# Sensing food quality by silicene nanosheets : a Density Functional Theory
study

Madhumita Kundu<EMAIL_ADDRESS>Department of Physics, Indian Institute
of Technology Guwahati, Assam 781039, India  Subhradip Ghosh
<EMAIL_ADDRESS>Department of Physics, Indian Institute of Technology
Guwahati, Assam 781039, India

###### Abstract

Volatile organic compounds (VOCs) emitted by food products are considered
markers for assessing quality of food. In this work, first-principles Density
Functional Theory (DFT) and Non-equilibrium Green’s function (NEGF) methods
have been employed to model chemo-resistive gas sensor based on two-
dimensional silicene based nanosheets that can sense the six different VOCs
emitted by standard food products. Our calculations with unpassivated and
flourine passivated silicene(F-silicene) sheets as sensor materials show that
flourine passivated silicene has significantly better sensitivity towards all
six VOC molecules (Acetone, Dimethylsulfide, Ethanol, Methanol, Methylacetate
and Toluene). Moreover, flourinated silicene sensor is found to be capable of
separately recognising four VOCs, a much better performance than r-GO used in
a recent experiment. We analyse the microscopic picture influencing sensing
capabilities of un-passivated and fluorinated silicene from the perspectives
of adsorption energy, charge transfer and changes in the electronic structure.
We find that better sensing ability of fluorinated silicene nanosheet can be
correlated with the changes in the electronic structures near the Fermi level
upon adsorption of different VOCs. The results imply that passivated silicene
can work better as a sensor than r-GO in case of generic food VOCs. The
results are important since modelling of various two-dimensional nano-sensors
can be done in the similar way for detection of more complex VOCs emitted by
specific food products.

##  I Introduction

Over past three decades, nano-science and technology have been omnipresent in
common man’s life. This is due to revolutionary advances in nano-electronics
Yu and Trinkle (2007) and nano-medicines Kim  _et al._ (2011), among other
applications. Since food and it’s consumption is an integral part of human
life, the quality and safety of food products is one of the key indices to
gauge improvements in standards of living. Quite naturally, nano-science based
applications have found their way into global industry surrounding food and
helped develop various parameters related to food quality and safety Duran and
Marcato (2013); Wang  _et al._ (2013). This has been possible, thanks to
advancements in intelligent packaging Sozer and Kokini (2009); de Azeredo
(2009); Arora and Padua (2010); Sekhon (2010) that tracks the trajectory of
food history and quality from producer to consumer, and takes preventive
measures to reduce wastage Yam  _et al._ (2005). The technology is based on
gas sensors that can detect and distinguish chemicals released during the
stages of maturation. The quality of food product, ascertained by its level of
deterioration is often assessed by assembling the gas sensors into arrays
leading to e-nose, an electronic machine designed to detect and discriminate
among complex odours Miguel and Escuder-Gilabert (2009). The odours are
primarily due to volatile organic compounds (VOCs) released by food in the
open space of the package. The sensor array can analyse the nature and content
of the VOCs and determine the stage of deterioration. VOCs, therefore, are
considered as markers of food spoilage.

Gas sensors based on nanomaterials are the most promising candidates for this
purpose as high surface to volume ratio, tunability of electronic structures
upon adsorption of gas molecules, high sensitivity and selectivity, fast
response time, low power consumption, small size of nanomaterials work as
advantages. Graphene, the prototype two-dimensional (2D) material has been
extensively explored for gas-sensing and bio-sensing applications Schedin  _et
al._ (2007); Robinson  _et al._ (2008); Fowler  _et al._ (2009); Lu  _et al._
(2009a); Wang  _et al._ (2019); Nag  _et al._ (2018). Graphene based
nanomaterials have also been used to detect and differentiate various VOCs for
breath analysis where they act as bio-markers for several terminal diseases
Tripathi  _et al._ (2016) and to remove pollutants like NO2 and SO2 from
atmosphere Kumar  _et al._ (2020). However, the sensors based on pristine
graphene have limited sensitivity and selectivity towards diverse VOCs
Tripathi  _et al._ (2016). One way to overcome such limitation is its
functionalisation with organic compounds,molecules,conducting polymers and
introduction of dopants or defect engineering Milowska and Majewski (2014a,
b); Nag  _et al._ (2014); Choi  _et al._ (2014); Rad (2015).

Due to the limitations posed by pure graphene and the structural complications
associated with its various composites, researchers, particularly ones
performing modelling and simulations, have explored other materials from 2D
family as potential gas sensors applicable to a wide spectrum of VOCs. A
plethora of first-principles Density Functional Theory Kohn and Sham (1965)
based simulations have been carried out in recent times with pristine
compounds like MoS2 Tian  _et al._ (2017), phosphorene Kou  _et al._ (2014);
Zhang  _et al._ (2015); Sun  _et al._ (2019), black phosphorous Ou  _et al._
(2019), HfTe2 Chakraborty and Johari (2020) and BC6N Aghaei  _et al._ (2021)
as sensors for VOC and hazardous gas detection.

