{"title":"用锰、钴、铜修饰的石墨烯基电阻传感器检测一氧化氮:Langmuir吸附和DFT方法","authors":"Fatemeh Mollaamin, M. Monajjemi","doi":"10.1108/sr-03-2023-0040","DOIUrl":null,"url":null,"abstract":"\nPurpose\nThe purpose of this paper is to investigate the ability of transition metals (TMs) of iron-, nickel- and zinc-doped graphene nanosheet for adsorption of toxic gas of nitric oxide (NO). The results of this paper have provided a favorable understanding of the interaction between TM-doped graphene nanosheet and NO molecule.\n\n\nDesign/methodology/approach\nA high performance of TM-doped graphene nanosheet as a gas sensor is demonstrated by modeling the material’s transport characteristics by means of the Langmuir adsorption and three-layered ONIOM/ density functional theory method. The Langmuir adsorption model has been done with a three-layered ONIOM using CAM-B3LYP functional and LANL2DZ and 6–311G (d, p) basis sets by Gaussian 16 revision C.01 program towards the formation of of NO→TM(Mn, Co, Cu)-doped on the Gr nanosheet.\n\n\nFindings\nThe changes of charge density for Langmuir adsorption of NO on Mn-, Co- and Cu-doped graphene nanosheet orderly have been achieved as: ΔQCo-doped = +0.309 >> ΔQMn-doped = −0.074 > ΔQCu-doped = −0.051. Therefore, the number of changes of charge density have concluded a more remarkable charge transfer for Mn-doped graphene nanosheet. However, based on nuclear magnetic resonance spectroscopy, the sharp peaks around Cu doped on the surface of graphene nanosheet and C19 close to junction of N2 and Co17 have been observed. In addition, Cu-doped graphene sheet has a large effect on bond orbitals of C8–Cu 17, C15–Cu 17 and C16–Cu17 in the adsorption of NO on the Cu-doped/Gr which has shown the maximum occupancy. The amounts of \n\nΔGads,NO→Mn−Co through IR computations based on polarizability have exhibited that \n\nΔGads,NO→Mn−Co has indicated the most energy gap because of charge density transfer from the nitrogen atom in NO to Mn-doped graphene nanosheet, though \n\nΔG(NO→Cu−C)0> \n\nΔG(NO→Co−C)0>ΔG(NO→Mn−C)0.\n\n\nOriginality/value\nThis research aims to explore the adsorption of hazardous pollutant gas of “NO” by using carbon nanostructure doped by “TM” of iron, nickel and zinc to evaluate the effectiveness of adsorption parameters of various TM-doped graphene nanosheets.\n","PeriodicalId":49540,"journal":{"name":"Sensor Review","volume":" ","pages":""},"PeriodicalIF":1.6000,"publicationDate":"2023-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Graphene-based resistant sensor decorated with Mn, Co, Cu for nitric oxide detection: Langmuir adsorption & DFT method\",\"authors\":\"Fatemeh Mollaamin, M. Monajjemi\",\"doi\":\"10.1108/sr-03-2023-0040\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\\nPurpose\\nThe purpose of this paper is to investigate the ability of transition metals (TMs) of iron-, nickel- and zinc-doped graphene nanosheet for adsorption of toxic gas of nitric oxide (NO). The results of this paper have provided a favorable understanding of the interaction between TM-doped graphene nanosheet and NO molecule.\\n\\n\\nDesign/methodology/approach\\nA high performance of TM-doped graphene nanosheet as a gas sensor is demonstrated by modeling the material’s transport characteristics by means of the Langmuir adsorption and three-layered ONIOM/ density functional theory method. The Langmuir adsorption model has been done with a three-layered ONIOM using CAM-B3LYP functional and LANL2DZ and 6–311G (d, p) basis sets by Gaussian 16 revision C.01 program towards the formation of of NO→TM(Mn, Co, Cu)-doped on the Gr nanosheet.\\n\\n\\nFindings\\nThe changes of charge density for Langmuir adsorption of NO on Mn-, Co- and Cu-doped graphene nanosheet orderly have been achieved as: ΔQCo-doped = +0.309 >> ΔQMn-doped = −0.074 > ΔQCu-doped = −0.051. Therefore, the number of changes of charge density have concluded a more remarkable charge transfer for Mn-doped graphene nanosheet. However, based on nuclear magnetic resonance spectroscopy, the sharp peaks around Cu doped on the surface of graphene nanosheet and C19 close to junction of N2 and Co17 have been observed. In addition, Cu-doped graphene sheet has a large effect on bond orbitals of C8–Cu 17, C15–Cu 17 and C16–Cu17 in the adsorption of NO on the Cu-doped/Gr which has shown the maximum occupancy. The amounts of \\n\\nΔGads,NO→Mn−Co through IR computations based on polarizability have exhibited that \\n\\nΔGads,NO→Mn−Co has indicated the most energy gap because of charge density transfer from the nitrogen atom in NO to Mn-doped graphene nanosheet, though \\n\\nΔG(NO→Cu−C)0> \\n\\nΔG(NO→Co−C)0>ΔG(NO→Mn−C)0.\\n\\n\\nOriginality/value\\nThis research aims to explore the adsorption of hazardous pollutant gas of “NO” by using carbon nanostructure doped by “TM” of iron, nickel and zinc to evaluate the effectiveness of adsorption parameters of various TM-doped graphene nanosheets.\\n\",\"PeriodicalId\":49540,\"journal\":{\"name\":\"Sensor Review\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":1.6000,\"publicationDate\":\"2023-06-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Sensor Review\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1108/sr-03-2023-0040\",\"RegionNum\":4,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"INSTRUMENTS & INSTRUMENTATION\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Sensor Review","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1108/sr-03-2023-0040","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"INSTRUMENTS & INSTRUMENTATION","Score":null,"Total":0}
Graphene-based resistant sensor decorated with Mn, Co, Cu for nitric oxide detection: Langmuir adsorption & DFT method
Purpose
The purpose of this paper is to investigate the ability of transition metals (TMs) of iron-, nickel- and zinc-doped graphene nanosheet for adsorption of toxic gas of nitric oxide (NO). The results of this paper have provided a favorable understanding of the interaction between TM-doped graphene nanosheet and NO molecule.
