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\n
1. Introduction
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Gas sensors technology has numerous applications in the automotive, industrial, domestic, and security sectors. In the automotive and industrial sectors, gas sensors are necessary to detect toxic and harmful gases for environment protection and human health (i.e., carbon monoxide). Sensor materials based on semiconducting metal oxides are one of the several technologies being used in the detection of pollutants [1]. This type of oxide materials is suitable for gas sensor applications due to their interesting structural, functional, physical, and chemical properties. To date, reports indicate that n-type semiconductor materials, such as SnO2 [2, 3], ZnO [4], and TiO2 [5] are most studied in gas sensing area. By contrast, a limited amount research works on p-type oxide semiconductor gas sensors have been found, the most studied being CuO, Co3O4, and NiO [6]. However, some sensor parameters such as gas sensitivity and working temperature still need to be improved. Therefore, additional studies are needed to improve the gas sensing characteristics of p-type semiconducting oxides by modifying factors such as synthesis conditions, structure, morphology, and composition.
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Among the p-type semiconductors, zinc cobaltite (ZnCo2O4) is a material with spinel-type structure, which has been mainly used as electrode for Li-ion batteries [7–11] and supercapacitors [12–15], due to its higher electrochemical performances and higher conductivities. To date, sensor devices based on nanostructured spinel ZnCo2O4 have particularly exhibited an excellent sensitivity toward liquefied petroleum gas [16–18], ethanol [19, 20], acetone [21], Cl2 [22], formaldehyde [23], and Xylene [24]. Additionally, several references [18–21, 25] reported its poor sensitivity to carbon monoxide (CO). On the other hand, a variety of synthesis methods have been developed on the preparation of ZnCo2O4 such as combustion [7], thermal decomposition [17], co-precipitation/digestion [18], W/O microemulsion [22], hydrothermal [9, 14], sol-gel [26], and surfactant-mediated method [27]. Recently, the colloidal route assisted by microwave radiation has provided an efficient and low cost synthesis method to obtain different types of nanostructured materials [28–31]. In this simple synthetic process, the addition of a surfactant agent plays a key role in the material\'s microstructure because the surfactant\'s ligands adsorb on the particles\' surface inhibiting the particle growth and modifying the particles\' microstructure [31–34]. Also, microwave radiation provides a rapid evaporation of the precursor solvent and a short reaction time in comparison with conventional heating [35, 36]. With this in mind, the synthesis of nanostructured ZnCo2O4 was done via a microwave-assisted colloidal method using zinc nitrate, cobalt nitrate, dodecylamine (as surfactant), and ethanol. Consequently, nanostructured ZnCo2O4 powder was characterized by X-ray powder diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM), and Raman spectroscopy. The potential application of nanostructured ZnCo2O4 as gas sensor was studied by measuring its sensitivity toward different CO concentrations and working temperatures.
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2. Synthesis, characterization, and gas sensing application of ZnCo2O4
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2.1. Synthesis
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For the preparation of nanostructured ZnCo2O4 by microwave-assisted colloidal method, first, 0.947 g of Zn(NO3)2⋅xH2O (Zinc nitrate hydrate), 2.91 g of Co(NO3)2⋅6H2O (cobalt nitrate hexahydrate), and 1 g of C12H27N (dodecylamine) were dissolved separately in 5 mL of ethanol and kept under stirring for 20 min, at room temperature. Then, the cobalt nitrate solution was added drop wise to the dodecylamine solution under stirring. Then, the zinc nitrate solution was slowly added, producing a wine color solution with pH = 2. This resulting colloidal solution was stirred for approximately 24 h. Then, the solvent evaporation was made by a domestic microwave oven (General Electric JES769WK) operated at low power (~140 W). During the evaporation process, microwave radiation was applied for a period of 1 min, over a period of 3 h. The resulting solid material was dried in air at 200°C for 8 h using a muffle-type furnace (Novatech). Finally, the obtained powders were calcined at 500°C for 5 h. For each thermal treatment, a heating rate of 100°C/h was used. The resulting powders were black. The general synthesis process is illustrated in Figure 1.
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Figure 1.
Schematic illustration of the synthesis of ZnCo2O4.
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2.2. Experimental techniques
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The structural characterization was performed by XRD using a PANalytical Empyrean system (CuKα, λ = 1.546 Å). The XRD patterns were recorded, at room temperature, in the 2θ range of 10–70° using a step size of 0.02°. The morphological characterization was made by SEM, TEM, high-resolution transmission electron microscopy (HRTEM), energy-dispersive X-ray (EDS), and high-angle annular dark-field/scanning transmission electron microscopy (HAADF-STEM). For SEM studies, a FEI-Helios Nanolab 600 system operated at 20 kV was used, while a FEI Tecnai-F30 system operated at 300 kV was used for TEM, HRTEM, EDS, and HAADF-STEM analysis. The optical characterization was done by Raman spectroscopy using a Thermo Scientific DXR confocal Raman with a 633 nm excitation source. The Raman spectrum was recorded from 150 to 800 cm−1, at room temperature, using a Laser power of 5 mW. The gas response (sensitivity) was acquired on pellets of ZnCo2O4 in the presence of several concentrations (1, 5, 50, 100, 200, and 300 ppm) of CO. The sensor devices were fabricated with 0.350 g of the nanostructured powder of ZnCo2O4, forming pellets with a thickness of 0.5 mm and a diameter of 12 mm. A TM20 Leybold detector was used to control the gas concentration and the partial pressure, and a digital-multimeter (Keithley) was put into use for the measurement of the electric resistance. A general schematic diagram of the gas sensing measurement system is shown in Ref. [33]. The sensitivity was defined as S = Ra/Rg, where Ra is the resistance measured in air and Rg is the resistances in gas [21, 24].
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2.3. Structural analysis
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Figure 2 shows a typical XRD pattern of the ZnCo2O4 powder obtained at a calcination temperature of 500°C. In this pattern, the main phase detected was the ZnCo2O4 crystal structure, which was identified by the JCPDF #23-1390 file. The sharp and strong peaks indicated a good crystallinity of the ZnCo2O4 sample. The diffraction peaks corresponded well to the (111), (220), (311), (222), (400), (422), (511), and (440) planes of the ZnCo2O4 spinel phase situated at 2θ = 18.95°, 31.21°, 36.80°, 38.48°, 44.73°, 55.57°, 59.28°, and 65.14°, respectively. The average crystal size, which was calculated by Scherrer\'s formula [38], using the XRD (311) plane, was ~24.7 nm. Since no secondary phase was detected in the XRD pattern of the ZnCo2O4, the synthesis procedure developed in this work allowed to obtain the crystalline phase of ZnCo2O4 without additional diffraction peaks. Thus, this method of synthesis might also be useful for the preparation of other oxide materials.
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Figure 2.
XRD pattern of the ZnCo2O4 powder after a thermal treatment at 500°C in air [37].