Graphene based nanocomposites have recently started to be used in food
industry for variety of purposes associated with food safety Sundramoorthy and
Gunasekaran (2014). With regard to sensing VOCs from food products,
chemoresistive responses of reduced Graphene oxide (r-GO) and it’s
nanocomposites with polythiopene have been experimentally measured upon
exposure to flows of VOCsTung  _et al._ (2012). Methanol (CH3OH), Ethanol
(C2H5OH), Acetone ((CH3)2OC), Methyl acetate ((CH3)2OCO), Dimethylsulfide
((CH3)2S) and Toluene (C6H5CH3) vapours were tested since they are the
prominent VOCs released from meat Bhattacharjee  _et al._ (2011), fruits
Fleming-Jones and Smith (2003) and vegetables Fleming-Jones and Smith (2010),
making them markers of food spoilage. Sensitivity and selectivity of the
sensor material were decided by measuring the resistance relative amplitude
defined as $A_{r}=\frac{R-R_{0}}{R_{0}}$, where $R(R_{0})$ is the resistance
of the sensor in presence of a particular VOC(nitrogen) vapour. It was found
that while pristine r-GO is unable to differentiate between the VOCs, it’s
nanocomposite would. No further comprehensive study on food VOCs using 2D
materials as sensors is available till date. However, several first-principles
DFT calculations on interactions of VOCs emitted from different fruits with
various 2D sensing materials like Germanene, Stanene and silicene have been
carried out Srimathi  _et al._ (2018); Nagarjan and Chandiramouli (2018a);
Bhavadharani  _et al._ (2019). These studies, although provided some insights
into the interplay of orbital hybridisation, charge transfer, work function
and subsequent influence of it’s response when interacted with different
organic molecules, the crucial aspect of device integrability was not
addressed. As an alternative to Graphene, silicene, among the single-component
2D materials, has shown substantial promises. In contrast to
$sp^{2}$-hybridised Graphene, silicene has a mixture of $sp^{2}$ and $sp^{3}$
hybridisation and hence exhibits a buckled structure. The buckling turns out
to be advantageous over planar structure of Graphene as it provides tunability
in the band gap upon application of external electric field. Accordingly,
silicene based Field Effect Transistors (FETs) have been explored. It turns
out that they demonstrate better efficiency than Graphene based FETs Ghosal
_et al._ (2023). The material has been extensively investigated with regard to
bio-sensing applications, through computational modelling. To this end,
silicene nanosheets and nanoribbons as bio-sensors have been investigated for
DNA sequencing Hussain  _et al._ (2018); Henry  _et al._ (2021); Alesheikh
_et al._ (2018); Li  _et al._ (2020) and sensing of important bio-molecules
like uric acid Tarun  _et al._ (2020), paracetamol Saikia  _et al._ (2017) and
pyrazinamide, the organic molecule used to treat tuberculosis Saikia  _et al._
(2016). silicene nanosheets have also been used for sensing of hazardous gases
Prasongkit  _et al._ (2015). The band structure of silicene changes
significantly upon surface passivation by functional groups. Hydrogenation
that is passivation of surface dangling bonds by hydrogen changes the zero gap
semiconductor to an insulator Osborn  _et al._ (2011) while fluorine
functionalisation yields a moderate band gap of 0.45 eV Jia  _et al._ (2015);
Zaminpayma and Payman (2016). This poses a possibility of band gap modulation
upon adsorption of different VOCs and its connection to differential
responses, giving rise to discrimination between VOCs, the desired goal. This,
certainly is an advantage over graphene or r-GO as sensing material.

In what follows, in this work, using DFT and non-equilibrium Green’s function
(NEGF) Dutta (2000) technique, we have investigated the changes in the
structural and electronic properties as well as sensing capabilities of
pristine and Fluorine (-F) functionalised silicene (F-silicene) nanosheets
upon adsorption of six prominent VOCs mentioned above that are indicators of
degradation of food products. The modifications in the electronic structures,
associated charge transfers and work functions of the gas adsorbed nanosheets
as well as the differences in these properties/parameters between bare and
functionalised nanosheets provide necessary insights into their comparative
capabilities as sensors to ascertain the degree of deterioration of food. The
ideas obtained are tested by simulating the sensing device and calculating
transport parameters that are directly related to the experimental quantities
like sensitivity and selectivity. We find that both sensitivity and
selectivity of silicene increase upon -F functionalisation at a moderate bias
voltage. This demonstrates that functionalised silicene can be an sensor,
alternative to Graphene based ones, in addressing quality of food products.

Figure 1: Relaxed structure of silicene: (a)top view, (c)side view. Relaxed
structure of F-silicene: (b) top view and (d) side view. Sites of adsorption
for (e) silicene and (f)F-silicene are shown.

##  II COMPUTATIONAL DETAILS

Important physical quantities at equilibrium and the electronic structures
associated with interactions due to adsorption of VOC molecules on silicene
nanosheets are calculated using DFT with Projector Augmented Wave (PAW) method
Blöchl (1994) as implemented in Vienna Ab initio simulation package(VASP)
Kresse and Furthmüller (1996); Feng  _et al._ (2020); Kresse and Joubert
(1999).Exchange-correlation part of the Hamiltonian is approximated using the
Generalized Gradient Approximation (GGA) with the Perdew-Burke-Ernzerhof (PBE)
parametrisation Perdew  _et al._ (1996). Long-range van der Waals (vdW)
interactions are considered by employing corrections proposed by GrimmeGrimme
(2006). $4\times 4\times 1$ fully relaxed supercells are used for the pristine
and -F functionalised silicene nanosheets (Figure 1). In order to avoid
interaction between two layers along $z$-direction, a vaccum of 30 Åis used.
The geometry optimisation of the composite system of molecule adsorbed on the
nanosheet is carried out using the conjugate gradient method. Plane wave basis
set upto 550 eV is considered. Convergence criteria for total energy and force
are kept at $10^{-6}$ eV and $0.04$ eV/Å, respectively. A $4\times 4\times 1$
Monkhorst-Pack Monkhorst and Pack (1976) $k$-point grid is used to sample the
Brillouin zone for self-consistent calculations. A $17\times 17\times 1$
$k$-mesh is used for calculations of electronic structures.

