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Cardamom essential oil (EO) is a rare oil of high scientific and economic interest due to its biofunctionality. This work aims to stabilize the EO by Pickering emulsions with nanocellulose, in the form of nanocrystals (CNC) or nanofibers (CNF), and to investigate the stability and chemical and physical interactions involved in the process. The emulsions were characterized by droplet size, morphology, stability, surface charges, Fourier transform infrared spectroscopy, FT-Raman, nuclear magnetic resonance, and scanning electron microscopy. Stable emulsions were prepared with cellulose morphologies and CNCs resulted in a 34% creaming index, while CNFs do not show instability. Emulsions indicate a possible interaction between nanocellulose, α-terpinyl acetate, and 1,8-cineole active essential oil compounds, where α-terpinyl acetate would be inside the drop and 1,8-cineole is more available to interact with cellulose. The interaction intensity depended on the morphology, which might be due to the nanocellulose's self-assembly around oil droplets and influence on oil availability and future application. This work provides a systematic picture of cardamomum derived essential oil Pickering emulsion containing nanocellulose stabilizers' formation and stability, which can further be extended to other value-added oils and can be an alternative for the delivery of cardamom essential oil for biomedical, food, cosmetics, and other industries. [ABSTRACT FROM AUTHOR]

The study of wetting characteristics of low-energy (e.g., superhydrophobic) liquid-repellent surfaces is of great importance towards optimal design of such micro/nano-engineered surfaces. The most common technique to accomplish this involves bringing a drop generated at the needle-tip close to the characterizing substrate with a goal to deposit it on the substrate, which often becomes a challenge when the surface energy of the drop-substrate combination is comparable to the needledrop system. In this paper, we proposed a new "needle-free" drop deposition technique, which overcomes this challenge for characterization the low-energy substrates. This is achieved by placing an additional low-energy substrate above the characterizing substrate and allowing the dropneedle combination to impact on this additional substrate. This technique is not only independent of the wetting properties of the needle and the characterizing substrate but is also independent of the liquid drop properties, thereby making it a very universal technique for characterizing substrate in air medium. [ABSTRACT FROM AUTHOR]

The present study established the genesis of free surface vortex inside a viscous liquid using a rotating cylindrical disc immersed in the liquid. The longitudinal axis of the disc is kept normal to the nominal interfacial plane and its rotation is characterized in terms of Froude number (1.50–4.49). This configuration is simulated using a grid-based volume of fluid technique in the air and high viscous polybutene pair. A dip in nominal interface profile is observed at low disc rotations (Froude number ≈ 1.50), however, the gradual progress of the rotational inertia (Froude number > 1.50) has resulted in elongation of the interface in the form of a free surface vortex progressing inside the liquid. The transient progress of vortex depicts bullet nose like interface tip during the early stages, which grows in the outward radial direction along with its downward motion due to the centrifugal effect of the surrounding liquid. The initial submergence ratio of the disc and its radius are shown as the important parameters governing the entrainment rate and shape of the vortex profiles. The physical understanding behind the formation of the vortex is revealed using axial and crosswise circulations of the surrounding liquid. [ABSTRACT FROM AUTHOR]

Nanostructured van der Waals (vdW) layered materials hold great potential for achieving smart surfaces with controllable wettability. Inspired by this possibility, we conduct Molecular Dynamics (MD) simulations of the wetting of nanostructured hexagonal boron nitride (hBN) surfaces. The nanostructure consists of periodically placed nanopillars made of hBN nanoribbons. We demonstrate that the polarity effect of the nanoribbon edges triggers wetting anisotropy of the nanoribbons: the vertical edges of the nanoribbons demonstrate a different wetting behavior as compared to the flat surfaces of the nanoribbons. Depending on the nature of the edge of the nanoribbon (armchair or zigzag), these vertical edges can be more hydrophilic for the zigzag edges or more hydrophobic for the armchair edges than the flat part. Such differences ensure that the nanostructured hBN surfaces become more hydrophilic (hydrophobic) as compared to the flat non-nanostructured hBN surfaces for cases where the edges of the nanoribbon are more hydrophilic (hydrophobic) than the flat part. Overall, the present study develops a most remarkable design space where by introducing nanopillars/nanoribbons on hBN and by merely changing the nature of the edges of these nanopillars, one can ensure atomistically thin coating of hBN with a wide range of wettability. [ABSTRACT FROM AUTHOR]

An analytical study was conducted for natural convection in a cavity of different aspects ratios with uniform volumetric heat generation. The results for velocity profiles when compared with the simulations obtained from Fluent were found to be in good agreement.

The automated identification of colors and their intensity from sensor images is a significant interest in field deployable and costeffective 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. [ABSTRACT FROM AUTHOR]