Ionic transport through a composite structure of N-ethyl-N-methylpyrrolidinium tetrafluoroborate organic ionic plastic crystals reinforced with polymer nanofibres

The incorporation of polyvinylidene difluoride (PVDF) electrospun nanofibres within N-ethyl-N-methylpyrrolidinium tetrafluoroborate, [C2mpyr][BF4] was investigated with a view to fabricating self-standing membranes for various electrochemical device applications, in particular lithium metal batteries. Significant improvement in mechanical properties and ionic conduction was demonstrated in a previous study, which also demonstrated the remarkably high performance of the lithium-doped composite material in a device. We now seek a fundamental understanding of the role of fibres within the matrix of the plastic crystal, which is essential for optimizing device performance through fine-tuning of the composite material properties. The focus of the current study is therefore a thorough investigation of the phase behaviour and conduction behaviour of the pure and the lithium-doped (as LiBF4) plastic crystal, with and without incorporation of polymer nanofibres. Analysis of the structure of the plastic crystal, including the effects of lithium ions and the incorporation of PVDF fibres, was conducted by means of synchrotron XRD. Ion dynamics were evaluated using VT solid-state NMR spectroscopy. ATR-FTIR spectroscopy was employed to gain insights into the molecular interactions of doped lithium ions and/or the PVDF nanofibres in the matrix of the [C2mpyr][BF4] composites. Preliminary measurements using PALS were conducted to probe structural defects within the pure materials. It was found that ion transport within the plastic crystal was significantly altered by doping with lithium ions due to the precipitation of a second phase in the structure. The incorporation of the fibres activated more mobile sites in the systems, but restricted ion mobility with different trends being observed for each ion species in each crystalline phase. In the presence of the fibres a strong interaction observed between the Li ion and the pyrrolidinium ring disappeared and formation of the second phase was prevented. As a result, an increased number of mobile lithium ions are released into the solid solution structure of the matrix, simultaneously removing the blocking effect of the second phase. Thus, ion conduction was remarkably improved within the Li-doped composite compared to the neat Li-doped plastic crystal.

Ultra-wideline 14N solid-state NMR as a method for differentiating polymorphs: glycine as a case study

Nitrogen-14 solid-state NMR (SSNMR) is utilized to differentiate three polymorphic forms and a hydrochloride (HCl) salt of the amino acid glycine. Frequency-swept Wideband, Uniform Rate, Smooth Truncated (WURST) pulses were used in conjunction with Carr-Purcell Meiboom-Gill refocusing, in the form of the WURST-CPMG pulse sequence, for all spectral acquisitions. The ¹⁴N quadrupolar interaction is shown to be very sensitive to variations in the local electric field gradients (EFGs) about the ¹⁴N nucleus; hence, differentiation of the samples is accomplished through determination of the quadrupolar parameters CQ and ηQ, which are obtained from analytical simulations of the ¹⁴N SSNMR powder patterns of stationary samples (i.e., static NMR spectra). Additionally, differentiation of the polymorphs is also possible via the measurement of ¹⁴N effective transverse relaxation time constants, T^eff2(¹⁴N). Plane-wave density functional theory (DFT) calculations, which exploit the periodicity of crystal lattices, are utilized to confirm the experimentally determined quadrupolar parameters as well as to determine the orientation of the ¹⁴N EFG tensors in the molecular frames. Several signal-enhancement techniques are also discussed to help improve the sensitivity of the ¹⁴N SSNMR acquisition method, including the use of selective deuteration, the application of the BRoadband Adiabatic INversion Cross-Polarization (BRAIN-CP) technique, and the use of variable-temperature (VT) experiments. Finally, we examine several cases where ¹⁴N VT experiments employing Carr-Purcell-Meiboom-Gill (CPMG) refocusing are used to approximate the rotational energy barriers for RNH3⁺ groups.

