The need for clean, renewable energy has driven the expansion of research focused on new materials for electrocatalysts, fuel cells, and batteries. These materials need to be plentiful, inexpensive, and have high energy capacity operating lifetimes to power a growing consumer market that is seeking alternatives to fossil-based fuel sources.[1] However, energy materials often operate at the nanoscale, which involves structural or morphological changes that can only be observed using transmission electron microscopy (TEM). To get more insight in the nanoscale changes, operando TEM can be used to observe a catalyst or battery material under its native operating conditions.Operando TEM techniques enable dynamic, real-time imaging of a material’s nanoscale processes as they occur within their functional environment.[2] This enables researchers to simultaneously follow structural and morphological changes and correlate those to the material’s electrochemical behavior. Operando studies using liquid electrolytes can utilize liquid-EM. In this case, a sample is encapsulated in a liquid environment between electron transparent membranes, which enables researchers to study reactions as they occur in an electrolyte environment.[1] This technique has enabled researchers to better understand the mechanistic pathways that can result in electrocatalyst deactivation [3,4] battery capacity fading [5-7]and identify targets within these materials to improve their performance.[8,9,10] However, as operando TEM combines multiple challenging topics, e.g. battery studies, electrolyte degradation, radiolysis and dose management, it has so far been a laborious undertaking for any one researcher alone.Successful operando liquid-EM experiments are challenging due to many factors, beginning with experiment design and workflow, and culminating complex analysis of multiple streams of large, often cumbersome, datasets. Here, we have set out to address and develop hardware and software tools which mitigate some of the most common challenges associated with operando liquid-EM electrochemical studies. Using a machine vision software platform called AXON, a holistic, workflow-driven approach to address key challenges and pain points associated with in-situ and operando TEM studies has been developed. New hardware and MEMs-based features integrated into the liquid-EM solution, Poseidon Select (Figure 1A), include the ability to perform electrochemical studies beyond room temperature, and improved stabilization of electrochemical signals for correlation with bulk studies. Moreover, adding a Luggin-Haber metal probe for electrical measurements allows for the use of any reference electrode compared to the limitation of a pseudo-reference electrode, e.g. platinum (Figure 1B).[11] On top of this, AXON uses learning algorithms for improved stabilization and tracking of dynamic samples, accurate dose quantification and management, consolidation of experimental parameters and metadata, and an intuitive, free-to-use, visual data analysis tool, AXON Studio.In this talk, examples of liquid-EM microscopy work for energy materials will be shown, as shown in Figure 1C-D. In this figure, copper nanograins were used to convert CO2 to multicarbon products while observing the morphological changes to the nanograins over time and simultaneously observing the electrochemical changes in the sample. Moreover, explanations and examples will be shown on how machine-vision software solutions can improve the workflow of electrochemical in-situ experiments. These workflow methods elevate the operation of in-situ experiments so that any one researcher can now operate microscope and in-situ cell simultaneously.[1] Yang, Y. et al. (2021) ACS Catalysis 11, 1136–1178.[2] Chenna, S. et al. (2012) ACS Catalysis, 2, 2395–2402.[3] Shi, F. et al. (2020) Chem, 6, 2257–2271.[4] Impagnatiello, A. et al. (2020) ACS Applied Energy Materials, 3, 2360–2371.[5] Bhatia, A. et al. (2021) Small Methods, 2100891.[6] He, K. et al. (2018) Nano Energy, 49, 338–345[7] Sasaki, Y. et al. (2021) Journal of Power Sources, 481, 228831.[8] Pu, S.D. et al. (2020) ACS Energy Letters, 5, 2283–2290.[9] Balaghi, S.E. et al. (2021) ACS Applied Materials & Interfaces, 13, 19927–19937.[10] Yang, Y. et al. (2023) Nature, 614, 262-270[11] Choudhary, S. et al (2022) Journal of the Electrochemical Society, 169, 111505 Figure 1
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Jan 1, 2024
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