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2.4. Morphological investigations
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Figure 3 shows the surface morphology of the ZnCo2O4 powder calcined at 500°C with different magnifications. Figure 3a exhibits a SEM image at low magnification, which revealed a surface with a high degree of porosity with pores of irregular shape. The average pore size was calculated around 724 nm. This extensive porosity has been associated with the emission of gas during the removal of organic matter in the calcination process [28]. At high magnification (Figure 3b), a large number of nanoparticles with irregular shape and size in the range of 50–110 nm were clearly observed. In colloidal chemistry, it is known that the formation of nanoparticles follows a nucleation and a growth mechanism [39]. In this mechanism, first, the nucleation is produced when the concentration of reagents reach the supersaturation limit for a short period of time. Consequently, the formation of large number of crystal nuclei occurs. Later, the process of growth of the particles is developed by diffusion. In our synthesis, the final solution does not present the supersaturation, although the nucleation process could occur when the zinc nitrate solution was added to the cobalt and dodecylamine solution, and the process of growth carried out is during the stirring of the final colloidal solution [31, 40]. On the other hand, the dodecylamine plays a key role in the microstructure of ZnCo2O4 particles, since the dodecylamine in the colloidal solution affects the particle growth via the saturating of nanocrystal surfaces and hence, results in the formation of ZnCo2O4 nanoparticles with a peculiar morphology (faceted nanoparticles were obtained) [28]. With thermal treatment at 500°C, the dodecylamine was finally removed from the ZnCo2O4 sample.
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Figure 3.
SEM images of the ZnCo2O4 powder at: (a) low (1,100x) and (b) high magnification (50,000x) [37].
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Figure 4 shows the typical TEM images of the ZnCo2O4 powder calcined at 500°C. Figure 4a exhibits a high concentration of nanoparticles, which was also observed by SEM. As can be seen Figure 4b, faceted nanoparticles with a pockmarked structure were clearly identified. The average particle size was ~75 nm, with a standard deviation of ±12.6 nm. A typical HRTEM image is displayed in Figure 4c. This image confirmed the presence of faceted nanoparticles and a value of 0.286 nm corresponded to the inter-planar d-spacing of the (200) plane of ZnCo2O4 spinel structure.
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Figure 4.
(a, b) TEM and (c) HRTEM images of ZnCo2O4.
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In order to investigate the nanoparticle’s composition, EDS line scan was performed on ZnCo2O4 powder. Figure 5 shows the corresponding analysis. Figure 5a shows a HAADF-STEM image of the ZnCo2O4 nanoparticles. The image confirms the presence of faceted nanoparticles with a pockmarked structure, which is consistent with the TEM images. In the EDS line scan, zinc, cobalt, and oxygen are observed across the linear mapping, confirming the presence of the expected elements, as seen in Figure 5b. In the central region X2, a decrease of the element composition is observed in comparison to point X1, which can be due to the irregular surface of the nanoparticle (pockmarked zone). It is also evident that cobalt exists in larger amount than zinc, corresponding to the target ratio of 1:2. However, the EDS line scan shows carbon (C) and copper (Cu) compounds, which are due to the sample support.
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Figure 5.
(a) HAADF-STEM image and (b) EDS elemental line scan of an individual ZnCo2O4 nanoparticle.
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2.5. Raman characterization
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The Raman spectrum shown in Figure 6 allowed us to confirm the formation of the ZnCo2O4 when a calcination temperature of 500°C was used. As shown in Figure 6, the Raman spectrum of the ZnCo2O4 powder shows five vibrational bands located at 182, 475, 516, 613, and 693 cm−1 corresponding to the five active Raman bands of ZnCo2O4 spinel structure [41]. However, the band at 204 cm−1 is a vibrational mode that could be generated by Co3O4 [42]. The formation of this oxide is due to the cation disorder (substitution of Zn2+ by Co2+) in the ZnCo2O4 spinel structure. As the Co3O4 possess a spinel structure same as the ZnCo2O4; therefore, they have similar XRD patterns and a deformation in the XRD pattern of ZnCo2O4 is not expected.
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Figure 6.
Raman spectrum of nanostructured ZnCo2O4 powder [37].
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2.6. Gas sensing application of ZnCo2O4
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The sensing performance of the ZnCo2O4 sensor was investigated on pellets fabricated from the nanostructured ZnCo2O4 powders and tested in different concentrations of CO. Figure 7 shows the variation of sensitivity against temperature at different concentrations of CO (1, 5, 50, 100, 200, and 300 ppm). As shown in Figure 7, only minor variations in sensitivity were measured at operating temperatures between 100 and 200°C in whole CO concentration range (1–300 ppm). For operating temperatures above 200°C, the sensitivity increased markedly from 5 to 300 ppm, with the maximum values of the sensitivity registered at 300°C.
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Figure 7.
Sensitivity of the ZnCo2O4 sensor as a function of the temperature.
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Figure 8 shows sensitivity of the ZnCo2O4 sensor toward different concentrations of CO at 100, 200, and 300°C. As seen in Figure 8a, a sensitivity value of 1 was maintained across the CO concentration range when the ZnCo2O4 sensor was operated at 100°C. On the contrary, when the ZnCo2O4 sensor was working at a temperature of 200°C (Figure 8a) and 300°C (Figure 8b), the sensitivity increased with an increase of CO concentration. At 200°C, the sensitivity values of the sensor were 1, 1.1, 1.5, 2.2, 2.8, and 3.1 for CO concentrations of 1, 5, 50, 100, 200, and 300 ppm, respectively. However, at 300°C and with same concentrations, the sensitivity values were 1, 2, 3.3, 84.5, 157.5, and 305, respectively. The observed increase in sensitivity with the concentration is due to increase in gas concentration and operation temperature. The increase of the sensitivity is also associated with increased oxygen desorption at high temperatures [43, 44]. Additionally, the ZnCo2O4 sensor showed a decrease in gas response when CO gas were removed from the vacuum chamber.
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Figure 8.
Sensitivity of the ZnCo2O4 sensor vs. concentration at different temperatures: (a) 100 and 200°C and (b) 300°C.
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It is known that the gas sensing mechanism of semiconducting materials is based on the changes of the electrical resistance produced by interaction between the target gas and chemisorbed oxygen ions [45, 46]. When oxygen is adsorbed on the semiconductor\'s surface, oxygen species are generated at the surface by taking electrons from the conduction band of the semiconductor. In general, molecular (O2¯) and ionic (O− and O2−) species are formed below 150°C and above this temperature, respectively [47]. Consequently, a space charge layer with thickness of ~100 nm is formed at the surface [6]. In our tests at temperatures above 100°C, the ionic species that adsorb chemically on the sensor are more reactive than molecular species that adsorb at temperatures below 100°C [30, 33, 48]. It means that below 100°C, the thermal energy is not enough to produce the desorption reactions of the oxygen and, therefore, an electrical response does not occur regardless of the gas concentration, as can see in Figure 8a. By contrast, above 100°C (in this case 200 and 300°C), the formation of ionic species at surface of the ZnCo2O4 occurs causing a chemical reaction with the gas and resulting in changes in the electrical resistance of the material (i.e., a high sensitivity is recorded) [48, 49]. Additionally, the conductivity mechanism of ZnCo2O4 sensor is strongly related to the crystallite size (D) and the space charge layer (L): if D >> 2L, the conductivity is limited by the Schottky barrier at the particle border; thus, gas detection does not depend on the size of the particle; if D = 2L, the conductivity and the gas sensing depend on the growing of necks formed by crystallites; and when D < 2L, the conductivity depends on the size of the crystallites [2]. In our case, the latter condition occurs while detecting the gases, since the average particle size is less than 100 nm; that is the reason why the conduction of the charge carriers (holes) takes place on the nanoparticles\' surface [6, 50].
