Heterogenized Molybdovanadophosphoric Acid on Amine-Functionalized SBA-15 for Selective Oxidation of Alkenes

Mono-vanadium substituted molybdovanadophosphoric acid has been immobilized on amine-functionalized SBA-15 and characterized by different techniques. Small angle X-ray scattering (SAXS) analysis and SEM technique provide evidence for the structural integrity of the amine-functionalized SBA-15 and for the molybdovanadophosphoric acid immobilized sample. Diffuse reflectance UV–vis, ³¹P, ⁵¹V-NMR and FT-IR data confirm the incorporation of molybdovanadophosphoric acid onto the amine-functionalized SBA-15. The textural properties of these materials were studied by nitrogen sorption studies. Oxidation of selected substrates, norbornene, cyclooctene, cyclohexene and styrene with aqueous hydrogen peroxide was carried out with the immobilized molybdovanadophosphoric acid catalyst (heterogenized) and the results were compared with that of the neat molybdovanadophosphoric acid catalyst (homogeneous). The selectivity of the desired products was higher with the immobilized catalyst. The immobilized catalyst could be separated after the reaction and the reusability of the catalyst has also been demonstrated.     

MCU based real-time temperature control system for universal microfluidic PCR chip

The analysis of genetic materials in the post-human genome project era has become an ever-expanding branch of research and thus routinely employed in majority of biochemical laboratories. Most of the diagnostic research area emphasizes on polymerase chain reaction for detecting pathogenic organisms. However, the conventional polymerase chain reaction requires expensive and sophisticated thermal cycler and is not handy owing to its large dimensions. Therefore, we fabricated a continuous-flow polymerase chain reaction chip on a PDMS based microfluidic platform to ease the hardship of the conventional system. Temperature being the most crucial factor in polymerase chain reaction, was monitored and regulated by thermostatic action using an on-line computer system. Indium tin oxide coated glass platform was used for heating as it is transparent and has good thermal conductivity under the influence of DC bias. The heating circuit used an ATMega 128 MCU to control the temperature. As a result, a precise and quick heating environment was maintained on the microfluidic chip to amplify the target DNA. We successfully amplified Lambda phage and Escherichia coli DNA on our chip to prove the practicality of the device.     

Time limitations and geometrical parameters in the design of microfluidic comparators

The ability to control the flow of particles (e.g., droplets and cells) in microfluidic environments can enable new methods for synthesis of biomaterials (Mann and Ozin in Nature 382:313–318, 1996), biocharacterization, and medical diagnosis (Pipper et al. in Nat Med 13:1259–1263, 2007). Understanding the factors that affect the particle passage can improve the control over the particles’ flow through microchannels (Vanapalli et al. in Lab Chip 9:982, 2009). The first step to understand the particle passage is to measure the resulting flow rate, induced pressure drop across the channel, and other parameters. Flow rates and pressure drops during passage of a particle through microchannels are typically measured using microfluidic comparators. Since the first microfluidic comparators were reported, a few design factors have been explored experimentally and theoretically, e.g., sensitivity (Vanapalli et al. in Appl Phys Lett 90:114109, 2007). Nevertheless, there is still a gap in the understanding of the temporal and spatial resolution limits of microfluidic comparators. Here we explore, theoretically and experimentally, the factors that affect the spatial and temporal resolution. We determined that the comparator sensitivity is defined by the device geometry adjacent and upstream the measuring point in the comparator. Further, we determined that, in order of importance, the temporal resolution is limited by the convective timescale, capacitive timescale due to channel expansion, and unsteady timescale due to the flow inertia. Finally, we explored the flow velocity limits by characterizing the transition between low to moderate Reynolds numbers (Re <<1 to Re ~ 50). The present work can guide the design of microfluidic comparators and clarify the limits of this technique.     

Contract signing, optimism, and advantage

A contract signing protocol lets two parties exchange digital signatures on a pre-agreed text. Optimistic contract signing protocols enable the signers to do so without invoking a trusted third party. However, an adjudicating third party remains available should one or both signers seek timely resolution. We analyze optimistic contract signing protocols using a game-theoretic approach and prove a fundamental impossibility result: in any fair, optimistic, timely protocol, an optimistic player yields an advantage to the opponent. The proof relies on a careful characterization of optimistic play that postpones communication to the third party.     

Selective Nanoscale Mass Transport across Atomically Thin Single Crystalline Graphene Membranes

Atomically thin single crystals, without grain boundaries and associated defect clusters, represent ideal systems to study and understand intrinsic defects in materials, but probing them collectively over large area remains nontrivial. In this study, the authors probe nanoscale mass transport across large‐area (≈0.2 cm²) single‐crystalline graphene membranes. A novel, polymer‐free picture frame assisted technique, coupled with a stress‐inducing nickel layer is used to transfer single crystalline graphene grown on silicon carbide substrates to flexible polycarbonate track etched supports with well‐defined cylindrical ≈200 nm pores. Diffusion‐driven flow shows selective transport of ≈0.66 nm hydrated K⁺ and Cl^? ions over ≈1 nm sized small molecules, indicating the presence of selective sub‐nanometer to nanometer sized defects. This work presents a framework to test the barrier properties and intrinsic quality of atomically thin materials at the sub‐nanometer to nanometer scale over technologically relevant large areas, and suggests the potential use of intrinsic defects in atomically thin materials for molecular separations or desalting.     

