Difference between revisions of "Template:Article of the week"

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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig2 Tremouilhac JOfChemoinfo2017 9.gif|240px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Bispo-Silva Geosciences23 13-11.png|240px]]</div>
'''"[[Journal:Chemotion ELN: An open-source electronic lab notebook for chemists in academia|Chemotion ELN: An open-source electronic lab notebook for chemists in academia]]"'''
'''"[[Journal:Geochemical biodegraded oil classification using a machine learning approach|Geochemical biodegraded oil classification using a machine learning approach]]"'''


The development of an [[electronic laboratory notebook]] (ELN) for researchers working in the field of chemical sciences is presented. The web-based application is available as open-source software that offers modern solutions for chemical researchers. The [[Chemotion ELN]] is equipped with the basic functionalities necessary for the acquisition and processing of chemical data, in particular work with molecular structures and calculations based on molecular properties. The ELN supports planning, description, storage, and management for the routine work of organic chemists. It also provides tools for communicating and sharing the recorded research data among colleagues. Meeting the requirements of a state-of-the-art research infrastructure, the ELN allows the search for molecules and reactions not only within the user’s data but also in conventional external sources as provided by SciFinder and PubChem. The presented development makes allowance for the growing dependency of scientific activity on the availability of digital [[information]] by providing open- source instruments to record and reuse research data. The current version of the ELN has been used for over half of a year in our chemistry research group, serving as a common infrastructure for chemistry research and enabling chemistry researchers to build their own databases of digital information as a prerequisite for the detailed, systematic investigation and evaluation of chemical reactions and mechanisms. ('''[[Journal:Chemotion ELN: An open-source electronic lab notebook for chemists in academia|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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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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