Reduction of loading effect by tungsten etchback in a magneticallyenhanced reactive ion etcher

The plug loading effect occurring during the etchback of tungsten was investigated in a magnetically enhanced reactive ion etcher using SF ₆/Ar mixtures. It was found that while the plug loading effect is independent of varying SF₆/Ar flow rate ratio and magnetic field intensity, it is reduced under the condition of high selectivity of tungsten relative to TiN which was achieved at high chamber pressure and low RF power. It is proposed that when TiN is used as a glue layer, the W etch rate enhancement in the plug is mainly controlled by a local loading effect. Under the optimized etchback conditions the plug loss was successfully controlled without the tungsten residue left on severe topology     

Production and properties of micro‐porous glas bubble zinc and aluminium composites

Micro‐porous syntactic foams were produced by means of integration of glass bubbles into aluminium and zinc matrices. Preforms of glass bubbles were pressure infiltrated with the alloys AlSi9Cu9 ans ZnAl4Cu using squeeze casting. The preforms were sintered thermically without the use of bonding agents. Using the combination of different sintering steps syntactic foams with locally different densities could be produced. The mechanical properties of the foams were tested indicating a high compression strength of the foams and a very good compression energy absorption. Furthermore, corrosion behaviour and behaviour at higher temperatures were investigated.     

From information to probability: An axiomatic approach—Inference is information processing

We define the very rich language of composed conditionals on a three‐valued logic and use this language as the communication tool between man and machine. Communication takes place for three reasons: knowledge acquisition, query, and response. Learning, thinking, and answering questions are of a pure information theoretical nature. The pivot of this knowledge processing concept is the amount of information (bit) we receive if a conditional becomes true. We follow an axiomatic approach to information theory rather than the classical probabilistic approach of Shannon; information comes first, and then comes probability. In the light of this philosophy, query and response experience new interpretations. Both, acquisition and response are realized by maximizing entropy and minimizing relative entropy, respectively. The iterative solution of these mathematical optimization problems gives new insights into the adaptation of prior knowledge to new information. Our expert system shell SPIRIT supports this kind of knowledge processing, which will be established by suitable examples. © 2003 Wiley Periodicals, Inc.     

Prediction of fire classification for wood based products. A multivariate statistical approach based on the cone calorimeter

Wood has long traditions as a building material, and is often used in construction elements, and as interior and exterior surfaces in the Nordic countries. In most applications, there are reaction to fire requirements to products used as surfaces, e.g. in escape routes and larger public spaces. Most wood products will therefore have to be treated with fire retardant (FR) agents to fulfil the strict requirements to properties connected to heat release and flame spread. Unfortunately, FR agents usually also increase the smoke production, as they cause a more incomplete combustion of the wood. The wood product manufacturers seek to find the optimal amount of FR additives where both heat release and smoke production in the classifying test are within the requirements given in the building regulations. This paper describes models for prediction of the European reaction to fire classes of wood products. The models are based on multivariate statistical analysis, and use test results from the cone calorimeter test as input. The presented models are, with very good precision, able to predict which Euroclass and additional smoke class a wood based product would obtain if it were to be tested in the single burning item test. Copyright © 2006 John Wiley & Sons, Ltd.     

Classification and prediction of the critical heat flux using fuzzy theory and artificial neural networks

A new method to predict the critical heat flux (CHF) is proposed, based on the fuzzy clustering and artificial neural network. The fuzzy clustering classifies the experimental CHF data into a few data clusters (data groups) according to the data characteristics. After classification of the experimental data, the characteristics of the resulting clusters are discussed with emphasis on the distribution of the experimental conditions and physical mechanism. The CHF data in each group are trained in an artificial neural network to predict the CHF. The artificial neural network adjusts the weight so as to minimize the prediction error within the corresponding cluster. Application of the proposed method to the KAIST CHF data bank shows good prediction capability of the CHF, better than other existing methods.     

Extraction of penicillin G from simulated media by an emulsion liquid membrane process

The extraction of penicillin G from simulated media was performed by water/oil/water (w/o/w) emulsion liquid membranes (ELMs) and studied under various operational conditions in a batch system. The degree of extraction achieved was between 80% and 95% under specific conditions. A concentration of greater than nine times the initial concentration of penicillin G in the external phase was obtained in the internal phase. The pH of the internal aqueous solution, containing a basic salt, was theoretically calculated on the basis of the amount of penicillin G transported into the internal phase. The calculated results agreed with the experimental data well and were used to select a suitable type and concentration of a basic salt in the internal phase to give a pH within the range 5 to 8 where penicillin G was stable after the termination of extraction. The extraction of penicillin G was successfully performed by the ELM process with sodium carbonate in the internal phase.