The researchers used molecular dynamics simulations to model asphaltene aggregate behavior in crude oil. They developed a simulation model using the MDynaMix program to model aggregates of 6 asphaltene molecules and 8 resin molecules in hexane solvent. Simulations of 2-32 nanoseconds found the asphaltene aggregates form stacked sheets with a distance of 3.74 angstroms between sheets. The aggregates had a density of 0.69 g/cm3 and limited mobility, diffusing around 3 times slower than hexane. The simulations provide insight into asphaltene aggregation and behavior in crude oil recovery.
chemical composition education "komposisi reaksi kimia"chusnaqumillaila
pada materi ini disajikan sebuah materi tentang komposisi reaksi kimia pada saat terjadinya peristiwa kimia. materi ini dibuat bertujuan untuk diberikan kepada para mahasiswa dan pelajar yang sedang mencari dan belajar memperdalam tentang materi komposisi kimia. semoga materi ini bermanfaat untuk semuanya
The branch of chemistry, which deals with the study of reaction rates and their mechanisms, called chemical kinetics.
Thermodynamics tells only about the feasibility of a reaction whereas chemical kinetics tells about the rate of a reaction.
For example, thermodynamic data indicate that diamond shall convert to graphite but in reality the conversion rate is so slow that the change is not perceptible at all.
chemical composition education "komposisi reaksi kimia"chusnaqumillaila
pada materi ini disajikan sebuah materi tentang komposisi reaksi kimia pada saat terjadinya peristiwa kimia. materi ini dibuat bertujuan untuk diberikan kepada para mahasiswa dan pelajar yang sedang mencari dan belajar memperdalam tentang materi komposisi kimia. semoga materi ini bermanfaat untuk semuanya
The branch of chemistry, which deals with the study of reaction rates and their mechanisms, called chemical kinetics.
Thermodynamics tells only about the feasibility of a reaction whereas chemical kinetics tells about the rate of a reaction.
For example, thermodynamic data indicate that diamond shall convert to graphite but in reality the conversion rate is so slow that the change is not perceptible at all.
Chapter10 section03 Percent Composition and Chemical Formulas By Hamdy Karim.Hamdy Karim
Students will learn about the Percent Composition and Chemical Formulas, also they will learn the difference between the empirical and molecular formulae!
A substituent effect is the change in a molecule’s reactivity when a substituent on the molecule is changed. In 1935, Louis Hammett designed a scale to measure influence of various substituents (X) at the meta- or para- positions on the acidity of benzoic acid.
Contributed by: Erika Aoyama and Megan Browning, University of Utah, 2016
Some reactions seem to obvious to fail, so what happens when they do? Quantum calculations yield invaluable insight to the nature of a reaction mechanism.
Presented at the Virtual Conference on Computational Chemistry VCCC 2014
Credit due to Guillermo Caballero on whose BSc thesis this presentation is based.
Predictive Modeling of Neutron Activation Analysis of Spent Nuclear Fuel for ...Raul Palomares
A method for the identification of observable radionuclides from neutron activation analysis of spent nuclear fuel was investigated. A predictive model was formulated using ORIGEN-ARP and nuclear decay data to predict neutron activation analysis results of two spent nuclear fuel samples with variable burnup values and cooling times. Model predictions were tested by performing thermal instrumental neutron activation analysis on the spent nuclear fuel samples using both cyclic and conventional irradiation methods. Preliminary results indicate neutron activation analysis was successful in identifying several stable and long-lived radionuclides predicted via model calculations but results appear limited to sample concentration. Spent nuclear fuel samples of higher specific activity are needed to further validate model results.
Chapter10 section03 Percent Composition and Chemical Formulas By Hamdy Karim.Hamdy Karim
Students will learn about the Percent Composition and Chemical Formulas, also they will learn the difference between the empirical and molecular formulae!
A substituent effect is the change in a molecule’s reactivity when a substituent on the molecule is changed. In 1935, Louis Hammett designed a scale to measure influence of various substituents (X) at the meta- or para- positions on the acidity of benzoic acid.
