Article
Impact of PHI TOF-SIMS instruments on scientific discovery
Looking back at last year and review some remarkable contributions in the TOF-SIMS community made possible with PHI nanoTOF instruments. The year 2024 saw an 18% increase in the number of hits for “TOF-SIMS” on Google Scholar across various industries. In the energy storage sector, researchers at the Shanghai Key Laboratory for R&D and Application of Metallic Functional Materials¹ used tin octoate to preferentially adsorb onto the copper substrate of the lithium metal battery’s current collector, thereby protecting against side reactions that reduce battery performance. In this work, they employed a nanoTOF II to generate chemical maps of the anode surface, monitoring species such as Li⁺, Li₂F⁺, LiCO₃⁺, and Sn⁺ to evaluate the effectiveness of the tin octoate coating.
At the School of Materials Science and Engineering, China University of Petroleum², researchers are working towards increasing the lifetime of sodium-chloride batteries. In this study, they developed a self-depassivated cathode (SDC) that incorporates an iodide anion to extend battery life. Using the PHI nanoTOF II to generate three-dimensional images of the sodium-chloride battery material, they demonstrated a reduced formation of Na₃Cl₂ in the SDC material compared to the original cathode material. The third article reviewed here, from the Beijing Institute of Technology³, utilized the nanoTOF II to image dendrite formation in all-solid-state sodium batteries. TOF-SIMS depth profiles were used to confirm findings obtained by other techniques, such as fluorescence tomography.
The PHI nanoTOF system also proved invaluable for research into material design and engineering. In one example⁴, a TRIFT V nanoTOF was employed to chemically map the surface of electrodes used in seawater electrolysis. In particular, the nanoTOF was crucial in confirming the formation of CrO₄²⁻, a necessary component of the catalytic electrolysis process. In another example⁵, researchers investigated energy irregularities in the lateral direction of photovoltaic devices. While vertical junctions have been extensively studied, optimizing these lateral junctions can also enhance perovskite solar cell performance. It was discovered that a Phenylpropanylamidinium (PPAd) passivation layer suppresses micro-inhomogeneity in both the lateral and vertical junctions. A nanoTOF 3 TOF-SIMS instrument was then used to generate three-dimensional images that unambiguously showed that the PPAd passivation layer resided exclusively at the surface of the perovskite film.
The final study in this 2024 year-in review comes from the Université Grenoble Alpes, CEA Leti in Grenoble, France6. In this work, the researchers developed a novel technique to combine the high spatial resolution of the unbunched imaging mode of the nanoTOF II with the high mass resolution spectral data of the bunched analytical mode. This integrated approach enables the precise identification and annotation of molecules within the cellular context at a lateral resolution of 100 nm. The two datasets were processed and merged using the non-negative matrix factorization (NMF) multivariate analysis (MVA), a technique that is rapidly gaining popularity within the TOF-SIMS community, as demonstrated in this study.
In typical TOF-SIMS analysis, there is a trade-off between high lateral resolution (HLR) and high mass resolution (HMR) due to the characteristics of the pulsed primary ion beam. When the pulse is focused to its smallest spot width, it yields excellent spatial definition, but the resulting longer ion pulse leads to poor mass resolution in the Time-of-Flight mass analyzer. For example, Figure 1B shows the high spatial definition of snow alga cells, whereas Figure 1A illustrates mass resolution that is insufficient to distinguish between Al⁺ and C₂H₃⁺. Conversely, “bunching” the primary ion pulse reduces its duration and achieves superior mass resolution (Figure 1C), but at the expense of image quality (Figures 1D and 1E).
Figure 1 - Comparison of High Lateral Resolution (A,B) and High Mass Resolution (C, D, E).
To bridge this gap, the two datasets were independently analyzed using NMF. The HLR dataset yielded a list of components with well-defined spatial features, though their chemical compositions could not be directly identified. In contrast, NMF applied to the HMR dataset produced components that allowed for confident chemical assignments based on the high-resolution spectra. By comparing and correlating the component images from both datasets, the researchers were able to merge the spatial and chemical information effectively. Figure 2 shows an example of this process for one component—the resin substrate.
Figure 2 - Results of the NMF analysis of each of the HLR and HMR data sets for Component A.
This process was repeated for each of the top 20 components identified by NMF, ensuring a comprehensive characterization of the sample. The analysis confirmed that NMF can accurately differentiate key cellular structures by correlating the chemical composition derived from high mass resolution data with the spatial details captured in high lateral resolution images. As seen in Figure 3, this approach enabled them to assign the chemical composition to specific areas in the high-resolution image, accurately identifying starch granules (B), lipid droplets (C), chloroplasts and nuclei (D), cell walls (E), and the cell cytoplasm (F).
Figure 3 - Six components identified by NMF analysis including resin (A), resin and starch granules (B), cell interior and lipid droplets (C), chloroplasts and nuclei (D), cell walls (E), and cell cytoplasm (F).
The ability to integrate high spatial and mass resolution data in such a precise manner represents a significant advancement in TOF-SIMS analysis. By leveraging NMF multivariate analysis, the researchers have demonstrated a robust approach for overcoming the inherent trade-off between lateral and mass resolution. This technique not only enhances the chemical specificity of imaging but also opens new avenues for investigating complex biological structures at an unprecedented level of detail. As TOF-SIMS methodologies continue to evolve, strategies like this will play a crucial role in expanding the technique’s applications across diverse scientific fields.
- https://doi.org/10.1038/s41563-024-01997-8
- https://doi.org/10.1038/s41467-024-51033-1
- sciadv.adr0676.pdf
- https://doi.org/10.1038/s41467-024-51130-1
- https://doi.org/10.1038/s41467-024-53953-4
- https://doi.org/10.1101/2024.07.15.603549