Work Function Lowering of Graphite by Sequential Surface Modifications: Nitrogen and Hydrogen Plasma Treatment

Akada, Keishi; Obata, Seiji; Saiki, Koichiro

doi:10.1021/acsomega.9b02208

Cited by 22 publications

(11 citation statements)

References 37 publications

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“…[ 55–57 ] It is worth noting that with the increase in H 2 plasma modification time, the CPD increases gradually, i.e., the decrease in work function of CSA graphene films, owing to the removal of functional groups and the introduction of hydrogen bonds by H 2 plasma. [ 58,59 ] Figure 3f shows the Hall mobility and resistivity of CSA graphene films with H 2 plasma surface modification. The resistivity of CSA graphene films first decreases gradually and then increases dramatically with the increase in H 2 plasma modification time.…”

Section: Resultsmentioning

confidence: 99%

Compacted Self‐Assembly Graphene with Hydrogen Plasma Surface Modification for Robust Artificial Electronic Synapses of Gadolinium Oxide Memristors

Chan

et al. 2020

Adv Materials Inter

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increasing the requirements of smart data computing and storage. However, for the conventional von Neumann computer architecture, owing to the separation between memory and computation functions, the data calculation rules and transmission speed between the central processing unit and memory segments limit the overall efficiency and computation time and dissipate a high processing power, which cannot satisfy the scenario of real-time applications. [1] By contrast, the human brain is a complex organ composed of ≈100 billion neurons and 60 trillion synapses with an average consumed power of ≈20 W and superior learning and cognitive abilities. [2] Therefore, an entirely new architecture concept of artificial intelligence (AI) has to be proposed to solve the limitations of latency and the issues of power consumption. For this purpose, deep learning is the core of AI based on the artificial neural networks, which is designed to mimic how the brain works. The neural networks consist of synapses, neurons, pulsed neural networks, perceptual systems, and so on. To simulate synapses, some nonvolatile memory devices have been proposed such as floating-gate (FG) flash memory, phase-change memory (PCM), memristor in resistive random-access memory (RRAM), and ferroic tunnel junctions. [3-6] Since the scaling of

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Section: Resultsmentioning

confidence: 99%

Compacted Self‐Assembly Graphene with Hydrogen Plasma Surface Modification for Robust Artificial Electronic Synapses of Gadolinium Oxide Memristors

Chan

et al. 2020

Adv Materials Inter

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“…We hypothesize that the emission from the graphite region has to do with surface contamination, specifically surface modification decreasing the work function after cooling from thermionic emission [6]. These findings demonstrate the importance of understanding the photoemission process for UEM photocathodes.…”

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confidence: 81%

Photocathode Investigation for Ultrafast Electron Microscopy

2021

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“…The correlation between the work function of graphene and the ORR activity has been reported for many graphene-based catalysts. − The work function of freestanding 3NG py is 5.10 eV, which is higher than that of Co-supported 3NG py with a work function of 4.54 eV. This implies that the Co substrate reduced the work function of pyridinic N-doped graphene.…”

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confidence: 87%

Pyridinic-Type N-Doped Graphene on Cobalt Substrate as Efficient Electrocatalyst for Oxygen Reduction Reaction in Acidic Solution in Fuel Cell

Haile

Hansen

Yohannes

et al. 2021

J. Phys. Chem. Lett.

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In this study, we use density functional theory to investigate the catalytic activity of graphene (G), single vacancy defective graphene (G SV ), quaternary N-doped graphene (NG Q ), and pyridinic N-doped graphene (NG py , 3NG py , and 4NG py ) on Co(0001) substrate for an oxygen reduction reaction (ORR) in proton exchange membrane fuel cells (PEMFCs). The results show pyridinic N-doped graphene on a Co support exhibited better performance than the NG Q on a Co support and free-standing systems. According to the results, ORR intermediates (*OOH, *O, and *OH) become more stable due to the presence of a Co substrate. The single pyridinic (3NG py ) layer placed on Co(0001) is the most active site. The overpotential for Co/3NG py is rather higher compared to pure Pt( 111) catalyst (0.65 V). Therefore, pyridinic N-doped graphene with a cobalt support could be a promising strategy to enhance the ORR activity of N-doped graphene in PEMFCs.

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Work Function Lowering of Graphite by Sequential Surface Modifications: Nitrogen and Hydrogen Plasma Treatment

Cited by 22 publications

References 37 publications

Compacted Self‐Assembly Graphene with Hydrogen Plasma Surface Modification for Robust Artificial Electronic Synapses of Gadolinium Oxide Memristors

Compacted Self‐Assembly Graphene with Hydrogen Plasma Surface Modification for Robust Artificial Electronic Synapses of Gadolinium Oxide Memristors

Photocathode Investigation for Ultrafast Electron Microscopy

Pyridinic-Type N-Doped Graphene on Cobalt Substrate as Efficient Electrocatalyst for Oxygen Reduction Reaction in Acidic Solution in Fuel Cell

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