The physisorption energy provides quantitative and qualitative information
about the interaction strength between the adsorbent and the adsorbate. The
adsorption energy of a single VOC molecule on the nanosheet can be expressed
as:

$\displaystyle
E_{\text{ad}}=\left[E_{\text{nanosheet+VOC}}-E_{\text{nanosheet}}-E_{\text{VOC}}\right]$
(1)

where $E_{\text{nanosheet+VOC}},E_{\text{nanosheet}}$ and $E_{\text{VOC}}$ are
total energies of VOC-nanosheet complex, pristine nanosheet and the isolated
VOC molecule, respectively.

To assess the performances of the silicene based sensor devices, electronic
transport through the VOC- nanosheet complexes are studied by the NEGF method
in conjunction with DFT as implemented in the TRANSIESTA Brandbyge  _et al._
(2002) package. The electrical current through the device with two metal
electrodes under a finite bias voltage $V_{b}$ can be calculated using the
Landauer-Buttiker formula as follows Meir and Wingreen (1992); Buttiker
(1988); Landauer (1957):

$\displaystyle I(V_{b})=\frac{2e}{h}\int
T\left(E,V_{b}\right)\left(f_{L}\left(E-\mu_{L}\right)-f_{R}\left(E-\mu_{R}\right)\right)$
(2)

$e$ represents the electronic charge, h is Planck’s constant,$T(E,V_{b})$ the
electronic transmission function and $f_{L}(E-\mu_{L})$ and $f_{R}(E-\mu_{R})$
are the Fermi functions in the left ($L$) and right ($R$) electrodes,
respectively. $\mu_{L}$ and $\mu_{R}$ are the chemical potentials of the left
and right electrodes, respectively. The bias energy window is between
$\mu_{L}=E_{f}-eV_{b}/2$ and $\mu_{R}=E_{f}+eV_{b}/2$; $E_{f}$, the average
Fermi energy, is given by $\left(\mu_{L}+\mu_{R}\right)/2$. The transmission
coefficient $T(\epsilon)$ of electrons with incident energy $\epsilon$ is
given by

$\displaystyle
T(\epsilon)=\Gamma_{L}\left(\epsilon\right)\mathcal{G}\left(\epsilon\right)\Gamma_{R}\left(\epsilon\right)\mathcal{G}^{\dagger}\left(\epsilon\right)$
(3)

Here, $\Gamma_{R}\left(\epsilon\right)$ and $\Gamma_{L}\left(\epsilon\right)$
are the coupling matrices for the right and left electrodes, respectively.
$\mathcal{G}\left(\epsilon\right)$ represents the Green’s function for the
system. Nanosheets of size $5\times 3\times 1$ and $k$-point grids of size
$10\times 1\times 100$ are used for all transport calculations.

##  III Results and Discussions

###  III.1 Structural properties

In Figure 1 (a) and (c), the top and side views of the optimised pristine
silicene sheet are shown. Corresponding structures for F-silicene are shown in
1 (b) and (d). The calculated lattice constant (Si-Si bond length) of pristine
silicene sheet is 3.84(2.27) Å, in good agreement with existing results
Hussain  _et al._ (2016); Zaminpayma and Payman (2016). In case of F-silicene,
the optimised Si-Si bond length doesn’t change much; the optimised Si-F bond
length is 1.6 Å, in agreement with other works Zaminpayma and Payman (2016);
Lu  _et al._ (2009b). The buckling parameter in either case is 0.47 Å, in
excellent agreement with the existing result of 0.4 ÅUmam  _et al._ (2018).

There are three sites of adsorption, marked as 1-3 in Figure 1 (e) in pristine
silicene sheet. For F-silicene, there is one extra site available due to -F
functional site (Figure 1(f)). For each of the six VOC molecule, we optimise
the geometry of the nanosheet-VOC complex and obtain the preferred site of
binding and the orientation of the gas molecule with respect to the nanosheet.
The sites of adsorption and the orientations of the six VOC molecules with
respect to pristine silicene(F-silicene) sheet are shown in Figure 2(Figure3).
Associated binding energies, sites of adsorption as numbered in Figure 1 and
the distances of the molecules from the sheets are tabulated in Table 1.

