Conductivity hysteresis in MXene driven by structural dynamics of nanoconfined water
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Time-resolved and ultrafast electron energy-loss spectroscopy (EELS) is an emerging technique for measuring photoexcited carriers, lattice dynamics, and near-fields across femtosecond to microsecond timescales. When performed in either a specialized scanning transmission electron microscope or ultrafast electron microscope (UEM), time-resolved and ultrafast EELS can directly image charge carriers, lattice vibrations, and heat dissipation following photoexcitation or applied bias. Yet, recent advances in theoretical calculations and electron optics are often required to realize the full potential of ultrafast EEL spectrum imaging. Here, in this review, we present a comprehensive overview of the recent progress in the theory and instrumentation of time-resolved and ultrafast EELS. We begin with an introduction to the technique, followed by a physical description of the loss function. We outline approaches for calculating and interpreting ground-state and transient EEL spectra spanning low-loss plasmons to core-level excitations analogous to x-ray absorption. We then survey the current state of time-resolved and ultrafast EELS techniques beyond photon-induced near-field electron microscopy, highlighting abilities to image carrier and thermal dynamics. Finally, we examine future directions enabled by emerging technologies, including electron beam monochromation, in situ and operando cells, laser-free UEM, and high-speed direct electron detectors. These advances position time-resolved and ultrafast EELS as a critical tool for uncovering nanoscale dynamic processes in quantum materials and solar energy conversion devices.
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The structural and microstructural responses of a model metal–organic framework material, Ni(3-methyl-4,4'-bipyridine)[Ni(CN) 4 ] (Ni-BpyMe or PICNIC-21), to CO 2 adsorption and desorption are reported for in situ small-angle X-ray scattering and X-ray diffraction measurements under different gas pressure conditions for two technologically important cases. These conditions are single or dual gas flow (CO 2 with N 2 , CH 4 or H 2 at sub-critical CO 2 partial pressures and ambient temperatures) and supercritical CO 2 (with static pressures and temperatures adjusted to explore the gas, liquid and supercritical fluid regimes on the CO 2 phase diagram). The experimental results are compared with density functional theory calculations that seek to predict where CO 2 and other gas molecules are accommodated within the sorbent structure as a function of gas pressure conditions, and hence the degree of swelling and contraction in the associated structure spacings and void spaces. Furthermore, these predictions illustrate the insights that can be gained concerning how such sorbents can be designed or modified to optimize the desired gas sorption properties relevant to enhanced gas recovery or to addressing carbon dioxide reduction through carbon mitigation, or even direct air capture of CO 2 .
The response of spent nuclear fuel (SNF) to hypothetical package drop events is of particular interest in the scope of spent fuel storage and transportation because of the mechanical shock encountered in such scenarios. Previous testing and modeling by the U.S. Department of Energy has demonstrated that the shock and vibration environment of normal shipping and handling conditions (excluding package drop events) is relatively benign and does not challenge the integrity of spent nuclear fuel. Cask drop events are worth considering because SNF packages are required to withstand free drops onto unyielding surfaces as part of their licensing basis. The acceleration experienced during drop events can be orders of magnitude higher, and thus more advanced models are needed to encompass potential nonlinear behavior of the fuel, such as spacer grid buckling and rod-to-rod impact. This work describes a number of finite element models developed to calculate the response of spent nuclear fuel to various hypothetical drop events that have been validated by package and fuel assembly drop tests conducted in the last decade. Sensitivity of the model response to factors such as package drop orientation, secondary impacts, and irradiated material properties as well as their potential impacts to fuel cladding integrity, was also investigated. Cask drops are not expected as a regular occurrence during SNF transportation, but this work helps raise the understanding of SNF mechanical loads to the point of consistency with the package design requirements.
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**SACLA** A crystallographic pump-power titration was first carried-out with pump laser fluences of 12, 30, 60 and 120 uJ.cm-2 and a time delay of 3 us. Then, a time-series was performed with a pump-laser fluence of 30 uJ.cm-2 (~2.4 absorbed photon per chromophore) and time-delays of 10 ns, 300 ns, 3 µs, 100 us and 3 ms. Finally, two time-delays (10 ns and 3 us) were collected with a pump laser fluence of 12 uJ.cm-2. Raw images, crystFEL streams and merged mtzs are available for all collected dat **SwissFEL** A time series with a pump laser fluence of 30 mJ.cm-2 was performed at time delys of 3 µs and 10 ms. Raw images, crystFEL streams and merged mtzs are available for all collected datasets. Refined detector geometry for each experimental campaign is also provided (crystFEL format)
**SACLA** A crystallographic pump-power titration was first carried-out with pump laser fluences of 12, 30, 60 and 120 ??J.cm-2 and a time delay of 3 ??s. Then, a time-series was performed with a pump-laser fluence of 30 ??J.cm-2 (~2.4 absorbed photon per chromophore) and time-delays of 10 ns, 300 ns, 3 ??s, 100 ??s and 3 ms. Finally, two time-delays (10 ns and 3 ??s) were collected with a pump laser fluence of 12 ??J.cm-2. Raw images, crystFEL streams and merged mtzs are available for all collected dat **SwissFEL** A time series with a pump laser fluence of 30 mJ.cm-2 was performed at time delys of 3 ??s and 10 ms. Raw images, crystFEL streams and merged mtzs are available for all collected datasets. Refined detector geometry for each experimental campaign is also provided (crystFEL format)
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The tension between accuracy and computational cost is a common thread throughout computational simulation. One such example arises in the modeling of mechanical joints. Joints are typically confined to a physically small domain and yet are computationally expensive to model with a high-resolution finite element representation. A common approach is to substitute reduced-order models that can capture important aspects of the joint response and enable the use of more computationally efficient techniques overall. Unfortunately, such reduced-order models are often difficult to use, error prone, and have a narrow range of application. In contrast, we propose a new type of reduced-order model, leveraging machine learning, that would be both user-friendly and extensible to a wide range of applications.
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