Characterization of carbon molecular sieves using methane and carbon dioxide as adsorptive probes

Nitrogen adsorption at 77 K is the current standard means for pore size determination of adsorbent materials. However, nitrogen adsorption reaches limitations when dealing with materials such as molecular sieving carbon with a high degree of ultramicroporosity. In this investigation, methane and carbon dioxide adsorption is explored as a possible alternative to the standard nitrogen probe. Methane and carbon dioxide adsorption equilibria and kinetics are measured in a commercially derived carbon molecular sieve over a range of temperatures. The pore size distribution is determined from the adsorption equilibrium, and the kinetics of adsorption is shown to be Fickian for carbon dioxide and non-Fickian for methane. The non-Fickian response is attributed to transport resistance at the pore mouth experienced by the methane molecules but not by the carbon dioxide molecules. Additionally, the change in the rate of adsorption with loading is characterized by the Darken relation in the case of carbon dioxide diffusion but is greater than that predicted by the Darken relation for methane transport. Furthermore, the proposition of inkbottle-shaped micropores in molecular sieving carbon is supported by the determination of the activation energy for the transport of methane and subsequent sizing of the pore-mouth barrier by molecular potential calculations.

The structural characterization of carbon molecular sieve membrane (CMSM) via gas adsorption

The microstructure of a carbon molecular sieve membrane (CMSM) is characterized using adsorption equilibrium information. The pore size distributions of the CMSM derived from N-2 and CH4 adsorption isotherm are found to be consistent with each other and in agreement with the results of gas permeation experiments as well as the general characteristics of such molecular sieve materials. (C) 2003 Elsevier B.V. All rights reserved.

Cosuppression of eukaryotic release factor 1-1 in Arabidopsis affects cell elongation and radial cell division

The role of the eukaryotic release factor 1 (eRF1) in translation termination has previously been established in yeast; however, only limited characterization has been performed on any plant homologs. Here, we demonstrate that cosuppression of eRF1-1 in Arabidopsis (Arabidopsis thaliana) has a profound effect on plant morphology, resulting in what we term the broomhead phenotype. These plants primarily exhibit a reduction in internode elongation causing the formation of a broomhead-like cluster of malformed siliques at the top of the inflorescence stem. Histological analysis of broomhead stems revealed that cells are reduced in height and display ectopic lignification of the phloem cap cells, some phloem sieve cells, and regions of the fascicular cambium, as well as enhanced lignification of the interfascicular fibers. We also show that cell division in the fascicular cambial regions is altered, with the majority of vascular bundles containing cambial cells that are disorganized and possess enlarged nuclei. This is the first attempt at functional characterization of a release factor in vivo in plants and demonstrates the importance of eRF1-1 function in Arabidopsis.

Accelerating, hyperaccelerating, and decelerating networks

Many growing networks possess accelerating statistics where the number of links added with each new node is an increasing function of network size so the total number of links increases faster than linearly with network size. In particular, biological networks can display a quadratic growth in regulator number with genome size even while remaining sparsely connected. These features are mutually incompatible in standard treatments of network theory which typically require that every new network node possesses at least one connection. To model sparsely connected networks, we generalize existing approaches and add each new node with a probabilistic number of links to generate either accelerating, hyperaccelerating, or even decelerating network statistics in different regimes. Under preferential attachment for example, slowly accelerating networks display stationary scale-free statistics relatively independent of network size while more rapidly accelerating networks display a transition from scale-free to exponential statistics with network growth. Such transitions explain, for instance, the evolutionary record of single-celled organisms which display strict size and complexity limits.