Abstract: A packed tower includes a packed tower housing having an inlet for receiving an effluent stream; an outlet for venting the effluent stream; a packed substrate housed within the packed tower housing between the inlet and the outlet, the packed substrate being configured to entrain at least some of particles from a fluid as the effluent stream flows therethrough; and a fan housed within the packed tower housing, the fan being configured to propel the effluent stream from the inlet towards the outlet and to remove at least some of the particles from the fluid. The fan helps to generate a negative pressure to draw the effluent stream into the packed tower and through the packed substrate, which reduces the back pressure improves the flow of effluent stream through the packed tower and upstream abatement apparatus and helps to improve the removal of particles from within the effluent stream.

Abstract: A method for using a neural network to deconvolute the effects due to surface topography from the effects due to the other physical property being measured in a scanning probe microscopy (SPM) or atomic force microscopy (AFM) image. In the case of a thermal SPM, the SPM probe is scanned across the surface of a sample having known uniform thermal properties, measuring both the surface topography and thermal properties of the sample. The data thus collected forms a training data set. Several training data sets can be collected, preferably on samples having different surface topographies. A neural network is applied to the training data sets, such that the neural network learns how to deconvolute the effects dues to surface topography from the effects dues to the variations in thermal properties of a sample.

Abstract: A method for using a neural network to deconvolute the effects due to surface topography from the effects due to the other physical property being measured in a scanning probe microscopy (SPM) or atomic force microscopy (AFM) image. In the case of a thermal SPM, the SPM probe is scanned across the surface of a sample having known uniform thermal properties, measuring both the surface topography and thermal properties of the sample. The data thus collected forms a training data set. Several training data sets can be collected, preferably on samples having different surface topographies. A neural network is applied to the training data sets, such that the neural network learns how to deconvolute the effects dues to surface topography from the effects dues to the variations in thermal properties of a sample.

Abstract: By employing a "modulated-temperature" heating program composed of a series of heat-isotherm stages, it is possible to separate the change in dimensions of an oriented material during heating into two contributions: a thermally "reversing" component which is due to linear thermal expansion and a "non-reversing" part arising from relaxation to the disordered state on heating above T.sub.g. Some preliminary results for biaxially drawn poly(ethylene terephthalate) film are presented.