ABSTRACT
Acid spun carbon nanotube (CNT) fibers were investigated for their field emission properties and performance was determined to be dependent on fiber morphology. The fibers were fabricated by wet-spinning of pre-made CNTs. Fiber morphology was controlled by a fabrication method and processing conditions, as well as purity, size, and type of the CNT starting material. The internal fiber structure consisted of CNT fibrils held together by van der Waals forces. Alignment and packing density of the CNTs affects the fiber's electrical and thermal conductivity. Fibers with similar diameters and differing morphology were compared, and those composed of the most densely packed and well aligned CNTs were the best field emitters as exhibited by a lower turn-on voltage and a larger field enhancement factor. Fibers with higher electrical and thermal conductivity demonstrated higher maximum current before failure and longer lifetimes. A stable emission current at 3 mA was obtained for 10 h at a field strength of <1 V µm(-1). This stable high current operation makes these CNT fibers excellent candidates for use as low voltage electron sources for vacuum electronic devices.
ABSTRACT
We report the application of an integrated computational approach for biomolecular structure determination at a low resolution. In particular, a neural network is trained to predict the spatial proximity of C-alpha atoms that are less than a given threshold apart, whereas a Kalman filter algorithm is employed to outline the biomolecular fold, with a constraints set that includes these pairwise atomic distances, and the distances and angles that define the structure as it is known from the protein's sequence. The results for Crambin demonstrate that this integrated approach is useful for molecular structure prediction at a low resolution and may also complement existing experimental distance data for a protein structure determination.
