Enhanced Combustion Characteristics of Bismuth Trioxide‐Aluminum Nanocomposites Prepared through Graphene Oxide Directed Self‐Assembly

We present a facile, spontaneous, and surfactant‐free method to controllably self‐assemble aluminum and bismuth trioxide nanoparticles through the introduction of graphene oxide as a self‐assembly directing agent. The self‐assembled nanocomposites demonstrate significant combustion performance improvements in comparison to randomly mixed aluminum and bismuth trioxide nanoparticles with enhanced pressure generation from 60 to 200 MPa, pressurization rate from 3 to 16 MPa μs⁻¹, burning rate from 1.15 to 1.55 km s⁻¹, and specific impulse from 41 to 71 s. The sensitivity of the self‐assembled aluminum and bismuth trioxide to electrostatic discharge was reduced by four orders of magnitude, without decreasing the combustion performance. Graphene oxide directed self‐assembly can be used to synthesize nanocomposites with diverse combustion properties and controlled ignition sensitivity, which lays the foundation for preparing multi‐functional, highly‐reactive, combustion systems in the future.     

Robust and computationally efficient superresolution algorithm

Superresolution is the process of combining information from multiple subpixel-shifted low-resolution images to form a high-resolution image. It works quite well under ideal conditions but deteriorates rapidly with inaccuracies in motion estimates. We model the original high-resolution image as a Markov random field (MRF) with a discontinuity adaptive regularizer. Given the low-resolution observations, an estimate of the superresolved image is obtained by using the iterated conditional modes (ICM) algorithm, which maximizes the local posterior conditional probability sequentially. The proposed method not only preserves edges but also lends robustness to errors in the estimates of motion and blur parameters. We derive theoretically the neighborhood structure for the posterior distribution in the presence of warping, blurring, and downsampling operations and use this to effectively reduce the overall computations. Results are given on synthetic as well as real data to validate our method.     

Measurements of benzo(a)pyrene in the city of Bombay for the evaluation of carcinogenic risk

Cigarette smoking, environmental chemicals and ionizing radiations are the three factors known to cause cancer in human beings. The relative importance of each of these is being constantly evaluated. There is an urgent need to monitor the environmental carcinogens on a large scale to assess the role of environmental chemicals in the incidence of cancer in human populations. Polycyclic aromatic hydrocarbons (PAH) are released into the atmosphere as a result of fossil fuel combustion and some of the PAH (e.g. benzo(a)pyrene) are recognised carcinogens. Measurement of benzo(a)pyrene in urban, suburban and rural regions of Bombay is carried out in order to evaluate the possible correlation with lung cancer incidence among different population groups. The variations in the concentration at the three sampling locations are discussed. The wide differences in the concentration at different locations seem to be very suitable for epidemiological investigations.     

Optimization of image processing parameters for large sets of in-process video microscopy images acquired from batch crystallization processes: Integration of uniform design and simplex search

A narrow particle size distribution with desired particle shape usually characterizes the expected product quality for pharmaceutical crystallization processes. Real-time estimation of particle size and shape from in-process video images is emerging as a new process analytical technology (PAT) tool for crystallization process monitoring and control. Any image processing algorithm involves a number of user-defined parameters and, typically, optimal values for these parameters are manually selected. Manual selection of optimal image processing parameters may become complex, time-consuming and unfeasible when there are a large number of images and particularly if these images are of varying qualities, as could happen in batch crystallization processes. This paper combines two optimization approaches to systematically locate optimal sets of image processing parameters — one approach is a model-based optimization method in conjunction with uniform experimental design; another approach is the Sequential Simplex Optimization method. Our study shows that these two approaches or a combination of them can successfully locate the optimal sets of parameters and the image processing results obtained with these parameters are better than those obtained via manual tuning. Combination of these two approaches also helps to overcome the drawbacks of each individual method. Our work also demonstrates that the optimal sets of parameters obtained from one batch of process images can also be successfully applied to another batch of process images that are obtained from the same system. The in-process video microscopy (PVM) images that are acquired from Monosodium Glutamate (MSG) seeded cooling crystallization process are used to demonstrate the workability of the proposed approach.     