Deciding branching time properties for asynchronous programs

Asynchronous programming is a paradigm that supports asynchronous function calls in addition to synchronous function calls. Programs in such a setting can be modeled by automata with counters that keep track of the number of pending asynchronous calls for each function, as well as a call stack for synchronous recursive computation. These programs have the restriction that an asynchronous call is processed only when the call stack is empty. The decidability of the control state reachability problem for such systems was recently established. In this paper, we consider the problems of checking other branching time properties for such systems. Specifically we consider the following problems — termination, which asks if there is an infinite (non-terminating) computation exhibited by the system; control state maintainability, which asks if there is a maximal execution of the system, where all the state visited lie in some “good” set; whether the system can be simulated by a given finite state system; and whether the system can simulate a given finite state system. We present decision algorithms for all these problems.     

Model predictive control of continuous drum granulation

This paper details a methodology for the design of a model predictive controller for a continuous granulation plant. The work is based on a non-linear one-dimensional population balance model (1D-PBM), which was parameterized using experimental step test data generated at a continuous granulation pilot plant installed at the University of Queensland, Australia. The main objective was to operate the granulator under optimal conditions while off-specification material was fed back into the granulator to increase the economy of the process. The final algorithm design combines elements of model predictive control (MPC) with gain scheduling to cancel non-linearities in the recycle flow. A model directly identified from the step test data was the basis for testing a model predictive controller. Simulations show that the efficiency and robustness of this granulation process can be improved by applying the proposed control strategy. Ongoing work focuses on the implementation of the proposed control strategy on a full scale industrial plant.     

Batch-mode semi-supervised active learning for statistical machine translation

The development of high-performance statistical machine translation (SMT) systems is contingent on the availability of substantial, in-domain parallel training corpora. The latter, however, are expensive to produce due to the labor-intensive nature of manual translation. We propose to alleviate this problem with a novel, semi-supervised, batch-mode active learning strategy that attempts to maximize in-domain coverage by selecting sentences, which represent a balance between domain match, translation difficulty, and batch diversity. Simulation experiments on an English-to-Pashto translation task show that the proposed strategy not only outperforms the random selection baseline, but also traditional active selection techniques based on dissimilarity to existing training data.     

On Fixed Cost k-Flow Problems

In the Fixed Cost k-Flow problem, we are given a graph G = (V, E) with edge-capacities {u _e ∣e ∈ E} and edge-costs {c _e ∣e ∈ E}, source-sink pair s, t ∈ V, and an integer k. The goal is to find a minimum cost subgraph H of G such that the minimum capacity of an st-cut in H is at least k. By an approximation-preserving reduction from Group Steiner Tree problem to Fixed Cost k-Flow, we obtain the first polylogarithmic lower bound for the problem; this also implies the first non-constant lower bounds for the Capacitated Steiner Network and Capacitated Multicommodity Flow problems. We then consider two special cases of Fixed Cost k-Flow. In the Bipartite Fixed-Cost k-Flow problem, we are given a bipartite graph G = (A ∪ B, E) and an integer k > 0. The goal is to find a node subset S ? A ∪ B of minimum size |S| such G has k pairwise edge-disjoint paths between S ∩ A and S ∩ B. We give an \(O(\sqrt {k\log k})\) approximation for this problem. We also show that we can compute a solution of optimum size with Ω(k/polylog(n)) paths, where n = |A| + |B|. In the Generalized-P2P problem we are given an undirected graph G = (V, E) with edge-costs and integer charges {b _v : v ∈ V}. The goal is to find a minimum-cost spanning subgraph H of G such that every connected component of H has non-negative charge. This problem originated in a practical project for shift design [11]. Besides that, it generalizes many problems such as Steiner Forest, k-Steiner Tree, and Point to Point Connection. We give a logarithmic approximation algorithm for this problem. Finally, we consider a related problem called Connected Rent or Buy Multicommodity Flow and give a log^3+?? n approximation scheme for it using Group Steiner Tree techniques.     

Application of wireless sensor networks to aircraft control and health management systems

Use of fly-by-wire technology for aircraft flight controls have resulted in an improved performance and reliability along with achieving reduction in control system weight. Implementation of full authority digital engine control has also resulted in more intelligent, reliable, light-weight aircraft engine control systems. Greater reduction in weight can be achieved by replacing the wire harness with a wireless communication network. The first step towards fly-by-wireless control systems is likely to be the ...