A hybrid hydrazine redox flow battery with a reversible electron acceptor.

Hydrazine is a pollutant with high hydrogen content, offering tremendous possibilities in a direct hydrazine fuel cell (DHFC) as it can be converted into electricity via benign end products. Due to the inner sphere nature of half-cell chemistries, hydrazine cross over triggers parasitic chemistry at the Pt-based air cathode of a state-of-the-art DHFC, overly complicating the already sluggish electrode kinetics at the positive electrode. Here, we illustrate that by altering the interfacial chemistry of the catholyte from inner sphere to outer sphere, the parasitic chemistry can be dissociated from the redox chemistry of the electron acceptor and the hybrid fuel cell can be driven by simple carbon-based cathodes. The reversible nature of an outer sphere catholyte leads to a hybrid fuel cell redox flow battery with performance metrics ∼4 times higher than a Pt-based DHFC-air configuration.

Nanoscale characterization of local structures and defects in photonic crystals using synchrotron-based transmission soft X-ray microscopy.

For the structural characterization of the polystyrene (PS)-based photonic crystals (PCs), fast and direct imaging capabilities of full field transmission X-ray microscopy (TXM) were demonstrated at soft X-ray energy. PS-based PCs were prepared on an O2-plasma treated Si3N4 window and their local structures and defects were investigated using this label-free TXM technique with an image acquisition speed of ~10 sec/frame and marginal radiation damage. Micro-domains of face-centered cubic (FCC (111)) and hexagonal close-packed (HCP (0001)) structures were dominantly found in PS-based PCs, while point and line defects, FCC (100), and 12-fold symmetry structures were also identified as minor components. Additionally, in situ observation capability for hydrated samples and 3D tomographic reconstruction of TXM images were also demonstrated. This soft X-ray full field TXM technique with faster image acquisition speed, in situ observation, and 3D tomography capability can be complementally used with the other X-ray microscopic techniques (i.e., scanning transmission X-ray microscopy, STXM) as well as conventional characterization methods (e.g., electron microscopic and optical/fluorescence microscopic techniques) for clearer structure identification of self-assembled PCs and better understanding of the relationship between their structures and resultant optical properties.

Classification of time-of-flight secondary ion mass spectrometry spectra from complex Cu-Fe sulphides by principal component analysis and artificial neural networks.

Artificial neural network (ANN) and a hybrid principal component analysis-artificial neural network (PCA-ANN) classifiers have been successfully implemented for classification of static time-of-flight secondary ion mass spectrometry (ToF-SIMS) mass spectra collected from complex Cu-Fe sulphides (chalcopyrite, bornite, chalcocite and pyrite) at different flotation conditions. ANNs are very good pattern classifiers because of: their ability to learn and generalise patterns that are not linearly separable; their fault and noise tolerance capability; and high parallelism. In the first approach, fragments from the whole ToF-SIMS spectrum were used as input to the ANN, the model yielded high overall correct classification rates of 100% for feed samples, 88% for conditioned feed samples and 91% for Eh modified samples. In the second approach, the hybrid pattern classifier PCA-ANN was integrated. PCA is a very effective multivariate data analysis tool applied to enhance species features and reduce data dimensionality. Principal component (PC) scores which accounted for 95% of the raw spectral data variance, were used as input to the ANN, the model yielded high overall correct classification rates of 88% for conditioned feed samples and 95% for Eh modified samples.

Chemometric and multivariate statistical analysis of time-of-flight secondary ion mass spectrometry spectra from complex Cu-Fe sulfides.

Time-of-flight secondary ion mass spectrometry (TOF-SIMS) spectra of mineral samples are complex, comprised of large mass ranges and many peaks. Consequently, characterization and classification analysis of these systems is challenging. In this study, different chemometric and statistical data evaluation methods, based on monolayer sensitive TOF-SIMS data, have been tested for the characterization and classification of copper-iron sulfide minerals (chalcopyrite, chalcocite, bornite, and pyrite) at different flotation pulp conditions (feed, conditioned feed, and Eh modified). The complex mass spectral data sets were analyzed using the following chemometric and statistical techniques: principal component analysis (PCA); principal component-discriminant functional analysis (PC-DFA); soft independent modeling of class analogy (SIMCA); and k-Nearest Neighbor (k-NN) classification. PCA was found to be an important first step in multivariate analysis, providing insight into both the relative grouping of samples and the elemental/molecular basis for those groupings. For samples exposed to oxidative conditions (at Eh ~430 mV), each technique (PCA, PC-DFA, SIMCA, and k-NN) was found to produce excellent classification. For samples at reductive conditions (at Eh ~ -200 mV SHE), k-NN and SIMCA produced the most accurate classification. Phase identification of particles that contain the same elements but a different crystal structure in a mixed multimetal mineral system has been achieved.