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In comparison with previous works, our ZnCo2O4 sensor fabricated on faceted nanoparticles showed a superior sensitivity toward CO (a sensitivity of ~305 in 300 ppm of CO at 300°C) than those sensors based on ZnCo2O4 nanoparticles [18], hierarchical porous ZnCo2O4 nano/microspheres [19], ZnCo2O4 nanotubes [20], porous ZnO/ZnCo2O4 hollow spheres [21], and nanowires-assembled hierarchical ZnCo2O4 microstructure [25], with sensitivities of 1 (50 ppm at 350°C), 2 (100 ppm at 175°C), 1.69 (400 ppm at 300°C), 1.1 (100 ppm at 275°C), and 29 (10 ppm at 300°C), respectively.
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2.7. Conclusions
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ZnCo2O4-faceted nanoparticles (~75 nm) were obtained by the simple and inexpensive microwave-assisted colloidal method, using dodecylamine as surfactant. This synthetic method is allowed to obtain the ZnCo2O4 at a calcination temperature of 500°C. The sensing tests showed that ZnCo2O4 sensor is highly sensitive to concentrations of 1–300 ppm of carbon monoxide at working temperatures above 100°C. Specifically, a maximum sensitivity of 305 was obtained for a CO concentration of 300 ppm at a working temperature of 300°C. The CO sensing response of ZnCo2O4 is better than that reported in previous investigations. Therefore, ZnCo2O4 can be considered as a potential candidate for gas sensing applications.
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Acknowledgments
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The authors are grateful to PRODEP for financial support under the project F-PROMEP-39/Rev-04 SEP-23-005. The authors also thank PROFOCIE 2016 for financial support and CONACYT-México grants: the National Laboratory for Nanoscience and Nanotechnology Research (LINAN). The authors are grateful to G. J. Labrada-Delgado, B. A. Rivera-Escoto, K. Gomez, Miguel Ángel Luna-Arias and Sergio Oliva for their technical assistance.
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\n',keywords:"spinel, nanoparticles, cobaltite, sensors, characterization, synthesis",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/54741.pdf",chapterXML:"https://mts.intechopen.com/source/xml/54741.xml",downloadPdfUrl:"/chapter/pdf-download/54741",previewPdfUrl:"/chapter/pdf-preview/54741",totalDownloads:1069,totalViews:353,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,dateSubmitted:"September 15th 2016",dateReviewed:"February 21st 2017",datePrePublished:null,datePublished:"July 12th 2017",dateFinished:null,readingETA:"0",abstract:"In this work, nanostructured ZnCo2O4 was synthesized via a microwave-assisted colloidal method, and its application as gas sensor for the detection of CO was studied. Typical diffraction peaks corresponding to the cubic ZnCo2O4 spinel structure were identified at calcination temperature of 500°C by X-ray powder diffraction. A high degree of porosity in the surface of the nanostructured powder of ZnCo2O4 was observed by scanning electron microscopy and transmission electron microscopy, faceted nanoparticles with a pockmarked structure were clearly identified. The estimated average particle size was approximately 75 nm. The formation of ZnCo2O4 material was also confirmed by Raman characterization. Pellets fabricated with nanostructured powder of ZnCo2O4 were tested as sensors using CO gas at different concentrations and temperatures. A high sensitivity value of 305–300 ppm of CO was measured at 300°C, indicating that nanostructured ZnCo2O4 had a high performance in the detection of CO.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/54741",risUrl:"/chapter/ris/54741",book:{slug:"nanostructured-materials-fabrication-to-applications"},signatures:"Juan Pablo Morán-Lázaro, Florentino López-Urías, Emilio Muñoz-\nSandoval, Oscar Blanco-Alonso, Marciano Sanchez-Tizapa,\nAlejandra Carreon-Alvarez, Héctor Guillén-Bonilla, María de la Luz\nOlvera-Amador, Alex Guillén-Bonilla and Verónica María\nRodríguez-Betancourtt",authors:[{id:"195379",title:"Dr.",name:"Maria De La Luz",middleName:null,surname:"Olvera Amador",fullName:"Maria De La Luz Olvera Amador",slug:"maria-de-la-luz-olvera-amador",email:"molvera@cinvestav.mx",position:null,institution:null},{id:"195825",title:"Dr.",name:"Juan Pablo",middleName:null,surname:"Morán-Lázaro",fullName:"Juan Pablo Morán-Lázaro",slug:"juan-pablo-moran-lazaro",email:"lazaro7mx27@gmail.com",position:null,institution:{name:"University of Guadalajara",institutionURL:null,country:{name:"Mexico"}}},{id:"196415",title:"Dr.",name:"Florentino",middleName:null,surname:"López-Urías",fullName:"Florentino López-Urías",slug:"florentino-lopez-urias",email:"flo@ipicyt.edu.mx",position:null,institution:null},{id:"196416",title:"Dr.",name:"Emilio",middleName:null,surname:"Muñoz-Sandoval",fullName:"Emilio Muñoz-Sandoval",slug:"emilio-munoz-sandoval",email:"ems@ipicyt.edu.mx",position:null,institution:null},{id:"196417",title:"Dr.",name:"Oscar",middleName:null,surname:"Blanco-Alonso",fullName:"Oscar Blanco-Alonso",slug:"oscar-blanco-alonso",email:"oblanco01@hotmail.com",position:null,institution:null},{id:"196421",title:"Dr.",name:"Héctor",middleName:null,surname:"Guillén-Bonilla",fullName:"Héctor Guillén-Bonilla",slug:"hector-guillen-bonilla",email:"hguillenbonilla@gmail.com",position:null,institution:null},{id:"196422",title:"Dr.",name:"Alex",middleName:null,surname:"Guillén-Bonilla",fullName:"Alex Guillén-Bonilla",slug:"alex-guillen-bonilla",email:"alexguillenbonilla@gmail.com",position:null,institution:null},{id:"196424",title:"Dr.",name:"Verónica María",middleName:null,surname:"Rodríguez-Betancourtt",fullName:"Verónica María Rodríguez-Betancourtt",slug:"veronica-maria-rodriguez-betancourtt",email:"veromrb@yahoo.com",position:null,institution:null},{id:"204477",title:"Dr.",name:"Marciano",middleName:null,surname:"Sanchez-Tizapa",fullName:"Marciano Sanchez-Tizapa",slug:"marciano-sanchez-tizapa",email:"msanchez@profesores.valles.udg.mx",position:null,institution:null},{id:"204478",title:"Dr.",name:"Alejandra",middleName:null,surname:"Carreon-Alvarez",fullName:"Alejandra Carreon-Alvarez",slug:"alejandra-carreon-alvarez",email:"alejandra.carreon@profesores.valles.udg.mx",position:null,institution:null}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Synthesis, characterization, and gas sensing application of ZnCo2O4",level:"1"},{id:"sec_2_2",title:"2.1. Synthesis",level:"2"},{id:"sec_3_2",title:"2.2. Experimental techniques",level:"2"},{id:"sec_4_2",title:"2.3. Structural analysis",level:"2"},{id:"sec_5_2",title:"2.4. Morphological investigations",level:"2"},{id:"sec_6_2",title:"2.5. Raman characterization",level:"2"},{id:"sec_7_2",title:"2.6. Gas sensing application of ZnCo2O4",level:"2"},{id:"sec_8_2",title:"2.7. Conclusions",level:"2"},{id:"sec_10",title:"Acknowledgments",level:"1"}],chapterReferences:[{id:"B1",body:'Yamazoe N, Shimanoe K. New perspectives of gas sensor technology. Sens. Actuators B. 2009;138:100-107. DOI: 10.1016/j.snb.2009.01.023\n'},{id:"B2",body:'Xu C, Tamaki J, Miura N, Yamazoe N. Grain size effects on gas sensitivity of porous SnO2-based elements. Sens. Actuators B. 1991;3:147-155. 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Department of Computer Science and Engineering, CUValles, University of Guadalajara, Ameca, Jalisco, Mexico
