Tuning the oscillatory dynamics of the Belousov�Zhabotinsky reaction using ruthenium nanoparticle decorated graphen

The classic Belousov�Zhabotinsky (BZ) reaction, which involves transition metal catalysed redox reactions, represents a family of nonlinear chemical oscillators. Here, we show that it is possible to tune the oscillatory dynamics of the BZ reaction by using a hybrid 2D material,�i.e., graphene-based nanosheets decorated with Ru nanoparticles. Specifically, we demonstrate that the frequency of chemical oscillations in a BZ reaction increases, by up to four-fold, when catalyzed by the Ru�graphene nanocomposite. We show that this observed behaviour is attributed to enhanced access to active catalytic sites on Ru nanoparticles, as well as the rapid shuttling of electrons facilitated by the highly conductive graphene platform. We further demonstrate that this enhancement of oscillations facilitated by the graphene platform can be simulated using the Oregonator model. Our numerical simulations reveal a strong correlation between the rate of charge transfer and the frequency of chemical oscillations. This ability of a 2D material, like graphene, to influence the dynamics of an oscillatory chemical reaction, as showcased in this work, is studied for the first time and opens up new avenues to tune the dynamics of chemical oscillators. We anticipate that these findings would enable us to design a variety of intrinsically powered biomimetic systems with controllable dynamic behavior.by D. Jaya Prasanna Kumar,�Sachin Verma, Kabeer Jasujaa� and��Pratyush Dayal

Optimal grade transition of a non-isothermal continuous reactor with multi-objective dynamic optimization approach

Dynamic optimization (DO) is a useful tool for carrying out grade transitions in polymer industry. Most open literature studies on DO emphasize such grade transitions using single objective optimization. However, there are multiple criteria which must be met simultaneously for economic benefits. In this work, we solve a multi-objective DO problem for free-radical polymerization of methyl methacrylate in a non-isothermal continuous stirred tank reactor. The process objectives considered in the DO activity include minimization of off-spec, minimization of grade transition time, and minimization of the averaged feed flowrate. The manipulated variables considered for this problem are the initiator and coolant flowrates. The DO problem is solved using control vector parameterization (CVP) approach with first order interpolation. The solution of the aforementioned multi-objective DO problem is obtained in terms of a trade-off curve, pareto curve, using non-dominated sorting genetic algorithm (NSGA II). The three-dimensional pareto front is then projected to each of the three pairs of the objectives for better visualization and analysis. Furthermore, three representative pareto solution points, namely the two end points and a utopia point are further analysed for of each of the bi-objective pareto solution curves.by Sandesh Shirude and Nitin Padhiya

Electroreduction of carbon dioxide into selective hydrocarbon using isomorphic atomic substitution in stable copper oxide

The conversion of carbon dioxide into selective hydrocarbons is vital for green energy generation. Due to the chemical instability and lower activity, environmentally stable transition metal oxides (e.g. CuO) are unpopular for CO2 electroreduction catalysis. Here, we demonstrate substitution of Cu with isomorphic atom i.e. Ni in the CuO and utilize it for improving the hydrocarbon selectivity by 4 times as compared to pristine CuO. Hydrocarbon formation is achieved at the lowest possible applied potential (-0.2 V RHE). This gives the overpotential of about 0.37 V for methane and 0.28 V for ethylene, the lowest ever reported. Employing the ionic interaction between Ni and Cu, this catalyst suppresses the hydrogen evolution reaction (HER) to improve the hydrocarbon selectivity prominently. It is observed that current normalized by the BET surface area gives 15 to 20 times enhancement in the case of Ni substituted CuO compared to undoped CuO. The in situ experiments indicate that Ni doped CuO prefers CO pathways compared to formate resulting into high hydrocarbon selectivity. The experimental observation is further supported by DFT studies, which reveals that the CO molecule is stabilized on Cu0.9375Ni0.0625O surface rather than the CHO intermediate, in comparison to the pristine CuO surface.by Subramanian Nellaiappan, Ritesh Kumar, C. Shivakumara, Silvia Irusta, Jordan A. Hachtel, Juan-Carlos Idrobo, Abhishek Kumar Singh, Chandra Sekhar Tiwary and Sudhanshu Sharm