In order to get insights into the efficiency of sensing, the first thing to
study is the adsorbent-adsorbate interaction of a potential gas sensor. The
common wisdom is that the interaction should be strong enough to hold the gas
molecule on the sensor. At the same time, the interaction strength should be
such that the molecule should be easily removable without affecting other
properties of the sensor. The strength of interaction can be gauged from
$E_{\text{ad}}$. In our case, we find the following trends: first, the
interaction strength for all six VOCs is weaker with F-silicene as the sensor
material and second, irrespective of the 2D sensor material, the interaction
strength in the ascending order is Methanol, Ethanol, Acetone, Methylacetate,
Dimethylsulfide, Toluene. The adsorption energy varies over a wide range: from
-0.17 eV for Methanol on F-silicene to -0.64 eV for Toluene on silicene. The
values suggest that except Toluene, all other molecules are moderately
physisorbed on the silicene and F-silicene nanosheets. The adsorption energies
compare well with existing results obtained for different 2D adsorbent
materials. Our results on Acetone are either better in case of adsorbent being
MoS2(-0.14 eV)Tian  _et al._ (2017), blue phosphorene (-0.25 eV)Sun  _et al._
(2019) or agree closely when the adsorbent is black phosphorous(-0.32 eV)Ou
_et al._ (2019) and BC6N (-0.3 eV)Aghaei  _et al._ (2021). For Ethanol, our
results agree closely too (-0.21 eV for MoS2 Tian  _et al._ (2017), -0.2 eV
for blue phosphorene Sun  _et al._ (2019), -0.24 eV for black phosphorous Ou
_et al._ (2019) and -0.38 eV for BC6N Aghaei  _et al._ (2021)). In case of
Toluene and Methanol, our results differ substantially when compared with
results with BC6N adsorbent (-0.91 eV for Toluene and -0.31 eV for Methanol)
Aghaei  _et al._ (2021). However, with black phosphorous as the nanosheet, our
results are in good agreement for Toluene (-0.5 eV) Ou  _et al._ (2019). It
is, therefore, expected that the performance of silicene and F-silicene as
sensors will be comparable to the standard 2D gas sensors.

In order to understand the trends in the adsorption energies, we look at the
optimised structures of the VOC molecules on two different nanosheets. From
Table 1, we find that except Methanol and Methylacetate, the molecules prefer
the same adsorption site, irrespective of the adsorbent. Acetone prefers one
of the Si-sites of the hexagon. Dimethylsulfide prefers the centre of the
hexagon while Toluene prefers a bridge site. Ethanol, Methanol and
Methylacetate prefer the centre of the hexagon(bridge site) when the sensing
material is pristine silicene (F-silicene). When physisorbed on pristine
silicene, the acetone molecule prefers an orientation almost parallel to the
nanosheet, with the oxygen atom closest to the sheet and at a distance of 3.43
Å. When the adsorbent is F-silicene, the molecule prefers the same orientation
but is at a greater distance of 3.97 Åfrom the nanosheet. That the interaction
distance $D$ increases substantially when the nanosheet is F-silicene, is also
observed in case of Methylacetate and Toluene. For the other three, the trend
is opposite. While Toluene molecules are adsorbed in an orientation parallel
to the sheet for both nanomaterials with H atom closest to the adsorbent,
Methylacetate is adsorbed with a tilted orientation with respect to the
nanosheets. When the nanomaterial is pristine silicene, the O atom is the
closest to it. The orientation changes in case of F-silicene, bringing H
closest to the adsorbent. The other three molecules prefer tilted orientations
with respect to both adsorbents. While H atom remains the closest to the
nanosheet in case of Ethanol and Methanol, it is the S atom in case of
Dimethylsulfide.

Among the six VOCs, Toluene has the maximum number of atoms and is devoid of
O. Dimethylsulfide is the only other molecule having no O atom. This explains
highest and the next highest $E_{\text{ad}}$ for these two. The hierarchy with
respect to $E_{\text{ad}}$ among the four VOCs containing O can be explained
in terms of number of atoms each molecule has. $E_{\text{ad}}$ is higher in
case of the molecule with greater number of atoms. The hierarchy among these
four can also be understood based upon the orientation of the molecules with
respect to the nano-sheets and the $D$ value. O atoms are closest to the
adsorbent in case of Acetone and Methylacetate while H atoms are the closest
for Ethanol and Methanol. Due to weaker Si-H bonds in comparison to Si-O
bonds, in case of pristine silicene the sensing material, $E_{\text{ad}}$ for
Acetone and Methylacetate adsorption on pristine silicene are larger. The
larger $D$ in case of Acetone explains smaller $E_{\text{ad}}$ for Acetone
than that of Methylacetate. Larger $E_{\text{ad}}$ of Ethanol, in comparison
to Methanol, can be explained the same way.

The decrease of $E_{\text{ad}}$ when silicene nanosheet is functionalised with
-F cannot be explained in terms of $D$. This is because an inverse
relationship between $D$ and $E_{\text{ad}}$, as expected, is observed only
for Acetone, Methylacetate and Toluene adsorption on F-silicene sheet. This
can be understood the following way: when silicene surface is functionalised
with -F, very strong Si-F covalent bonds are formed. Since -F is highly
electronegative, a protective layer is created on the silicene surface that
prevents silicene to react with VOC molecules. The strong Si-F bonds too make
it difficult for VOC molecules to react. Thus the binding of VOCs is
relatively weak when the nanosheet is -F passivated silicene.

Figure 2: (a-c) Top and (d-f) side views of optimised geometries after (a,d)
Acetone, (b,e) Dimethylsulfide, and (c,f)Ethanol physisorbed on silicene
nanosheet. (g-i)Top and (j-l) side views of optimised geometries after (g,j)
Methanol, (h,k) Methylacetate and (i,l) Toluene physisorbed on silicene
nanosheet. Figure 3: (a-c) Top and (d-f) side views of optimised geometries
after (a,d) Acetone, (b,e) Dimethylsulfide, and (c,f)Ethanol physisorbed on
F-silicene nanosheet. (g-i)Top and (j-l) side views of optimised geometries
after (g,j) Methanol, (h,k) Methylacetate and (i,l) Toluene physisorbed on
F-silicene nanosheet. Table 1:  Site of adsorption, Adsorption Energy
($E_{\text{ad}}$) and equilibrium distance between 2D sheet and VOC
Molecule($D$) for the systems considered in this work.

