SEI growth on Lithium metal anodes in solid-state batteries quantified with coulometric titration time analysis.

Lithium-metal batteries with a solid electrolyte separator are promising for advanced battery applications, however, most electrolytes show parasitic side reactions at the low potential of lithium metal. Therefore, it is essential to understand how much (and how fast) charge is consumed in these parasitic reactions. In this study, a new electrochemical method is presented for the characterization of electrolyte side reactions occurring on active metal electrode surfaces. The viability of this new method is demonstrated in a so-called anode-free stainless steel ∣ Li6PS5Cl ∣ Li cell. The method also holds promise for investigating dendritic lithium growth (and dead lithium formation), as well as for analyzing various electrolytes and current collectors. The experimental setup allows easy electrode removal for post-mortem analysis, and the SEI's heterogeneous/layered microstructure is revealed through complementary analytical techniques. We expect this method to become a valuable tool in the future for solid-state lithium metal batteries and potentially other cell chemistries.

Identification of Lithium Compounds on Surfaces of Lithium Metal Anode with Machine-Learning-Assisted Analysis of ToF-SIMS Spectra.

Detailed knowledge about contamination and passivation compounds on the surface of lithium metal anodes (LMAs) is essential to enable their use in all-solid-state batteries (ASSBs). Time-of-flight secondary ion mass spectrometry (ToF-SIMS), a highly surface-sensitive technique, can be used to reliably characterize the surface status of LMAs. However, as ToF-SIMS data are usually highly complex, manual data analysis can be difficult and time-consuming. In this study, machine learning techniques, especially logistic regression (LR), are used to identify the characteristic secondary ions of 5 different pure lithium compounds. Furthermore, these models are applied to the mixture and LMA samples to enable identification of their compositions based on the measured ToF-SIMS spectra. This machine-learning-based analysis approach shows good performance in identifying characteristic ions of the analyzed compounds that fit well with their chemical nature. Moreover, satisfying accuracy in identifying the compositions of unseen new samples is achieved. In addition, the scope and limitations of such a strategy in practical applications are discussed. This work presents a robust analytical method that can assist researchers in simplifying the analysis of the studied lithium compound samples, offering the potential for broader applications in other material systems.

High resolution imaging and 3D analysis of Ag nanoparticles in cells with ToF-SIMS and delayed extraction.

Within this study, the authors use human mesenchymal stem cells incubated with silver nanoparticles (AgNPs) as a model system to systematically investigate the advantages and drawbacks of the fast imaging delayed extraction mode for two-dimensional and three-dimensional (3D) analyses at the cellular level. The authors compare the delayed extraction mode with commonly employed measurement modes in terms of mass and lateral resolution, intensity, and dose density. Using the delayed extraction mode for single cell analysis, a high mass resolution up to 4000 at m/z = 184.08 combined with a lateral resolution up to 360 nm is achieved. Furthermore, the authors perform 3D analyses with Ar-clusters (10 keV) and O2+ (500 eV) as sputter species, combined with Bi3+ and delayed extraction for analysis. Cell compartments like the nucleus are visualized in 3D, whereas no realistic 3D reconstruction of intracellular AgNP is possible due to the different sputter rates of inorganic and organic cell materials. Furthermore, the authors show that the sputter yield of Ag increases with the decreasing Ar-cluster size, which might be an approach to converge the different sputter rates.

Strontium release from Sr2+-loaded bone cements and dispersion in healthy and osteoporotic rat bone.

Drug functionalization of biomaterials is a modern and popular approach in biomaterials research. Amongst others this concept is used for the functionalization of bone implants to locally stimulate the bone healing process. For example strontium ions (Sr2+) are administered in osteoporosis therapy to stimulate bone growth and have recently been integrated into bone cements. Based on results of different analytical experiments we developed a two-phase model for the transport of therapeutically active Sr2+-ions in bone in combination with Korsmeyer-Peppas kinetics for the Sr2+ release from bone cement. Data of cement dissolution experiments into water in combination with inductively coupled plasma mass spectrometry (ICP-MS) analysis account for dissolution kinetics following Noyes-Whitney rule. For dissolution in α-MEM cell culture media the process is kinetically hindered and can be described by Korsmeyer-Peppas kinetics. Time of flight secondary ion mass spectrometry (ToF-SIMS) was used to determine the Sr2+ diffusion coefficient in healthy and osteoporotic trabecular rat bone. Therefore, bone sections were dipped in aqueous Sr2+-solution by one side and the Sr2+-profile was measured by classical SIMS depth profiling. The Sr2+ mobility can be described by a simple diffusion model and we obtained diffusion coefficients of (2.28±2.97)⋅10-12cm2/s for healthy and of (1.55±0.93)⋅10-10cm2/s for osteoporotic bone. This finding can be explained by a different bone nanostructure, which was observed by focused ion beam scanning electron microscopy (FIB-SEM) and transmission electron microscopy (TEM). Finally, the time and spatially resolved drug transport was calculated by finite element method for the femur of healthy and osteoporotic rats. The obtained results were compared to mass images that were obtained from sections of in vivo experiments by ToF-SIMS. The simulated data fits quite well to experimental results. The successfully applied model for the description of drug dispersion can help to reduce the number of animal experiments in the future.