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An innovative process of effective ‘fishing, trapping and killing’ of Escherichia coli(E. coli) in contaminated water samples using paper strips is proposed here. A naturally occurring sugar, d-glucose, has been adsorbed onto blotting paper strips to facilitate the ‘fishing’ of E. colicells by inducing their chemotactic response towards the chemoattractant (glucose) concentration gradient. By dipping paper strips laced with 0.1 M glucose in contaminated water samples, we report a maximum bacterial removal efficiency of 99.5% after a 90 minute duration. The porous nature of the paper strip facilitates the ‘trapping’ of the bacterial cells within its inherent porous network. Antimicrobial paper strips produced through the adsorption of bactericidal Moringa oleiferacationic protein (MOCP) were used to ‘kill’ the ‘trapped’ E. colithrough prolonged contact. Finally, we have combined these individual processes into an integrated ‘fishing, trapping, and killing’ water treatment system, whereby the top and the bottom paper strips containing chemoattractant glucose are glued with the middle one containing the antimicrobial MOCP. Based on the results of the study, we anticipate that this approach can lead to the development of a new generation of inexpensive and portable water treatment devices which can passively remove and neutralize deleterious bacteria from water.

The automated identification of colors and their intensity from sensor images is a significant interest in field deployable and cost-effective smartphone-based water monitoring solutions. Artificial Intelligence (AI) has been extensively used in automated image processing applications specifically when there is no recognizable pattern in the image data. AI considerably outperforms conventional detection techniques using image analysis in such scenarios. In the present work, we have developed an Artificial Intelligence (AI) based mobile application platform, that can capture the sensor image using an inbuilt smartphone camera, identify the presence of sensing parameters and classify the level of the same based on color intensity recognized in the training sets of the captured image using deep convolutional neural networks (CNN). As a test case, we have implemented the developed AI-based mobile application platform to monitor the water quality for bacterial contamination where the sensor images are classified into the presence or absence of bacteria based on visual appearance. Our method is seen to detect the presence with a ∼99.99% accuracy which is an improvement in the detection accuracy of the already established method in this regard where manual visual inspection is carried out to classify the sensor images. The considerable enhancement in detection accuracy can be attributed to the elimination of subjective decision making which inevitable consequence of human intervention in the reported test case.

In the present work we investigated the applicability of microfluidics for detection of myoglobin in blood samples using a dielectrophoresis (DEP) technique. We have proposed a new method of detecting myoglobin using immunoassay assisted by DEP. This paper focuses on the fabrication of the proposed device as well as the sample preparation for myoglobin detection.