Collaborative Improvement Electrochemical Properties of Supercapacitor Electrodes by Loading MoS2 Nanosheets on Biomass Hierarchical Porous Carbon

MoS2, a typical two-dimensional transition metal sulfide nanomaterial, has attracted much attention for supercapacitor electrode materials due to its high theoretical capacity. Herein, MoS2 nanosheets growing on a hierarchical porous carbon (HPGC) derived from pomelo peel are prepared via hydrothermal method. The curled MoS2 nanosheets uniformly grow and distribute on the conductive hierarchical porous carbon matrix, which made the electrodes materials possess a high specific surface area (320.2 m2/g). Simultaneously, the novel structure enhances the conductivity of MoS2, alleviates capacity attenuation and guarantees the interface stability. Furthermore, the MoS2/HPGC show a great enhancement in supercapacitor performance and deliver a remarkable specific capacitance of 411.4 F/g at the current density of 0.5 A/g. The initial capacitance retention rate is approximately 94.3% after 2000 cycles. It turns out that the synergistic effects between the MoS2 nanosheets and HPGC contribute to high specific capacity, excellent rate performance and ultra-long cycle life. This work provides a new idea for the design and development of MoS2 composites as the electrode materials of supercapacitors.

Synthesis of /Single-Walled Carbon Nanotubes Integrated into Nanostructured Composites as Cathode Materials in High Performance Lithium-Ion Batteries

Vanadium pentoxide (V2O5)-anchored single-walled carbon nanotube (SWCNT) composites have been developed through a simple sol–gel process, followed by hydrothermal treatment. The resulting material is suitable for use in flexible ultra-high capacity electrode applications for lithium-ion batteries. The unique combination of V2O5 with 0.2 wt.% of SWCNT offers a highly conductive three-dimensional network. This ultimately alleviates the low lithium-ion intercalation seen in V2O5 itself and facilitates vanadium redox reactions. The integration of SWCNTs into the layered structure of V2O5 leads to a high specific capacity of 390 mAhg−1 at 0.1 C between 1.8 to 3.8 V, which is close to the theoretical capacity of V2O5 (443 mAhg−1). In recent research, most of the V2O5 with carbonaceous materials shows higher specific capacity but limited cyclability and poor rate capability. In this work, good cyclability with only 0.3% per cycle degradation during 200 cycles and enhanced rate capability of 178 mAhg−1 at 10 C have been achieved. The excellent electrochemical kinetics during lithiation/delithiation is attributed to the chemical interaction of SWCNTs entrapped between layers of the V2O5 nanostructured network. Proper dispersion of SWCNTs into the V2O5 structure, and its resulting effects, have been validated by SEM, TEM, XPS, XRD, and electrical resistivity measurements. This innovative hybrid material offers a new direction for the large-scale production of high-performance cathode materials for advanced flexible and structural battery applications.

Encapsulating Metal-Organic-Foam Derived Nanocages into a Microcapsule for Shuttle Effect-Suppressive Lithium-Sulfur Batteries

Long-term stable secondary batteries are highly required. Here, we report a unique microcapsule encapsulated with metal organic foams (MOFs)-derived Co3O4 nanocages for a Li-S battery, which displays good lithium-storage properties. ZIF-67 dodecahedra are prepared at room temperature then converted to porous Co3O4 nanocages, which are infilled into microcapsules through a microfluidic technique. After loading sulfur, the Co3O4/S-infilled microcapsules are obtained, which display a specific capacity of 935 mAh g−1 after 200 cycles at 0.5C in Li-S batteries. A Coulombic efficiency of about 100% is achieved. The constructed Li-S battery possesses a high rate-performance during three rounds of cycling. Moreover, stable performance is verified under both high and low temperatures of 50 °C and −10 °C. Density functional theory calculations show that the Co3O4 dodecahedra display large binding energies with polysulfides, which are able to suppress shuttle effect of polysulfides and enable a stable electrochemical performance.

Multifactorial engineering of biomimetic membranes for batteries with multiple high-performance parameters

Lithium–sulfur (Li–S) batteries have a high specific capacity, but lithium polysulfide (LPS) diffusion and lithium dendrite growth drastically reduce their cycle life. High discharge rates also necessitate their resilience to high temperature. Here we show that biomimetic self-assembled membranes from aramid nanofibers (ANFs) address these challenges. Replicating the fibrous structure of cartilage, multifactorial engineering of ion-selective mechanical, and thermal properties becomes possible. LPS adsorption on ANF surface creates a layer of negative charge on nanoscale pores blocking LPS transport. The batteries using cartilage-like bioinspired ANF membranes exhibited a close-to-theoretical-maximum capacity of 1268 mAh g−1, up to 3500+ cycle life, and up to 3C discharge rates. Essential for safety, the high thermal resilience of ANFs enables operation at temperatures up to 80 °C. The simplicity of synthesis and recyclability of ANFs open the door for engineering high-performance materials for numerous energy technologies.

