We report new advancements in the determination and high-resolution structural analysis of beam-sensitive metal organic frameworks (MOFs) using microcrystal electron diffraction (MicroED) coupled with focused ion beam milling at cryogenic temperatures (cryo-FIB). A microcrystal of the beam-sensitive MOF, ZIF-8, was ion-beam milled in a thin lamella approximately 150 nm thick. MicroED data were collected from this thin lamella using an energy filter and a direct electron detector operating in counting mode. Using this approach, we achieved a greatly improved resolution of 0.59 Å with a minimal total exposure of only 0.64 e−/A2. These innovations not only improve model statistics but also further demonstrate that ion-beam milling is compatible with beam-sensitive materials, augmenting the capabilities of electron diffraction in MOF research.
more »
« less
Understanding Ion-Beam Damage to Air-Sensitive Lithium Metal With Cryogenic Electron and Ion Microscopy
Abstract It is essential to understand the nanoscale structure and chemistry of energy storage materials due to their profound impact on battery performance. However, it is often challenging to characterize them at high resolution, as they are often fundamentally altered by sample preparation methods. Here, we use the cryogenic lift-out technique in a plasma-focused ion beam (PFIB)/scanning electron microscope (SEM) to prepare air-sensitive lithium metal to understand ion-beam damage during sample preparation. Through the use of cryogenic transmission electron microscopy, we find that lithium was not damaged by ion-beam milling although lithium oxide shells form in the PFIB/SEM chamber, as evidenced by diffraction information from cryogenic lift-out lithium lamellae prepared at two different thicknesses (130 and 225 nm). Cryogenic energy loss spectroscopy further confirms that lithium was oxidized during the process of sample preparation. The Ellingham diagram suggests that lithium can react with trace oxygen gas in the FIB/SEM chamber at cryogenic temperatures, and we show that liquid oxygen does not contribute to the oxidation of lithium process. Our results suggest the importance of understanding how cryogenic lift-out sample preparation has an impact on the high-resolution characterization of reactive battery materials.
more »
« less
- Award ID(s):
- 2309043
- PAR ID:
- 10503697
- Publisher / Repository:
- Advanced Access Publication
- Date Published:
- Journal Name:
- Microscopy and Microanalysis
- Volume:
- 29
- Issue:
- 4
- ISSN:
- 1431-9276
- Page Range / eLocation ID:
- 1350 to 1356
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Abstract This work describes cryogenic ex situ lift out (cryo-EXLO) of cryogenic focused ion beam (cryo-FIB) thinned specimens for analysis by cryogenic transmission electron microscopy (cryo-TEM). The steps and apparatus necessary for cryo-EXLO are described. Methods designed to limit ice contamination include use of an anti-frost lid, a vacuum transfer assembly, and a cryostat. Cryo-EXLO is performed in a cryostat with the cryo-shuttle holder positioned in the cryogenic vapor phase above the surface of liquid N2 (LN2) using an EXLO manipulation station installed inside a glove box maintained at < 10% relative humidity and inert (e.g., N2 gas) conditions. Thermal modeling shows that a cryo-EXLO specimen will remain vitreous within its FIB trench indefinitely while LN2 is continuously supplied. Once the LN2 is cut off, modeling shows that the EXLO specimen will remain vitreous for over 4 min, allowing sufficient time for the cryo-transfer steps which take only seconds to perform. Cryo-EXLO was applied successfully to cryo-FIB-milled specimen preparation of a polymer sample and plunge-frozen yeast cells. Cryo-TEM of both the polymer and the yeast shows minimal ice contamination with the yeast specimen maintaining its vitreous phase, illustrating the potential of cryo-EXLO for cryo-FIB-TEM of beam-sensitive, liquid, or biological materials.more » « less
-
Understanding the behavior of pressure increases in lithium-ion (Li-ion) cells is essential for prolonging the lifespan of Li-ion battery cells and minimizing the safety risks associated with cell aging. This work investigates the effects of C-rates and temperature on pressure behavior in commercial lithium cobalt oxide (LCO)/graphite pouch cells. The battery is volumetrically constrained, and the mechanical pressure response is measured using a force gauge as the battery is cycled. The effect of the C-rate (1C, 2C, and 3C) and ambient temperature (10 ◦C, 25 ◦C, and 40 ◦C) on the increase in battery pressure is investigated. By analyzing the change in the minimum, maximum, and pressure difference per cycle, we identify and discuss the effects of different factors (i.e., SEI layer damage, electrolyte decomposition, lithium plating) on the pressure behavior. Operating at high C-rates or low temperatures rapidly increases the residual pressure as the battery is cycled. The results suggest that lithium plating is predominantly responsible for battery expansion and pressure increase during the cycle aging of Li-ion cells rather than electrolyte decomposition. Electrochemical impedance spectroscopy (EIS) measurements can support our conclusions. Postmortem analysis of the aged cells was performed using scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDS) to confirm the occurrence of lithium plating and film growth on the anodes of the aged cells. This study demonstrates that pressure measurements can provide insights into the aging mechanisms of Li-ion batteries and can be used as a reliable predictor of battery degradation.more » « less
-
Understanding the behavior of pressure increases in lithium-ion (Li-ion) cells is essential for prolonging the lifespan of Li-ion battery cells and minimizing the safety risks associated with cell aging. This work investigates the effects of C-rates and temperature on pressure behavior in commercial lithium cobalt oxide (LCO)/graphite pouch cells. The battery is volumetrically constrained, and the mechanical pressure response is measured using a force gauge as the battery is cycled. The effect of the C-rate (1C, 2C, and 3C) and ambient temperature (10 °C, 25 °C, and 40 °C) on the increase in battery pressure is investigated. By analyzing the change in the minimum, maximum, and pressure difference per cycle, we identify and discuss the effects of different factors (i.e., SEI layer damage, electrolyte decomposition, lithium plating) on the pressure behavior. Operating at high C-rates or low temperatures rapidly increases the residual pressure as the battery is cycled. The results suggest that lithium plating is predominantly responsible for battery expansion and pressure increase during the cycle aging of Li-ion cells rather than electrolyte decomposition. Electrochemical impedance spectroscopy (EIS) measurements can support our conclusions. Postmortem analysis of the aged cells was performed using scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDS) to confirm the occurrence of lithium plating and film growth on the anodes of the aged cells. This study demonstrates that pressure measurements can provide insights into the aging mechanisms of Li-ion batteries and can be used as a reliable predictor of battery degradation.more » « less
-
Abstract High-throughput materials research is strongly required to accelerate the development of safe and high energy-density lithium-ion battery (LIB) applicable to electric vehicle and energy storage system. The artificial intelligence, including machine learning with neural networks such as Boltzmann neural networks and convolutional neural networks (CNN), is a powerful tool to explore next-generation electrode materials and functional additives. In this paper, we develop a prediction model that classifies the major composition (e.g., 333, 523, 622, and 811) and different states (e.g., pristine, pre-cycled, and 100 times cycled) of various Li(Ni, Co, Mn)O2(NCM) cathodes via CNN trained on scanning electron microscopy (SEM) images. Based on those results, our trained CNN model shows a high accuracy of 99.6% where the number of test set is 3840. In addition, the model can be applied to the case of untrained SEM data of NCM cathodes with functional electrolyte additives.more » « less
An official website of the United States government

