Ammonia synthesis over Ru/C catalysts with different carbon supports promoted by barium and potassium compounds

Ammonia synthesis over ruthenium catalysts supported on different carbon materials using Ba or K compounds as promoters has been investigated. Ba(NO3)(2), KOH, and KNO3 are used as the promoter or promoter precursor, and activated carbon (AC), activated carbon fiber (ACF). and carbon molecular sieve (CMS) are used as the support. The activity measurement for ammonia synthesis was carried out in a flow micro-reactor under mild conditions: 350-450 degreesC and 3.0 MPa. Results show that KOH promoter was more effective than KNO3. and that Ba(NO3)(2) was the most effective promoter among the three. The roles of promoters can be divided into the electronic modification of ruthenium, the neutralization of surface functional groups on the carbon support and the ruthenium precursor. The catalyst with AC as the support gave the highest ammonia concentration in the effluent among the supports used, while the catalyst with ACF as the support showed the highest turnover-frequency (TOF) value. It seems that the larger particles of Ru on the carbon supports are more active for ammonia synthesis in terms of TOF value. (C) 2001 Elsevier Science B.V. All rights reserved.

Butene catalytic cracking to propene and ethene over potassium modified ZSM-5 catalysts

Catalytic cracking of butene over potassium modified ZSM-5 catalysts was carried out in a fixed-bed microreactor. By increasing the K loading on the ZSM-5, butene conversion and ethene selectivity decreased almost linearly, while propene selectivity increased first, then passed through a maximum (about 50% selectivity) with the addition of ca. 0.7-1.0% K, and then decreased slowly with further increasing of the K loading. The reaction conditions were 620 degrees C, WHSV 3.5 h(-1), 0.1 MPa 1-butene partial pressure and 1 h of time on stream. Both by potassium modification of the ZSM-5 zeolite and by N(2) addition in the butene feed could enhance the selectivity towards propene effectively, but the catalyst stability did not show any improvement. On the other hand, addition of water to the butene feed could not only increase the butene conversion, but also improve the stability of the 0.7%K/ZSM-5 catalyst due to the effective removal of the coke formed, as demonstrated by the TPO spectra. XRD results indicated that the ZSM-5 structure of the 0.07% K/ZSM-5 catalyst was not destroyed even under this serious condition of adding water at 620 degrees C.

Channel formation and intermediate range order in sodium silicate melts and glasses

Kargl, Florian; Meyer, A.; Horbach, J.; Kob, W., (2004) 'Channel formation and intermediate range order in sodium silicate melts and glasses', Physical Review Letters 93(2) pp.027801 RAE2008

A fully integrated multi-channel detector for electron spectroscopy

Langstaff, David; Bushell, A.; Chase, T.; Evans, D.A., (2005) 'A fully integrated multi-channel detector for electron spectroscopy', Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms 238 pp.219-223 RAE2008 Synchrotron Radiation in Materials Science ? Proceedings of the 4th Conference on Synchrotron Radiation in Materials Science 4th Conference on Synchrotron Radiation in Materials Science

On the Interaction between TCP and the Wireless Channel in CDMA2000 Networks

In this work, we conducted extensive active measurements on a large nationwide CDMA2000 1xRTT network in order to characterize the impact of both the Radio Link Protocol and more importantly, the wireless scheduler, on TCP. Our measurements include standard TCP/UDP logs, as well as detailed RF layer statistics that allow observability into RF dynamics. With the help of a robust correlation measure, normalized mutual information, we were able to quantify the impact of these two RF factors on TCP performance metrics such as the round trip time, packet loss rate, instantaneous throughput etc. We show that the variable channel rate has the larger impact on TCP behavior when compared to the Radio Link Protocol. Furthermore, we expose and rank the factors that influence the assigned channel rate itself and in particular, demonstrate the sensitivity of the wireless scheduler to the data sending rate. Thus, TCP is adapting its rate to match the available network capacity, while the rate allocated by the wireless scheduler is influenced by the sender's behavior. Such a system is best described as a closed loop system with two feedback controllers, the TCP controller and the wireless scheduler, each one affecting the other's decisions. In this work, we take the first steps in characterizing such a system in a realistic environment.