Contributed by: Erika Aoyama and Megan Browning, University of Utah, 2016
Some reactions seem to obvious to fail, so what happens when they do? Quantum calculations yield invaluable insight to the nature of a reaction mechanism.
Presented at the Virtual Conference on Computational Chemistry VCCC 2014
Credit due to Guillermo Caballero on whose BSc thesis this presentation is based.
Predictive Modeling of Neutron Activation Analysis of Spent Nuclear Fuel for ...Raul Palomares
A method for the identification of observable radionuclides from neutron activation analysis of spent nuclear fuel was investigated. A predictive model was formulated using ORIGEN-ARP and nuclear decay data to predict neutron activation analysis results of two spent nuclear fuel samples with variable burnup values and cooling times. Model predictions were tested by performing thermal instrumental neutron activation analysis on the spent nuclear fuel samples using both cyclic and conventional irradiation methods. Preliminary results indicate neutron activation analysis was successful in identifying several stable and long-lived radionuclides predicted via model calculations but results appear limited to sample concentration. Spent nuclear fuel samples of higher specific activity are needed to further validate model results.
The cholesteric liquid-crystal poly[oxycarbonyl-1,4-phenylene-oxy-1,4 terephthaloyl-oxy-1,4-phenylenecarbonyloxy(
1,2-dodecane)] [C34H36O8]n, named PTOBDME, synthesized by polycondensation reaction from
equimolar quantities of TOBC and the racemic mixture of glycol (R-S-1,2 dodecanediol), exhibits unexpected
optical activity and chiral morphology. The structure of racemic-PTOBDME, under different polymerization
kinetics conditions, is analyzed by conventional NMR techniques and compared with those of polymer
enantiomers R-PTOBDME and S-PTOBDME obtained starting R(+)1,2 and S(-)1,2-dodecanediol respectively.
Molecular models based on the NMR signals intensities are proposed. The optical activity of racemic-
PTOBDME is evaluated by measuring the ORD values during kinetics study, and compared to the chiral
polymers. Each enantiomeric polymer seems to present the same stereoregular head-tail, isotactic structure than
the racemic, which we explain by the higher reactivity of the primary hydroxyl than the secondary one in the
glycol through polycondensation. For each enantiomer, two independent sets of signals were observed by NMR,
explained as two diastereomeric helical conformers: gg and gt, related with two possible staggered
conformations, along the copolymer backbone. Chirality in racemic-PTOBDME is proposed to be due to the
kinetic resolution of a preferable helical diastereomer, such as Sgt, with respect to the possible four forms, while
the R/S ratio of asymmetric carbon atoms remained 50:50. Chiral amplification is observed in R-PTOBDME and
S-PTOBDME due to a helical screw sense excess. Optimum yield was obtained for racemic PTOBDME, after
120 minutes polycondensated and decanted in toluene for 24 hours. Two weeks later a second fraction
precipitated from the toluene mother liquor with 67.6% chiral excess. After eight months and two weeks a third
fraction precipitated with 85.2% chiral excess.
LSSC2011 Optimization of intermolecular interaction potential energy paramete...Dragan Sahpaski
Optimization of intermolecular interaction potential energy parameters for Monte-Carlo and Molecular dynamics simulations using Genetic Algorithms (GA)
Incorporation of Linear Scaling Relations into Automatic Mechanism Generation...Richard West
Presented at the 2017 AIChE Annual Meeting on October 31, 2017, by Richard H. West and C. Franklin Goldsmith.