Synthesis of an indium oxide nanoparticle embedded graphene three-dimensional architecture for enhanced lithium-ion storage

Indium oxide nanoparticles were synthesised by using a facile and scalable strategy. The as-prepared nanoparticles (20-40 nm) were in situ and homogeneously distributed in a three-dimensional (3D) graphene architecture subsequently during the fabrication process. The obtained nanocomposite acts as a high capacity anode material for lithium-ion batteries and demonstrates good cycle stability. A drastically enhanced capacity of 750 mA h g^-1 in comparison with that of bare In2O3 nanoparticles can be maintained after 100 cycles, along with an improved high rate performance (210 mA h g^-1 at 1 A g^-1 and 120 mA h g^-1 at 2 A g^-1). The excellent performance is linked with the indium oxide nanoparticles and the unique 3D interconnected porous graphene structure. The highly conductive and porous 3D graphene structure greatly enhances the performance of lithium-ion batteries by protecting the nanoparticles from the electrolyte, stabilizing the nanoparticles during cycles and buffering the volume expansion upon lithium insertion.

Self-cleaning wool: effect of noble metals and silica on visible-light-induced functionalities of nano TiO2 colloid

© 2014 The Textile Institute. This study intends to enhance the functionality of titanium dioxide (TiO2) nanoparticles applied to wool fabrics under visible light. Herein, TiO2, TiO2/SiO2, TiO2/Metal, and TiO2/Metal/SiO2 nanocomposite sols were synthesized and applied to wool fabrics through a low-temperature sol–gel method. The impacts of three types of noble metals, namely gold (Au), platinum (Pt), and silver (Ag), on the photoefficiency of TiO2 and TiO2/SiO2 under visible light were studied. Different molar ratios of Metal toTiO2 (0.01, 0.1, 0.5, and 1%) were employed in synthesizing the sols. Photocatalytic efficiency of fabrics was analyzed through monitoring the removal of red wine stain and degradation of methylene blue under simulated sunlight and visible light, respectively. Also, the antimicrobial activity against Escherichia coli (E. coli) bacterium and the mechanical properties of fabrics were investigated. Through applying binary and ternary nanocomposite sols to fabrics, an enhanced visible-light-induced self-cleaning property was imparted to wool fabrics. It was concluded that the presence of silica and optimized amount of noble metals had a synergistic impact on boosting the photocatalytic and antimicrobial activities of coated samples. The fabrics were further characterized using attenuated total reflectance, energy-dispersive X-ray spectrometry, and scanning electron microscopy images.

Mixed-norm sparse representation for multi view face recognition

Face recognition with multiple views is a challenging research problem. Most of the existing works have focused on extracting shared information among multiple views to improve recognition. However, when the pose variation is too large or missing, 'shared information' may not be properly extracted, leading to poor recognition results. In this paper, we propose a novel method for face recognition with multiple view images to overcome the large pose variation and missing pose issue. By introducing a novel mixed norm, the proposed method automatically selects candidates from the gallery to best represent a group of highly correlated face images in a query set to improve classification accuracy. This mixed norm combines the advantages of both sparse representation based classification (SRC) and joint sparse representation based classification (JSRC). A trade off between the ℓ1-norm from SRC and ℓ2,1-norm from JSRC is introduced to achieve this goal. Due to this property, the proposed method decreases the influence when a face image is unseen and has large pose variation in the recognition process. And when some face images with a certain degree of unseen pose variation appear, this mixed norm will find an optimal representation for these query images based on the shared information induced from multiple views. Moreover, we also address an open problem in robust sparse representation and classification which is using ℓ1-norm on the loss function to achieve a robust solution. To solve this formulation, we derive a simple, yet provably convergent algorithm based on the powerful alternative directions method of multipliers (ADMM) framework. We provide extensive comparisons which demonstrate that our method outperforms other state-of-the-arts algorithms on CMU-PIE, Yale B and Multi-PIE databases for multi-view face recognition.