Fusion Artmap: A Neural Network Architecture for Multi-Channel Data Fusion and Classification

Fusion ARTMAP is a self-organizing neural network architecture for multi-channel, or multi-sensor, data fusion. Single-channel Fusion ARTMAP is functionally equivalent to Fuzzy ART during unsupervised learning and to Fuzzy ARTMAP during supervised learning. The network has a symmetric organization such that each channel can be dynamically configured to serve as either a data input or a teaching input to the system. An ART module forms a compressed recognition code within each channel. These codes, in turn, become inputs to a single ART system that organizes the global recognition code. When a predictive error occurs, a process called paraellel match tracking simultaneously raises vigilances in multiple ART modules until reset is triggered in one of them. Parallel match tracking hereby resets only that portion of the recognition code with the poorest match, or minimum predictive confidence. This internally controlled selective reset process is a type of credit assignment that creates a parsimoniously connected learned network. Fusion ARTMAP's multi-channel coding is illustrated by simulations of the Quadruped Mammal database.

Fusion ARTMAP: An Adaptive Fuzzy Network for Multi-Channel Classification

Fusion ARTMAP is a self-organizing neural network architecture for multi-channel, or multi-sensor, data fusion. Fusion ARTMAP generalizes the fuzzy ARTMAP architecture in order to adaptively classify multi-channel data. The network has a symmetric organization such that each channel can be dynamically configured to serve as either a data input or a teaching input to the system. An ART module forms a compressed recognition code within each channel. These codes, in turn, beco1ne inputs to a single ART system that organizes the global recognition code. When a predictive error occurs, a process called parallel match tracking simultaneously raises vigilances in multiple ART modules until reset is triggered in one of thmn. Parallel match tracking hereby resets only that portion of the recognition code with the poorest match, or minimum predictive confidence. This internally controlled selective reset process is a type of credit assignment that creates a parsimoniously connected learned network.

Profiling side-channel attacks on cryptographic algorithms

Traditionally, attacks on cryptographic algorithms looked for mathematical weaknesses in the underlying structure of a cipher. Side-channel attacks, however, look to extract secret key information based on the leakage from the device on which the cipher is implemented, be it smart-card, microprocessor, dedicated hardware or personal computer. Attacks based on the power consumption, electromagnetic emanations and execution time have all been practically demonstrated on a range of devices to reveal partial secret-key information from which the full key can be reconstructed. The focus of this thesis is power analysis, more specifically a class of attacks known as profiling attacks. These attacks assume a potential attacker has access to, or can control, an identical device to that which is under attack, which allows him to profile the power consumption of operations or data flow during encryption. This assumes a stronger adversary than traditional non-profiling attacks such as differential or correlation power analysis, however the ability to model a device allows templates to be used post-profiling to extract key information from many different target devices using the power consumption of very few encryptions. This allows an adversary to overcome protocols intended to prevent secret key recovery by restricting the number of available traces. In this thesis a detailed investigation of template attacks is conducted, along with how the selection of various attack parameters practically affect the efficiency of the secret key recovery, as well as examining the underlying assumption of profiling attacks in that the power consumption of one device can be used to extract secret keys from another. Trace only attacks, where the corresponding plaintext or ciphertext data is unavailable, are then investigated against both symmetric and asymmetric algorithms with the goal of key recovery from a single trace. This allows an adversary to bypass many of the currently proposed countermeasures, particularly in the asymmetric domain. An investigation into machine-learning methods for side-channel analysis as an alternative to template or stochastic methods is also conducted, with support vector machines, logistic regression and neural networks investigated from a side-channel viewpoint. Both binary and multi-class classification attack scenarios are examined in order to explore the relative strengths of each algorithm. Finally these machine-learning based alternatives are empirically compared with template attacks, with their respective merits examined with regards to attack efficiency.