Indian Institute of Technology Madras

About: Indian Institute of Technology Madras is a facility organization based out in Chennai, Tamil Nadu, India. It is known for research contribution in the topics: Catalysis & Heat transfer. The organization has 20118 authors who have published 36499 publications receiving 590447 citations.

Nitrogen doped graphene nanoplatelets as catalyst support for oxygen reduction reaction in proton exchange membrane fuel cell

Abstract: Graphene nanoplatelets have been synthesized by thermal exfoliation of graphitic oxide and nitrogen doped graphene nanoplatelets have been obtained by nitrogen plasma treatment. Graphene nanoplatelets and nitrogen doped graphene nanoplatelets have been used as a catalyst support for platinum nanoparticles for oxygen reduction reactions in proton exchange membrane fuel cells. Platinum nanoparticles were dispersed over these support materials using the conventional chemical reduction technique. The morphology and structure of the graphene based powder samples were studied using X-ray diffraction, Raman spectroscopy, transmission electron microscopy and X-ray photoelectron spectroscopy. A full cell was constructed with platinum loaded nitrogen doped graphene nanoplatelets and the results have been compared with platinum loaded graphene nanoplatelets. A maximum power density of 440 and 390 mW cm−2 has been obtained with platinum loaded nitrogen doped graphene and platinum loaded graphene nanoplatelets as ORR catalysts respectively. Nitrogen plasma treatment created pyrrolic nitrogen defects, which act as good anchoring sites for the deposition of platinum nanoparticles. The improved performance of fuel cells with N-G as catalyst supports can be attributed to the increased electrical conductivity and improved carbon–catalyst binding.

Identification of humans using gait

Abstract: We propose a view-based approach to recognize humans from their gait. Two different image features have been considered: the width of the outer contour of the binarized silhouette of the walking person and the entire binary silhouette itself. To obtain the observation vector from the image features, we employ two different methods. In the first method, referred to as the indirect approach, the high-dimensional image feature is transformed to a lower dimensional space by generating what we call the frame to exemplar (FED) distance. The FED vector captures both structural and dynamic traits of each individual. For compact and effective gait representation and recognition, the gait information in the FED vector sequences is captured in a hidden Markov model (HMM). In the second method, referred to as the direct approach, we work with the feature vector directly (as opposed to computing the FED) and train an HMM. We estimate the HMM parameters (specifically the observation probability B) based on the distance between the exemplars and the image features. In this way, we avoid learning high-dimensional probability density functions. The statistical nature of the HMM lends overall robustness to representation and recognition. The performance of the methods is illustrated using several databases.