Adsorped 2D Adsorption $E_{\text{ad}}$ $D$ molecule sheet site (eV) (Å)
silicene 1 -0.34 3.43 Acetone ((CH3)2CO) F-silicene 1 -0.32 3.97 silicene 2
-0.42 3.55 Dimethylsulfide ((CH3)2S) F-silicene 2 -0.35 3.09 silicene 2 -0.32
2.76 Ethanol (C2H5OH) F-silicene 3 -0.24 2.6 silicene 2 -0.25 2.89 Methanol
(CH3OH) F-silicene 3 -0.17 2.3 silicene 2 -0.39 2.92 Methylacetate ((CH3)2OCO)
F-silicene 3 -0.35 3.36 silicene 3 -0.64 3.18 Toluene (C6H5CH3) F-silicene 3
-0.48 3.91

Figure 4: i) Total DOS (ii) DOS of silicene and (iii) DOS of VOC molecule in
(a)Acetone-silicene (b)Dimethylsulfide-silicene (c)Ethanol-silicene
(d)Methanol-silicene (e)Methylacetate-silicene and (f)Toluene-silicene
systems.  Figure 5: (i) Total DOS (ii) DOS of silicene (iii) DOS of F and (iv)
DOS of VOC molecule in (a)Acetone-F-silicene (b)Dimethylsulfide-F-silicene
(c)Ethanol-F-silicene (d)Methanol-F-silicene (e)Methylacetate-F-silicene and
(f)Toluene-F-silicene systems.

###  III.2 Electronic Structure

More profound understanding of VOC adsorption on the nanosheets and their
possible consequences with regard to sensing abilities of these 2D materials
can be obtained by analysing the electronic structures of the VOC-nanomaterial
complexes. In Figures 4 and 5 we show the total and component-projected
densities of states (DOS) of the systems considered in this work. The Total
densities of states of the VOC complexes with silicene as the adsorbent
(Figure 4) show that there is very little change in the electronic structure,
particularly close to the Fermi level, upon adsorption of different VOCs. The
major difference in the total DOS occurs around -1 eV to -2 eV and between -3
eV to -4 eV, due to the states of the VOC molecules. For all cases, the VOC
states are contributed by the valence states of all constituent atoms. The
structure around -1 eV for Acetone-silicene complex (Figure 4(a)) is due to
the hybridisation of Si, C and O $p$ and H $s$ states. The presence of two
-CH3 functional group symmetrically networked with the C-O bond leads to near
equal contributions of all atoms of the molecule. In case of adsorbed
Dimethylsulfide, its states near -1 eV are contributed largely by the $3p$
states of S and $s$ states of the two H atom connected to the two C atoms and
are closer to the surface of the nanosheet. The deeper states around -4 eV
have contributions from valence orbitals of all atoms in the molecule. For
Ethanol, upon adsorbed in silicene, the states around -2 eV are contributed by
$p$ orbitals of O, $p$ orbitals of C and the $s$ orbitals of two H attached to
them, situated closer to the sheet after relaxation (Figure 2(c) and (f)). For
the adsorbed Methanol, the states around -2 eV are similarly contributed by
the O $p$ and $s$ states from the two H atoms attached to the C atom (Figure
2(g) and (j)) while $p$ states of O closer to silicene surface and those of
two C atoms connected (Figure 2(h) and (k)) are the major contributors to the
states near -2 eV for Methylacetate-silicene complex. The DOS of toluene
adsorbed silicene is somewhat different from the rest. This is due to weak
contributions from Toluene around -2 eV. The major contributions from the
constituents of Toluene molecule are located deeper in the valence band. The
features in the densities of states imply that the charge transfer between the
VOC and the nanosheet will be maximum for Acetone and least for Toluene VOC.
Also, since there is no noteworthy changes in the electronic structures upon
adsorption of different VOCs, the selectivity of silicene sensor may be poor.

The adsorption of VOCs on F-silicene sheet produces remarkable changes in the
DOS (Figure 5). A common feature found in all six cases is that the projected
DOS of the molecules display features of an isolated molecule. The molecular
DOS now consists of sharp discrete peaks. This is an artefact of the weak
reactivity with the nanosheet due to the protective layer formed by the -F
functional group as discussed in the previous sub-section. However, in this
case we find significant changes in the DOS of the systems. As mentioned
earlier, functionalisation by -F opens up a semiconducting gap. Adsorption of
different VOC molecule produces discrete molecular states (contributed by the
valence states of the constituents aligned closer to the surface of the nano-
material) very close to the Fermi levels of the systems. The position of this
isolated state creates a noticeable difference between electronic structures
of different VOC-F-silicene complexes. This feature can be quite useful from
the perspective of differences in charge transfers and sensitivity when
F-silicene is used as a sensor material. The band structures of the six
compounds presented in Figure 6 demonstrate this more clearly. In all cases,
an isolated flat band appears below the Fermi level. This band represents the
highest occupied molecular orbital (HOMO) of a given complex. This acts as a
shallow donor state Zhang  _et al._ (2015), reduces the band gap of the
systems substantially, rendering the systems like a n-type semiconductor. The
new band gap $E_{n}$, calculated as the energy difference between the
conduction band minimum and the HOMO, is 0.055, 0.053, 0.281, 0.195, 0.346 and
0.137 eV for Acetone,Dimethylsulfide,Ethanol,Methanol,Methylacetate and
Toluene adsobed F-silicene, respectively. Such considerable reductions in the
band gap imply lower activation energies required for the electrons in the
shallow donor states for charge transport, an advantage from the perspective
of transport applications.

