Measurements of the open circuit voltage of Li-ion cells have been extensively used as a non-destructive characterisation tool. Another technique based on entropy change measurements has also been applied for this purpose. More recently, both techniques have been used to make qualitative statements about aging in Li-ion cells. One proposed cause of cell failure is point defect formation in the electrode materials. The steps in voltage profiles, and the peaks in entropy profiles are sensitive to order/disorder transitions arising from Li/vacancy configurations, which are affected by the host lattice structures. We compare the entropy change results, voltage profiles and incremental capacity (dQ/dV) obtained from coin cells with spinel lithium manganese oxide (LMO) cathodes, Li1+yMn2-yO4, where excess Li y was added in the range 0 ≤ y ≤ 0.2. A clear trend of entropy and dQ/dV peak amplitude decrease with excess Li amount was determined. The effect arises, in part, from the presence of pinned Li sites, which disturb the formation of the ordered phase. We modelled the voltage, dQ/dV and entropy results as a function of the interaction parameters and the excess Li amount, using a mean field approach. For a given pinning population, we demonstrated that the asymmetries observed in the dQ/dV peaks can be modelled by a single linear correction term. To replicate the observed peak separations, widths and magnitudes, we had to account for variation in the energy interaction parameters as a function of the excess Li amount, y. All Li-Li repulsion parameters in the model increased in value as the defect fraction, y, increased. Our paper shows how far a computational mean field approximation can replicate experimentally observed voltage, incremental capacity and entropy profiles in the presence of phase transitions.

In-situ diagnostic tools have become established to as a means to understanding the aging processes that occur during charge/discharge cycles in Li-ion batteries (LIBs). One electrochemical thermodynamic technique that can be applied to this problem is known as entropy profiling. Entropy profiles are obtained by monitoring the variation in the open circuit potential as a function of temperature. The peaks in these profiles are related to phase transitions, such as order/disorder transitions, in the lattice. In battery aging studies of cathode materials, the peaks become suppressed but the mechanism by which this occurs is currently poorly understood. One suggested mechanism is the formation of point defects. Intentional modifications of LIB electrodes may also lead to the introduction of point defects. To gain quantitative understanding of the entropy profile changes that could be caused by point defects, we have performed Monte Carlo simulations on lattices of variable defect content. As a model cathode, we have chosen manganese spinel, which has a well-described order-disorder transition when it is half filled with Li. We assume, in the case of trivalent defect substitution (M=Cr,Co) that each defect M permanently pins one Li atom. This assumption is supported by Density Functional Theory (DFT) calculations. Assuming that the distribution of the pinned Li sites is completely random, we observe the same trend in the change in partial molar entropy with defect content as observed in experiment: the peak amplitudes become increasing suppressed as the defect fraction is increased. We also examine changes in the configurational entropy itself, rather than the entropy change, as a function of the defect fraction and analyse these results with respect to the ones expected for an ideal solid solution. We discuss the implications of the quantitative differences between some of the results obtained from the model and the experimentally observed ones.

Connected devices present new opportunities to advance design through data collection in the wild, similar to the way digital services evolve through analytics. However, it is still unclear how live data transmitted by connected devices informs the design of these products, going beyond performance optimisation to support creative practices. Design can be enriched by data captured by connected devices, from usage logs to environmental sensors, and data about the devices and people around them. Through a series of workshops, this paper contributes industry and academia perspectives on the future of data-driven product design. We highlight HCI challenges, issues and implications, including sensemaking and the generation of design insight. We further challenge current notions of data-driven design and envision ways in which future HCI research can develop ways to work with data in the design process in a connected, rich, human manner.

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