Graphene on Paper: A Simple, Low-Cost Chemical Sensing Platform

Graphene layers have been transferred directly on to paper without any intermediate layers to yield G-paper. Resistive gas sensors have been fabricated using strips of G-paper. These sensors achieved a remarkable lower limit of detection of similar to 300 parts per trillion (ppt) for NO2, which is comparable to or better than those from other paper-based sensors. Ultraviolet exposure was found to dramatically reduce the recovery time and improve response times. G-paper sensors are also found to be robust against minor strain, which was also found to increase sensitivity. G-paper is expected to enable a simple and inexpensive low-cost flexible graphene platform

Homing abilities of the tropical primitively eusocial paper wasp Ropalidia marginata

Compared to our extensive knowledge about the navigation and homing abilities of ants and bees, we know rather little about these phenomena in social wasps. Here, we report the homing abilities of the tropical primitively eusocial wasp Ropalidia marginata and the factors that affect their homing success. To determine from how far these wasps can return to their nests, we transported foragers blindfold and released them at gradually increasing distances from their nests in four cardinal directions. Their homing success was determined by checking their presence on their nests on three consecutive nights. All foragers (56 individuals, 115 releases) returned back from an area of 0.73 +/- A 0.25 km(2) on the day of release (minimal homing area), whereas 83.8 % of the foragers (217 individuals, 420 releases) returned when we enlarged the area of release to 6.22 +/- A 0.66 km(2) around their nests (maximal homing area). Of 66 releases, no wasps returned from beyond the maximal homing area. The minimal homing area might be familiar to the foragers because they probably routinely forage in this area and the maximal homing area represents the maximum distances from which the wasps are capable of returning to their nests, with or without familiarity.

A Cause and Effect Analysis of Motion Trajectories for Modeling Actions

An action is typically composed of different parts of the object moving in particular sequences. The presence of different motions (represented as a 1D histogram) has been used in the traditional bag-of-words (BoW) approach for recognizing actions. However the interactions among the motions also form a crucial part of an action. Different object-parts have varying degrees of interactions with the other parts during an action cycle. It is these interactions we want to quantify in order to bring in additional information about the actions. In this paper we propose a causality based approach for quantifying the interactions to aid action classification. Granger causality is used to compute the cause and effect relationships for pairs of motion trajectories of a video. A 2D histogram descriptor for the video is constructed using these pairwise measures. Our proposed method of obtaining pairwise measures for videos is also applicable for large datasets. We have conducted experiments on challenging action recognition databases such as HMDB51 and UCF50 and shown that our causality descriptor helps in encoding additional information regarding the actions and performs on par with the state-of-the art approaches. Due to the complementary nature, a further increase in performance can be observed by combining our approach with state-of-the-art approaches.

Covalent interlocking of glucose oxidase and peroxidase in the voids of paper: enzyme-polymer ``spider webs''

A modular, general method for trapping enzymes within the voids of paper, without chemical activation of cellulose, is reported. Glucose oxidase and peroxidase were crosslinked with poly(acrylic acid) via carbodiimide chemistry, producing 3-dimensional networks interlocked in cellulose fibers. Interlocking prevented enzyme activity loss and enhanced the washability and stability.