Figure 6: Band Structure of (a) Acetone-F-silicene (b)Dimethylsulfide-F-
silicene (c)Ethanol-F-silicene (d)Methanol-F-silicene (e) Methylacetate-F-
silicene and (f)Toluene-F-silicene systems.The shallow donor state in each
case is marked by arrow. Figure 7: Charge Transfer and Work functions before
and after adsorption of different VOCs on silicene and F-silicene.

###  III.3 Charge transfer and Work function

In order to substantiate the inferences drawn from analysis of the electronic
structures of the VOC-nanosheet complexes, in Figure 7, we present the results
of charge transfer $Q$ and work function $\phi$ for VOCs adsorbed on both
silicene and F-silicene nanosheets. The charges on each component of the
complexes are obtained from Bader charge analysis Yu and Trinkle (2011). The
work function $\phi$ is computed from $\phi=E_{\text{vac}}-E_{F}$;
$E_{\text{vac}}$ and $E_{F}$ are the vacuum energy and Fermi energy in each
case, respectively. $Q<(>)0$ indicates charge transfer from the
molecule(nanosheet) to the nanosheet (molecule).

Figure 8: (a) Top and (b) side views of the schematic structural model of a
gas sensor based on silicene with two semi-infinite electrodes . The areas
marked by square boxes represent semi-infinite electrodes.The central part of
the figure where the molecule is physisorped (Dimethylsulfide is shown here)
is the scattering region. (c) and (d) represent the same but for F-silicene
gas sensor. The physisorbed molecule shown in the schematics is Acetone.

Analysing the trends in $Q$, we find that the charge transfers, in general,
are small, irrespective of the nanosheet. This is consistent with the fact
that the adsorption in the present context is physiorption only. The other
noticeable features are the following: (a) $Q<0$ when the nano-sheet is
F-silicene. This corroborates the donor levels obtained in the band
structures. (b)With silicene as the nanosheet, $Q>0$ for all cases except
adsorption of Dimethylsulfide. Dimethylsulfide has 2 lone pairs coming from S.
As a result it acts as a donor instead of acceptor. (c) $Q$ is greater in
magnitude when the nano-sheet is F-silicene. This is consistent with the fact
that the molecular electronic states lie deeper in energies (Figure 4) when
molecules are adsorbed in pristine silicene. The greater charge transfer for
F-silicene is also an artefact of the presence of highly electronegative -F on
the surface of the 2D material. (d) With silicene as the adsorbent, there is
hardly any charge transfer between Toluene and silicene ($Q=0.006e$) while it
is maximum in case of Acetone adsorbate ($Q=0.055e$). For the other four
molecules, $Q$ varies between 0.042 and 0.047. This corroborates the
implications stated out by analysis of the DOS. (e) For both adsorbents,
dimethylsulfide enforces the largest charge transfer. (f) In case of
F-silicene nanosheet, charge transfer amounts are almost identical when
Acetone and Toluene are adsorbed ($Q=-0.058e,-0.055e$ for Acetone and Toluene,
respectively). Qualitatively the same is seen when the other three VOCs are
adsorped. However, $Q$ is less in their cases: -0.012e, -0.020e and -0.016e
for Methanol, Ethanol and Methylacetate, respectively. Once again, such
qualitative behaviour validates the conclusions from the electronic
structures. It is to be noted that the appreciable changes in the charge
transfers upon adsorption of different molecules on an adsorbate are supposed
to affect it’s resistance that can be experimentally measured and is directly
connected to the sensitivity when used as a sensor.

In an adsorbate-adsorbent complex, if the absorbate is a acceptor (donor), the
Fermi energy of the system is lowered( elevated) increasing (decreasing) the
work function as a consequence. Work function $\phi$ of pristine silicene
(F-silicene) is 4.56 eV (6.17 eV). Higher $\Phi$ of F-silicene is due to
strong Si-F bonds as opposed to unpassivated silicene surface. From Figure 7,
we find that $\Phi$ of the compounds after adsorption of VOCs on pristine
silicene increase only slightly (4.57 eV for Toluene adsorped to 4.61 for
Methanol adsorped silicene); the only exception is Dimethylsulfide where
$\phi$=4.51 eV. This is understandable as the molecule is a donor. Large
changes in $\phi$ are observed when the adsorbent is F-silicene. The changes
are hardly noticeable (with respect to $\phi$ of F-silicene before adsorption)
for Methylacetate, Ethanol and Methanol. Large changes are observed in case of
other three molecules: $\Phi$=5.73 eV, 5.82 eV and 5.96 eV for
Dimethylsulfide, Acetone and Toluene, respectively. This trend of $\phi$
correlates with $Q$; larger the $Q$, lower is the work function. The results,
therefore, indicate possible selectivity of these three molecules when
F-silicene is used as a sensing material. On the other hand, the results imply
that silicene, when used as a sensor for these VOCs, may not be able to
discriminate between them as has been inferred from the electronic structure.

Figure 9: I-V characteristic of (a)silicene and (b)F-silicene sensors with and
without adsorption of the six VOCs separately.