Department of Computer Science and Engineering, CUValles, University of Guadalajara, Ameca, Jalisco, Mexico
'},{corresp:null,contributorFullName:"Verónica María Rodríguez-Betancourtt",address:null,affiliation:'
Department of chemistry, CUCEI, University of Guadalajara, Guadalajara, Jalisco, Mexico
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1. Introduction
A broad range of proteins and peptides, for various purposes of enhancement, such as human growth hormone (hGH), i.e., somatropin, can be obtained from the illicit market. These products are mainly marketed as lyophilized formulations in small glass containers often without labelling. The customers are exposed to a range of potential harms, besides from the active components, including bacterial and fungal or viral infections which may arise from the fact that they are administered parenterally.
Figure 1A illustrates the total number of injection vials containing white lyophilized product cake being seized by the Swedish Customs during nine years in the past, i.e., 2010–2018. A large proportion of these samples, i.e., 64%, contained human growth hormone or melanotan II. About a third of the seized vials, i.e., 27%, did not contain any active peptide or protein, while the remaining 9% of the vials contained other compounds, Figure 1B.
Figure 1.
Schematic illustration of the number of seized illicit products during 2010–2018 in Sweden (A), as well as the active peptides/proteins that have been identified in theses samples (B).
The concept of a proteolytic peptide pattern, i.e., protein peptide mapping (PPM), being characteristic of a protein was first demonstrated by SDS-PAGE [1]. In 1989, peptide sequencing by automated Edman degradation had a cycle-time of nearly one hour per amino acid residue. Samples of interest often contained complex mixtures of proteins, which usually required separation by SDS-PAGE followed by electroblotting onto a polyvinylidene fluoride (PVDF) membrane [2]. However, a more rapid approach to peptide sequencing is “peptide mass fingerprinting” (PMF). By PMF, proteins are enzymatically cleaved in a predictable manner and the sizes of the generated peptide fragments are specific for different proteins. Subsequent analysis of the obtained peptides by mass spectrometry (MS) generates mass-to-charge ratio (m/z) values in the mass spectrum which in turn give rise to a characteristic “peptide mass fingerprint” of the protein [3, 4]. The fingerprint serves to identify the protein by comparison with in silico digests, i.e., search engines attempt to match peptides from in silico digested proteins to those measured by the mass spectrometer [5, 6, 7, 8, 9]. Peptide mass fingerprinting with MS, which was first demonstrated with fast atom bombardment ionization in 1981, provides the possibility of identifying a protein at nanogram-level [5, 10, 11, 12]. Trypsin is a commonly used proteolytic enzyme for PMF, since it is relatively cheap, highly selective, and generates peptides with an average size of about 8–10 amino acids which are ideally suited for analysis by MS. It cleaves principally on the C-terminal side of arginine and lysine with the exception of Arg-Pro and Lys-Pro [2]. Limitations to protein identification by PMF include; I) The protein sequence must be present in a database for a successful protein identification. II) Proteins with extensive post-translational modifications may fail to yield good matches [13]. III) Different isoforms of a protein or alternatively spliced proteins may not be distinguished if the unique sequence regions are not observed in the peptide map. IV) Incomplete proteolytic digestion and differences in peptide ionization provide an incomplete mass fingerprint of the protein. Therefore, a complementary approach to PMF for protein identification is the use of tandem mass spectrometry (MS/MS), whereby tryptic peptide ions from the first stage of MS are dissociated along the backbone and then separated and detected in a second stage of MS to identify primary amino acid sequences [14, 15, 16]. Tandem mass spectrometry in conjunction with PMF provides even more specificity, thereby facilitating the identification [17, 18].
Since the innovation of sensitive commercial instrumentation based on MALDI-TOF MS in 1992, the technique has been widely used for protein identification due to its high sensitivity and mass accuracy, speed, extremely low material consumption, absence of multiple charge mass signals and relatively high tolerance toward additives and contaminants such as salts, matrix components and excipients [19, 20, 21, 22, 23, 24, 25, 26]. Furthermore, MALDI is a micro-destructive analytical technique and the remaining material on the MALDI target plate can be archived for later analysis. The high sensitivity of MALDI implies that only a small aliquot of the digested protein is required for mass analysis, and the remainder can be used for alternative measurements. MALDI provides additional information regarding the primary structure of the protein by sequencing of selected tryptic peptide ions in post source decay (PSD) mode [27, 28, 29, 30, 31, 32, 33, 34]. MALDI in-source decay (ISD) is another attractive method which generates partial sequence information of intact proteins with up to 20–50 amino acid residues [35], Figure 2.
Figure 2.
MALDI in source decay analysis of a suspected illegal somatropin sample. The blue marked amino acid asp (D) is the deamidated form of Asn (N).
The sequence information from MALDI-PSD or MALDI-ISD analyses can be used to validate protein identification. The singly charged ions generated by MALDI-TOF-MS are a mixture of b-, y- and a-ions accompanied by ions resulting from neutral loss of ammonia or water [36, 37, 38, 39].
PMF-based protein identification is accomplished by searching a protein sequence database using different search engines such as ProFound [40], Mascot [41], or SEQUEST [15]. A value-based scoring system has been developed that facilitates the identification without accompanying amino acid data [42, 43]. Parameters which are considered to be important for the identification include; molecular mass, protein sequence coverage and the number of matching peptides [42]. However, presence of a signature peptide, being unique for a protein, facilitates the PMF-based identifications [44]. Prior reports suggest that a minimum of four matching peptides and a sequence coverage of at least 20% is necessary for positive PMF-based protein identification [45, 46]. The other alternative strategy for protein identification is the top down approach, where intact molecule ions are subjected to gas-phase fragmentation [47].
