Liquid surface established by standing waves is used as a dynamically reconfigurable template to assemble microscale materials into ordered, symmetric structures in a scalable and parallel manner. We illustrate broad applicability of this technology by assembling diverse materials from soft matter, rigid bodies, individual cells, cell spheroids and cell-seeded microcarrier beads.

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Voltage-based battery metrics are ubiquitous and essential in battery manufacturing diagnostics. They enable electrochemical “fingerprinting” of batteries at the end of the manufacturing line and are naturally scalable, since voltage data is already collected as part of the formation process which is the last step in battery manufacturing. Yet, despite their prevalence, interpretations of voltage-based metrics are often ambiguous and require expert judgment. In this work, we present a method for collecting and analyzing full cell near-equilibrium voltage curves for end-of-line manufacturing process control. The method builds on existing literature on differential voltage analysis (DVA or dV/dQ) by expanding the method formalism through the lens of reproducibility, interpretability, and automation. Our model revisions introduce several new derived metrics relevant to manufacturing process control, including lithium consumed during formation and the practical negative-to-positive ratio, which complement standard metrics such as positive and negative electrode capacities. To facilitate method reproducibility, we reformulate the model to account for the “inaccessible lithium problem” which quantifies the numerical differences between modeled versus true values for electrode capacities and stoichiometries. We finally outline key data collection considerations, including C-rate and charging direction for both full cell and half cell datasets, which may impact method reproducibility. This work highlights the opportunities for leveraging voltage-based electrochemical metrics for online battery manufacturing process control.

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The assembly of microscale materials offers unique opportunities for tissue engineering, material science, microelectronics, and microphotonics. U. Demirci and co‐workers show on page 5936 that liquid surfaces established by standing waves can be used as dynamically reconfigurable templates for assembling diverse symmetric and periodic structures from microscale hydrogels, cells, and cell spheroids, to neuron‐seeded microcarrier beads.

The formation process is the last step in the lithium ion battery manufacturing process but plays an outsize role in determining cell costs [1], [2]. A typical formation protocol consists of multiple low-rate charge-discharge cycles which can take many days to complete. During formation, the solid-electrolyte interface (SEI) is created through irreversible lithium reduction reactions with the electrolyte at the negative electrode surface. Formation protocols are difficult to optimize in part because their impact to long term cell lifetime and safety remain under debate. For example, while some authors have reported that the voltage window and charging rate during formation could impact cycle life[3], [4], others have found that formation protocols as fast as 14 hours[5] or ones causing lithium plating[6] have little to no impact on cycle life. The current evidence suggests that there remain opportunities to optimize the formation protocol for a given cell chemistry and design. However, finding an optimal formation protocol will require a more detailed understanding of the long-term impact on cell degradation and with consideration for cell safety. In this study, we design a test sequence and identify diagnostic signals to elucidate the impact of a fast formation protocol[5] on the long-term cycle life behavior of NMC/graphite cells. The study consists of 40 identical pouch cells built in-house at the University of Michigan Battery Lab. Half of the cells were subjected to the fast formation protocol and the remaining cells used a baseline formation protocol. Each set of cells were further split into two groups to be cycled at room temperature and inside a temperature-controlled oven at 45°C. The aging test consists of 1C charge-discharge cycles between 3.0V and 4.2V. Periodic reference performance tests were inserted as part of the aging test to provide additional diagnostic signals, including cell internal resistances as a function of state of charge and low-rate voltage curves for differential capacity analysis. Cells were stopped after discharge capacities were measured to be below 50%. At the end of the test, the degree of cell swelling was measured. The results showed that the cells with fast formation had a longer cycle life compared to control, but the same cells also showed higher cell-to-cell variability towards the end of life. Nearly all cells with fast formation showed moderate to severe swelling when cycled at 45°C, in contrast to the cells with baseline formation which showed little to no volume change. Taken together, these results suggest that it is possible for a fast formation protocol to yield similar or better cycle life but still potentially compromise the safety of the cell through increased gas generation rates. We also monitored the cycle life of several cells that showed a non-monotonic current behavior during formation attributable to a reversible lithium plating process. The cycle life and resistance growth of these cells fell within the expected range of values after considering cell-to-cell variation, suggesting that the presence of transient lithium plating during formation does not have a long-term impact to cell lifetime. Finally, in exploring the diagnostic signals, we identify two beginning-of-life signals that showed positive correlation with the maximum cycle count and cell resistance measured at the end-of-life: (1) the cell resistance measured at low states of charge, and (2) the voltage decay during a rest step inserted at the end of the last formation charge cycle. This last finding revitalizes the possibility of designing cell lifetime predictors using simple metrics measured at the beginning of life. [1] D. L. Wood, J. Li, and C. Daniel, J. Power Sources, vol. 275, pp. 234–242, 2015 [2] K. Kuhlmann, S. Wolf, C. Pieper, G. Xu, and J. Ahmad, Bost. Consult. Gr., pp. 1–22, 2018 [3] T. S. Pathan, M. Rashid, M. Walker, W. D. Widanage, and E. Kendrick, J. Phys. Energy, vol. 1, no. 4, p. 044003, 2019 [4] C. Mao et al., J. Power Sources, vol. 402, no. July, pp. 107–115, 2018 [5] D. L. Wood, J. Li, and S. J. An, Joule, vol. 3, no. 12, pp. 2884–2888, 2019 [6] M. Ryan et al., J. Power Sources, vol. 484, pp. 0–7, 2021 Figure 1