Differentiation on crystallographic orientation dependence of hydrogen diffusion in α-Fe and γ-Fe: DFT calculation combined with SKPFM analysis

Xing, Baihui; Gao, Ruizhe; Wu, Min; Wei, Haotian; Chi, Shuanghe; Hua, Zhengli

doi:10.1016/j.apsusc.2023.156395

Cited by 7 publications

(1 citation statement)

References 53 publications

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“…Many spatial resolution methods for indirect observation of hydrogen have been developed, such as the silver decorating technique (Schober and Dieker, 1983), hydrogen microprint technique (HMT) (Qin et al , 2022) and scanning Kelvin probe force microscopy (SKPFM) (Xing et al , 2023). Among these techniques for observing hydrogen, the HMT has been highly sought after by researchers.…”

Section: Introductionmentioning

confidence: 99%

Effect of hydrogen on the pitting corrosion behavior of Incoloy 825

Li,

Yao,

Guan

et al. 2024

ACMM

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Purpose In this paper, the authors aim to study the effect of hydrogen on the pitting corrosion behavior of Incoloy 825, a commonly used material for heat exchanger tubes in hydrogenated heat exchangers. Design/methodology/approach The pitting initiation and propagation behaviors were investigated by electrochemical and chemical immersion experiments and observed and analyzed by scanning electron microscope and energy dispersive spectrometer methods. Findings The results show that hydrogen significantly affects the electrochemical behavior of Incoloy 825; the self-corrosion potential decreased from −197 mV before hydrogen charging to −263 mV, −270 mV and −657 mV after hydrogen charging, and the corrosion current density increased from 0.049 µA/cm2 before hydrogen charging to 2.490 µA/cm2, 2.560 µA/cm2 and 2.780 µA/cm2 after hydrogen charging. The pitting susceptibility of the material increases. Originality/value Hydrogen is enriched on the precipitate, and the pitting corrosion also initiates at that location. The synergistic effect of hydrogen and precipitate destroys the passive film on the metal surface and promotes pitting initiation.

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Section: Introductionmentioning

confidence: 99%

Effect of hydrogen on the pitting corrosion behavior of Incoloy 825

Li,

Yao,

Guan

et al. 2024

ACMM

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How scanning probe microscopy can be supported by artificial intelligence and quantum computing?

Pregowska,

Roszkiewicz,

Osial

et al. 2024

Microscopy Res & Technique

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The impact of Artificial Intelligence (AI) is rapidly expanding, revolutionizing both science and society. It is applied to practically all areas of life, science, and technology, including materials science, which continuously requires novel tools for effective materials characterization. One of the widely used techniques is scanning probe microscopy (SPM). SPM has fundamentally changed materials engineering, biology, and chemistry by providing tools for atomic‐precision surface mapping. Despite its many advantages, it also has some drawbacks, such as long scanning times or the possibility of damaging soft‐surface materials. In this paper, we focus on the potential for supporting SPM‐based measurements, with an emphasis on the application of AI‐based algorithms, especially Machine Learning‐based algorithms, as well as quantum computing (QC). It has been found that AI can be helpful in automating experimental processes in routine operations, algorithmically searching for optimal sample regions, and elucidating structure–property relationships. Thus, it contributes to increasing the efficiency and accuracy of optical nanoscopy scanning probes. Moreover, the combination of AI‐based algorithms and QC may have enormous potential to enhance the practical application of SPM. The limitations of the AI‐QC‐based approach were also discussed. Finally, we outline a research path for improving AI‐QC‐powered SPM.Research Highlights Artificial intelligence and quantum computing as support for scanning probe microscopy. The analysis indicates a research gap in the field of scanning probe microscopy. The research aims to shed light into ai‐qc‐powered scanning probe microscopy.

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