One-pot synthesis of high-capacity silicon anodes via on-copper growth of a semi-conducting, porous polymer

Silicon-based anodes with lithium ions as charge carriers have the highest predicted theoretical specific capacity of 3579 mA h g (for LiSi). Contemporary electrodes do not achieve this theoretical value largely because conventional production paradigms rely on the mixing of weakly coordinated components. In this paper, a semi-conductive triazine-based graphdiyne polymer network is grown around silicon nanoparticles directly on the current collector, a copper sheet. The porous, semi-conducting organic framework (i) adheres to the current collector on which it grows via cooperative van der Waals interactions, (ii) acts effectively as conductor for electrical charges and binder of silicon nanoparticles via conjugated, covalent bonds, and (iii) enables selective transport of electrolyte and Li-ions through pores of defined size. The resulting anode shows extraordinarily high capacity at the theoretical limit of fully lithiated silicon. Finally, we combine our anodes in proof-of-concept battery assemblies using a conventional layered Ni-rich oxide cathode.

Single-dispersed polyoxometalate clusters embedded on multilayer graphene as a bifunctional electrocatalyst for efficient Li-S batteries

The redox reactions occurring in the Li-S battery positive electrode conceal various and critical electrocatalytic processes, which strongly influence the performances of this electrochemical energy storage system. Here, we report the development of a single-dispersed molecular cluster catalyst composite comprising of a polyoxometalate framework ([Co4(PW9O34)2]10−) and multilayer reduced graphene oxide. Due to the interfacial charge transfer and exposure of unsaturated cobalt sites, the composite demonstrates efficient polysulfides adsorption and reduced activation energy for polysulfides conversion, thus serving as a bifunctional electrocatalyst. When tested in full Li-S coin cell configuration, the composite allows for a long-term Li-S battery cycling with a capacity fading of 0.015% per cycle after 1000 cycles at 2 C (i.e., 3.36 A g−1). An areal capacity of 4.55 mAh cm−2 is also achieved with a sulfur loading of 5.6 mg cm−2 and E/S ratio of 4.5 μL mg−1. Moreover, Li-S single-electrode pouch cells tested with the bifunctional electrocatalyst demonstrate a specific capacity of about 800 mAh g−1 at a sulfur loading of 3.6 mg cm−2 for 100 cycles at 0.2 C (i.e., 336 mA g−1) with E/S ratio of 5 μL mg−1.

High-energy and long-life aluminum−sulfur battery: Employment of electrocatalytic function into continuous multiple reactions within quasi-solid-state electrolyte

Aluminum−sulfur (Al−S) batteries of ultrahigh energy-to-price ratios are promising for next-generation energy storage, while they suffer from large charge/discharge voltage hysteresis and short lifespan. Herein, an electrocatalyst-boosting quasi-solid-state Al−S battery is proposed, in which sulfur is anchored on the cobalt/nitrogen co-doped graphene (S@CoNG, as the positive electrode) and chloroaluminate-based ionic liquid (IL) is encapsulated into metal-organic frameworks (IL@MOF, as the quasi-solid-state electrolyte). Mechanistically, the Co−N bonds in CoNG act as electrocatalytic center to continuous induce breaking of Al−Cl bonds and S−S bonds and accelerate the kinetics of sulfur conversion, endowing the Al−S battery with much shortened voltage gap of 0.32 V and 0.98 V in the discharge voltage plateau. Within quasi-solid-state IL@MOF electrolytes, shuttle effect of polysulfides has been inhibited, which stabilizes the process of reversible sulfur conversion. Consequently, the assembled Al−S battery presents high specific capacity of 820 mAh g−1 and 78% capacity retention after 300 cycles. This concept here offers novel insights to design practical Al−S batteries for stable energy storage.

Machine Learning as a Tool for Specific Capacity Prediction of Prospective Potassium Battery Electrodes

Application of data science and machine learning (ML) techniques in the domain of materials science has been increasing by leaps and bounds recently. With the help of ML, through input features derived from available databases we can rapidly screen materials based on our desired output. Capacity is one of the important parameters for choosing suitable electrode materials for high energy storage metal ion battery. Exploration of suitable electrode materials for metal ion batteries other than Li ion batteries (LIBs) has been deficient, though there is a need to develop alternative battery technologies with higher energy storage characteristics and environmental safety. In this work, we have considered Li, Na and K-ion electrode materials and their available battery data from Materials Project database to predict specific capacity of prospective K-ion battery electrode materials. Suitable features have been considered and developed to train the various ML algorithms. Mean Absolute Percentage Error has been considered as the performance metrics for assessment of different ML algorithms and among them, kernel ridge regression has been adopted as the most useful to predict the capacity of unknown electrode materials for K-ion battery. Using the value of specific capacity, the number of intercalated K ions in the formula unit of the non-intercalated electrode material compounds have also been calculated. DFT calculations have also been performed to verify the results obtained through ML. Our result shows ML is an encouraging alternative to computationally demanding DFT process as it can screen electrode materials rapidly for battery.