Redesigning Solvation Structure toward Passivation-Free Magnesium Metal Batteries.

Simple magnesium (Mg) salt solutions are widely considered as promising electrolytes for next-generation rechargeable Mg metal batteries (RMBs) owing to the direct Mg2+ storage mechanism. However, the passivation layer formed on Mg metal anodes in these electrolytes is considered the key challenge that limits its applicability. Numerous complex halogenide additives have been introduced to etch away the passivation layer, nevertheless, at the expense of the electrolyte's anodic stability and cathodes' cyclability. To overcome this dilemma, here, we design an electrolyte with a weakly coordinated solvation structure which enables passivation-free Mg deposition while maintaining a high anodic stability and cathodic compatibility. In detail, we successfully introduce a hexa-fluoroisopropyloxy (HFIP-) anion into the solvation structure of Mg2+, the weakly [Mg-HFIP]+ contact ion pair facilitates Mg2+ transportation across interfaces. As a consequence, our electrolyte shows outstanding compatibility with the RMBs. The Mg||PDI-EDA and Mg||Mo6S8 full cells use this electrolyte demonstrating a decent capacity retention of ∼80% over 400 cycles and 500 cycles, respectively. This represents a leap in cyclability over simple electrolytes in RMBs while the rest can barely cycle. This work offers an electrolyte system compatible with RMBs and brings deeper understanding of modifying the solvation structure toward practical electrolytes.

Color computational ghost imaging by deep learning based on simulation data training.

We present a new color computational ghost imaging strategy using a sole single-pixel detector and training by simulated dataset, which can eliminate the actual workload of acquiring experimental training datasets and reduce the sampling times for imaging experiments. First, the relative responsibility of the color computational ghost imaging device to different color channels is experimentally detected, and then enough data sets are simulated for training the neural network based on the response value. Because the simulation process is much simpler than the actual experiment, and the training set can be almost unlimited, the trained network model has good generalization. In the experiment with a sampling rate of only 4.1%, the trained neural network model can still recover the image information from the blurry ghost image, correct the color distortion of the image, and get a better reconstruction result. In addition, with the increase in the sampling rate, the details and color characteristics of the reconstruction result become better and better. Feasibility and stability of the proposed method have been verified by the reconstruction results of the trained network model on the color objects of different complexities.

New Insights into Phase-Mechanism Relationship of Mgx MnO2 Nanowires in Aqueous Zinc-Ion Batteries.

In response to the call for safer energy storage systems, rechargeable aqueous manganese-based zinc-ion (Zn-ion) batteries using mild electrolyte have attracted extensive attention. However, the charge-storage mechanism and structure change of manganese-based cathode remain controversial topics. Herein, a systematic study to understand the electrochemical behavior and charge storage mechanism based on a 3 × 3 tunnel-structured Mgx MnO2 as well as the correspondence between different tunnel structures and reaction mechanisms are reported. The energy storage mechanism of the different tunnel structure is surface faradaic dissolution/deposition coupled with an intercalation mechanism of cations in aqueous electrolyte, which is confirmed by in situ X-ray diffraction, in situ Raman and ex situ extended X-ray absorption fine structure. The deposition process at the cathode is partially reversible due to the accumulation of a birnessite layer on the surface. Compared to smaller tunnels, the 3 × 3 tunnel structure is more conducive to deposit new active materials from the electrolyte. Therefore, pristine Mgx MnO2 nanowires with large tunnels display an excellent cycling performance. This work sheds light on the relationship between the tunnel structure and Mn2+ deposition and provides a promising cathode material design for aqueous Zn-ion batteries.

Electronic Structure Modulation in MoO2 /MoP Heterostructure to Induce Fast Electronic/Ionic Diffusion Kinetics for Lithium Storage.

Transition metal oxides (TMOs) are considered as the prospective anode materials in lithium-ion batteries (LIBs). Nevertheless, the disadvantages, including large volume variation and poor electrical conductivity, obstruct these materials to meet the needs of practical application. Well-designed mesoporous nanostructures and electronic structure modulation can enhance the electron/Li-ions diffusion kinetics. Herein, a unique mesoporous molybdenum dioxide/molybdenum phosphide heterostructure nanobelts (meso-MoO2 /MoP-NBs) composed of uniform nanoparticles is obtained by one-step phosphorization process. The Mott-Schottky tests and density functional theory calculations demonstrated that meso-MoO2 /MoP-NBs possesses superior electronic conductivity. The detailed lithium storage mechanism (solid solution reaction for MoP and partial conversion for MoO2 ), small change ratio of crystal structure and fast electronic/ionic diffusion behavior of meso-MoO2 /MoP-NBs are systematically investigated by operando X-ray diffraction, ex situ transmission electron microscopy, and kinetic analysis. Benefiting from the synergistic effects, the meso-MoO2 /MoP-NBs displays a remarkable cycling performance (515 mAh g-1 after 1000 cycles at 1 A g-1 ) and excellent rate capability (291 mAh g-1 at 8 A g-1 ). These findings can shed light on the behavior of the electron/ion regulation in heterostructures and provide a potential route to develop high-performance lithium-ion storage materials.