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MYU Tokyo
S & M 1993

Prediction of the Property of Colorimetric Sensor Array
Based on Density Functional TheoryHaiyang Gu,1,3* Xingyi Huang,2 Quansheng Chen,1,2 Yanhui Sun,1Chin Ping Tan,1,4 ZhaoJun Wei,3 and Riqin Lv1
1

School of Bio and Food Engineering, Chuzhou University, Chuzhou 239000, China
School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China
3
School of Food Science and Engineering, Hefei University of Technology, Hefei 230000, China
4Department of Food Technology, Faculty of Food Science and Technology, Universiti Putra Malaysia,
43400, Malaysia2(Received July 17, 2019; accepted September 18, 2019)

Keywords: colorimetric sensor array, density functional theory, volatile organic compounds, sensor
designThe importance of a rapid, sensitive, and selective colorimetric sensor array (CSA) for
the discrimination of a wide range of food quality attributes has been recognized in the foodindustry. Here, the density functional theory (DFT) method at the B3LYP/LANL2DZ level
was used to analyze the molecular reactions between a CSA and volatile organic compounds(VOCs). The energy gaps for iron phthalocyanine (FePc) binding to VOCs show that FePc-O2
and FePc-N2 have relatively smaller energy gaps; this causes the process of FePc binding to O2
and N2 through more than one pathway. Because of the binding energy, FePc is sensitive to N2,
followed by trimethylamine, propane, and acetone, whereas H2S, ethanol, and ethyl acetate may
not be sensitive to FePc. We suggest that the DFT-based method is useful for the theoreticaldesign of CSAs and perhaps other sensors containing metal phthalocyanine.

1.

Introduction

Freshness is one of the main indicators that reflect food quality attributes during storage,processing, marketing, and consumption. Spoilage is a complex process that makes foodunacceptable or undesirable for consumers owing to the changes in sensory characteristics. In
addition to its effect on sensory characteristics, the spoilage of food products probably causes
serious illness and even death for consumers. Therefore, a rapid method of monitoring thechanges in the quality of food products should be developed for the food industry. Volatileorganic compounds (VOCs) are one of the most important indicators for detecting andevaluating food freshness. Each food product has a characteristic profile of VOCs during eachstage of the entire food chain.
Over the previous decades, a series of traditional methods, such as gas chromatography-mass
spectrometry (GC-MS),(1) and electronic nose (EN),(2) have been developed for the detection*Corresponding author: e-mail: guhaiyang51@163.com
https://doi.org/10.18494/SAM.2019.2520
ISSN 0914-4935 © MYU K.K.
https://myukk.org/

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Sensors and Materials, Vol. 31, No. 10 (2019)and evaluation of food quality. However, almost all of these methods are based on physicaladsorption and van der Waals interactions, which are the least selective and weakest molecularinteractions between sensors and analytes. Here, a novel sensor-based method [i.e., colorimetricsensor array (CSA)] is developed to detect VOCs from food or other sources. According toprevious studies, this helpful method is proved to have both high sensitivity and high selectivity
to VOCs. It shows enormous discriminatory power for similar molecules, such as 2-picolineand 3-picoline. Moreover, it is also 20000-fold more sensitive than the methods based on
physical adsorption and van der Waals interactions in theoretical and experimental studies.(3)In addition to its high sensitivity and high selectivity, this method is essentially unresponsive
to water from 2000 to 20000 ppm in an environment where GC-MS and EN are difficult toperform. Therefore, this method is regarded as helpful in detecting and evaluating the foodquality for the food industry. In this paper, we therefore intend to provide a design strategy fora CSA at the molecular level for the rapid detection of VOCs in food.

2.

Materials and Methods

2.1

CSA

The schematic diagrams of the reactions between the CSA and an analyte are shown in Fig.
1. A typical CSA composed of a series of metalloporphyrins and other dyes show sensitivity
to VOCs.(4) The reactive sites of metalloporphyrin are the metal atom in the center of theporphyrin plane, which is in agreement with previous studies.(5,6) Figure 1(a) shows an imageof the CSA before reacting with an analyte. After exposure to the analyte, the image landscapeshows a small change compared with the original image, which is difficult to be discriminatedwith the naked eye, as shown in Fig. 1(b). Therefore, the difference image between theimages obtained before and after exposure to the analyte is always required to obtain a uniquefingerprint for each analyte. A typical difference image is produced by subtracting the beforereaction image from the after-reaction image, as shown in Fig. 1(c).

