The statistics of ToF-SIMS data revisited and introduction of the empirical Poisson correction

Lithium Self-Diffusion in a Polymer Electrolyte for Solid-State Batteries: ToF-SIMS/ssNMR Correlative Characterization and Modeling Based on Lithium Isotopic Labeling

Manufacturers aim to commercialize efficient and safe batteries by finding new strategies. Solid-state electrolytes can be seen as an opportunity to develop batteries with a high energy density. They allow the use of lithium foil as the anode, increasing the energy density. Also, they are composed of nonflammable materials making them safer than liquid electrolytes. However, to enhance the electrochemical performances of forthcoming solid-state lithium metal batteries, phenomena governing ionic conductivity have yet to be mastered in such devices. Lithium isotopic tracing was successfully used in previous works to further understand lithium ion transport mechanisms in batteries. Time-of-flight secondary ion mass spectrometry (ToF-SIMS) and 6/7Li high-resolution solid-state nuclear magnetic resonance (ssNMR) spectroscopy are two complementary techniques probing local and global scale, respectively. Both techniques can distinguish lithium isotopes. Here, four polymer membranes were elaborated with the same lithium concentration, but with various isotopic enrichments from 7.6 to 95.4% of 6Li. The selected material was a poly(ethylene oxide) (PEO) membrane containing lithium bis(trifluoromethylsulfonyl)imide (LiTFSI) as lithium salt. They are widely studied in the lithium battery field. First, reliable ToF-SIMS and ssNMR methodologies were validated in light of the converging results. They led to accurate determination of lithium isotopic abundance of polymer membranes with a 1 or a 2% uncertainty, respectively. Then, the developed methodologies were applied to characterize lithium self-diffusion in a polymer membrane. Furthermore, numerical simulations based on a two-dimensional diffusion model compared with ToF-SIMS analyses allowed us to extract a lithium self-diffusion coefficient of 1.6 × 10-12 m2·s-1 at 60 °C, which complements other published values. The robust methodologies described in this work can be extended to various applications and materials. They stand as powerful strategies to better understand lithium ionic transport, especially in multiphase materials, for example, in hybrid solid-state electrolytes.

Applications of multivariate analysis and unsupervised machine learning to ToF-SIMS images of organic, bioorganic, and biological systems

Time-of-flight secondary ion mass spectrometry (ToF-SIMS) imaging offers a powerful, label-free method for exploring organic, bioorganic, and biological systems. The technique is capable of very high spatial resolution, while also producing an enormous amount of information about the chemical and molecular composition of a surface. However, this information is inherently complex, making interpretation and analysis of the vast amount of data produced by a single ToF-SIMS experiment a considerable challenge. Much research over the past few decades has focused on the application and development of multivariate analysis (MVA) and machine learning (ML) techniques that find meaningful patterns and relationships in these datasets. Here, we review the unsupervised algorithms—that is, algorithms that do not require ground truth labels—that have been applied to ToF-SIMS images, as well as other algorithms and approaches that have been used in the broader family of mass spectrometry imaging (MSI) techniques. We first give a nontechnical overview of several commonly used classes of unsupervised algorithms, such as matrix factorization, clustering, and nonlinear dimensionality reduction. We then review the application of unsupervised algorithms to various organic, bioorganic, and biological systems including cells and tissues, organic films, residues and coatings, and spatially structured systems such as polymer microarrays. We then cover several novel algorithms employed for other MSI techniques that have received little attention from ToF-SIMS imaging researchers. We conclude with a brief outline of potential future directions for the application of MVA and ML algorithms to ToF-SIMS images.

Exploiting Generative Design for 3D Printing of Bacterial Biofilm Resistant Composite Devices

As the understanding of disease grows, so does the opportunity for personalization of therapies targeted to the needs of the individual. To bring about a step change in the personalization of medical devices it is shown that multi-material inkjet-based 3D printing can meet this demand by combining functional materials, voxelated manufacturing, and algorithmic design. In this paper composite structures designed with both controlled deformation and reduced biofilm formation are manufactured using two formulations that are deposited selectively and separately. The bacterial biofilm coverage of the resulting composites is reduced by up to 75% compared to commonly used silicone rubbers, without the need for incorporating bioactives. Meanwhile, the composites can be tuned to meet user defined mechanical performance with ±10% deviation. Device manufacture is coupled to finite element modelling and a genetic algorithm that takes the user-specified mechanical deformation and computes the distribution of materials needed to meet this under given load constraints through a generative design process. Manufactured products are assessed against the mechanical and bacterial cell-instructive specifications and illustrate how multifunctional personalization can be achieved using generative design driven multi-material inkjet based 3D printing.