Flexible and Conductive Membrane with Ultrahigh Porosity and Polysulfide-Conversion Catalytic Activity for Large-Scale Preparation of Li–S Batteries

Abstract

Li−S batteries still have various problems, including volume expansion, low S conductivity, and shuttling effect of polysulfides (LiPSs), requiring resolution. Herein, polyvinylidene fluoride (PVDF) is combined with carbon nanotubes (CNTs) and Fe 2 O 3 through a one-step phase-inversion process to form a highly flexible and conductive membrane as a functional Li−S battery interlayer. The CNT skeleton is tightly entangled and crosslinked by PVDF to form a hierarchically porous framework, which is suitable for fast Li + and electron transport and volumetric expansion of S and facilitated electrolyte diffusion. Additionally, the strongly negative F atoms can accelerate Li + transport, promoting the fast redox reaction between S species and Li + . Furthermore, the uniformly doped Fe 2 O 3 nanoparticles provide abundant active sites and broad reaction interfaces for highly efficient LiPS anchoring and catalytic conversion. Li−S batteries with the membrane as the interlayer exhibit an excellent cycle performance and reversible capacity of 631.2 mAh g −1 after 400 cycles at 1 C. Furthermore, in situ Raman spectroscopy shows that long-chain Li 2 S 8 decreases, indicating that the interlayer can alleviate the shuttle of soluble LiPSs. This proposed strategy advances the practical application of Li−S batteries due to the facile and scalable production of the flexible membrane.

Scalable SPAN Membrane Cathode with High Conductivity and Hierarchically Porous Framework for Enhanced Ion Transfer and Cycling Stability in Li–S Batteries

“…53 The diffusion coefficient of Li + is inversely proportional to σ 2 , indicating D Li+ is proportional to the slope of the straight line in the low-frequency region. 54 Therefore, the D Li+ value of the CNT 1 @SPAN freestanding electrode is higher than that of the freestanding electrode and the SPAN without CNT wrapping, which confirms that the reasonable holey structure facilitates the diffusion of Li ions. Figure S8 shows the EIS plots containing CNT 1 @S 0 PAN and CNT 1 @SPAN.…”

“…Meanwhile, the equations of D Li + = R 2 T 2 0.5 A 2 n 4 F 4 C 4 σ 2 and Z ′ = R 1 + R 2 + σω –0.5 can be applied to explain the relationship between the diffusion coefficient and σ . The diffusion coefficient of Li + is inversely proportional to σ 2 , indicating D Li+ is proportional to the slope of the straight line in the low-frequency region . Therefore, the D Li+ value of the CNT 1 @SPAN freestanding electrode is higher than that of the freestanding electrode and the SPAN without CNT wrapping, which confirms that the reasonable holey structure facilitates the diffusion of Li ions.…”

WS2-TiO2 Heterostructure Catalyst for Boosting the Polysulfide Adsorption–Conversion in Lithium–Sulfur Batteries

“…Energy storage systems such as lithium-ion batteries (LIBs), sodium-ion batteries (SIBs), , Mg-ion batteries (MIBs), lithium–sulfur (Li–S) batteries, and solid-state alkali-metal batteries , are regarded as the most promising power sources for portable devices and electric vehicle energy storage devices. , Li–S batteries have been widely studied because of their high theoretical energy density (2600 Wh kg –1 ). − However, their commercial application is limited by a low sulfur utilization (<80%) and limited lifespan (<500 cycles). There are mainly four reasons as follows: (i) the poor utilization of sulfur species due to the low conductivity of sulfur and its discharge product Li 2 S 2 /Li 2 S; , (ii) the loss of sulfur species and the corrosion of the Li metal anode caused by the “shuttling effect” of intermediate lithium polysulfides (LPSs); ,, (iii) the poor cycle stability and low Coulombic efficiency caused by the sluggish conversion kinetics of LPSs and the huge volume expansion (about 80%) of S 8 and Li 2 S 2 /Li 2 S; , and (iv) the loss of ion/electron transportation and active sulfur species caused by the depressed deposition and oxidation of the insoluble lithium sulfide (Li 2 S 2 /Li 2 S) on the cathode …”

Machine Learning Enhanced High‐Throughput Fabrication and Optimization of Quasi‐2D Ruddlesden–Popper Perovskite Solar Cells

“…[ 62,63 ] In 2018, Allam et al developed a ML model to accurately predict the band gap of RPPs based only on the number of layers, ionic radii, and charge states. [ 64 ] Starostin et al applied ML to predict the phase of perovskites from X‐ray diffraction data, [ 65 ] while Zhang et al developed a chemically interpretable model to predict the stability of perovskites in extreme conditions, [ 66 ] and Chen et al developed a ML model to screen ABO 3 oxides for their potential to form perovskite structures. [ 67 ] Zhang et al developed a ML model to predict whether 2D lead halide perovskites would form the Ruddlesden–Popper or Dion–Jacobson phase.…”