The development of freestanding flexible electrodes with high capacity and long cycle-life is a central issue for lithium-ion batteries (LIBs). Here, we use bacteria absorption of metallic Mn(2+) ions to in situ synthesize natural micro-yolk-shell-structure Mn2P2O7-carbon, followed by the use of vacuum filtration to obtain Mn2P2O7-carbon@reduced graphene oxides (RGO) papers for LIBs anodes. The Mn2P2O7 particles are completely encapsulated within the carbon film, which was obtained by carbonizing the bacterial wall. The resulting carbon microstructure reduces the electrode-electrolyte contact area, yielding high Coulombic efficiency. In addition, the yolk-shell structure with its internal void spaces is ideal for sustaining volume expansion of Mn2P2O7 during charge/discharge processes, and the carbon shells act as an ideal barrier, limiting most solid-electrolyte interphase formation on the surface of the carbon films (instead of forming on individual particles). Notably, the RGO films have high conductivity and robust mechanical flexibility. As a result of our combined strategies delineated in this article, our binder-free flexible anodes exhibit high capacities, long cycle-life, and excellent rate performance.

Early in the engineering design cycle, it is difficult to quantify product reliability or compliance to performance targets due to insufficient data or information to model uncertainties. Probability theory cannot be, therefore, used. Design decisions are usually based on fuzzy information that is vague, imprecise qualitative, linguistic or incomplete. Recently, evidence theory has been proposed to handle uncertainty with limited information as an alternative to probability theory. In this paper, a computationally efficient design optimization method is proposed based on evidence theory, which can handle a mixture of epistemic and random uncertainties. It quickly identifies the vicinity of the optimal point and the active constraints by moving a hyperellipse in the original design space, using a reliability-based design optimization (RBDO) algorithm. Subsequently, a derivative-free optimizer calculates the evidence-based optimum, starting from the close-by RBDO optimum, considering only the identified active constraints. The computational cost is kept low by first moving to the vicinity of the optimum quickly and subsequently using local surrogate models of the active constraints only. Two examples demonstrate the proposed evidence-based design optimization method.

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We design a carbon-free Si/polyaniline hybrid anode composed of porous Si dendrites and oxalic acid doped polyaniline coatings.