###  III.4 Sensing performances

Understanding of sensing capabilities of silicene based gas sensors is done by
modelling the nano-device. We have considered a two-probe model where the left
and right electrodes act as the source and drain of electrons. They are
basically extensions of the central scattering region where adsorption of a
VOC molecule takes place. As a result, their chemical potentials are same
($\mu_{L}=\mu_{R}$). In Figure 8 (a) and (b), we show the device setup with
pristine and F-silicene, respectively. The central region is smoothly joined
to the electrodes that are periodic in the transport direction ($z$ in this
case). To screen out the perturbations in the sensing regions, the electrode
regions are extended into the central scattering region. The $I-V$
characteristics with and without gas adsorptions by silicene and F-silicene
are shown in Figure 9 (a) and (b), respectively. Application of a bias voltage
$V_{b}$ to the device elevates the Fermi level of the left electrode with
respect to the right electrode. When $V_{b}$ supersedes the threshold voltage
at which conduction band minimum of the right electrode matches with the
valence band maximum of the left electrode, current starts to flow. As seen in
Figure 9, this threshold is 0V(0.5 V) for pristine silicene (F-silicene). The
values are consistent with the electronic band gaps of the bare materials.
$I-V$ characteristics curves for VOC adsorped silicene are linear while those
for F-silicene are parabolic. In pristine silicene device, the curves are
barely separable from one another. The current for VOC adsorbed silicene is
always higher than that of bare silicene. The current reaches a maximum of 10
$\mu$A at 1.5 V when Dimethylsulfide is adsorbed. The lowest current is
observed in case of Toluene adsorption.

Figure 10: (a) Sensitivity of silicene and F-silicene towards the six VOCs
considered in this work (b) Selectivity of silicene and F-silicene for
detection of Acetone.

For F-silicene device, the current curves corresponding to each VOC-complex
starts to get separated from each other around a bias voltage of 0.75V. For
each VOC-F-silicene complex, maximum of current is higher than the one
corresponding to pristine silicene device. This suggests better sensing
capability of F-silicene as predicted in earlier sub-sections. In here,
highest current is observed for Acetone-F-silicene system with a maximum of
19.4 $\mu$A at 2V. Toluene and Dimethylsulfide are the two recording next
highest currents. The $I-V$ curves for the other three VOC complexes are
barely separable from one another and the bare F-silicene. This behaviour is
consistent with the qualitative trends in charge transfer and work functions.

Direct assessment of sensing capability and one to one comparison with
experimental results is done by calculating sensitivity in terms of
differential conductance. The sensitivity $S$(%) is given by

$\displaystyle S=\frac{|g-g_{0}|}{g_{0}}\times 100$ (4)

where $g$($g_{0}$) is the zero-bias conductance of the sensor material when
VOC molecule is adsorbed(without the molecular adsorption). With a finite bias
$V_{b}$, the conductance can be expressed as
$g\left(V_{b}\right)=\frac{2e^{2}}{h}T\left(\mu=E_{F}-eV_{b}\right)$; $T$ is
the transmission coefficient, $E_{F}$ the Fermi energy, $h$ the Planck’s
constant and $e$ the electronic charge. Applied bias voltage $V_{b}$ can
effect changes in the transmission by tuning the chemical potential $\mu$.
Conductance is a better measure than current because (i) the result is
directly comparable to experiments where they measure differential resistance
and (ii) it allows a better distinction of the change of signal as the
conductance through the sensor changes by a quantum unit of $2e^{2}/h$ in its
interaction with the VOC.

In Figure 10(a), we compare the sensitivities of silicene and F-silicene
sensors with regard to the six food VOC molecules. The results presented are
for $V_{b}=1.23$ V. For silicene gas sensor, the maximum sensitivity of only
about 5 % is obtained for Dimethylsulfide. The lowest sensitivity is obtained
for Toluene ($\sim 1$ %). The sensitivities for Acetone, Methanol and
Methylacetate are almost same. The sensitivity of Ethanol is slightly higher.
The trend is consistent with the behaviour of charge transfer for VOC-silicene
complexes. A remarkable improvement is obtained in case of F-silicene sensor.
The sensitivity for Acetone increases nearly 16 times reaching near 30%. The
sensitivities of Toluene and Dimethylsulfide too improves significantly
reaching near 14 % and 10 % respectively. For the rest three VOCs too,
sensitivities improve to near 5%. The sensitivities with regard to Acetone and
Toluene, obtained in our calculations, are either better or comparable with
respect to results obtained with other 2D sensors. For example, sensitivity
for Acetone and Toluene with BC6N sensor are 6.1 % and 14.7 % while those with
defective-BC6N are 1.7 % and 8.8 % only Aghaei  _et al._ (2021). For Ethanol
and Methanol, sensitivity of F-silicene sensor is somewhat poor as compared to
sensitivity obtained with other 2D materials. With BC6N, the sensitivity for
Ethanol and Methanol are 14.7 % and 9.5 % respectively. It is 61 % and 8.8 %
with defective BC6N, 21 % for both with MoSe2 Nagarjan and Chandiramouli
(2018b) and 9 % for Ethanol with Black Phosphorous Ou  _et al._ (2019)

In Figure 10(b), we show comparative abilities of silicene and F-silicene
sensors in discriminating the molecules. Sensitivity of Acetone has been
considered the reference and the ratio of it’s sensitivity to that of another
molecule is considered quantitative measure of selectivity of that molecule.
The selectivity using silicene sensor is poor in comparison with F-silicene.
Infact, silicene sensor cannot separate Methanol and Methylacetate at all. On
the contrary, F-silicene can clearly distinguish between Acetone,
Dimethylsulfide, Toluene and Methylacetate. The selectivities of Ethanol and
Methanol are very close, 4.6 and 4.9 respectively. Therefore, we can conclude
that F-silicene can separate out at least four VOCs emitted by food products.
The performance is significantly better than r-GO sensors Bhattacharjee  _et
al._ (2011) which could not discriminate between any of the six VOCs.

