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Fe2O3 is an appealing anode material due to its high specific capacity (1007 mAh g− 1), low cost, natural abundance, and nontoxicity. However, its unstable structure during cycling processes has hindered its potential. In this study, we present a “green” synthesis method to fabricate stable porous Fe2O3 encapsulated in a buffering hollow structure (p-Fe2O3@h-TiO2) as an effective anode material for Li-ion batteries. The synthesis process only involves glucose as an “etching” agent, without the need for organic solvents or difficult-to-control environments. Characterizations of the nanostructures, chemical compositions, crystallizations, and thermal behaviors for the intermediate/final products confirm the formation of p-Fe2O3@h-TiO2. The synthesized Fe2O3 anode material effectively accommodates volume change, decreases pulverization, and alleviates agglomeration, leading to a high capacity that is over eleven times greater than that of the as-received commercial Fe2O3 after a long cycling process. This work provides an attractive, “green” and efficient method to convert commercially abundant resources like Fe2O3 into effective electrode materials for energy storage systems. 

Reversible phenotypic flexibility allows organisms to better match phenotypes to prevailing environmental conditions and may produce fitness benefits. Costs and constraints of phenotypic flexibility may limit the capacity for flexible responses but are not well understood nor documented. Costs could include expenses associated with maintaining the flexible system or with generating the flexible response. One potential cost of maintaining a flexible system is an energetic cost reflected in the basal metabolic rate (BMR), with elevated BMR in individuals with more flexible metabolic responses. We accessed data from thermal acclimation studies of birds where BMR and/or M_sum(maximum cold-induced metabolic rate) were measured before and after acclimation, as a measure of metabolic flexibility, to test the hypothesis that flexibility in BMR (ΔBMR), M_sum(ΔM_sum), or metabolic scope (M_sum − BMR; ΔScope) is positively correlated with BMR. When temperature treatments lasted at least three weeks, three of six species showed significant positive correlations between ΔBMR and BMR, one species showed a significant negative correlation, and two species showed no significant correlation. ΔM_sumand BMR were not significantly correlated for any species and ΔScope and BMR were significantly positively correlated for only one species. These data suggest that support costs exist for maintaining high BMR flexibility for some bird species, but high flexibility in M_sumor metabolic scope does not generally incur elevated maintenance costs.

 

Quantum simulation is a prominent application of quantum computers. While there is extensive previous work on simulating finite-dimensional systems, less is known about quantum algorithms for real-space dynamics. We conduct a systematic study of such algorithms. In particular, we show that the dynamics of a d -dimensional Schrödinger equation with η particles can be simulated with gate complexity O ~ ( η d F poly ( log ⁡ ( g ′ / ϵ ) ) ) , where ϵ is the discretization error, g ′ controls the higher-order derivatives of the wave function, and F measures the time-integrated strength of the potential. Compared to the best previous results, this exponentially improves the dependence on ϵ and g ′ from poly ( g ′ / ϵ ) to poly ( log ⁡ ( g ′ / ϵ ) ) and polynomially improves the dependence on T and d , while maintaining best known performance with respect to η . For the case of Coulomb interactions, we give an algorithm using η 3 ( d + η ) T poly ( log ⁡ ( η d T g ′ / ( Δ ϵ ) ) ) / Δ one- and two-qubit gates, and another using η 3 ( 4 d ) d / 2 T poly ( log ⁡ ( η d T g ′ / ( Δ ϵ ) ) ) / Δ one- and two-qubit gates and QRAM operations, where T is the evolution time and the parameter Δ regulates the unbounded Coulomb interaction. We give applications to several computational problems, including faster real-space simulation of quantum chemistry, rigorous analysis of discretization error for simulation of a uniform electron gas, and a quadratic improvement to a quantum algorithm for escaping saddle points in nonconvex optimization. 

With plenty of charges and rich functional groups, bovine serum albumin (BSA) protein provides effective transport for multiple metallic ions inside blood vessels. Inspired by the unique ionic transport function, we develop a BSA protein coating to stabilize Li anode, regulate Li+ transport, and resolve the Li dendrite growth for Li metal batteries (LMBs). The experimental and simulation studies demonstrate that the coating has strong interactions with Li metal, increases the wetting with electrolyte, reduces the electrolyte/Li side reactions, and significantly suppresses the Li dendrite formation. As a result, the BSA coating exhibits excellent stability in the electrolyte and improves the performance of Li|Cu and Li|Li cells as well as the LiFePO4|Li batteries. This work reveals that LMBs can benefit from the biological function of BSA, i.e., special transport capability of metallic ions, and lays an important foundation in design of protein-based materials for effectively enhancing the electrochemical performance of energy storage systems.