vishinkin & Haick, 2015). Indeed, volatile

organic compounds (VOCs) are becoming

increasingly recognized as potential biomarkers

associated with disease. Artificial olfaction is the

automated simulation of the sense of smell through

the use of electronic nose devices (e-noses),

comprised by an array of chemical sensors with

https://orcid.org/0000-0001-8496-1746

https://orcid.org/0000-0003-2376-6749

https://orcid.org/0000-0001-7675-5926

https://orcid.org/0000-0002-1851-8110

https://orcid.org/0000-0002-4586-3024

partial selectivity coupled with signal-processing and

pattern recognition tools. The most common and

commercially available gas sensing materials include

metal oxide semiconductors (Dey, 2018) or

conducting polymers (Park et al., 2017). However,

their main drawbacks include low long-term sensor

stability, high maintenance, cumbersome and

complex instrumentation, and most importantly low

selectivity. Thus, it is of high interest to develop

better and competitive alternatives, by adapting the

components of the sensors in order to enhance their

selectivity, reliability and portability (Son et al.,

2017). In biological olfaction, odorant binding

proteins and olfactory receptors are the main tools

used to address the difficult problem of selectivity. In

artificial olfaction, these find limitations as they are

Oliveira, A., Ramou, E., Teixeira, G., Palma, S. and Roque, A.

Incorporation of VOC-Selective Peptides in Gas Sensing Materials.

DOI: 10.5220/0010797200003123

In Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) - Volume 1: BIODEVICES, pages 25-34

ISBN: 978-989-758-552-4; ISSN: 2184-4305

difficult to produce, as well as to stabilise and

interface with electronic systems for signal

transduction, long-term storage and repeated use

(Barbosa et al., 2018).

Among the alternative options to tune selectivity,

peptides are one of the most attractive choices due to

their robustness, chemical diversity, compact size,

and their adaptability to extreme environments and

safely long-term storage (Cui et al., 2012; Sankaran

et al., 2012). Furthermore, they can be developed to

bind distinct targets through rational design or

discovery by panning of phage display libraries

(Kuang et al., 2010).

In this work, we investigated the incorporation of

a previously reported peptide (peptide P1), and two

designed derivatives (peptides P2 and P3) able to

discriminate single carbon deviations among benzene

and derivatives such as toluene and xylene (Ju et al.,

2015), into gel-like sensing materials yielding

electrical and optical signals in the presence of VOCs.

Two gel formulations were studied: a gelatin matrix

gelated in an ionic liquid environment

([BMIM][DCA]) (ionogels yielding an electrical

signal by monitoring the changes in ionic

conductance of the formulation) and the same matrix

with a liquid crystal component (hybrid gels yielding

an optical signal due to their birefringence). The

modified versions of P1 were obtained through the

addition of non-natural amino acids at the C-terminal

- norleucine (Nle - P2 peptide) and biphenylalanine

(Bip, - P3 peptide) - with the purpose to facilitate their

binding in the ionic liquid-liquid crystal interface, due

to their structural resemblance with 5CB (Figure 1).

These materials offer the possibility to add the

peptide selective moiety with unprecedented simple

procedures that take advantage of self-assembly and

autonomous compartmentalization, avoiding harsh

chemical reactions and peptide covalent attachment

onto surfaces.

2 MATERIALS AND METHODS

2.1 Materials and Reagents

Gelatin from bovine skin (gel strength ൎ 225 g;

Bloom, Type B), was purchased from SigmaAldrich.

The liquid crystal 4-cyano-4’-pentylbiphenyl (5CB),

was acquired from TCI Europe, and the Ionic liquid

1-Butyl-3-methylimidazolium chloride

([BMIM][DCA], ˃98%)) was purchased from

IoLiTec. Peptides 1, 2 and 3 were purchased from

Genecust (purity >95,9%). Ethanol (purity 99.8%)

and Fluorescein isothiocyanate (FITC) Isomer I (99%

purity) were purchased from Sigma-Aldrich, while

benzene, hexane, xylene and toluene were supplied

by Fisher Scientific, and acetone (purity 99.5%) was

purchased from Honeywell.

2.2 Confirmation of Peptide P1

Binding by Multi-Parametric

Surface Plasmon Resonance

(MP-SPR)

For the MP-SPR studies, an Au-glass slide was

modified with 3-mercaptopropionic acid (3-MPA), a

linker with a hydroxyl group and a sulfhydryl group,

which binds to the Au surface thiols. For the

immobilization of the P1 peptide (10 uM in 20 mM

sodium phosphate solution, at pH 7) on the surface

EDC/NHS chemistry was used, and Ethanolamine

was used as a blocking agent for extremities where P1

was not bound. This method was used to produce the

sample placed in the peptide chamber, whereas the

control chamber contained an Au-glass slide with all

the previously described functionalization, minus the

immobilization of the peptide step. The MP-SPR

signal variation within time was measured by using

two wavelengths, 670 nm and 785 nm (mDeg), being

the average of the signal measured at the two

wavelengths defined as ∆mDeg.