https://www.aiche.org/conferences/aiche-annual-meeting/2017/proceeding/paper/304c-incorporation-linear-scaling-relations-automatic-mechanism-generation-heterogeneous-catalysis
Abstract:
To predict the selectivity and reactivity of novel catalysts at industrially relevant conditions requires a detailed microkinetic mechanism, comprising many elementary reactions. Recent advances such as such as the work of Ulissi et al [5] use a combination of scaling relations, machine learning, and DFT calculations, to gradually refine a microkinetic model until the rate limiting steps have been calculated with sufficient accuracy to be confident that they are correctly identified. However, such a system requires as input a comprehensive kinetic model containing all the possible pathways. Our recently developed Reaction Mechanism Generator for Heterogeneous Catalysis (RMG-Cat) [3], built upon the open-source RMG software primarily used for gas-phase pyrolysis and combustion [1,2], can provide such mechanisms ab inito: the user supplies just the initial conditions (eg. reactant composition, temperature, pressure) and the software predicts all the possible reactions, estimates the thermochemical and kinetic parameters, solves the governing equations, and decides which reaction pathways to include and explore further. RMG-Cat makes its decisions regarding which pathways to explore and which to ignore, using the estimated parameters, so it is important that the estimates are reasonable, even if the important parameters will be refined with more accurate calculations later in the model development process.
Linear Scaling Relations (LSRs) can provide reasonable estimates of adsorption energies in a very computationally efficient manner [4-6]. We have now implemented linear scaling relationships for the estimation of adsorption energies in the RMG-Cat software. Our database of parameters is organized in a hierarchical tree structure, enabling detailed functional group descriptions to be used when data are available and more general descriptions to be used when necessary. We include parameters to describe many adsorbates on a range of metal surfaces, and a framework to re-train the parameters whenever new data are available.
[1] Gao, C.W. et al., Comput. Phys. Commun., 203, 212-225, (2016) http://doi.org/10.1016/j.cpc.2016.02.013
[2] RMG - Reaction Mechanism Generator, open-source software, RMG-Py. http://reactionmechanismgenerator.github.io
[3] Goldsmith, C. F., West, R. H., J. Phys. Chem. C., 121 (18), 9970–9981 http://doi.org/10.1021/acs.jpcc.7b02133
[4] Medford, A. J. et al., Topics in Catalysis (2013) 57, 135-142
[5] Ulissi, Z. W. et al., Nature Comm. (2017) 8, 14621-14627
[6] Hummelshøj, J.S. et al., Angewandte Chemie International Edition (2011) 51, 272-274
The Futures of the Physic-Chemical and Thermoelectric Properties of the InTe-...theijes
By the methods of the physic-chemical analysis as well the measurements of microhadness and picknomentric density the semiconducting system InTe-Cu2ZnSnS4 has been investigated and its state diagram has been plotted. Guazibinarity of the system with restricted homogeneous fields has established on the basis of both initial components. The boundaries of solid solutions at 300K reach on side of InTe ~ 2mol% and on side of Cu2ZnSnS4 ~ 13mol%. The coordinates of the system eutectic correspond to the composition ~23mol% Cu2ZnSnS4 and temperature ~813K. A study of thermoelectric and galvanomagnetic parameters of solid solutions (InTe)1-x(Cu2ZnSnS4)x showed dispersion of electrons from the ionized admixture at low temperatures, and at higher temperatures from thermal vibrations of crystalline lattice. Heat transfer takes place according to one-phonon mechanism. The alloy of solid solution (InTe)0,98(Cu2ZnSnS4)0,02 possesses a high value of thermoelectric efficiency.
A facile method to prepare CdO-Mn3O4 nanocompositeIOSR Journals
CdO-Mn3O4 nanocomposite has been prepared by a simple solvothermal method using a domestic microwave oven. Cadmium acetate, manganese acetate and urea were used as the precursors and ethylene glycol as the solvent. The as-prepared sample was annealed for 1 hour in each case at different temperatures, viz. 100, 200 and 300°C. The as-prepared and annealed samples were characterized by X-ray diffraction and scanning electron microscopic analyses. Results indicate that annealing at 300°C is required to get the sample with high phase purity and homogeneity. The present study indicates that the method adopted can be considered as an economical and scalable one to prepare the proposed nanocomposite with reduced size, phase purity and homogeneity.
