Stable delivery of nano-beams for advanced nano-scale analyses

Ohashi, Haruhiko; Yamazaki, Hiroshi; Yumoto, Hirokatsu; Koyama, Takahisa; Senba, Yasunori; Takeuchi, Tomoyuki; Terada, Yasuko; Suzuki, Motohiro; Kawamura, N.; Mizumaki, Masaichiro; Nariyama, Nobuteru; Takeshita, Kunikazu; Fujiwara, Akihiko; Uruga, Tomoya; Goto, Shunji; Yamamoto, Masaki; Takata, Masaki; Ishikawa, Tetsuya

doi:10.1088/1742-6596/425/5/052018

Cited by 6 publications

(2 citation statements)

References 7 publications

(6 reference statements)

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“…Recently, nano-beams (50 Â 50 nm 2 ) have also become available for XRF and XAFS. 139 The spatial resolution of micro-PIXE is also at the submicron level 140 and the analytical method is capable of determining trace elements in tissues at a several ppm level. Both SR-XRF and micro-PIXE cause little damage to tissue specimens.…”

Section: Microbeam Elemental Analysismentioning

confidence: 99%

The key role of atomic spectrometry in radiation protection

Zheng¹,

Tagami²,

Homma-Takeda³

et al. 2013

J. Anal. At. Spectrom.

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Section: Microbeam Elemental Analysismentioning

confidence: 99%

The key role of atomic spectrometry in radiation protection

Zheng¹,

Tagami²,

Homma-Takeda³

et al. 2013

J. Anal. At. Spectrom.

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“…Doing so will not only enhance our comprehension but will also enable us to leverage these findings more effectively for the greater benefit. For example, the study by Ohashi et al [13] underscores the necessity for the stable delivery of nano-beams in facilitating advanced nanoscale analyses. However, the diminutive dimensions of these structures pose unique challenges pertaining to their dynamic behavior, notably when exposed to vibrational forces [14,15].…”

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confidence: 99%

A neural state-space-based model predictive technique for effective vibration control in nano-beams

Alsubaie

2023

Front. Phys.

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Model predictive control (MPC) is a cutting-edge control technique, but its susceptibility to inaccuracies in the model remains a challenge for embedded systems. In this study, we propose a data-driven MPC framework to address this issue and achieve robust and adaptable performance. Our framework involves systematically identifying system dynamics and learning the MPC policy through function approximations. Specifically, we introduce a system identification method based on the Deep neural network (DNN) and integrate it with MPC. The function approximation capability of DNN enables the controller to learn the nonlinear dynamics of the system then the MPC policy is established based on the identified model. Also, through an added control term the robustness and convergence of the closed-loop system are guaranteed. Then the governing equation of a non-local strain gradient (NSG) nano-beam is presented. Finally, the proposed control scheme is used for vibration suppression in the NSG nano-beam. To validate the effectiveness of our approach, the controller is applied to the unknown system, meaning that solely during the training phase of the neural state-space-based model we relied on the data extracted from the time history of the beam’s deflection. The simulation results conclusively demonstrate the remarkable performance of our proposed approach in effectively suppressing vibrations.

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Microscopic synchrotron X-ray analysis of mercury waste in simulated landfill experiments

Takaoka

Kusakabe

Shiota

et al. 2023

J Mater Cycles Waste Manag

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Mercury enters into the environment or waste streams because it is present as an impurity in natural minerals. Mercury must be appropriately managed as an hazardous waste. In this study, a waste layer of artificial mercury sulfide mixed with incinerator ash and sewage sludge compost in a simulated landfill experiment for 5 years was analyzed using microscopic synchrotron X-ray to obtain basic knowledge of mercury behavior in a landfill. Mapping by synchrotron X-ray revealed the distribution of mercury-containing particles in the waste layer. In most cases, the movement of mercury sulfide was not considered significant even within a microscopic range; however, water flows could enhance the movement of mercury sulfide particles. When disposing of mercury sulfide, “concentrated placement” or solidification, rather than mixing with other wastes, was more effective at preventing mercury leaching in lysimeters. The chemical form of mercury sulfide in each lysimeter was confirmed by X-ray absorption fine structure (XAFS) analysis, which showed that most of the mercury was present as metacinnabar and had not undergone any changes, indicating that it was extremely stable. The microscopic synchrotron X-ray analysis proved very useful for studying the behavior of mercury waste in a simulated landfill experiment.

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Stable delivery of nano-beams for advanced nano-scale analyses

Cited by 6 publications

References 7 publications

The key role of atomic spectrometry in radiation protection

The key role of atomic spectrometry in radiation protection

A neural state-space-based model predictive technique for effective vibration control in nano-beams

Microscopic synchrotron X-ray analysis of mercury waste in simulated landfill experiments

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