###  III.5 Recovery Time

The re-usability of a sensor is dependent on how fast the desorption of a gas
molecule takes place. This is quantified by sensor recovery time given as

$\displaystyle\tau=\nu_{0}^{-1}{\rm exp}(-E_{\text{ad}}/k_{B}T),$ (5)

$\nu_{0}$ is the operating frequency, $T$ the temperature, and $k_{B}$ the
Boltzmann constant. In order for a gas sensor to be reusable $\tau\lesssim
10^{5}$s Yong  _et al._ (2017). In Table 2, we present calculated values of
$\tau$ for both silicene and F-silicene sensor.

Molecule | silicene | F-silicene
---|---|---
Acetone | $1.15\times 10^{-7}$ | $3.89\times 10^{-7}$
Dimethylsulfide | $2.29\times 10^{-5}$ | $7.57\times 10^{-7}$
Ethanol | $4.38\times 10^{-7}$ | $1.58\times 10^{-8}$
Methanol | $3.27\times 10^{-8}$ | $1.13\times 10^{-9}$
Methylacetate | $5.95\times 10^{-6}$ | $1.2\times 10^{-6}$
Toluene | $0.18$ | $2.85\times 10^{-4}$
Table 2: Recovery time $\tau$ (in s)of silicene and F-silicene gas sensors to
sense food VOCs. The calculations are done at room temperature ($T=300$K) and
under visible light ($\nu_{0}=10^{12}$ s-1)

The results suggest that both silicene and F-silicene can work as reusable
sensors for sensing the VOCs from food materials. Calculated recovery times
for silicene based sensors turn out to be smaller than those for pristine and
defected- BC6N. $\tau$ of pristine(defected) BC6N sensors in case of
Acetone,Ethanol,Methanol and Toluene desorption are $2.6\times 10^{-6}(740)$
s, $2.3\times 10^{-6}(4.9)$ s, $2.1\times 10^{-7}(4.2)$ s and $2\times
10^{3}(6.8\times 10^{3})$ s, respectively, orders of magnitude higher than
silicene based sensors. Thus the silicene and F-silicene nanosheets will
function more efficiently as sensors as compared to many other 2D materials.

##  IV Conclusions

Usage of nano-sensors to detect quality of food products is a relatively new
addition in the field of nano-science and nanotechnology. Using a combination
of DFT based first-principles method and non-equilibrium Green’s function
technique, we have investigated in detail the sensing abilities of two-
dimensional silicene and -F functionalised silicene nanosheets for standard
VOCs emitted by canonical food products like meat, fruits and vegetables. We
have systematically analysed the structural parameters, the energetics, the
electronic structures and charge transfers when silicene based 2D materials
are used to adsorb the VOC molecules. Based upon these, we have concluded that
F-silicene to be a better candidate as sensors. Our insights from such
analysis of microscopic physics are then put to test by modelling sensor
devices and making a direct comparison with the experimental quantities. We
find that F-silicene indeed has a much better sensitivity than pristine
silicene as far as VOCs from food products are concerned. The sensitivities
for select molecules obtained by using F-silicene as the sensing material
compare well with the available results using other 2D materials as sensors.
Moreover, silicene-F is able to clearly discriminate between at least four
molecules, Acetone, Dimethylsulfide, Methylacetate and Toluene. The
selectivity is comparable for the other two. A comparison with the
experimental findings with r-GO as sensor material Tung  _et al._ (2012) shows
that F-silicene is better in discriminating between the VOCs from food
materials while r-GO fails to do so. Finally, we establish the re-usability of
silicene based sensors to detect these VOCs by computing the desorption time
under excitation by visible light. We find that physiorption of the adsorbates
actually help in faster desorption of molecules. This in turn should enhance
the sensor’s re-usability Aghaei  _et al._ (2016). This work carries immense
relevance on two larger counts: first, to our knowledge this is the first work
using first-principles based computations to investigate sensing performances
of 2D materials in the field of food technology, and second, this work opens
up possibilities to model 2D materials based sensors to detect and separate
more complex VOC molecules emitted from specific food products. For example,
VOCs like 3-Carene, Longifolene and D-limonene are found experimentally in
Papaya fruits Kushwaha  _et al._ (2021). However, no e-nose sensor using these
VOCs as non-invasive bio-markers to determine the ripening stage of Papaya is
yet available. Our study can be extended for such cases in future.

## Acknowledgement

The authors gratefully acknowledge the Department of Science and Technology,
India, for the computational facilities under Grant No. SR/FST/P-II/020/2009
and IIT Guwahati for the PARAM ISHAN and PARAM KAMRUPA supercomputing facility
where all computations are performed. SG would like to thank Prof. Ramgopal
Uppaluri and Ms Paushali Mukherjee, IIT Guwahati, for useful discussions.

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