Bias field inconsistency correction of motion-scattered multislice MRI for improved 3D image reconstruction

A common solution to clinical MR imaging in the presence of large anatomical motion is to use fast multislice 2D studies to reduce slice acquisition time and provide clinically usable slice data. Recently, techniques have been developed which retrospectively correct large scale 3D motion between individual slices allowing the formation of a geometrically correct 3D volume from the multiple slice stacks. One challenge, however, in the final reconstruction process is the possibility of varying intensity bias in the slice data, typically due to the motion of the anatomy relative to imaging coils. As a result, slices which cover the same region of anatomy at different times may exhibit different sensitivity. This bias field inconsistency can induce artifacts in the final 3D reconstruction that can impact both clinical interpretation of key tissue boundaries and the automated analysis of the data. Here we describe a framework to estimate and correct the bias field inconsistency in each slice collectively across all motion corrupted image slices. Experiments using synthetic and clinical data show that the proposed method reduces intensity variability in tissues and improves the distinction between key tissue types.     

Hands‐On CFD Educational Interface for Engineering Courses and Laboratories

This study describes the development, implementation, and evaluation of an effective curriculum for students to learn computational fluid dynamics (CFD) in introductory and intermediate undergraduate and introductory graduate level courses/laboratories. The curriculum is designed for use at different universities with different courses/laboratories, learning objectives, applications, conditions, and exercise notes. The common objective is to teach students from novice to expert users who are well prepared for engineering practice. The study describes a CFD Educational Interface for hands‐on student experience, which mirrors actual engineering practice. The Educational Interface teaches CFD methodology and procedures through a step‐by‐step interactive implementation automating the CFD process. A hierarchical system of predefined active options facilitates use at introductory and intermediate levels, encouraging self‐learning, and eases transition to using industrial CFD codes. An independent evaluation documents successful learning outcomes and confirms the effectiveness of the interface for students in introductory and intermediate fluid mechanics courses.     

High Energetic High Density Materials: A BASIC Program (FCALC) for the design and selection of efficient high energetic materials containing nitro and azido groups

A program named “FCALC” has been developed in BASIC language to calculate the detonation volumes (through a factor F) for various explosives as well as for any new organic structure. The number and kind of substiments that need to be incorporated into an unsubstituted organic compound in order to achieve maximum F factor, and thus maximum detonation velocity, can also be predicted. The program calculates F factors for a user-defined set of combinations of substituents.     

Integration of an Axially Continuous Graphene with Functional Metals for High-Temperature Electrical Conductors

Demands for effective high-temperature electrical conductors continue to increase with the rapid adoption of electric vehicles. However, the use of conventional copper-based conductors is limited to relatively low temperatures due to their poor oxidation resistance and microstructural instability. Here, a highly conductive and thermally stable nickel-graphene-copper (NiGCu) wire that combines the advantages of graphene and its metallic components is developed. The NiGCu wire consists of a conductive copper core, an oxidation-resistant nickel shell, and axially continuous graphene embedded between them. The experiments on 10–80 µm diameter NiGCu wires demonstrate substantial enhancements in electrical properties and thermal stability across a variety of metrics. For instance, the smallest NiGCu wires have a 61.2% higher current density limit, 307.6% higher conductivity, and an order of magnitude smaller change in resistivity compared to conventional Ni-coated Cu counterparts after annealing at 650 °C. By performing both innovative experiments and simulations using different sizes of NiGCu wires, the diffusion coefficients of metals are quantified, for the first time to the best knowledge, through continuous graphene. These results indicate that the dramatic improvement in thermo-electrical properties is enabled by the embedded graphene layer which reduces Ni Cu interdiffusion by ≈10⁴ times at 550 °C and 650 °C.