Proteins with posttranslational modifications, such as glycosylation, present additional challenges since the masses of the modified peptides are different and thus do not contribute to the identification. In such cases, the protein can be analyzed by capillary electrophoresis (CE), in order to explore the heterogeneity of the protein followed by comparison of its electropherogram with that of the corresponding reference standard [13, 48].
2. Experimental
2.1 Sample preparation
MALDI-TOF-MS is very tolerant to salts and sample matrices, hence it is seldom necessary to desalt the sample. However, sometimes it is necessary to use a C18 micro-column in order to fractionate a complex sample or enhance the target analyte concentration.
The sample to be analyzed is mixed with a matrix solution (1:1, v:v), e.g. sinapinic acid (SA) or alpha-cyano-4-hydroxycinnamic acid (ACHCA). One μl of the mixture is deposited on the MALDI target plate and allowed to air-dry (i.e., the dried-droplet method) before being placed in the mass spectrometer [19, 49].
2.2 Proteolysis
The analyte to be digested is dissolved in ammonium bicarbonate (50 mM, pH 7.9). The intact sample is directly analyzed by MALDI in order to determine the molecular mass of the analyte. Then, 200 μl of the solution is digested by addition of 2–10 μl trypsin (200 μg/ml in 10 mM HCl). The reaction is carried out at room temperature or at 37°C for 30 minutes up to 24 hours, depending on peptide or protein in question. It has been found that 30 minutes digestion of somatropin at room temperature generated enough tryptic fragments for the MALDI analyses [50]. For more complex proteins, such as human chorionic gonadotropin, the required time period for proteolysis is found to be 24 hours at 37°C. Insulin porcine is digested at 37°C for 12 hours, while other peptides are digested at 37°C for 4 hours. In order to enable alkylation of the cysteine residues in a protein or peptide, it is reduced by using DTT or 2-mercaptoethanol (ME) followed by labelling of the free thiol groups with 2-iodoacetamide. The alkylation is carried out through the following procedure:
2.5 μl 100 mM ME is added to 10 μl of the protein solution.
The protein is then incubated at 50°C for 15 minutes to reduce the S-S linkages.
2.5 μl 2-iodoacetamide (100 mM) is added into the mixture to interact with free sulfide groups of the cysteine residues at +4°C for 15 to 60 minutes in darkness.
2.5 μl (10 μg/mL) trypsin is added to the mixture for the digestion. The reaction is performed at room temperature or at 37°C [13, 50].
2.3 Apparatus and operating conditions
MALDI-TOF analyses are performed using either an Autoflex or an Autoflex Max (Bruker Daltonics, Bremen, Germany) reflector type time-of-flight mass spectrometer, equipped with a pulsed nitrogen laser working at 337 nm and a smartbeam II laser working at 355 nm, respectively. The Autoflex instrument is operated in the positive ion mode with delayed extraction at an accelerating voltage of 20 kV and a variable voltage reflectron. The parameter settings are optimized to analyze peptides in reflectron mode. Before analysis, the instrument is externally calibrated with Bruker Daltonics standard peptide or protein mixtures. Peptide mass peaks occurring due to autolysis of trypsin (porcine) such as 842.51 and 2211.10 Da are also used for internal calibration. Mass spectra are obtained by averaging 250 laser shots (5× 50 shots) at different positions on the sample surface. All samples being used for post source decay (PSD) analysis are analyzed in the reflectron mode. The autoflex Max instrument TOF/TOF (2 kHz MS and 200 Hz MS/MS) operates in the positive ion mode. Metastable fragmentation is induced by laser (355 nm) without the further use of collision gas. The lyophilized samples are dissolved in 300 μL ammonium bicarbonate buffer (50 mM, pH 7.5). The liquid samples are diluted with same buffer. The wells of MALDI plate are spotted with 1 μl sample/matrix solution (1:1, v:v) and allowed to air dry before being placed in the mass spectrometer. ACHCA is used for analysis of peptides. About 20 mg of ACHCA is mixed in 1 ml of ethanol: acetonitrile (ACN) (1: 1 v/v) and 0.1% trifluoroacetic acid (TFA). SA is used for protein analysis. Two different solutions of SA in water and ethanol are made as follows: 1 - Saturated solution of SA in ethanol and 0.1% TFA; 2 - Saturated solution of SA in 50% acetonitrile (ACN) and 0.1% TFA. Solution 1 is first applied on the MALDI plate on which the sample mixed with SA in 50% ACN and 0.1% TFA (1: 1) is then applied.
3. Results and discussion
Illegally distributed lyophilized or liquid products being suspected to contain pharmacologically active peptides were seized by the Swedish customs. The analyte to be identified is analyzed in both reflectron and linear modes in order to determine its molecular mass, Figure 3. Large peptides and proteins are then exposed to trypsin digestion in order to obtain peptide-mass map upon MALDI analysis in reflectron mode. Small peptides are, on the other hand, analyzed in reflectron mode and/or PSD mode directly. This strategy was applied to the identification of the following peptides and proteins, Figure 4 and Table 1.
Figure 3.
The sample to be identified is analyzed in both reflectron and linear modes in order to determine the molecular mass of the analyte. Depending on the size of the molecule it will be exposed to enzymatic digestion in order to be identified through PMF. Small peptides used to be identified by de novo sequencing in PSD mode.
Figure 4.
The primary structure of the analyzed peptides. (A) Somatoliberin, (B) AOD, (C) GHRP-2, (D) glycine-GHRP-2, (E) GHRP-6, (F) glycine-GHRP-2, (G) Ipamorelin, (H) MGF, (I) long-R3-IGF (disulfide bridges: C6-C48; C47-C52 and C18-C61; asp at position 3 is replaced by Arg), (J) insulin Aspart, (K) insulin porcine, (L) DSIP, (M) Thymosine β4, (N) Melanotan II, (O) Bremelanotide, (P) Dermorphin and (Q) BPC 157. For molecular structures of somatropin and hCG see references [13] and [50].
3.1 Identification of somatropin (hGH)
Recombinant hGH or somatropin consists of 191 amino acids with two disulfide bridges (Cys53-Cys165 and Cys182-Cys189) and promotes proteinogenesis as well as fat mobilization and oxidation [51, 52, 53]. Recombinant hGH is used as a prescription drug to treat children’s growth disorders and adult growth hormone deficiency. In the belief that the beneficial impact of somatropin on the growth can be extrapolated to healthy individuals, it is abused by bodybuilders and athletes [54]. However, many users are unaware of the correct dosage and how to prepare the solution for giving an injection. It has been demonstrated that supra-physiological dosages can have fatal consequences [55]. Apart from the undesired consequences following the abuse of somatropin, our investigations have shown that the illegally marketed products contained high levels of impurities such as endotoxins [50]. Endotoxins are associated with Gram-negative bacteria which can cause severe immune response and diseases in humans [56, 57]. Somatropin was identified through PMF and MALDI-ISD, see Figure 2 [48, 58, 59]. The availability of a compendial reference standard has made it possible to apply double injection capillary zone electrophoresis (DICZE) for both identification and impurity determination of somatropin products [50, 58, 59]. The DICZE-method provided complementary information on the native protein, providing a side by side comparison between the electrophoretic patterns of the reference standard and the analyte to be identified [50].
