The catalytic reduction of nitric oxide in automobile exhause gases--I. Tests of iron-containing materials.
Abstract The efficiencies of iron-containing catalysts for the reduction of nitric oxide in simulated and real exhaust gases are reported. The catalyst bed was seven times the swept volume of the engine. The best results were obtained when the engines were run about 10 per cent rich of stoicheiometric. The rate at which the catalyst was poisoned by conventional fuel additives was shown to be reduced by raising the working temperature of the reactor.
Nitric Oxide: Surface Reactions and Removal from Auto Exhaust
Abstract In the last 10 years an extensive research and development effort has been made to implement the catalytic removal of noxious constituents from automotive exhaust. This effort is finally coming to fruition as this paper is going into print. Vehicles sold in the United States in 1975 will be, in a significant proportion, equipped with catalytic devices. The incorporation of a miniature chemical conversion plant into millions of moving objects operating under widely varying conditions, while being required to assure reliable performance for extended usage periods, has called for the combined skills of scientists, engineers, and designers in several industries. Even so, at present only the catalytic oxidation of carbon monoxide and hydrocarbons has reached the implementation stage. The catalytic removal of nitrogen oxides has turned out to be a still more difficult task. This review will summarize very briefly the present state-of-art of the catalytic devices for the treatment of NO, emissions, but ...
An automated retinal image quality grading algorithm
This paper introduces an algorithm for the automated assessment of retinal fundus image quality grade. Retinal image quality grading assesses whether the quality of the image is sufficient to allow diagnostic procedures to be applied. Automated quality analysis is an important preprocessing step in algorithmic diagnosis, as it is necessary to ensure that images are sufficiently clear to allow pathologies to be visible. The algorithm is based on standard recommendations for quality analysis by human screeners, examining the clarity of retinal vessels within the macula region. An evaluation against a reference standard data-set is given; it is shown that the algorithm's performance correlates closely with that of clinicians manually grading image quality.