Accelerating
the redox reaction of polysulfides via catalysis
is an effective way to suppress the shuttling effect in
lithium–sulfur (Li–S) cells. However, recent studies
have mainly focused on the singular function of the catalyst, i.e., either oxidation or reduction of polysulfides. As
such, the goal of rapid cycling of sulfur species remains to be highly
desired. Herein, a Pt-carbide composite as a bifunctional catalyst
was developed to simultaneously accelerate both the reduction of soluble
polysulfides and the oxidation of insoluble Li2S/Li2S2. Typically, a Pt-NbC composite was synthesized
by growing Pt nanoparticles on the surface of NbC, and the resultant
intimate interface in the hybrid is a key component for the bifunctional
catalysis. During the reduction process, polysulfides could be grabbed
on the surface of NbC via strong adsorption, and
then these trapped polysulfides could be catalytically converted by
Pt nanoparticles. During the oxidation process, both NbC and Pt exhibited
catalytic activities for the dissolution of Li2S. This
process could lead to the renewal of the surface of the catalyst.
By combining the sulfur cathode with a Pt-NbC-CNT (Pt-NbC anchored
on a carbon nanotube)-coated separator, the cell was able to demonstrate
a high initial capacity of 1382 mAh g–1 at a current
density of 0.2C. Furthermore, the cell was able to achieve an exceptional
rate capability of 795 mAh g–1 at 5C, and it was
also able to show significantly inhibited self-discharge behavior.
Thus, this work explores the catalyst design and the mechanism of
a bifunctional catalyst for the performance enhancement in Li–S
cells.
Lithium
metal is considered as a strongly attractive anode candidate
for the high-energy-storage field, but its dreadful dendrite growth
has haunted its commercialization progress. Herein, we develop a lithiophilic
Nb2O5-embedded three-dimensional (3D) carbon
nanofiber network (Nb2O5-CNF) as a scaffold
to preload molten Li for the fabrication of dendrite-free composite
anode. The in situ lithiation reaction between molten Li and Nb2O5 nanocrystals results in the formation of nanosize
Li
x
Nb
y
O nanoparticles,
which can serve as preferred sites that regulate nucleation/growth
behavior of Li during the plating process. Besides, due to its high
structural stability and abundant internal inner space, the 3D CNF
network can function as a reservoir to confine the dimensional expansion
of “hostless Li”. The resulting Li composite anodes
exhibit enlarged active areas and reduced interfacial energy barriers,
delivering a prolonged cycling of 1000 h with an ultralow hysteresis
of 52 mV and dendrite-free morphology in a symmetric cell (1.0 mA
cm–2). Coupled with the LiFePO4 cathode,
the Li@Nb2O5-CNF anode sustains a reversible
capacity of 163 mAh g–1 with an excellent capacity
retention of 93.0% after 370 cycles at 0.5C. This all-around strategy
of lithiophilic sites coupled with a 3D conductive nanofiber matrix
may shed light on promising applications of high-capacity and dendrite-free
Li-metal batteries.
Fe/AC bifunctional catalysts provide an environmentally friendly strategy for the efficient catalytic conversion of low-density polyethylene into valuable fuel products.
A precise three-dimensional (3D) microstructure is crucial in porous materialbased sciences and technologies, whereas accurate and efficient microstructure reconstruction using two-dimensional (2D) images remains challenging. Here, the strategy of ascertaining nanopore boundaries in focused ion beam-scanning electron microscopy images using experimentally guided image segmentation and deep learning-based reconstruction is proposed for precise reconstruction and heat transfer performance prediction. We demonstrate that uncertain boundaries in 2D images and pore characteristics in reconstructed 3D microstructures can be mathematically linked through area proportion (AP)-determined image segmentation. By calibrating the AP value once using reliable experimental data, accurate reconstruction of porous materials with different pore structures can be achieved with pore characteristic errors of <3%. Compared to randomly generated models, microstructures reconstructed using the proposed method have a better description in the connectivity of solid particles. Porosity-related transport property simulations of reconstructed microstructures reveal the remarkable boundary-identifying ability of the proposed strategy. Benefiting from ascertained porous structures, thermal conductivities in different directions could be predicted using 2D images with accuracy >99% and efficiency <1 s.
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