Carbon deposition on MgO-supported Pt nanoparticles from ethylene decomposition was studied by in situ transmission electron microscopy (TEM) at the atomic level. An imaging strategy was established for controlling beam-gas-sample interactions that minimizes beam-induced changes of the reaction. Using this strategy, it was possible to observe how carbon encapsulation occurs on Pt nanoparticles and the role of the Pt surface morphology. The evidence suggests that multiple partial layers grew simultaneously prior to full encapsulation of the nanoparticle. The growth of carbon on Pt nanoparticles was found to induce significant changes in the nanoparticle shape, resulting in particles becoming rounder as coking progressed. Closer examination of the surface structure revealed that in some cases carbon growth induced step formation.
TitlePredicting the cetane number of furanic biofuel candidates using an improved artificial neural network based on molecular structure b s t r a c tThe next generation of alternative fuels is being investigated through advanced chemical and biological production techniques for the purpose of finding suitable replacements for diesel and gasoline while lowering production costs and increasing process yields. Chemical conversion of biomass to fuels provides a plethora of pathways with a variety of fuel molecules, both novel and traditional, which may be targeted. In the search for new fuels, an initial, intuition-driven evaluation of fuel compounds with desired properties is required. Due to the high cost and significant production time needed to synthesize these materials at a scale sufficient for exhaustive testing, a predictive model would allow chemists to preemptively screen fuel properties of potentially desirable fuel candidates. Recent work has shown that predictive models, in this case artificial neural networks (ANN's) analyzing quantitative structure property relationships (QSPR's), can predict the cetane number (CN) of a proposed fuel molecule with relatively small error. A fuel's CN is a measure of its ignition quality, typically defined using prescribed ASTM standards and a cetane testing engine. Alternatively, the analogous derived cetane number (DCN), obtained using an Ignition Quality Tester (IQT), is a direct measurement alternative to the CN that uses an empirical inverse relationship to the ignition delay found in the constant volume combustion chamber apparatus. DCN data points acquired using an IQT were utilized for model validation and expansion of the experimental database used in this study. The present work improves on an existing model by optimizing the model architecture along with the key learning variables of the ANN and by making the model more generalizable to a wider variety of fuel candidate types, specifically the class of furans and furan derivatives, by including specific molecules for the model to incorporate. The new molecules considered include tetrahydrofuran, 2-methylfuran, 2-methyltetrahydrofuran, 5,5 0 -(furan-2-ylmethylene)bis(2-methyl furan), 5,5 0 -((tetrahydrofuran-2-yl)methylene)bis(2-methyltetrahydrofuran), tris(5-methylfuran-2-yl) methane, and tris(5-methyltetrahydrofuran-2-yl)methane. Model architecture adjustments improved the overall root-mean-square error (RMSE) of the base database predictions by 5.54%. Additionally, through the targeted database expansion, it is shown that the predicted cetane number of the furanbased molecules improves on average by 49.21% (3.74 CN units) and significantly for a few of the individual molecules. This indicates that a selected subset of representative molecules can be used to extend the model's predictive accuracy to new molecular classes. The approach, bolstered by the improvements http://dx
Due to the high cost and time required to synthesize alternative fuel candidates for comprehensive testing, an Artificial Neural Network (ANN) can be used to predict fuel properties, allowing researchers to preemptively screen desirable fuel candidates. However, the accuracy of an ANN is limited by its error, measured by the root mean square error (RMSE), standard deviation, and r-squared values derived from a given input database. The present work improves upon an existing model for predicting the Cetane Number (CN) by changing the neuron activation function of the ANN from sigmoid to rectified linear unit (ReLU). This change to the ANN's architecture provides an increase in accuracy by reducing the RMSE by 21.4% (1.35 CN units), the average standard deviation across models by 28%, and increasing the r-squared value by 0.0492 across a wide range of molecular structures. Additionally, by using the ReLU activation function, input data is not required to be normalized, which reduces the likelihood of an inaccurate prediction on future fuel candidates which may have input parameters outside the range of normalization. Increasing the accuracy of the predictive ANN in this way will allow researchers to obtain more accurate fuel property predictions for promising fuel candidates.
Octane sensitivity (OS), defined as the research octane number (RON) minus the motor octane number (MON) of a fuel, has gained interest among researchers due to its effect on knocking conditions in internal combustion engines. Compounds with a high OS enable higher efficiencies, especially within advanced compression ignition engines. RON/MON must be experimentally tested to determine OS, requiring time, funding, and specialized equipment. Thus, predictive models trained with existing experimental data and molecular descriptors (via quantitative structure property relationships, QSPR) would allow for the preemptive screening of compounds prior to performing these experiments. The present work proposes two methods for predicting the OS of a given compound: using artificial neural networks (ANNs) trained with QSPR descriptors to predict RON and MON individually to compute OS (derived octane sensitivity, dOS), and using ANNs trained with QSPR descriptors to directly predict OS. 25 ANNs were trained for both RON and MON and their test sets achieved an overall 6.4% and 5.2% error, respectively. 25 additional ANNs were trained for both dOS and OS; dOS calculations were found to have 15.3% error while predicting OS directly resulted in 9.9% error. A chemical analysis of the top QSPR descriptors for RON/MON and OS is conducted, highlighting desirable structural features for high-performing molecules and offering insight into the inner mathematical workings of ANNs; such chemical interpretations study the interconnections between structural features, descriptors, and fuel performance showing that connectivity, structural diversity, and atomic hybridization consistently drive fuel performance.
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