Initial Reactivity of Linkages and Monomer Rings in Lignin Pyrolysis Revealed by ReaxFF Molecular Dynamics

The initial conversion pathways of linkages and their linked monomer units in lignin pyrolysis were investigated comprehensively by ReaxFF MD simulations facilitated by the unique VARxMD for reaction analysis. The simulated molecular model contains 15 920 atoms and was constructed on the basis of Adler’s softwood lignin model. The simulations uncover the initial conversion ratio of various linkages and their linked aryl monomers. For linkages and their linked monomer aryl rings of α-O-4, β-O-4 and α-O-4 & β-5, the C_α/C_β ether bond cracking dominates the initial pathway accounting for at least up to 80% of their consumption. For the linkage of β–β & γ-O-α, both the C_α–O ether bond cracking and its linked monomer aryl ring opening are equally important. Ring-opening reactions dominate the initial consumption of other 4-O-5, 5-5, β-1, β-2, and β-5 linkages and their linked monomers. The ether bond cracking of C_α–O and C_β–O occurs at low temperature, and the aryl ring-opening reactions take place at relatively high temperature. The important intermediates leading to the stable aryl ring opening are the phenoxy radicals, the bridged five-membered and three-membered rings and the bridged six-membered and three-membered rings. In addition, the reactivity of a linkage and its monomer aryl ring may be affected by other linkages. The ether bond cracking of α-O-4 and β-O-4 linkages can activate its neighboring linkage or monomer ring through the formed phenoxy radicals as intermediates. The important intermediates revealed in this article should be of help in deepening the understanding of the controlling mechanism for producing aromatic chemicals from lignin pyrolysis

Investigation of model scale effects on coal pyrolysis using ReaxFF MD simulation

<p>ReaxFF MD is a promising method for exploring complex chemical reactions, allowing large coal molecular systems simulated at high temperature. Due to the amorphous and diverse nature of coal chemical structure, model scale effect on the simulation of coal pyrolysis can be a big issue, which was investigated by comparing heat-up ReaxFF MD simulations of three Liulin coal models with 2338, 13,498 and 98,900 atoms. ReaxFF MD simulation results show its consistency in the observed trends of weight loss profiles and product generations lumped by C number for different model scales. The small-scale coal model facilitates observation of reaction sites, but can hardly access reasonable evolving trends of products and reactions. ReaxFF MD simulations of the coal model in middle scale can obtain much better evolving trends of pyrolyzates and reproduce some reaction pathways. But certain fluctuations or randomness of reactions still exist in the evolution trends of representative products. The diversity of chemical reactions in coal pyrolysis can be much more accessible by employing the large-scale coal model, thus permitting distinguishable evolving trends in pyrolyzates.</p

Creating a Reaction Data Set Labeled with Reaction Class for Automated Reaction Classification for ReaxFF Molecular Dynamics Simulations of Realistic Fuel Pyrolysis

Pyrolysis chemistry is important in both engine combustion and industrial utilization of various fuels. Understanding pyrolysis chemistry is challenging due to the large number of reactions involved and the explosion of intermediate species structures in the radical-driven process. Since the bond changes reflect the very core information on a reaction, automatic reaction classification based on reaction centers can be useful to peak at a simplified reaction view of a complex pyrolysis process. This work proposes and implements a scheme to build a reaction data set labeled with the reaction class for reactions from reactive molecular dynamics simulations using ReaxFF (ReaxFF MD) in generating global reactions in pyrolysis of realistic fuel mixtures. The major steps include the automated conversion of reactions into elementary-like reactions with a pseudosingle reaction center, automatic extraction of extended reaction centers, reaction class defining, and manual labeling. There are 46 reaction classes defined in total based on both pyrolysis reaction knowledge and reaction observations from ReaxFF MD simulations of realistic hydrocarbon fuel pyrolysis. With the effort to have as adequate number of reactions as possible labeled for each reaction class defined, 7862 reactions were manually labeled with reaction classes for the data set of 26,881 elementary-like reactions that cover major pyrolysis reaction classes of typical hydrocarbon fuel components of n-paraffins, iso-paraffins, olefins, cycloparaffins, and aromatics. The reaction data set has been used in the scheme of SRG-Reax to build a semisupervised machine learning model of tri-training to predict the reaction classes of pyrolysis reactions. Through automated reaction classification, 30 major reaction classes involved in a total of 3479 pyrolysis reactions of real RP-3 fuel containing 45 components unravel the overall pyrolysis reaction characteristics of the fuel system. With additional reaction classes defined and reaction data labeled, the approach can be used for various fuels

Creating a Reaction Data Set Labeled with Reaction Class for Automated Reaction Classification for ReaxFF Molecular Dynamics Simulations of Realistic Fuel Pyrolysis