Biomass is considered as a potential source of energy production.Gasification can be employed to convert
dilute biomass energy source in to gaseous products holding concentrated form of energy. A steady state model for fluidized
bed biomass gasifier is developed based on reaction kinetics and hydrodynamic aspects of fluidization. The presence of
sorbent for absorption of carbon dioxide from the product gas is also incorporated in the model.The developed model
predicts the variation of syngas composition, temperature, pressure and velocity along the height of gasifier. Experiments
were carried out in a lab scale fluidized bed biomass gasifier and the results were used to validate the model.An increase of
50.35% in H2 mole fraction and a decrease of 50.88 % in CO2 mole fraction were observed when CaO was used as the
sorbent.
1. Computer Simulation of Asphaltene Aggregate Molecules in Crude Oil
David Woo, Aleksey Vishnyakov
Department of Chemical and Biochemical Engineering, Rutgers University
Introduction
Asphaltene Molecules
Oil fraction soluble in toluene, insoluble in heptane
Primarily consists of polyaromatic material
Most problematic fraction in crude oils, such as plugging
Unconventional energy source and can be an important factor in enhanced oil recovery
Objectives of Research:
To improve the overall understanding of asphaltene behavior in crude oil using coarse-grained models &
simulation tools
Develop a simulation model for asphaltene aggregates using MDynaMix program
MDynaMix: Molecular Dynamics Program
General purpose molecular dynamics program for simulation of either rigid or flexible molecules
Uses standard molecular-mechanics force field including electrostatic and Lennard-Jones potentials
Utilizes terms describing angles, dihedral angles, and covalent bonds
Force Field
The general form of the force field implemented in the MDynaMix Program:
𝑼 = 𝑼 𝐋𝐉 + 𝑼 𝐞𝐥 + 𝑼 𝐛𝐨𝐧𝐝 + 𝑼 𝐚𝐧𝐠 + 𝑼𝐭𝐨𝐫𝐬 + 𝑼𝐢𝐦𝐩𝐫
Lennard-Jones potential plot
Bonds/Angles
MDynaMix Simulation Methodology
Model Simulation
Sum of Lennard-Jones potential taken over non-bonded
atom pairs
Sum of electrostatic interactions between non-bonded
atom pairs
Harmonic bonds & harmonic covalent angles
Amber-type and MM3-type torsion angles
“Improper torsions” to enforce planar structure of polyaromatics
k = force constant ϴ0 = equilibrium angle M = multiplicity factor
y = 1.4201x + 25.586
0
500
1000
1500
2000
2500
3000
3500
4000
0 500 1000 1500 2000 2500 3000
MSD(A)
Time (ps)
MSD vs Time (Resins)
0
500
1000
1500
2000
2500
0 1000 2000 3000 4000 5000 6000
MSD(A)
Time (ps)
MSD vs Time (asphaltene aggregate)
Insufficient statistics,
no linear region
Structure of molecules
Hexane (solvent)
Asphaltene (MW 600-2000Da,
low H/C ratio, polyaromatic,
most polar oil fraction)
Resin (polyaromatic+aliphatic,
higher H/C)
Molecule name Molecular Weight H/C Ratio
Asphaltene 773 Da 0.946
Resin 353 Da 1.54
Hexane 86 Da 2.33
Visualizations in Visual Molecular Dynamics
Simulations of individual molecules:
MDynaMix program employed
Lennard Jones parameters for aromatic carbons and alkanes from literature papers to create
the molecules for simulation.
Bonds between carbon atoms of 1.4 Å and 120˚ angles
Timestep of 0.01 fs, total 1200 steps.
Aggregate/Micelle Simulation
Simulation procedure
A micelle/aggregate of 6 asphaltenes, and 8 resin
molecules was created in periodic box and
compacted to experimental densities at P=100atm
and 300K
The aggregate was placed in a spherical void in bulk
solvent
Three NPT MD simulations were performed at T =
300K and P =1 atm
Timestep of 2 fs
Total simulation length 2 to 32 ns.
Box size about 60 x 60 x 60 Å3
A density of 0.69 g/cm3 is calculated after the
simulation, somewhat higher than the density of
hexane.
