Metal Organic Framework Crystals in Mixed‐Matrix Membranes: Impact of the Filler Morphology on the Gas Separation Performance

Mixed‐matrix membranes comprising NH₂‐MIL‐53(Al) and Matrimid or 6FDA‐DAM have been investigated. The metal organic framework (MOF) loading has been varied between 5 and 20 wt%, while NH₂‐MIL‐53(Al) with three different morphologies, nanoparticles, nanorods, and microneedles has been dispersed in Matrimid. The synthesized membranes have been tested in the separation of CO₂ from CH₄ in an equimolar mixture. At 3 bar and 298 K for 8 wt% MOF loading, incorporation of NH₂‐MIL‐53(Al) nanoparticles leads to the largest improvement compared to nanorods and microneedles. The incorporation of the best performing filler, i.e., NH₂‐MIL‐53(Al) nanoparticles, into the highly permeable 6FDA‐DAM has a larger effect, and the CO₂ permeability increases up to 85% with slightly lower selectivities for 20 wt% MOF loading. Specifically, these membranes have a permeability of 660 Barrer with a CO₂/CH₄ separation factor of 28, leading to a performance very close to the Robeson limit of 2008. Furthermore, a new non‐destructive technique based on Raman spectroscopy mapping is introduced to assess the homogeneity of the filler dispersion in the polymer matrix. The MOF contribution can be calculated by modeling the spectra. The determined homogeneity of the MOF filler distribution in the polymer is confirmed by focused ion beam scanning electron microscopy analysis.     

The importance of access to information and knowledge coordination on quality and economic benefits obtained from Six Sigma

Wireless Networks - This paper presents a structural equation model that relates knowledge coordination with access to information in the process of implementing Six Sigma and their impact on the...     

48 Gbit/s InP DHBT MS-DFF with very low time jitter

A master-slave D-type flip-flop (MS DFF) fabricated in a self-aligned InP DHBT technology is presented. The packaged circuit shows full-rate clock operation at 48 Gbit/s. Very low time jitter and good retiming capabilities are observed. Layout aspects, packaging and measurement issues are discussed in particular     

A 1.4GS/s 9-bit 45 dB power control RFDAC for digital radio transmitters

This paper presents a high speed, 9-bit RF Digital-to-Analog Converter based on a new architecture implemented in a 0.13 μm BiCMOS process and able to adjust the output power by 45 dB to meet gain control requirements of the new communications standards with a SFDR >25 dBc. The maximum test-demonstrated frequency is 1.4 GHz and the chip dissipates <25 mW.     

Analog circuits optimization based on evolutionary computation techniques

This paper presents a new design automation tool, based on a modified genetic algorithm kernel, in order to improve efficiency on the analog IC design cycle. The proposed approach combines a robust optimization with corner analysis, machine learning techniques and distributed processing capability able to deal with multi-objective and constrained optimization problems. The resulting optimization tool and the improvement in design productivity is demonstrated for the design of CMOS operational amplifiers.     

Network‐layer security for the Internet of Things using TinyOS and BLIP

The design of standard communications and security mechanisms for resource‐constrained sensing applications and devices may provide an important contribution for its integration with the Internet and consequently towards the realization of what we nowadays identify as the Internet of Things. This vision will only be realizable if appropriate security mechanisms are available, and in this context we target the design and experimental evaluation of security mechanisms for communications at the network‐layer with sensing devices (smart objects) using the standard IPv6 protocol. Our work proposes and evaluates the usage of new compressed security headers for the network layer with smart objects. We implement and evaluate what is, as far as we know, the first proposal of security at the network layer experimentally evaluated using the TinyOS operating system and its networking stack. As we verify in the course of our evaluation study, various scenarios employing network‐layer secure communications involving smart objects are feasible, particularly when security mechanisms are designed to benefit from cross‐layer interactions that allow the optimization of expensive cryptographic operations. Copyright © 2012 John Wiley & Sons, Ltd.     

Improved CO2 Capture from Flue Gas by Basic Sites,Charge Gradients,and Missing Linker Defects on Nickel Face Cubic Centered MOFs

