Microscale 3D Printing of Nanotwinned Copper

Nanotwinned (nt)‐metals exhibit superior mechanical and electrical properties compared to their coarse‐grained and nanograined counterparts. nt‐metals in film and bulk forms are obtained using physical and chemical processes including pulsed electrodeposition (PED), plastic deformation, recrystallization, phase transformation, and sputter deposition. However, currently, there is no process for 3D printing (additive manufacturing) of nt‐metals. Microscale 3D printing of nt‐Cu is demonstrated with high density of coherent twin boundaries using a new room temperature process based on localized PED (L‐PED). The 3D printed nt‐Cu is fully dense, with low to none impurities, and low microstructural defects, and without obvious interface between printed layers, which overall result in good mechanical and electrical properties, without any postprocessing steps. The L‐PED process enables direct 3D printing of layer‐by‐layer and complex 3D microscale nt‐Cu structures, which may find applications for fabrication of metamaterials, sensors, plasmonics, and micro/nanoelectromechanical systems.     

A mathematical model for order splitting in a multiple supplier single-item inventory system

In this paper we develop a mathematical model which considers multiple-supplier single-item inventory systems. The lead times of the suppliers and demand arrival rate are random variables. All shortages are backordered. Continuous review (s, Q) policy has been assumed. When the inventory level hits the reorder level, the total order is split among n suppliers. The problem is to determine the reorder level and order quantity for each supplier so that the expected total cost per time unit, including ordering cost, procurement cost, inventory holding cost and shortage cost is minimized. We also conduct extensive numerical experiments to show the advantages of our model compared to the relevant models in the literature. In addition, some managerial insights are observed.     

Formation of homogenous nanofibers using silicon microneedle spinnerets

We report the use of a silicon microfabricated device as a new spinneret for electrospinning purposes. This device has been realized on silicon substrates using a deep reactive ion etching process. To make proper holes in the center of microneedles, a rotating angle deposition method followed by vertical etching of silicon is employed. By using these needles as fluid nozzles in the electrospinning process, poly vinyl alcohol solution with a concentration of 7?% has been converted into nanofibers. The formation of nanofibers has been investigated using field emission scanning electron microscopy. Using this process, nanofibers with a diameter of 100–200?nm are realized where the dispersion is less than 50?nm. Finally, the effects of needle size and the applied voltage have been investigated on the diameter of nanofibers.     

Molecular tracking: A concept for side-draw distillation column design

A simple approach is introduced to locate a side-draw tray for ternary and multi-component mixtures with middle boiling component(s) present in the system at trace levels. The concept is based on a probability function defined by the thermodynamic properties of the system. The advantage of this method over existing methods is the ability to quickly and efficiently provide a feasible configuration of the distillation unit without relying on rigorous optimization or trial and error approaches. Moreover, it provides an intuitive understanding of the movements of the middle boiling components in the column.     

Integrated design and control of reactive distillation processes using the driving force approach

Superior controllability of reactive distillation (RD) systems, designed at the maximum driving force (design-control solution) is demonstrated in this article. Binary or multielement single or double feed RD systems are considered. Reactive phase equilibrium data, needed for driving force analysis and design of the RD system, is generated through an in-house property prediction tool. Rigorous steady-state simulation is carried out in ASPEN plus in order to verify that the predefined design targets and dynamics are met. A multiobjective performance function is employed to evaluate the performance of the RD system in terms of energy consumption, sustainability metrics (total CO₂ footprint), and control performance. Controllability of the designed system is evaluated using indices like the relative gain array (RGA) and Niederlinski index (N_I ), to evaluate the degree of loop interaction, as well as through dynamic simulations using proportional-integral (PI) controllers and model predictive controllers (MPC). The design-control of the RD systems corresponding to other alternative designs that do not take advantage of the maximum driving force is also investigated. The analysis shows that the RD designs at the maximum driving force exhibit enhanced controllability and lower carbon footprint than the alternative RD designs.     

