Incorporating Pyrrolic and Pyridinic Nitrogen into a Porous Carbon made from C60 Molecules to Obtain Superior Energy Storage.

Nitrogen-doped porous carbon is obtained by KOH activation of C60 in an ammonia atmosphere. As an anode for Li-ion batteries, it shows a reversible capacity of up to ≈1900 mA h g-1 at 100 mA g-1 . Simulations suggest that the superior Li-ion storage may be related to the curvature of the graphenes and the presence of pyrrolic/pyridinic group dopants.

Supercapacitors: A Hierarchical Carbon Derived from Sponge-Templated Activation of Graphene Oxide for High-Performance Supercapacitor Electrodes (Adv. Mater. 26/2016).

H. Ji, Y. Zhu, and co-workers demonstrate a 3D hierarchically porous carbon by introducing a polyurethane sponge to template graphene oxide into a 3D interconnected structure while KOH activation generates abundant micropores in its backbone. As described on page 5222, a supercapacitor assembled with this carbon material achieves a high energy density of 89 W h kg(-1) (64 W h L(-1) ) and outstanding power density due to its shortened ion transport distance in three dimensions.

A Hierarchical Carbon Derived from Sponge-Templated Activation of Graphene Oxide for High-Performance Supercapacitor Electrodes.

A hierarchical porous carbon is fabricated by introducing a polyurethane sponge to a template graphene oxide into a 3D interconnected structure, while KOH activation generates abundant micropores in its backbone. Supercapacitors assembled with this carbon achieve a high energy density of 89 W h kg(-1) (64 W h L(-1) ) and outstanding power density due to the shortened ion-transport distance in 3D.

Creating Pores on Graphene Platelets by Low-Temperature KOH Activation for Enhanced Electrochemical Performance.

KOH activation of microwave exfoliated graphite oxide (MEGO) is investigated in detail at temperatures of 450-550 °C. Out of the activation temperature range conventionally used for the preparation of activated carbons (>600 °C), the reaction between KOH and MEGO platelets at relatively low temperatures allows one to trace the structural transition from quasi-two-dimensional graphene platelets to three-dimensional porous carbon. In addition, it is found that nanometer-sized pores are created in the graphene platelets at the activation temperature of around 450 °C, leading to a carbon that maintains the platelet-like morphology, yet with a specific surface area much higher than MEGO (e.g., increased from 156 to 937 m(2) g(-1) ). Such a porous yet highly conducting carbon shows a largely enhanced electrochemical activity and thus improved electrochemical performance when being used as electrodes in supercapacitors. A specific capacitance of 265 F g(-1) (185 F cm(-3) ) is obtained at a current density of 1 A g(-1) in 6 m KOH electrolyte, which remains 223 F g(-1) (156 F cm(-3) ) at the current density of 10 A g(-1) .

Assembling carbon quantum dots to a layered carbon for high-density supercapacitor electrodes.

It is found that carbon quantum dots (CQDs) self-assemble to a layer structure at ice crystals-water interface with freeze- drying. Such layers interconnect with each other, forming a free-standing CQD assembly, which has an interlayer distance of about 0.366 nm, due to the existence of curved carbon rings other than hexagons in the assembly. CQDs are fabricated by rupturing C60 by KOH activation with a production yield of ~15 wt.%. The CQDs obtained have an average height of 1.14 nm and an average lateral size of 7.48 nm, and are highly soluble in water. By packaging annealed CQD assembly to high density (1.23 g cm(-3)) electrodes in supercapacitors, a high volumetric capacitance of 157.4 F cm(-3) and a high areal capacitance of 0.66 F cm(-2) (normalized to the loading area of electrodes) are demonstrated in 6 M KOH aqueous electrolyte with a good rate capability.

Highly sensitive surface-enhanced Raman scattering based on multi-dimensional plasmonic coupling in Au-graphene-Ag hybrids.

We report an efficient surface-enhanced Raman scattering (SERS) substrate by utilizing the multi-dimensional plasmonic coupling in Au nanoparticle (NP)-graphene-Ag NP hybrid structures. An ultrasensitive SERS detection with a limit of down to 10(-13) M has been achieved when the sandwiched hybrid film is fabricated on an Ag substrate.

Manipulating size of Li3V2(PO4)3 with reduced graphene oxide: towards high-performance composite cathode for lithium ion batteries.

Lithium vanadium phosphate (Li3V2(PO4)3, LVP)/reduced graphene oxide (rGO) composite is prepared with a rheological method followed by heat treatment. The size and interface of LVP particles, two important merits for a cathode material, can be effectively tuned by the rGO in the composite, which plays as surfactant to assist sol-gelation and simultaneously as conductive carbon coating. As a consequence, the composite with 7.0 ± 0.4 wt.% rGO shows a capacity of 141.6 mAh g(-1) at 0.075 C, and a rate capacity of 119.0 mAh g(-1) at 15 C with respect to the mass of LVP/rGO composite, and an excellent cycling stability that retains 98.7% of the initial discharge capacity after 50 cycles. The improved electrochemical performance is attributed to the well-controlled rGO content that yields synergic effects between LVP and rGO. Not only do the rGO sheets reduce the size of LVP particles that favor the Li(+) ion migration and the electron transfer during charging and discharging, but also contribute to the reversible lithium ions storage.

Morphology cluster and prediction of growth of human brain pyramidal neurons.

Predicting neuron growth is valuable to understand the morphology of neurons, thus it is helpful in the research of neuron classification. This study sought to propose a new method of predicting the growth of human neurons using 1 907 sets of data in human brain pyramidal neurons obtained from the website of NeuroMorpho.Org. First, we analyzed neurons in a morphology field and used an expectation-maximization algorithm to specify the neurons into six clusters. Second, naive Bayes classifier was used to verify the accuracy of the expectation-maximization algorithm. Experiment results proved that the cluster groups here were efficient and feasible. Finally, a new method to rank the six expectation-maximization algorithm clustered classes was used in predicting the growth of human pyramidal neurons.