2.3 Incorporation of the Peptides into

Ionogels

For the production of ionogel sensors we used the

same protocol as previously described with the

addition of peptide solutions (Abid Hussain et al.,

2017). The final formulation was pipetted onto

interdigitated golden electrodes deposited on

untreated glass slides and spread into a thin film using

a TQC film applicator (Automatic Film Applicator

Standard, TQC) with a 15 μm thickness. After

production, the peptide ionogels were left to dry on a

sealed clean petri dish, in a humidity box with

environmental control. Control ionogels (without the

incorporation of a peptide component) were also

prepared, for comparison purposes.

2.4 Incorporation of the Peptides into

Hybrid Gels

Peptide hybrid gels were formulated by mixing all

components plus the peptide solution following

protocols previously described (C. Esteves et al.,

2019; A. Hussain et al., 2017). When gelation occurs,

the gel compositions were deposited on top of

BIODEVICES 2022 - 15th International Conference on Biomedical Electronics and Devices

untreated glass slides and spread into thin films using

a TQC film applicator (Automatic Film Applicator

Standard, TQC) with a 30 μm thickness. After

production, the gels were stored within an

environment of controlled humidity. Control hybrid

gels (without a peptide component) were also

prepared for comparison purposes.

2.5 Hybrid Gel Characterization

The formation of ionic liquid droplets and the

morphological characteristics and differences

between the hybrid gels containing the P1, P2 and P3

peptides were assessed via a ZEISS, Observer.Z1

Polarized Optical Microscope (POM) equipped with

an Axiocam 503 color camera. Pictures were taken

with crossed polarizers and in bright field and were

processed with the ZEN 203 software.

Regarding the morphological stability of the

hybrid gels upon storage over time, POM photos with

crossed polarizers of the same region of interest were

taken for a 30-day period. A costume-made python

scrip (Python 3.6.2) was used to analyze the photos.

The scrip uses binary images to calculate the

differences between them.

2.6 VOC Sensing using Ionogels

All the prepared ionogels (peptide-based and

controls) were assessed in an in-house tailor-made

electrical e-nose device (Hussain et al., 2017). The

device detects changes in the conductance upon VOC

adsorption and desorption. The sensors were placed

in a hermetically sealed array chamber and exposed

to a sequence of six volatiles – hexane, benzene,

toluene, xylene, ethanol and acetone – similar to the

investigation of Ju et al. (Ju et al., 2015). The solvents

were kept in a bath thermostatized at 37ºC and their

respective vapors were pumped to the array chamber

(exposure period) followed with ambient air via a

second air pump (recovery period). The films were

exposed to each gas analyte for 45 consecutive cycles,

each cycle consisting of 5 sec of exposure followed

by 10 sec of recovery, in total 7 min and 30 sec of test

duration per VOC. The electrical signals of the

sensors were acquired at a sampling rate of 90 Hz, and

assays were performed in duplicates.

Processing and evaluation of the signals obtained

was performed, using methods described in our

previous publications (C. Esteves et al., 2019; S. I. C.

J. Palma et al., 2019; Rodrigues et al., 2020; Santos et

al., 2019b). Python programming tools were used to

process the signals retrieved by the e-nose device.

For the evaluation of the sensors’ performance

regarding VOC identification, machine learning

methods were applied to the individual cycles.

Briefly, twelve features representative of the signals

morphology were extracted from each cycle and used

as input variables in an automatic classifier based on

Support Vector Machines (SVM) algorithm, using a

radial basis kernel and hyperparameters C = 100 and

y = 0.1. The dataset for each sensor formulation was

composed by 30 cycles per VOC. Two thirds of this

dataset were used as training set and one third as

validation set for the automatic classifier. Accuracy

bar plots, which exhibit the percentage of the correct

predictions of the classifier were used to represent the

sensors performance.

2.7 VOC Sensing using Hybrid Gels

The optical e-nose device is designed to monitor the

light transmitted by the hybrid sensors and convert it

into voltage. The detailed setup has been described in

our previous works (Esteves et al., 2019; Hussain et

al., 2017; Palma et al., 2019; Rodrigues et al., 2020).