-20
0
20
40
60
80
100
120
140
160
0 2 4 6 8 10 12 14
RDF
R
Intermolecular RDF for asphaltene core
carbons
Conclusion
Developed a simulated model for asphaltene aggregate system using MDynaMix program
Density of 0.69 g/cm^3 calculated while distance between asphaltene sheets is 3.74 Å
Mobility of asphaltene aggregate is not very fast because it is a random process
Simulation models significantly impact the search for advanced techniques for asphaltene oil recovery and precipitation
prevention
asphaltene aggregate (solvent not shown)
“stacking” of polyaromatic compounds
Aggregate is kept together by dispersion/π-π interactions and
h-bonds
Resins partially dissolved in hexane, partly “stacked” with
asphaltenes
Determining the distance between the stacks
Asphaltene in transport pipe plugging Fractal images of asphaltene
Distance
between
sheets
(3.74Å)
Acknowledgements and References
TraPPE-UA force field used for hydrocarbons treated as pseudo-atoms
Lennard-Jones parameters determined from single component VLCC
United-atom torsion potential for alkanes
𝑢tors = 𝑐0 + 𝑐1 1 + cos𝜑 + 𝑐2 1 − cos 2𝜑 + 𝑐3[1 + cos 3𝜑 ]
Pseudo-atom 𝝈 (Å) 𝜺/𝒌 𝐁 (K)
CH3 (sp3) 3.75 98.0
CH2 (sp2) 3.95 46.0
CH (aromatic) 3.695 50.5
Torsion 𝒄 𝟎
𝒌 𝐁
[K]
𝒄 𝟏
𝒌 𝐁
[K]
𝒄 𝟐
𝒌 𝐁
[K]
𝒄 𝟑
𝒌 𝐁
[K]
𝐶𝐻 𝑥 − (𝐶𝐻2) − (𝐶𝐻2)
− 𝐶𝐻 𝑦
0 335.03 -68.19 791.32
(1)Martin, M. G.; Siepmann, J. I. Novel Configurational-Bias Monte Carlo Method For Branched Molecules. Transferable Potentials for Phase Equilibria. 2. United-Atom Description of Branched
Alkanes. The Journal of Physical Chemistry B J. Phys. Chem. B. 1999, 103, 4508–4517.
(2)Wick, C. D.; Martin, M. G.; Siepmann, J. I.; Schure, M. R. Simulating Retention In Gas–Liquid Chromatography: Benzene, Toluene, and Xylene Solutes. International Journal of Thermophysics.
2001, 22, 111–122.
(3)Pacheco-Sánchez, J. H.; Zaragoza, I. P.; Martínez-Magadán, J. M. Asphaltene Aggregation Under Vacuum at Different Temperatures by Molecular Dynamics. Energy & Fuels Energy Fuels. 2003,
17, 1346–1355.
(4)Tanaka, R.; Sato, E.; Hunt, J. E.; Winans, R. E.; Sato, S.; Takanohashi, T. Characterization Of Asphaltene Aggregates Using X-Ray Diffraction and Small-Angle X-Ray Scattering. Energy & Fuels
Energy Fuels. 2004, 18, 1118–1125.
(5)Harris, K. R. Temperature And Density Dependence of the Self-Diffusion Coefficient of n-Hexane from 223 to 333 K and up to 400 MPa. Journal of the Chemical Society, Faraday Transactions 1:
Physical Chemistry in Condensed Phases J. Chem. Soc., Faraday Trans. 1. 1982, 78, 2265–2274.
(6)Zhigilei, L. Diffusion, http://people.virginia.edu/~lz2n/mse627/notes/diffusion.pdf.
RDF first
maximum
(4Å)
r/AA
Estimated distance between asphaltenes in stack agrees with experimental
data [3,4] 3.6-4AA
Diffusion of resins and mobility of the aggregate as a whole in
MD simulations can be described using MSD
Calculated from MSD using Einstein relationship
D=1.4201 × 10−9 𝑀2
𝑆
~3 times slower than hexane diffusion