Design/methodology/approach
A high performance of TM-doped graphene nanosheet as a gas sensor is demonstrated by modeling the material’s transport characteristics by means of the Langmuir adsorption and three-layered ONIOM/ density functional theory method. The Langmuir adsorption model has been done with a three-layered ONIOM using CAM-B3LYP functional and LANL2DZ and 6–311G (d, p) basis sets by Gaussian 16 revision C.01 program towards the formation of of NO→TM(Mn, Co, Cu)-doped on the Gr nanosheet.
Findings
The changes of charge density for Langmuir adsorption of NO on Mn-, Co- and Cu-doped graphene nanosheet orderly have been achieved as: ΔQCo-doped = +0.309 >> ΔQMn-doped = −0.074 > ΔQCu-doped = −0.051. Therefore, the number of changes of charge density have concluded a more remarkable charge transfer for Mn-doped graphene nanosheet. However, based on nuclear magnetic resonance spectroscopy, the sharp peaks around Cu doped on the surface of graphene nanosheet and C19 close to junction of N2 and Co17 have been observed. In addition, Cu-doped graphene sheet has a large effect on bond orbitals of C8–Cu 17, C15–Cu 17 and C16–Cu17 in the adsorption of NO on the Cu-doped/Gr which has shown the maximum occupancy. The amounts of
ΔGads,NO→Mn−Co through IR computations based on polarizability have exhibited that
ΔGads,NO→Mn−Co has indicated the most energy gap because of charge density transfer from the nitrogen atom in NO to Mn-doped graphene nanosheet, though
ΔG(NO→Cu−C)0>
ΔG(NO→Co−C)0>ΔG(NO→Mn−C)0.
Originality/value
This research aims to explore the adsorption of hazardous pollutant gas of “NO” by using carbon nanostructure doped by “TM” of iron, nickel and zinc to evaluate the effectiveness of adsorption parameters of various TM-doped graphene nanosheets.
期刊介绍:
Sensor Review publishes peer reviewed state-of-the-art articles and specially commissioned technology reviews. Each issue of this multidisciplinary journal includes high quality original content covering all aspects of sensors and their applications, and reflecting the most interesting and strategically important research and development activities from around the world. Because of this, readers can stay at the very forefront of high technology sensor developments.
Emphasis is placed on detailed independent regular and review articles identifying the full range of sensors currently available for specific applications, as well as highlighting those areas of technology showing great potential for the future. The journal encourages authors to consider the practical and social implications of their articles.
All articles undergo a rigorous double-blind peer review process which involves an initial assessment of suitability of an article for the journal followed by sending it to, at least two reviewers in the field if deemed suitable.
Sensor Review’s coverage includes, but is not restricted to:
Mechanical sensors – position, displacement, proximity, velocity, acceleration, vibration, force, torque, pressure, and flow sensors
Electric and magnetic sensors – resistance, inductive, capacitive, piezoelectric, eddy-current, electromagnetic, photoelectric, and thermoelectric sensors
Temperature sensors, infrared sensors, humidity sensors
Optical, electro-optical and fibre-optic sensors and systems, photonic sensors
Biosensors, wearable and implantable sensors and systems, immunosensors
Gas and chemical sensors and systems, polymer sensors
Acoustic and ultrasonic sensors
Haptic sensors and devices
Smart and intelligent sensors and systems
Nanosensors, NEMS, MEMS, and BioMEMS
Quantum sensors
Sensor systems: sensor data fusion, signals, processing and interfacing, signal conditioning.