3.2 Identification of human somatoliberin
Human somatoliberin, growth hormone-releasing hormone (GHRH), constitutes of 44 amino acids without any post-translational modification or disulfide bridge. Somatoliberin was first isolated from two pancreatic islet cell tumors, and subsequently from normal human hypothalamus [60, 61, 62]. The MALDI results from determination of the molecular mass, PMF and amino acid sequence revealed that the Asn8 (N), Gly15 (G) and Met27 (M) residues have, respectively, been replaced by Gln8 (Q), Ala15 (A) and Leu27 (L) during the synthesis, see Figures 4 and 5. The peptide was successfully identified by PMF and de-novo sequencing of three of the tryptic peptides.
Figure 5.
MALDI-PMF (A) and MALDI-PSD (B) analysis of somatoliberin.
3.3 Identification of an anti-obesity drug (AOD)
The AOD peptide is a fragment of the C-terminus of human growth hormone (fragment 177–191) where a tyrosine is added at the N-terminus. It is a cyclic peptide consisting of 16 amino acids with a disulfide bridge between cysteine residues at positions 7 and 14 in the peptide chain [63], Figure 4 and Table 1. The fragment is the minimum length of the hGH sequence that retains the lipolytic and antilipogenic properties of hGH [63, 64, 65]. The molecular peptide masses of its tryptic peptides complied with the peptide map of hGH fragment 177–191. The existence of the disulfide bridge between C7 and C14 was confirmed upon analysis of the non-reduced tryptic sample, Figure 6. This peptide has also been employed as a signature peptide for the identification of hGH [48, 50]. The amino acid sequences of three selected tryptic peptides were also confirmed.
Figure 6.
MALDI-PSD analysis of AOD.
3.4 Identification of growth hormone releasing peptides (GHRP)
GHRP, including GHRP-2, GHRP-6, Gly-GHRP-2, Gly-GHRP-6 and ipamorelin, as an agonist of the gut peptide ghrelin is an endogenous ligand for the growth hormone secretagogue receptor [66, 67]. Ghrelin strongly stimulates food intake and GH release in humans [68, 69, 70]. These peptides were identified through de-novo sequencing. The amino acid sequence of GHRP-6 differs slightly from that of GHRP-2, i.e., the amino acid residues dA and Naphthyl alanine (NalA) in GHRP2 are replaced by H and dW in GHRP-6, Figure 4 and Table 1 [70].
Ipamorelin is a penta-peptide, being derived from GHRP-1 [71]. Ipamorelin like the other GHR-peptides, stimulates production of growth hormone [72]. Incorporation of aminoisobutyric acid (Aib) in the peptide chain increases the stability of the peptide, Figure 4 [73].
3.5 Identification of mechano growth factor (MGF) and long-R3 insulin-like growth factor (IGF-1)
MGF is a unique, spliced variant of IGF-1. MGF induces muscle cell proliferation in response to muscle stress and injury [74]. MGF and Long-R3-IGF1 were identified in several confiscated samples. Long-R3-IGF-1, an analogue of IGF-1, has 13 additional amino acids at its N-terminus, Figure 4 and Table 1. IGF-1 mediates the anabolic and mitogenic activity of GH [75, 76, 77]. MGF and Long-R3-IGF1 were identified by sequence coverages of 100% and 43%, respectively, Table 2 and Figure 7.
Illegally distributed peptides and proteins that have been analyzed by MALDI-ToF-MS and DICZE. The monoisotopic mass (Mmass) of the analytes and the employed analytical methodology is indicated.
Identification by peptide mass fingerprinting using enzymatic degradation as well as other modifications.
De novo sequencing by MALDI- post source decay.
Protein sequencing by MALDI- in source decay.
Identification and/or impurity profiling by double injection capillary zone electrophoresis.
Average molecular mass.
Bovin albumin was detected in some of the samples.
MALDI peptide mass fingerprinting-data from analysis of mechano growth factor.
Glu-C cleaves at the C-terminus of either aspartic or glutamic acid residues.
The amino acid sequence of the peptide was determined.
Figure 7.
MALDI analysis of intact long-R3-IGF and MALDI-PSD analysis of two tryptic peptides, i.e., m/z 1667.771 and m/z 1763.887.
3.6 Identification of insulin porcine and insulin aspart
Insulin regulates the cellular uptake, utilization, and storage of glucose, amino acids, and fatty acids and inhibits the breakdown of glycogen, protein, and fat. Since more than one decade ago the illegal use of insulin has been noticed [78]. However, the misuse and wrong administration of insulin could cause the, so called, dead in the bed syndrome [79]. In bodybuilding, insulin works such as testosterone or hGH to consolidate muscle tissue. Insulin also prevents breakdown of muscles and vanishes rapidly from the body, since it has a very short half-time (t1/2) [80].
Several illegal products containing insulin porcine or aspart have been analyzed. Insulin is composed of two peptide chains, i.e., A and B, which are joined by two inter-chain disulfide bonds. The A chain also contains an intra-chain disulfide bond, Figure 4. The results summarized in Table 3, demonstrate the applied strategy for the identification of porcine and insulin aspart. The insulin molecules were reduced using a potent reducing agent, i.e., 2-mercaptoethanol (ME). MS-analysis of the reduced samples resulted in a mass spectrum consisting of several signals from both reduced A and B chains. The A and B chains generated three and four signals, respectively, corresponding to the ME-modified peptide as described in Table 3. It is to be noted that the amino acid residues P and A at positions 28 and 30 in the B-chain, respectively, have been replaced by D and T in insulin aspart. Therefore, these insulin molecules are distinguished upon these differences. The tryptic digestion of the B chain yielded three peptide fragments of different sizes, Figure 8 and Table 3. The molecular masses of these peptides were determined accurately, and the amino acid sequence of the tryptic peptides were determined in PSD-mode.
Insulin
Theoretical m/z [M + H]+
Determined m/z [M + H]+
Porcine (intact)
5774.635
5774.632
Aspart (intact)
5822.612
5822.618
Peptide chains from Insulin porcine:
[A-chain + Na]+
2404.990
2404.758
[A-chain +1ME + Na]+a
2480.988
2480.769
[B-chain + H]+
3398.682
3398.460
[B-chain +1ME + H]+a
3474.680
3474.486
Peptide chain from Insulin aspart:b
[B-chain + H]+
3446.667
3446.434
[B-chain + Na]+
3468.648
3468.487
[B-chain +1ME + H]+a
3522.665
3522.422
[B-chain - (GFFYTDKT) + H]+c
2487.228
2487.030
[B-chain - (GFFYTDKT) + 1ME + H]+a, c
2563.226
2563.302
Tryptic peptides from Insulin aspart:
[GFFYTDK + H]+
877.399
877.317
[GFFYTDKT + H]+
978.457
978.446
[B-chain - (GFFYTDKT) + H]+c, d
2487.217
2487.030
Tryptic peptides from Insulin porcine:
[GFFYTPK + H]+
859.425
859.345
[GFFYTPKA + H]+
930.462
930.337
[B-chain - (GFFYTPKA) + H]+c
2487.228
2487.234
[B-chain-(GFFYTPKA) + 1ME + H]+ a, c
2563.226
2563.129
Table 3.
