MXene-mediated regulation of local electric field surrounding polyoxometalate nanoparticles for improved lithium storage

Abstract

Due to their capability of reversibly accepting multi lithium ions, polyoxometalates (POMs) have been widely regarded as promising candidates for electrochemical lithium storage. Nevertheless, the insulating nature of POMs hinders fast migration kinetics of lithium within the bulk of these materials. Herein, we propose the introduction of a local electric field surrounding the POM nanoparticles consisting of Mn and V where the concomitant Coulomb forces can accelerate the migration of lithium ions. After rationally hybridizing POMs with MXene nanosheets, the imbalanced charge distribution emerging at their interface produces the local electric field, thereby leading to a 250-fold increase of lithium diffusion coefficient. In this regard, a capacitive contribution as high as 81.7% at 1.0 mV s −1 is observed. Moreover, the POM nanoparticles could densely assemble on the surface of MXene nanosheets, offering highly packed electrodes and thus high volumetric capacities. Due to the improved lithiumion transfer kinetics, the POMs/MXenes composites are paired with activated carbon to produce lithium-ion capacitors which could offer a high energy density of 195.5 W h kg −1 and a large power capability of 3800 W kg −1 . The findings in this work could build a clear relationship between materials with different conductivities for designing electrode materials.

Huge Electron Sponge of Polyoxometalate toward Advanced Lithium-Ion Storage

“…A Keggin-type polyoxometalate (PMo 12 ) was first reported to be used as the anode material of LIBs in 2012, and it was found that each PMo 12 molecule reversibly transfers 24 electrons during the lithiation and delithiation processes . From then on, some smaller POMs, including PMo 12 , − PMo 10 V 2 , − PW 12 , , SiW 12 , Co 8 W 12 , and so on, which are combined with functional support materials, such as MXene, − organic linkers, , MOFs, − CNTs, , and GO, , were mainly reported for energy storage.…”

Reshaping the material research paradigm of electrochemical energy storage and conversion by machine learning

“…Although the high-throughput computational screening contributes to a partial solution to this obstacle, the extremely-high computer configuration is inevitably needed, which largely restricts the wide use of this method. [8][9][10][11] To promote the large deployment of renewable energy, the key materials for EESC are supposed to offer a long duration. To determine this parameter experimentally is almost impossible, thus making an accurate prediction of the calendar life of these materials is strongly needed.…”

“…The search for key materials of EESC is generally realized via laborious and costly trial‐and‐error processes, which cannot meet the imminent requirement of “Carbon Neutrality.” Although the high‐throughput computational screening contributes to a partial solution to this obstacle, the extremely‐high computer configuration is inevitably needed, which largely restricts the wide use of this method 8–11 . To promote the large deployment of renewable energy, the key materials for EESC are supposed to offer a long duration.…”