Understanding morphological evolution of Griseofulvin particles into hierarchical microstructures during liquid antisolvent precipitation

Controlling morphology of active pharmaceutical ingredients (APIs) during crystallization/precipitation is essential for pharmaceutical development since the pharmaceutical powder properties such as solubility, flowability, and dissolution rates are morphology dependent. The objective of this work was to understand the morphological evolution of a poorly water-soluble antifungal drug, griseofulvin (GF), during liquid antisolvent (LAS) precipitation in the presence of ultrasound and additives. GF was found to precipitate as hierarchical structures in the presence of different additives and in the absence of ultrasound. An umbrella-like morphology was observed when hydroxypropyl methylcellulose was used, hexagonal particles elongated along the central axis were obtained in the presence of Tween 80, and the use of polyvinylpyrrolidone yielded long needle-like particles. The most fascinating morphology was observed in the case of bovine serum albumin and no ultrasound, where the GF particles precipitated as six-branched hierarchical structures. Interestingly, the morphology of 6-month-old GF particles reveals that the outline of the overall morphology of initial unfilled skeletons resembled the bipyramidal morphology that would form when particles are completely filled/fused due to Ostwald ripening. The size of GF particles typically varied from 30 to 50 ?m when no ultrasound was used. Time-resolved scanning electron microscopy (SEM) studies imply that interesting morphologies of GF particles observed in the absence of ultrasound could be the result of aggregation and fusion of a large number of small particles formed in the beginning of the precipitation process. These smaller particles fuse to form primary bipyramidal particles which then undergo diffusion-limited growth through Ostwald ripening and secondary nucleation on specific particle faces due to selective adsorption of additives depending on the functional groups present on those particular faces. In contrast to the no ultrasound situation, the use of ultrasound along with the additives resulted in the formation of completely filled octahedron/bipyramidal GF particles irrespective of the additive used. These particles were significantly smaller with sizes ranging from 4 to 6 ?m. Use of ultrasound improves micromixing and alters the particle growth mechanism from diffusion limited to integration controlled resulting in smaller and well-formed GF particles.by Rupanjali Prasad and Sameer V. Dalv

Exploring temporal differences in 3D convolutional neural networks

Traditional 3D convolutions are computationally expensive, memory intensive, and due to large number of parameters, they often tend to overfit. On the other hand, 2D CNNs are less computationally expensive and less memory intensive than 3D CNNs and have shown remarkable results in applications like image classification and object recognition. However, in previous works, it has been observed that they are inferior to 3D CNNs when applied on a spatio-temporal input. In this work, we propose a convolutional block which extracts the spatial information by performing a 2D convolution and extracts the temporal information by exploiting temporal differences, i.e., the change in the spatial information at different time instances, using simple operations of shift, subtract and add without utilizing any trainable parameters. The proposed convolutional block has same number of parameters as of a 2D convolution kernel of size nxn, i.e. n^2, and has n times lesser parameters than an nxnxn 3D convolution kernel. We show that the 3D CNNs perform better when the 3D convolution kernels are replaced by the proposed convolutional blocks. We evaluate the proposed convolutional block on UCF101 and ModelNet datasets.by Gagan Kanojia, Sudhakar Kumawat and Shanmuganathan Rama

Deep neural network (DNN) surrogate models for the accelerated design of optical devices and systems

by Ravi Hegd

Contemporary dalit identity in Marathi Bhim geet

by Tushar Meshra

Synthesis of bentonite clay based geopolymer and its application in treatment of expansive soil

by Kaling Taki and Sudhanshu Sharm

Microstructural analysis of Aluminum-Molybdenum surface composites by friction stir processing

by V. P. Mahesh, and Amit Aror

Uncertainties of shear forces and bending moments in retaining wall due to earthquake loading

by V. Solanki, Prajakta R. Jadhav and Amit Prashan