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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Denuders.jpg|200px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Bispo-Silva Geosciences23 13-11.png|240px]]</div>
A '''[[denuder]]''' is a cylindrical or annular conduit or tube internally coated with a reagent that selectively reacts with a stable flow of gas drawn through the conduit. The gas molecules diffuse to the walls while the [[analyte]] contained in the gas is transmitted outwards via laminar flow, collected, and analyzed. Effectiveness of the system depends primarily "on a complete discrimination between the gas species and particulate matter."
'''"[[Journal:Geochemical biodegraded oil classification using a machine learning approach|Geochemical biodegraded oil classification using a machine learning approach]]"'''


Additional non-linear denuder geometries have also been tried with mixed results. Coiled configurations increased collection efficiency but lost larger particulate. A parallel multi-tube diffusion denuder has also been tried and found to increase collection efficiency. Other geometries include honeycombed, annular, and parallel plate. The development of the annular denuder in particular allowed researchers to overcome the inefficiencies of cylindrical denuders, allowing operation at larger flow rates (up to 30 times that of cylindrical denuders), shorter sampling periods, and less particle loss. ('''[[Denuder|Full article...]]''')<br />
[[Chromatography|Chromatographic]] oil analysis is an important step for the identification of biodegraded petroleum via peak visualization and interpretation of phenomena that explain the oil geochemistry. However, analyses of chromatogram components by geochemists are comparative, visual, and consequently slow. This article aims to improve the chromatogram analysis process performed during geochemical interpretation by proposing the use of [[convolutional neural network]]s (CNN), which are deep learning techniques widely used by big tech companies. Two hundred and twenty-one (221) chromatographic oil images from different worldwide basins (Brazil, USA, Portugal, Angola, and Venezuela) were used. The [[open-source software]] Orange Data Mining was used to process images by CNN. The CNN algorithm extracts, pixel by pixel, recurring features from the images through convolutional operations ... ('''[[Journal:Geochemical biodegraded oil classification using a machine learning approach|Full article...]]''')<br />
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Latest revision as of 13:37, 13 May 2024

Fig1 Bispo-Silva Geosciences23 13-11.png

"Geochemical biodegraded oil classification using a machine learning approach"

Chromatographic oil analysis is an important step for the identification of biodegraded petroleum via peak visualization and interpretation of phenomena that explain the oil geochemistry. However, analyses of chromatogram components by geochemists are comparative, visual, and consequently slow. This article aims to improve the chromatogram analysis process performed during geochemical interpretation by proposing the use of convolutional neural networks (CNN), which are deep learning techniques widely used by big tech companies. Two hundred and twenty-one (221) chromatographic oil images from different worldwide basins (Brazil, USA, Portugal, Angola, and Venezuela) were used. The open-source software Orange Data Mining was used to process images by CNN. The CNN algorithm extracts, pixel by pixel, recurring features from the images through convolutional operations ... (Full article...)
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