Pyrolysis chemistry is important in both engine combustion and industrial utilization of various fuels. Understanding pyrolysis chemistry is challenging due to the large number of reactions involved and the explosion of intermediate species structures in the radical-driven process. Since the bond changes reflect the very core information on a reaction, automatic reaction classification based on reaction centers can be useful to peak at a simplified reaction view of a complex pyrolysis process. This work proposes and implements a scheme to build a reaction data set labeled with the reaction class for reactions from reactive molecular dynamics simulations using ReaxFF (ReaxFF MD) in generating global reactions in pyrolysis of realistic fuel mixtures. The major steps include the automated conversion of reactions into elementary-like reactions with a pseudosingle reaction center, automatic extraction of extended reaction centers, reaction class defining, and manual labeling. There are 46 reaction classes defined in total based on both pyrolysis reaction knowledge and reaction observations from ReaxFF MD simulations of realistic hydrocarbon fuel pyrolysis. With the effort to have as adequate number of reactions as possible labeled for each reaction class defined, 7862 reactions were manually labeled with reaction classes for the data set of 26,881 elementary-like reactions that cover major pyrolysis reaction classes of typical hydrocarbon fuel components of n-paraffins, iso-paraffins, olefins, cycloparaffins, and aromatics. The reaction data set has been used in the scheme of SRG-Reax to build a semisupervised machine learning model of tri-training to predict the reaction classes of pyrolysis reactions. Through automated reaction classification, 30 major reaction classes involved in a total of 3479 pyrolysis reactions of real RP-3 fuel containing 45 components unravel the overall pyrolysis reaction characteristics of the fuel system. With additional reaction classes defined and reaction data labeled, the approach can be used for various fuels

Creating a Reaction Data Set Labeled with Reaction Class for Automated Reaction Classification for ReaxFF Molecular Dynamics Simulations of Realistic Fuel Pyrolysis

Pyrolysis chemistry is important in both engine combustion and industrial utilization of various fuels. Understanding pyrolysis chemistry is challenging due to the large number of reactions involved and the explosion of intermediate species structures in the radical-driven process. Since the bond changes reflect the very core information on a reaction, automatic reaction classification based on reaction centers can be useful to peak at a simplified reaction view of a complex pyrolysis process. This work proposes and implements a scheme to build a reaction data set labeled with the reaction class for reactions from reactive molecular dynamics simulations using ReaxFF (ReaxFF MD) in generating global reactions in pyrolysis of realistic fuel mixtures. The major steps include the automated conversion of reactions into elementary-like reactions with a pseudosingle reaction center, automatic extraction of extended reaction centers, reaction class defining, and manual labeling. There are 46 reaction classes defined in total based on both pyrolysis reaction knowledge and reaction observations from ReaxFF MD simulations of realistic hydrocarbon fuel pyrolysis. With the effort to have as adequate number of reactions as possible labeled for each reaction class defined, 7862 reactions were manually labeled with reaction classes for the data set of 26,881 elementary-like reactions that cover major pyrolysis reaction classes of typical hydrocarbon fuel components of n-paraffins, iso-paraffins, olefins, cycloparaffins, and aromatics. The reaction data set has been used in the scheme of SRG-Reax to build a semisupervised machine learning model of tri-training to predict the reaction classes of pyrolysis reactions. Through automated reaction classification, 30 major reaction classes involved in a total of 3479 pyrolysis reactions of real RP-3 fuel containing 45 components unravel the overall pyrolysis reaction characteristics of the fuel system. With additional reaction classes defined and reaction data labeled, the approach can be used for various fuels

Quantitative Comparison of Raman Activities, SERS Activities, and SERS Enhancement Factors of Organothiols: Implication to Chemical Enhancement

Studying the correlation between the molecular structures of SERS-active analytes and their SERS enhancement factors is important to our fundamental understanding of SERS chemical enhancement. Using a common internal reference method, we quantitatively compared the Raman activities, SERS activities, and SERS enhancement factors for a series of organothiols that differ significantly in their structural characteristics and reported chemical enhancements. We find that while the tested molecules vary tremendously in their normal Raman and SERS activities (by more than 4 orders of magnitude), their SERS enhancement factors are very similar (the largest difference is less than 1 order of magnitude). This result strongly suggests that SERS chemical enhancement factors are not as diverse as initially believed. In addition to shedding critical insight on the SERS phenomena, the common internal reference method developed in this work provides a simple and reliable way for systematic investigation of the correlation between molecular structures and their normal Raman and SERS activities

<i>Carex diaoluoshanica</i>.

<p>A. Habit. B. Stigmas. C. Terminal staminate spike. D. Inflorescence(pistillate spikes enclosed in bladeless sheaths). E. Pistillate glume. F. Flowering phase lateral pistillate spikes. G. Fruit period lateral pistillate spikes. H. Perigynium. J. Nutlet. Photographs by Hu-biao Yang.</p

Morphological comparison between <i>Carex jianfengensis</i> and <i>C</i>. <i>zunyiensis</i>.

<p>(Figs <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0136373#pone.0136373.g001" target="_blank">1</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0136373#pone.0136373.g002" target="_blank">2</a>).</p

Effects of burial depth and stolon internode length on growth of <i>Mikania micrantha</i>.

<p>Total biomass, leaf biomass, stolon biomass and root biomass of the surviving fragments are given. Error bars represent the mean ± SE. One-way ANOVAs with post-hoc Duncan’s tests were used for the multiple comparison analyses (data were ln-transformed prior to analyses), and significant differences (at the significance level of <i>P</i>=0.05) between two treatments are marked with the use of different symbols.</p

<i>Carex jianfengensis</i> A. Habit, B. Inflorescence, C. Inflorescence, D. Lateral pistillate spike, E. Terminal staminate spike, F. Bract sheaths.

<p><i>Carex jianfengensis</i> A. Habit, B. Inflorescence, C. Inflorescence, D. Lateral pistillate spike, E. Terminal staminate spike, F. Bract sheaths.</p