Graphene-based resistant sensor decorated with Mn, Co, Cu for nitric oxide detection: Langmuir adsorption & DFT method

IF 1.6 4区 工程技术 Q3 INSTRUMENTS & INSTRUMENTATION Sensor Review Pub Date : 2023-06-13 DOI:10.1108/sr-03-2023-0040
Fatemeh Mollaamin, M. Monajjemi
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引用次数: 5

Abstract

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.
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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
目的研究掺杂铁、镍和锌的过渡金属石墨烯纳米片对一氧化氮(NO)有毒气体的吸附能力。本文的研究结果为研究掺杂tm的石墨烯纳米片与NO分子之间的相互作用提供了有利的认识。通过Langmuir吸附和三层niom /密度泛函理论方法对材料的输运特性进行建模,证明了tm掺杂石墨烯纳米片作为气体传感器的高性能。采用高斯16修正C.01程序,利用CAM-B3LYP功能层和LANL2DZ及6-311G (d, p)基集,建立了三层niom在Gr纳米片上NO→TM(Mn, Co, Cu)掺杂形成的Langmuir吸附模型。在Mn-、Co-和cu掺杂的石墨烯纳米片上,NO在Langmuir吸附过程中电荷密度的变化规律为:ΔQCo-doped = +0.309 >> ΔQMn-doped =−0.074 > ΔQCu-doped =−0.051。因此,电荷密度的变化次数得出了mn掺杂石墨烯纳米片的电荷转移更为显著的结论。然而,基于核磁共振波谱,在石墨烯纳米片表面Cu掺杂和C19靠近N2和Co17交界处的周围观察到尖锐的峰。此外,cu掺杂石墨烯片对C8-Cu 17、C15-Cu 17和C16-Cu17在cu掺杂/Gr上吸附NO时的键轨道影响较大,占据率最大。通过基于极化率的红外计算,ΔGads,NO→Mn−Co的量表明,ΔGads,NO→Mn−Co由于电荷密度从NO中的氮原子转移到Mn掺杂的石墨烯纳米片上,虽然ΔG(NO→Cu−C)0> ΔG(NO→Co−C)0>ΔG(NO→Mn−C)0,但由于电荷密度从NO转移到Mn掺杂的石墨烯纳米片上,其能隙最大。本研究旨在探索掺杂铁、镍、锌“TM”的碳纳米结构对“NO”有害污染物气体的吸附,评价不同掺杂TM的石墨烯纳米片吸附参数的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Sensor Review
Sensor Review 工程技术-仪器仪表
CiteScore
3.40
自引率
6.20%
发文量
50
审稿时长
3.7 months
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.
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