Split internal-loop photobioreactor for Scenedesmus sp. microalgae:Culturing and hydrodynamics

In this study, the evaluation of the performance of the split internal loop photobioreactor for culturing a species of green microalgae, Scenedesmus sp. under different operating superficial gas velocity and during a different time of growth (i.e., starting for the first day until end day of the culturing process) was addressed. The evaluation of the performance of the split internal loop photobioreactor was included assessing the density, pH, temperature, vis-cosity, surface tension, the optical density, cell population, dry biomass, and chlorophyll of the culture medium of the microalgae culturing. Additionally, the hydrodynamics of a Split Internal-Loop Photobioreactor with microalgae culturing was comprehensively quantified. Radioactive particle tracking (RPT) and gamma-ray com-puted tomography (CT) techniques were applied for the first time to quantify and address the influence of microalgae culture on the hydrodynamic parameters. The hydrodynamics parameters such as local liquid veloc-ity field, shear stresses, turbulent kinetic energy, and local gas holdup profiles were measured at different super-ficial gas velocities as well as under different times of algae growth. The obtained results indicate that the flow distribution may significantly affect the performance of the photobioreactor, which may have substantial effects on the cultivation process. The obtained experimental data can serve as benchmark data for the evaluation and validation of computational fluid dynamics (CFD) codes and their closures. This, in turn, allows us to develop ef-ficient reactors and consequently improving the productivity and selectivity of these photobioreactors.     

Quick integrative optimizers for minimizing the error of neural computing in pan evaporation modeling

To achieve an efficient methodology for approximating pan evaporation (E_P), this study offers two metaheuristic-integrated predictors. Shuffled complex evolution (SCE) and electromagnetic field optimization (EFO) are two of the fastest metaheuristic algorithms that are synthesized with artificial neural network (ANN). By doing this, the ANN is optimized in a noticeably shorter time compared to its integration with other metaheuristic techniques. Five-year climatic data of the Bakersfield station (California, USA) with an 80:20 ratio are used for developing and testing the methods. The proposed hybrids are implemented with appropriate population sizes (20 and 35 for the SCE and EFO, respectively) and their results are compared to a single ANN. Accuracy evaluation (correlation coefficients > 0.99) professed that the neural network with both conventional and sophisticated trainers is a competent approach for the E_P simulation. Besides, it was observed that the error of prediction by the ANN-SCE and ANN-EFO is 6.02 and 9.27% lower than the single ANN, respectively. Therefore, the used strategies can enhance the applicability of the ANN. The time elapsed in the optimization using SCE and EFO was 479.0 and 281.9 s, respectively. A comparison between these algorithms revealed that the EFO is both a faster and more accurate optimizer. The ANN-EFO is accordingly recommended as a new efficient model for predicting the E_P.

     

Torque redistribution and time regulation methods for actuator of fault-tolerant parallel robots

In this article, we investigate and employ torque redistribution and time regulation approaches to track a preplanned trajectory while avoiding actuator saturation for redundantly actuated, fault-tolerant parallel robots encountering actuator failure. The torque redistribution method utilizes actuation redundancies (if available) after joint failure and/or saturation to alter the torque requirements of the joints so as to avoid saturation. The time regulation method is based upon the alteration of the time to completion of the defined joint-space trajectory. By searching for a suitable time scaling factor to change (reduce) the inertia load requirements, the robot attempts to slow down or speed up as it moves along the defined trajectory to avoid saturation. The preferred implementation strategy is to use the torque redistribution approach when redundancy is available and actuation capacity is sufficient, otherwise the time regulation approach is employed. For both methods, the computed torque technique, consisting of a model reference algorithm in the feedforward process, and a proportional plus integral plus derivative (PID) controller, to deal with modeling errors and disturbances in the feedback process, are utilized.     

Microarray Image Processing and Quality Control

Image processing is an important stage of every microarray experiment. Reliability of this stage strongly influences the results of data analysis performed on extracted gene expressions. Multiple methods related to array recognition, spot segmentation and measurement extraction have emerged in this area over past several years. Currently there are various commercial and freeware packages available, which perform microarray image analysis. This paper attempts to review microarray image analysis as a whole and to make some experimental comparison of several computational schemes for signal segmentation and measurement extraction. Also we provide a detailed discussion of automated image quality control for use with microarray images.