The adsorptive properties of the isoreticular series [Ni₈(OH)₄(H₂O)₂(BDP_X)₆] (H₂BDP_X = 1,4‐bis(pyrazol‐4‐yl)benzene‐4‐X with X = H (1), OH (2), NH₂ (3)) can be enhanced by postsynthetic treatment with an excess of KOH in ethanol. In the case of X = H, NH₂, this treatment leads to partial removal of the organic linkers, deprotonation of coordinated water molecules and introduction of extraframework cations, giving rise to materials of K[Ni₈(OH)₅(EtO)‐(H₂O)₂(BDP_X)_5.5] (1@KOH, 3@KOH) formulation, in which the original framework topology is maintained. By contrast, the same treatment with KOH in the [Ni₈(OH)₄(H₂O)₂(BDP_OH)₆] (2) system, enclosing the more acidic phenol residues, leads to a new material containing a larger fraction of missing linker defects and extra‐framework cations as well as phenolate residues, giving rise to the material K₃[Ni₈(OH)₃(EtO)(H₂O)₆(BDP_O)₅] (2@KOH), which also conserves the original face cubic centered (fcu) topology. It is noteworthy that the introduction of missing linker defects leads to a higher accessible pore volume with a concomitant increased adsorption capacity. Moreover, the creation of coordinatively unsaturated metal centers, charge gradients, and phenolate nucleophilic sites in 2@KOH gives rise to a boosting of CO₂ capture features with increased adsorption heat and adsorption capacity, as proven by the measurement of pulse gas chromatography and breakthrough curve measurements of simulated flue gas.     

Superparamagnetic Ag@Co‐Nanocomposites on Granulated Cation Exchange Polymeric Matrices with Enhanced Antibacterial Activity for the Environmentally Safe Purification of Water

Cation exchange polymeric matrices are widely used in water treatment protocols to reduce the mineral content of hard waters, even for human consumption. However, they are not antibacterial and flowing bacteria can be trapped in their structures and proliferate, thus acting as microbial contamination sources. Here, Ag@Co‐nanoparticles (Ag@Co‐NPs) with a low‐cost superparamagnetic Co⁰‐core and an antibacterial Ag‐shell are synthesized on granulated cation exchange polymeric matrices under soft reaction conditions. The presence of these NPs provides the final nanocomposite (NC) with additional functionalities (superparamagnetism and antibacterial activity) making it ideal for water purification applications. Ag@Co‐NPs are synthesized in situ on four cation exchange polymeric matrices containing either strong (sulfonic) or weak (carboxylic) acid functional groups homogeneously distributed (C‐type) or concentrated on an external shell (SST‐type) by the intermatrix synthesis (IMS) method. The NCs are characterized (metal content, NP size and distribution, metal oxidative state, and metal release) and evaluated for water purification applications.     

Robustness of optimal channel reservation using handover prediction in multiservice wireless networks

The aim of our study is to obtain theoretical limits for the gain that can be expected when using handover prediction and to determine the sensitivity of the system performance against different parameters. We apply an average-reward reinforcement learning approach based on afterstates to the design of optimal admission control policies in mobile multimedia cellular networks where predictive information related to the occurrence of future handovers is available. We consider a type of predictor that labels active mobile terminals in the cell neighborhood a fixed amount of time before handovers are predicted to occur, which we call the anticipation time. The admission controller exploits this information to reserve resources efficiently. We show that there exists an optimum value for the anticipation time at which the highest performance gain is obtained. Although the optimum anticipation time depends on system parameters, we find that its value changes very little when the system parameters vary within a reasonable range. We also find that, in terms of system performance, deploying prediction is always advantageous when compared to a system without prediction, even when the system parameters are estimated with poor precision.     

Diagnosis of learner dropout based on learning styles for online distance learning

The amount of data generated by computer systems in Online Distance Learning (ODL) contains rich information. One example of this information we define as the Learner Learning Trail (LLT), which is the sequence of interactions between the students and the virtual environment. Another example is the Learner Learning Style (LLS), which is associated with the student behavior and choices during the learning process. This information can be used to identify learner behavior and learning style. We perceived, after the study of related literature, that the research field of learner diagnosis for ODL does not apply the conjoint use of LLT and LLS. In this article, we propose a model capable of integrating data generated from the behavior of students in ODL with cognitive aspects of them, such as their Learning Styles, by crossing LLT with LLS. We also propose the CPAD method (Collect, Preprocessing, Analysis, Diagnosis), which is implemented by collecting the raw data regarding learning activities, preprocessing the data into structured time sequences, analyzing the sequences regarding the learning styles and using this analysis to diagnose the learner behavior. We selected the dropout to investigate, once the dropout rate in ODL is a real problem in universities around the world. In addition, the dropout is a student decision which can be associated with previous students behaviors. We performed a study with 202 learners to evaluate if learning styles are capable of explaining aspects of the student behavior. The results suggest that Sequential/Global learning style dimension is more capable of explaining the dropout than the other dimensions. Also, we performed four classification experiments to verify how the dimensions of Felder-Silverman Learning Style Model influence the learner diagnosis. We perceived that the Sequential/Global dimension could provide a higher accuracy average with lower variation independently of the diagnosis technique.