Increase of catalyst utilization in polymer electrolyte membrane fuel cells by shape-selected Pt nanoparticles

In the present work, we succeeded in supporting predominantly cuboctahedral Pt nanoparticles onto high surface area carbons while maintaining their shape. These novel catalysts were applied in a realistic fuel cell set-up for the first time and showed remarkable fuel cell performance. A 95% fraction of cuboctahedral Pt nanoparticles was synthesized using tetradecyltrimethylammonium bromide (TTAB) as a stabilizer. Transmission electron micrographs of the synthesized samples demonstrated the presence of monodispersed cuboctahedral particles of 12 nm in size. Cyclic voltammetry (CV) studies of the unsupported cuboctahedral nanoparticles revealed the presence of Pt (110) and (100) facets. The shape-selected Pt nanoparticles were let to absorb onto Vulcan carbon by a simple dispersing procedure to obtain supported shape-selected Pt nanoparticles. Only by this gentle adsorption step of the surfactant-stabilized nanoparticles on the carbonaceous support material, the nanoparticles retained their shape. Finally an MEA was fabricated using the supported shape-selected nanoparticles and tested in a realistic H₂-PEM fuel cell environment. In terms of Pt utilization, shape-selected Pt particles were found to be more effective by a factor of four in weight compared to the commercial catalyst.     

New, low-cost, high-power poly(o-anisidine-co-metanilic acid)/activated carbon electrode for electrochemical supercapacitors

A poly(o-anisidine-co-metanilic acid)/activated carbon composite is evaluated as an active material for electrochemical supercapacitors. Poly(o-anisidine-co-metanilic acid) (PASM) is potentiodynamically deposited on an activated carbon (AC)-coated stainless-steel substrate, in a supporting electrolyte of 1.0 M H₂SO₄ containing dissolved o-anisidine and metanilic acid, at a sweep rate of 50 mV s⁻¹. Scanning electron micrographs show a uniformly deposited, thick PASM film on the activated carbon. Electrochemical techniques, such as impedance analysis, cyclic voltammetry and galvanostatic experiments, are carried out to investigate the suitability of the PASM/AC electrode for supercapacitor applications. A maximum specific capacitance of 576 F g⁻¹ is obtained for PASM/AC at a 5 mA cm⁻² current density.     

Hybrid semi-blind image watermarking in redundant wavelet domain

Multimedia Tools and Applications - Image watermarking in wavelet domain has been found useful for copyright protection and rightful ownership. Classical wavelet transforms, like discrete wavelet...     

Influence of neotectonic activity on groundwater salinity and playa development in the Mendha river catchment,western India

The groundwater salinity of Mendha river, one of the important streams that feed Sambhar playa in Rajasthan in western India, was studied to understand the effect of neotectonic activity on groundwater quality and salinity. We attempted to decouple the tectonic control of salinity and its contribution in the development of playa deposits in Rajasthan. Multiresolution, multidate satellite data products such as IRS‐1C, IRS‐1D LISS‐III, PAN and Landsat MSS were digitally enhanced and analysed to model the morphotectonic evolution and hydrological regime of the region. Electrical conductance data from spatially distributed points in the Mendha river basin were correlated with the aquifer geometry deciphered from borehole lithologs and lineaments and major geomorphic features interpreted from satellite images. The results of the study reveal that the aquifer geometry is controlled by subsurface structures that have been influenced by neotectonic activity in the past 8–9 ka, significantly influencing the hydrological regime and salinity of Sambhar playa.     

TopoAct: Visually Exploring the Shape of Activations in Deep Learning

Deep neural networks such as GoogLeNet, ResNet, and BERT have achieved impressive performance in tasks such as image and text classification. To understand how such performance is achieved, we probe a trained deep neural network by studying neuron activations, i.e.combinations of neuron firings, at various layers of the network in response to a particular input. With a large number of inputs, we aim to obtain a global view of what neurons detect by studying their activations. In particular, we develop visualizations that show the shape of the activation space, the organizational principle behind neuron activations, and the relationships of these activations within a layer. Applying tools from topological data analysis, we present TopoAct , a visual exploration system to study topological summaries of activation vectors. We present exploration scenarios using TopoAct that provide valuable insights into learned representations of neural networks. We expect TopoAct to give a topological perspective that enriches the current toolbox of neural network analysis, and to provide a basis for network architecture diagnosis and data anomaly detection.     

Hybrid NSCT domain multiple watermarking for medical images

Multimedia Tools and Applications - Medical image watermarking is a challenging area of research. High bandwidth, secure transmission of patient’s data among hospitals and hiding capacity are...