The sensors are placed in a hermetically sealed

detection chamber and each one is paired with a

polarizer and a corresponding analyzer. The VOC

experiments, as well as the data analysis of the signals

were conducted and processed as described in the

previous section.

3 RESULTS AND DISCUSSION

3.1 Assessing VOC Selectivity in

Ionogel: Electrical e-Nose

The affinity and selectivity of peptide P1 towards

VOCs was firstly assessed by MP-SPR. Our results

indicated a preferred binding of the peptide P1

towards VOCs following the order benzene<xylene<

toluene<ethanol<acetone<hexane, as can be seen by

the increase of the ΔmDeg values (see Table 1).

The three peptides were then incorporated into the

ionogel gelatin matrices and spread as thin films onto

interdigitated electrodes to be tested in an in-house

developed electrical e-nose. VOCs adsorption to the

ionogels affects the ion mobility within the materials.

Therefore, the ionogels admittance changes and an

electric response can be obtained. This is a reversible

process upon VOC desorption and the basis for

electrical VOC sensing with the electric E-nose (S. I.

C. J. Palma et al., 2019).

Incorporation of VOC-Selective Peptides in Gas Sensing Materials

Figure 1: Chemical structure of (a) peptide 1, (b) peptide 2,

dicyanamide ([BMIM] [DCA]) and (e) liquid crystal 4-

Cyano-4’-pentylbiphenyl (5CB).

Table 1: Summary-results of the interaction between the P1

functionalized Au surface and the VOCs. ∆mDeg (peptide-

control) = ∆mDeg peptide-∆mDeg control, where ∆mDeg

peptide is the average of the signal measured at the 670 and

785 nm wavelengths in the peptide chamber, and ∆mDeg

control is the average of the signal measured at the 670 and

785 nm wavelengths in the control chamber. Relative signal

variation = the relative signal representing how much the

signal increased on the peptide chamber, when compared to

the control chamber.

VOCs

ΔmDeg

(Peptide-

Control)

Relative

signal

variation

Acetone 5.76 ± 2.16 0.21

Ethanol 2.20 ± 3.63 0.33

Benzene 32.54 ± 3.35 5.25

lene 14.69 ± 2.97 1.20

Toluene 5.61 ± 4.36 0.46

Hexane 6.19 ± 3.94 0.17

Through the sequential exposure of the peptide

ionogel formulations to the 6 tested volatiles - hexane,

benzene, toluene, xylene, ethanol and acetone -

variations in the conductance of the sensors were

monitored in real time and after signal processing (as

described in Materials and Methods section) typical

relative amplitude responses are presented in Figure

2(a).

The sensor responses exhibit a variability in their

profiles, characteristic of the corresponding gas

analyte exposure. For example, all of the sensors

during the linear 6C-alkyl chain hexane exposure

respond with a downwards curve, while all other

volatiles generate an upwards response signal. The

majority of the sensors respond rather quickly (within

seconds) upon VOCs exposure, during which most

signals never reach a plateau, with the exception of

xylene in control and P3 ionogels. Upon recovery,

almost all sensor signals return to the baseline in a

similar way.

After signal collection machine learning-based

tools were implemented to analyze the data. The

signals were divided into cycles and were then

normalized. A set of features corresponding to the

curve morphology (C. Esteves et al., 2019; S. I. C. J.

Palma et al., 2019; Santos et al., 2019a) was extracted

and used as input for an SVM-based automatic

classifier. The accuracy % of correct VOC prediction

is presented in an accuracy bar plot seen in Figure

2(b), depicting that the tested ionogel compositions

exhibit distinct selectivity for the tested VOCs.

For example, P2 and P3-based sensors were able

to discriminate and identify hexane and xylene,

respectively, with an 100% accurate classification.

This could be associated with the presence of the

similar non-canonical aminoacid norleucine and

biphenylalanine moieties in P2 and P3 peptides

respectively and their similarity to the structure of the

volatiles in question, possibly enhancing the

interaction between the sensors and the volatiles. On

a similar vein, the control ionogel provides great

classification results for acetone and ethanol, with a

100% accuracy. The accuracy scores for the P1

ionogel were not as high (e.g. for toluene and xylene)

which suggests that certain signal profiles produced

by the formulation exhibited similar characteristics,

thus not allowing the distinction of the corresponding

analytes. Overall, the control ionogel formulation

achieved the best global accuracy score (91%)

followed by the P3-based ionogel (86%).

These results indicate that the incorporation of the

peptides onto the gelatin ionogels did not yield an

improvement of selectivity towards benzene and

aromatic compounds. This is mainly due to the fact

that the control ionogel already displayed excellent

discriminating behavior towards the particular VOCs

tested, as conductance and selectivity are already

driven by the ionic liquid component.

