Probing the dynamics of nanoparticle growth in a flame using synchrotron radiation

Flame synthesis is one of the most versatile and promising technologies for large-scale production of nanoscale materials. Pyrolysis has recently been shown to be a useful route for the production of single-walled nanotubes, quantum dots and a wide variety of nanostructured ceramic oxides for catalysis and electrochemical applications. An understanding of the mechanisms of nanostructural growth in flames has been hampered by a lack of direct observations of particle growth, owing to high temperatures (2,000 K), rapid kinetics (submillisecond scale), dilute growth conditions (10(-6) volume fraction) and optical emission of synthetic flames. Here we report the first successful in situ study of nanoparticle growth in a flame using synchrotron X-ray scattering. The results indicate that simple growth models, first derived for colloidal synthesis, can be used to facilitate our understanding of flame synthesis. Further, the results indicate the feasibility of studies of nanometre-scale aerosols of toxicological and environmental concern.     

Diterpene structure elucidation from 13cnmr spectra with inductive logic programming

We present a novel application ofInductive Logic Programming (ILP) to the problem of diterpene structure elucidation from 13 CNMR spectra. Diterpenes are organic compounds oflow molecular weight with a skeleton of 20 carbon atoms. They are of significant chemical and commercial interest because oftheir use as lead compounds in the search for new pharmaceutical effectors. The interpretation of diterpene 13 CNMR spectra normally requires specialists with detailed spectroscopic knowledge and substantial experience in natural products chemistry, specifically knowledge on peak patterns and chemical structures. Given a database ofpeak patterns for diterpenes with known structure, we apply several ILP approaches to discover correlations between peak patterns and chemical structure. The approaches used include first - order inductive learning, relational instance based learning, induction oflogical decision trees, and inductive constraint logic. Performance close to that of domain experts is achieved, which suffices for practical use.     

High-sensitivity and high-resolution low-energy ion scattering

Low-Energy Ion Scattering (LEIS or ISS) is used to selectively analyze the atomic composition of the outer atomic layer of surfaces. In addition, the spectrum gives (non-destructively) the in-depth distribution. Using a double toroidal energy analyzer with parallel energy detection and time-of-flight filtering a high sensitivity and mass resolution of LEIS is achieved. This is demonstrated for a highly dispersed catalyst of Pt/Au on γ-alumina. The improved depth resolution is illustrated for self-assembled monolayers of alkanethiols (12-20 carbon atoms) on gold. Even for these low Z carbon atoms a clear shift of 8 eV/carbon atom is observed (using 1.5 keV ⁴He⁺ ion scattering). This opens many new possibilities for studies of ultra-thin diffusion barriers, high-k dielectrics and biosensors.     

Daily dietary lithium intake in Belgium using duplicate portion sampling

 For the first time, daily dietary lithium intake for adults in Belgium has been evaluated by duplicate portion sampling, the heating of the samples in a microwave oven and atomic absorption spectrometric determination of the element. The mean intake value for adults (8.6±4.6 μg/day) is very low compared to the scarce literature data. Since the lithium requirement of humans and animals is still unknown, no comparison could be made with RDA values.     

Learning directed probabilistic logical models: ordering-search versus structure-search

We discuss how to learn non-recursive directed probabilistic logical models from relational data. This problem has been tackled before by upgrading the structure-search algorithm initially proposed for Bayesian networks. In this paper we show how to upgrade another algorithm for learning Bayesian networks, namely ordering-search. For Bayesian networks, ordering-search was found to work better than structure-search. It is non-obvious that these results carry over to the relational case, however, since there ordering-search needs to be implemented quite differently. Hence, we perform an experimental comparison of these upgraded algorithms on four relational domains. We conclude that also in the relational case ordering-search is competitive with structure-search in terms of quality of the learned models, while ordering-search is significantly faster.     

Modeling and simulation of the engagement dynamics of a wet friction clutch system subjected to degradation: An application to condition monitoring and prognostics

In this paper, a friction model appropriate for wet friction clutches based on the extension of the Generalized Maxwell Slip (GMS) friction model is integrated to a four-DOF lumped-mass-spring-damper system which represents a typical SAE#2 test setup. Degradation models expressing the evolutions of the friction model parameters are also proposed, where the structure of the degradation models is inspired from experimental results obtained in the earlier work. This way, the engagement dynamics of the clutch system during the useful lifetime can be simulated. It appears that the previously developed pre-and postlockup features extracted from the simulated signals obtained in this study are qualitatively in agreement with the experimental results. Those features show their predictive behaviors that confirm their feasibility to be used for clutch monitoring and prognostics. Furthermore, the models and simulation procedure discussed in this paper can be employed for developing and evaluating prognostics algorithms for wet friction clutch applications.