(a)

(b)

(c)

Fig. 1. (Color online) Schematic diagrams of CSA based on metalloporphyrin. Color images of the sensor arrayobtained (a) before and (b) after exposure to analyte. (c) Difference image obtained by subtracting the beforereaction image from the after-reaction image.

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2.2

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Computational method

The CSA model used is represented by a simple iron phthalocyanine (FePc) without any
meso substituent. The initial unligated FePc and VOCs were obtained from the Cambridge
Crystallographic Data Centre (CCDC). Each model consists of a planar phthalocyanin ring andthe ligand lies along an axis perpendicular to the plane of the phthalocyanin ring. The FePcVOC model was used to represented the CSA binding with an analyte by adding VOC molecules
above the phthalocyanin ring at distances of 3–4 Å.(7) The FePc and a similar FePc-O2 model
are shown in Fig. 1. All calculations were implemented using Gaussian 09. Each model wascalculated by applying the density functional theory (DFT) to B3LYP/LANL2DZ. The DFTbased method at this level was shown to be very efficient in the calculation of the geometries,
energies, and charges of FePc and similar FePc complexes. To avoid any shortcomings,
geometry optimization was carried out at three different spin states (low, intermediate, and high).(6)3.

Results and Discussion

3.1

Relative energy

According to a previous study,(8) all further calculations were carried out on the basis of themost stable geometry structure. The energy change associated with the geometry structuresat three possible spin states (i.e., low-spin doublet, intermediate-spin quartet, and high-spin
sextet) is the relative energy. The relative energy is the energy gap based on the most stableone. Figure 2 shows the relative energies for the FePc before and after exposure to different
VOCs. It is important to note that FePc, FePc-H2S, and FePc-L1,4,5 have the most stable

Fig. 2. (Color online) Relative energy obtained by subtracting the most stable energy from the unstable energy
for each FePc and its complexes. L1 is trimethylamine, L2 is ethanol, L3 is propane, L4 is acetone, and L5 is ethyl
acetate.

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Sensors and Materials, Vol. 31, No. 10 (2019)geometry structures at doublet, whereas FePc-N2, FePc-L3,4 and FePc-O2 have the most stablegeometry structures at quartet and sextet. In addition to the results of the analysis of the moststable geometry structures, the energy gaps between the most stable state and other states
are observed to affect the binding capability of the FePc sensor. It is interesting to note thatFePc-O2, FePc-N2, and FePc-L1,2,3,4 have smaller energy gaps, and FePc-H2S and FePc-L5 havelarger energy gaps. The smaller energy gaps probably mean that the binding process between
the CSA and the VOCs is through more than one pathway, making the CSA react easily andmore efficiently, as reported previously.(9)3.2

Optimized molecular structure

All calculations were initiated from the appropriate geometry structures optimized at fixed
spin multiplicity. Besides the effect of the geometry structure, the binding pattern also affects
the capability of the CSA to bind VOCs. There are two binding patterns, the end-on(10) andside-on.(11) The end-on bent binding was proved to be well established by X-ray structure
determination.(12) Furthermore, the side-on binding in the P450 model with a methyl mercaptide
axial ligand for the heme iron had higher energy than the end-on bent binding with the O2geometry.(13) Therefore, the end-on pattern was selected to construct the FePc-VOCs. After
optimizing the process, the molecular structures of FePc before and after exposure to VOCs
were obtained and are shown in Fig. 3. The distances between the Fe atom in the center of themetalloporphyrin plane and the VOCs ranged from 1.996 to 4.143 Å, which is in agreementwith the results of a previous theoretical study.(7) In Fig. 3, for the unligated metalloporphyrin,
the distances of Fe-Cl are 2.245 Å for doublet, 2.349 Å for quartet, and 2.320 Å for sextet. It
is clearly observed that the molecular structures were changed at the different spin states. As
shown in Fig. 3, the Fe atom moved out of the metallophthalocyanine plane toward the VOCs.
The complexes of FePc-VOCs showed a very interesting spin-structure relationship. Theirdistances are 0.019–0.156 Å for the doublet, 0.064–0.249 Å for the quartet, and 0.113–0.471 Åfor the sextet. FePc-N2 has the largest out-of-planarity because of its negative ion, which isconsistent with a previous study.(14)
3.3