BIODEVICES 2022 - 15th International Conference on Biomedical Electronics and Devices

Figure 2: Electrical e-nose results for Control, P1, P2 and P3-based ionogels. (a) Typical cycle signals of all the sensors upon

exposure to different VOCs. Each curve represents the average and standard deviation of at least 19 replicate cycles from the

same sensor. VOC exposure periods (5 s) are highlighted in grey, and each cycle corresponds in total to 15 s. (b) Comparison

of the VOC prediction accuracies obtained for all the ionogels tested in the electrical e-nose.

3.2 Assessing VOC Selectivity in

Hybrid Gels: Optical e-Nose

The peptides P1, P2 and P3 were incorporated into

hybrid gel formulations by simply adding them to the

formulation and promoting autonomous self-

assembly of the different moieties into the hybrid gel

compartments. The formation of ionic liquid – liquid

crystal droplets was observed by bright field and

polarized optical microscopy (Figure 3).

All the hybrid gels (control and peptide-based)

exhibit polydisperse 5CB droplets as previously

noted (Esteves et al., 2019; Hussain et al., 2017;

Palma et al., 2019), featuring a radial configuration

which under the POM gives rise to a distinctive

Maltese cross pattern (Drzaic, 1995). The

characteristic core defect located in the center of the

droplet can be observed in the bright field pictures

(Esteves et al., 2019; Hussain et al., 2017) – seen in

Figure 3 (a), (e) and (j). The radial droplet profile

Incorporation of VOC-Selective Peptides in Gas Sensing Materials

Figure 3: Representative POM images of control hybrid gel (a), (e) and (i)), peptide 1 hybrid gel (b), (f) and (j)), peptide 2

hybrid gel (c), (g) and (k)) and peptide 3 hybrid gel (d), (h) and (l)).

suggests that the liquid crystal molecules adopt a

homeotropic alignment near the ionic liquid interface,

which is attributed to the interactions occurring

between the alkyl chain of the ionic liquid and 5CB

(C. Esteves et al., 2019; Carina Esteves et al., 2020;

A. Hussain et al., 2017).

We need to point out that in the case of P2-based

and P3-based hybrid compositions, apart from the

radial droplets, some irregularly shaped and randomly

oriented droplets were also observed. This finding

suggests that both the Nle (in the case of the P2

peptide) and Bip (in the case of the P3 peptide)

moieties interfere with the liquid crystal anchoring on

the ionic liquid interface.

The peptide-based hybrid gels were tested in the

optical e-nose. Exposure to the tested gas analytes

results in a disorganization of the liquid crystal

component, triggering a phase transition to the

isotropic state. The signal responses are the

collaborative responses of the individual

compartments of the gel formulations to the

corresponding analyte. For example, hydrophobic

VOCs, such as hexane and the aromatic benzene,

xylene and toluene, are more likely to interact mainly

with the oil phase formed by the liquid crystal

molecules inside the droplets. Protic VOCs, and those

forming hydrogen bonds (e.g. ethanol), tend to

interact not only with the LC droplets but also with

the gelatin matrix itself, as previously reported

(Esteves et al., 2019; Hussain et al., 2017). The sensor

responses were repeatable and exhibited features

(such as signal profile, response/recovery profile)

characteristic of the tested volatiles.

Signal processing, analysis and presentation were

conducted using the same tools as in the electrical e-

nose results, described in the previous section. In

Figure 4(a) relative amplitude responses for each

tested hybrid peptide gel compositions and for all the

studied gas analytes is shown. The baseline on each

individual curve represents the initial light state of a

sensor, due to the presence of the liquid crystal

droplets. Upon VOC exposure the liquid crystal

component becomes isotropic, thus it cannot alter the

polarization of the transmitted light (which

subsequently cannot pass through the analyzer). This

generates an upwards response curve to a dark(er)

state for the sensor.

It is possible to observe that each sensor holds a very

characteristic amplitude signature signal, related to

the different volatiles. For example, a delayed

response upon analyte exposure is observed (e.g.

hexane for control and P1-based hybrid gels), or the

cases where a plateau in the response is reached (e.g.

the control and P3-based sensors for all volatiles, with

the exception of hexane and ethanol), and a very

distinctive flat response from P1-based sensor

towards ethanol.