MALDI-TOF-MS analysis of insulin porcine and aspart.
Beta mercaptoethanol (ME) was used as reducing agent.
The A-chains of insulin aspart and Insulin porcine are identical.
Trypsinated B-chain.
These peptides originate from insulin aspart, see Figure 8.
Figure 8.
MALDI analysis of insulin aspart; analysis of reduced B-chain (A), MALDI-PSD analysis of tryptic B-chain (B), see Table 3.
Double-injection capillary electrophoresis has also been applied for the identification of insulin molecules [81].
3.7 Identification of delta sleep-inducing peptide (DSIP)
The nonapeptide delta DSIP was first isolated from the cerebral venous blood of rabbits in an induced state of sleep during the mid-70s [82]. It was primarily believed to be involved in sleep regulation due to its apparent ability to induce slow-wave sleep in rabbits. However, it has been demonstrated that short-term treatment of chronic insomnia with DSIP is not likely to be of major therapeutic benefit [83]. The peptide is marketed illegally presumably for the treatment of insomnia. The peptide was directly exposed to the PSD analysis in order to confirm its molecular mass and amino acid sequence, Figure 4 and Table 1.
3.8 Identification of thymosin β4
Synthetic thymosin is a peptide consisting of 43 amino acids with artificial acetylation of the N-terminus, see Figure 4 and Table 1. Thymosin has the potential of playing a significant role in tissue development, maintenance, repair, pathology and other important biological activities [84]. Some important biological activities of thymosin are related to the peptide sequence L17KKTET22 [85]. Illegally distributed thymosin products are claimed to promote a variety of beneficial biological functions, such as muscle building. The peptide was identified through PMF and de-novo sequencing of the tryptic peptides, Table 4.
MALDI peptide mass fingerprinting data from analysis of thymosin β4.
The amino acid sequence of the peptide was determined in the PSD mode.
N.D. = Not detected.
3.9 Identification of human chorionic gonadotropin (hCG)
Human chorionic gonadotropin (hCG) is a glycoprotein hormone consisting of α (92 amino acids) and β-subunits (145 amino acids) being noncovalently associated [86]. These subunits are, however, highly cross-linked internally through disulfide bridges, i.e., the α-subunit has five disulfide bridges [87], while the β-subunit has six [87, 88]. The protein is heavily glycosylated where oligosaccharides are attached to the protein backbone through asparagine and serine residues and constitute approximately 30% of the molecular mass [89]. The protein has been identified using MALDI-TOF-MS and DICZE [13, 50]. Approximately 40% of the amino acid sequence of hCG was confirmed upon PMF, Table 5 [13].
Peptide fragments
Peptide position in the peptide chain
Theoretical m/z [M + H]+
Determined m/z [M + H]+
AYPTPLR
α-hCG; 36–42
817.446
817.482
TMLVQK
α-hCG; 46–51
719.402
719.414
STNR
α-hCG; 64–67
477.231
477.165
VTVMGGFK
α-hCG; 68–75
838.439
838.471
SK
β-hCG; 1–2
234.135
243.142
PR
β-hCG; 7–8
272.161
271.996
EPLR
β-hCG; 3–6
514.288
514.291
EPLRPR
β-hCG; 3–8
767.442
767.469
DVR
β-hCG; 61–63
389.204
389.228
FESIR
β-hCG; 64–68
651.336
651.359
Table 5.
MALDI-PMF and MALDI-PSD analysis of human chorionic gonadotropin. The identified peptides from the α and β subunits are presented in the table below.
The identification was confirmed by DICZE analysis of illegal samples together with the corresponding reference standard [13, 50].
3.10 Identification of melanotan II (MII) and bremelanotide
Melanotan, a melanocortin receptor agonist, is a cyclic-lactam bridge heptapeptide which induces melanogenesis (i.e., tanning of the skin), by activation of the MC1 receptor, being an analogue to alpha melanocyte hormone (α-MSH) [90]. The cyclic, lactam bridged structure of MII induces increased lipophilicity, Figure 4 [91].
Skin-tanning products that claim to contain MII are being advertised and sold on the illicit drug market. Injection of MII can result in systemic toxicity and rhabdomyolysis [90]. Bremelanotide (formerly PT-141) is an active metabolite of MII, Table 1.
These peptides were identified through the top-down approach by MALDI in PSD mode as illustrated in Figure 9.
Figure 9.
MALDI-PSD analysis of melanotan II.
3.11 Identification of dermorphin
Dermorphin is a μ-opioid receptor-binding peptide that causes both central and peripheral effects [92], Figure 4 and Table 1. This peptide, being originally isolated from the skin of the south American tree frog Phyllomedusa sauvagii, is classified as one of the strongest mammalian endogenous analgesic opioids [93, 94]. Dried frog skin containing dermorphin, has been used as a therapeutic agent by the Matses tribes of the upper Amazonian basin, to treat cuts during hunting expeditions [95]. The analgetic effects of dermorphin has been demonstrated in rat, horse, dog and white sea cod [92, 94]. It has been used illegally in horse racing as a pain killing agent, allowing horses to run even if injured.
This peptide, which was detected in several samples, was identified by MALDI in the PSD mode, Figure 10. The molecular structure was confirmed by NMR spectroscopy.
Figure 10.
MALDI-PSD analysis of dermorphin.
3.12 Identification of body protecting compound 157 (BPC 157)
BPC 157 being a partial sequence of body protecting compound (BPC) (Mmass = 40 kDa) is a synthetic peptide, which is composed of fifteen amino acids, Figure 4 and Table 1. BPC was discovered and isolated from mouse gastric juice in response to stress stimuli in the gut mucosa [96]. BPC 157 is also known as Bepcin and PL. 14,736 or PL 10 [97]. This peptide fragment was speculated to be responsible for the BPC’s physiological and protective effects [96]. However, it is unclear whether this peptide is endogenous to humans. BPC 157 is suggested to aid in tendon, ligament and muscle healing, and therefore its use as a quick injury healing in the sporting world is appealing. However, no proper clinical trials in human subjects have yet been performed to investigate the healing capability and the harmful effects of this compound [97].
BPC 157 was recently identified in several confiscated vials for injection. The identification was carried out by MALDI in both PSD and reflectron modes, Figure 11. The amino acid sequence of the peptide was confirmed by NMR spectroscopy and LC-QTOF-MS.
Figure 11.
MALDI-PSD analysis of BPC 157.
