DERIVATIZATION OF STABLE, SOLUBLE REDOX-ACTIVE ORGANIC MATERIALS FOR NON-AQUEOUS REDOX FLOW BATTERIES

In screening active materials for redox flow batteries (RFBs) – in which solubility is important to raise the volumetric energy density – scientists are slave to trial and error, modifying organic molecules in an attempt to optimize (increase) solubility without compromising other important properties such as stability and redox potential. A trained chemist can often predict the trends of solubility with the structural modifications in the neutral state of the materials, but when it’s come to the charged state of the materials, it doesn’t follow the same trend as the neutral species and relative values are hard to predict. The solubility of a wide variety of phenothiazine derivatives – in both relevant states of charge (neutral and radical cation) – in a nonaqueous electrolyte is measured using an NMR solubility technique. To predict the solubility trends, different experimental and computational parameters can be incorporated. The volumetric energy density of the RFB also depends on the cell voltage. Compared to phenothiazine derivative carbazole derivatives have higher oxidation potentials, hence higher cell voltages can be achieved. Therefore, different structural modifications on carbazole core were studied to enhance other important properties such as solubility, stability, and electrochemical reversibility, under the non-aqueous electrolyte environments

Detecting water in visual image streams from UAV with flight constraints

Unmanned Ariel Vehicles (UAVs) require identifying water surfaces during flight maneuvers, mainly for safety in execution and its applications. We introduce two novel techniques to identify water surfaces from front-facing and downward-facing cameras mounted on a UAV. The first method — UNet-RAU, a unique architecture based on UNet and Reflection Attention Units, segments water pixels from front-facing camera views, utilizing the reflection property of water surfaces. On the On-Road and Off-Road datasets of Puddle-1000, UNet-RAU improved its performance by 2% over the state-of-the-art FCN-RAU. Additionally, the UNet-RAU generated an F1-score of 80.97% on our Drone-Water-Front dataset. The second method — Dense Optical Flow based Water Detection (DOF-WD), detects water surfaces in videos of downward-facing cameras. This method utilizes downwash-generated ripples and natural texture features on a water surface to identify water in low and high altitudes, respectively. We empirically validated the performance of the DOF-WD method using our Drone-Water-Down dataset

Listening to the Giants : Using Elephant Infra-Sound to Solve the Human-Elephant Conflict

The continuing human-elephant conict in Sri Lanka has resulted in loss of human as well as elephant lives. Detecting and localizing elephants is an essential component of any viable solution to this problem. Currently, we conduct feasibility tests on using low cost sensors to detect elephants from a long distance, leveraging the infra-sounds emitted by them. In this paper we present the test environment that we have set up for this purpose and some preliminary, but promising results