BIODEVICES 2022 - 15th International Conference on Biomedical Electronics and Devices

Figure 4: Optical e-nose results for the for Control, P1, P2 and P3-based hybrid gels. (a) Typical cycle signals of all the

sensors upon exposure to different VOCs. Each curve represents the average and standard deviation of at least 19 replicate

cycles from the same sensor. VOC exposure periods (5 s) are highlighted in grey, and each cycle corresponds in total to 15 s.

(b) Comparison of the VOC prediction accuracies obtained for all the sensors tested in the optical e-nose.

Another interesting observation is that although

the 10 s recovery period seems to be sufficient for the

liquid crystal to completely recuperate from

isotropisation in the control hybrid formulation

(allowing the sensors signal to return to the initial

baseline levels), the majority of the peptide-based

sensors appear to take longer to completely recover

within the 10 s period. This is evident in the case of

P1 sensors with benzene, xylene and acetone, and

both P2 and P3 sensors with all volatiles – with the

exception of hexane. It should be noted that this

recovery pattern was not detected for the ionogel

sensors previously analyzed.

When looking at the VOC prediction accuracy bar

plot (seen in Figure 4(b)) it is noticeable that

selectivity towards benzene and hexane from the P1-

Incorporation of VOC-Selective Peptides in Gas Sensing Materials

based sensor was achieved. Overall, the P1-based

hybrid gel was able to distinguish and classify all the

tested volatiles presenting a 74% score of global

accuracy.

4 CONCLUSIONS

In this work we studied the incorporation of peptides

in two distinct groups of gas sensitive materials.

Ionogels, comprised of gelatin and ionic liquid,

tailored for electrical gas sensing and hybrid gels,

containing gelatin, ionic liquid and liquid crystal,

designed for optical gas sensing. A peptide (P1)

known to discriminate single carbon deviations

among benzene and derivatives was used as a model,

along with two modified versions (P2 and P3). The

set of ionogels were tested in an electrical e-nose,

designed to monitor changes in the conductance of

the sensors, and selectivity towards hexane and

xylene was observed in the case of P2 and P3 ionogel

sensors, respectively. Regarding the hybrid gels, the

incorporation of peptide P1 did not disrupt the self-

assembly of the ionic liquid crystal droplets

(suggesting that peptide P1 is mainly distributed in

the matrix), while the incorporation of peptides P2

and P3 disrupt some droplets, although the majority

exhibit a radial configuration. The set of hybrid gels

were tested in an optical e-nose, designed to monitor

the light transmitted from the materials. An enhanced

discrimination of benzene was observed for the P1-

based gel and for ethanol in the case of both P2 and

P3 hybrid gels. As a final note, we would like to

highlight the complementarity of the two distinct gel

formulations responses, since the combination of both

optical and electrical prediction accuracies could

provide the best classification accuracies for the

tested VOCs. For example, the P2 ionogel was more

Table 2: Relative accuracy scores of VOC prediction from

every ionogel and hybrid gel peptide sensors’ tested. The

relative accuracy score is defined as the difference between

the peptide-based accuracy score and the control accuracy

score.

Relative accuracy

ionogels (%)

Relative accuracy

hybrid gels (%)

VOCs P1 P2 P3 P1 P2 P3

Acetone -3 20 18 -36 -25 -6

Ethanol 13 44 44 -49 -18 -22

Benzene 34 10 14 -20 -9 -22

lene 33 13 14 -29 2 -3

Toluene 45 -2 34 -33 19 21

Hexane 18 -31 11 -50 -23 2

selective towards hexane and toluene whereas the P2

hybrid gel was more selective towards ethanol,

suggesting that additional information and enhanced

selectivity can be obtained regarding discrimination

of volatiles, by using a combinatorial sensors array in

a dual-mode e-nose. An overall view of the relative

accuracy results from all sensors, regarding

discrimination of each VOC, can be seen in Table 2.

ACKNOWLEDGEMENTS

This project has received funding from the European

Research Council (ERC) under the EU Horizon 2020

research and innovation programme [grant reference

SCENT-ERC-2014-STG-639123, (2015-2022)] and

by national funds from FCT - Fundação para a

Ciência e a Tecnologia, I.P., in the scope of the

project UIDP/04378/2020 and UIDB/04378/2020 of

the Research Unit on Applied Molecular Biosciences

– UCIBIO and the project LA/P/0140/2020 of the

Associate Laboratory Institute for Health and

Bioeconomy - i4HB, which is financed by national

funds from financed by FCT/MEC

(UID/Multi/04378/2019). The authors thank

FCT/MCTES for the PhD grants

SFRH/BD/128687/2017 and PD/BD/139800/2018.

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BIODEVICES 2022 - 15th International Conference on Biomedical Electronics and Devices