Mulliken charge analysis

The chemical reactions between the CSA and VOCs involve bond formation and bondbreaking, which are closely related to the charge distribution. The relationship between thecharge configurations of the metal and nitrogen atoms may affect the binding of the CSA
to VOCs. Figure 4 shows the charge relationship between the Fe and nitrogen atoms. TheFePc and FePc-VOCs show larger positive charges on Fe atoms and more negative charges onnitrogen atoms, which are reflected by FePc, FePc-L2,5, and FePc-H2S data points at the left topof the line. On the other hand, FePc and FePc-L1,3 have larger negative charges on Fe atoms andmore positive charges on nitrogen atoms at the lower right corner of the lines. It is interesting
to note that the unligated FePc has the largest positive charge on the Fe atom, whereas FePc hasthe lowest positive charge. The reason for this may be that N2 is a better electron donor than theSensors and Materials, Vol. 31, No. 10 (2019)3071

Fig. 3. (Color online) Optimized molecular structures for further calculation of FePc and its complexes. Δ is thedistance between the Fe atom and the metalloporphyrin plane. Data for the singlet is without parentheses, triplet
data is within the parentheses, and quintet data is within the brackets.

Fig. 4. (Color online) Strong linear relationships of charge distribution between Fe and N atoms in the center ofthe metalloporphyrin plane for different complex models.

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Fig. 5.

(Color online) Binding energy of CSA for different VOCs.

others, which makes the Fe atom exhibit a more negative charge. When the VOC used is a goodelectron donor, there will be more electrons available in the metalloporphyrin system.
3.4

Binding energy

T h e r e a c t io n p r o c e s s b e t we e n t h e C SA a n d t h e VO C s c a n b e w r it t e n a sFePc + VOCs → FePc − VOCs. The energy change associated with the CSA before and after
exposure to VOCs, which reflects the property for the binding of the CSA to different VOCs,
was represented by binding energy (BE). After obtaining the energy of the CSA before
and after exposure to VOCs, the binding energy can be calculated as BE = EFePc + EVOCs− EFePc−VOCs. Figure 5 shows the binding energy (kcal/mol) for all the FePc-VOCs. It isinteresting to note that FePc-N2 exhibits the largest binding energy, followed by FePc-L1,3,4 andO2, whereas FePc-L2,5 and H2S have relatively smaller binding energies. The reason for thismay be that N2 is a better electron donor than the others, making the Fe atom in the center of themetalporphyrin plane take the largest negative charge. However, L2 is not as good an electron
donor as the others, which makes the Fe atom exhibit the largest positive charge. When theVOC used is a good electron donor, there may be higher binding energy through the reaction
process between the CSA and the VOCs.

4.

Conclusion

A DFT-based method for the design of a CSA with different VOCs was developed at themolecular level. This theoretical study was carried out at the B3LYP/LAN2DZ level on thebasis of an optimized molecule. The sensitivity of the CSA for different VOCs was representedby binding energy. The binding energies of the CSA before and after exposure to VOCs werecalculated to measure binding capability profiles as a sensitivity indicator of the CSA. The

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analysis of binding energies indicated that FePc is sensitive to FePc-N2, followed by L1, L3, L4,and O2, whereas L2, L5, and H2S may not easily react with FePc. This result suggests that theDFT-based method is feasible for the theoretical design of the CSA. This method may also behelpful for other types of sensor design. Further study is required to investigate other types ofCSA containing metalloporphyrin or other molecules.

Acknowledgments
This study was sponsored by the National Natural Science Foundation of China (31671932),
the National Natural Science Foundation of China (31701685), the Program of Study Abroadfor Young Scholars (gxgwfx2019061), and the Science and Technology Program of Chuzhou(2019ZN006).

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