4. Conclusions
The proposed methods, based on PMF by MALDI-TOF-MS as well as analysis with DICZE, provided an efficient procedure for the identification of peptides and proteins in illegally distributed samples. The use of trypsin as a proteolytic enzyme generated peptide fragments which covered 40 to 80% of the amino acid sequences of the analyzed proteins. The presence of a signature peptide in the peptide map facilitated the analyte identification considerably. MALDI-TOF-MS was also applied in the PSD mode for the amino acid sequencing of selected tryptic peptides as well as small peptides, such as ipamorelin.
The double-injection CE method provided complementary information on the native protein in the presence of a reference standard. This provided the possibility of performing a comparison between the electrophoretic patterns of the reference standard and the analyte to be identified. In addition, the double-injection based identifications were carried out by comparing the corrected migration time of the analyte and the observed migration time of the reference standard.
Abbreviations
ACHCA
α-cyano-4-hydroxycinnamic acid
ACN
Acetonitrile
Aib
Aminobutyric acid
BPC
Body protecting compound
DICZE
Double-injection capillary electrophoresis
DSIP
Delta sleep-inducing peptide
GH
Growth hormone
GHRP
Growth hormone releasing peptide
GHRH
Growth hormone releasing hormone (somatoliberin)
hCG
Human chorionic gonadotropin
hGH
Human growth hormone
IGF-1
Insulin like growth factor 1
ISD
In source decay
Nle
Norleucine
PMF
Protein mass fingerprinting
PSD
Post source decay
SA
Sinapinic acid
TFA
Trifluoroacetic acid
\n',keywords:"matrix-assisted laser desorption/ionization time-of-flight mass spectrometry, double-injection capillary electrophoresis, illegally distributed proteins and peptides",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/74510.pdf",chapterXML:"https://mts.intechopen.com/source/xml/74510.xml",downloadPdfUrl:"/chapter/pdf-download/74510",previewPdfUrl:"/chapter/pdf-preview/74510",totalDownloads:146,totalViews:0,totalCrossrefCites:0,dateSubmitted:"September 28th 2020",dateReviewed:"December 2nd 2020",datePrePublished:"January 5th 2021",datePublished:null,dateFinished:"December 21st 2020",readingETA:"0",abstract:"An analytical strategy based on matrix-assisted laser desorption/ionization (MALDI) time-of-flight (TOF) mass spectrometry (MS) for identification of peptides and proteins in illegally distributed products is presented. The identified compounds include human growth hormone (hGH), human somatoliberin, anti-obesity drug (AOD), growth hormone releasing peptides (GHRP-2 and GHRP-6), Glycine-GHRP-2 and Glycine-GHRP-6, ipamorelin, insulin aspart and porcine, delta sleep-inducing peptide (DSIP), thymosin β4, insulin like growth factor (IGF), mechano growth factor (MGF), human chorionic gonadotropin (hCG), melanotan II, bremelanotide, dermorphin and body protecting compound (BPC 157). The identification of proteins was mainly based on peptide mass fingerprinting, i.e., bottom up approach, while the smaller peptides were identified through de-novo sequencing. In cases when a reference standard was available, complementary identification was performed by capillary electrophoresis in double-injection mode (DICE), where a suspicious product was compared with the reference standard through two consecutive injections within the same electrophoretic run.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/74510",risUrl:"/chapter/ris/74510",signatures:"Ahmad Amini, Torgny Rundlöf, Henrik Lodén, Johan A. Carlsson, Martin Lavén, Ezra Mulugeta, Karin Björk, Torbjörn Arvidsson, Iréne Agerkvist and Anette Perolari",book:{id:"10387",title:"Mass Spectrometry",subtitle:null,fullTitle:"Mass Spectrometry",slug:null,publishedDate:null,bookSignature:"Dr. Goran Mitulović",coverURL:"https://cdn.intechopen.com/books/images_new/10387.jpg",licenceType:"CC BY 3.0",editedByType:null,editors:[{id:"212804",title:"Dr.",name:"Goran",middleName:null,surname:"Mitulović",slug:"goran-mitulovic",fullName:"Goran Mitulović"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:null,sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Experimental",level:"1"},{id:"sec_2_2",title:"2.1 Sample preparation",level:"2"},{id:"sec_3_2",title:"2.2 Proteolysis",level:"2"},{id:"sec_4_2",title:"2.3 Apparatus and operating conditions",level:"2"},{id:"sec_6",title:"3. Results and discussion",level:"1"},{id:"sec_6_2",title:"3.1 Identification of somatropin (hGH)",level:"2"},{id:"sec_7_2",title:"3.2 Identification of human somatoliberin",level:"2"},{id:"sec_8_2",title:"3.3 Identification of an anti-obesity drug (AOD)",level:"2"},{id:"sec_9_2",title:"3.4 Identification of growth hormone releasing peptides (GHRP)",level:"2"},{id:"sec_10_2",title:"3.5 Identification of mechano growth factor (MGF) and long-R3 insulin-like growth factor (IGF-1)",level:"2"},{id:"sec_11_2",title:"3.6 Identification of insulin porcine and insulin aspart",level:"2"},{id:"sec_12_2",title:"3.7 Identification of delta sleep-inducing peptide (DSIP)",level:"2"},{id:"sec_13_2",title:"3.8 Identification of thymosin β4",level:"2"},{id:"sec_14_2",title:"3.9 Identification of human chorionic gonadotropin (hCG)",level:"2"},{id:"sec_15_2",title:"3.10 Identification of melanotan II (MII) and bremelanotide",level:"2"},{id:"sec_16_2",title:"3.11 Identification of dermorphin",level:"2"},{id:"sec_17_2",title:"3.12 Identification of body protecting compound 157 (BPC 157)",level:"2"},{id:"sec_19",title:"4. Conclusions",level:"1"},{id:"sec_22",title:"Abbreviations",level:"1"}],chapterReferences:[{id:"B1",body:'Cleveland DW, Fischer SG, Kirschner MW, Laemmli UK. Peptide mapping by limited proteolysis in sodium dodecyl sulfate and analysis by gel electrophoresis. Journal of Biological Chemistry. 1977; 252: 1102-1106.'},{id:"B2",body:'Matsudaira P. Sequence from picomole quantities of proteins electroblotted onto polyvinylidene difluoride membranes. Journal of Biological Chemistry. 1987;262:10035-10038.'},{id:"B3",body:'Domon B, Aebersold R. Mass Spectrometry and protein analysis. Science. 2006;312:212-217. DOI: 10.1126/science.1124619.'},{id:"B4",body:'Han X, Aslanian A, Yates III JR. Mass spectrometry for proteomics. Current Opinion in Chemical Biology. 2008;12:483-490. DOI: 10.1016/j.cbpa.2008.07.024.'},{id:"B5",body:'Yates III JR, Speicher S, Griffin PR, Hunkapiller T. Peptide mass maps: a highly informative approach to protein identification. 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DOI: 10.1002/dta.2152.'}],footnotes:[],contributors:[{corresp:"yes",contributorFullName:"Ahmad Amini",address:"ahmad.amini@lakemedelsverket.se",affiliation:'
Swedish Medical Products Agency, P.O. Box 26, Dag Hammarskjölds väg 42, Sweden
Swedish Medical Products Agency, P.O. Box 26, Dag Hammarskjölds väg 42, Sweden
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