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Chain by chain Monte Carlo simulatio...

Demirel, Derya.

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Chain by chain Monte Carlo simulations for polymerization processes.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Chain by chain Monte Carlo simulations for polymerization processes./
作者:	Demirel, Derya.
面頁冊數:	244 p.
附註:	Source: Dissertation Abstracts International, Volume: 77-11(E), Section: B.
Contained By:	Dissertation Abstracts International77-11B(E).
標題:	Chemical engineering. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10133695
ISBN:	9781339922706

Chain by chain Monte Carlo simulations for polymerization processes.
Demirel, Derya.

Chain by chain Monte Carlo simulations for polymerization processes. - 244 p.

Source: Dissertation Abstracts International, Volume: 77-11(E), Section: B.

Thesis (Ph.D.)--Illinois Institute of Technology, 2016.

Predicting chain microstructures is an important task for polymer scientists, that is made more interesting and challenging by the polydisperse nature of polymer molecules. In this work, a new method, called "Chain-by-Chain Monte Carlo Method", is presented for simulating chain microstructures one-by-one or chain-by-chain. CBC-MC is a new hybrid method that uses the mean-field background information as concentrations of polymer populations and small molecules from the deterministic solver to provide an environment in which we stochastically simulate chains one-by-one with kinetic Monte Carlo. The deterministic solver in this work uses method of moments. The main advantage of CBC-MC is that the use of the deterministic solver allows the elimination of the computational load associated with simulation of the whole ensemble. Method is suited for chemistries, or situations in which chain architecture develops slowly with respect to the background environment, such as controlled reversible-deactivation radical polymerizations. In this thesis, CBC-MC is applied to two case studies for synthesis of gradient copolymers. Gradient distribution of chain properties is analyzed in all cases since it is relatively more challenging and interesting. Chain properties are compared to results from method of moments and kinetic Monte Carlo method for different sample sizes and are found to be in perfect agreement. Results confirm that if applicable, full information regarding the microstructure of chains can be obtained using this method with reduced simulation times and smaller sample sizes. This method is also applied to a non-linear copolymerization leading to gelation. Effect of a gradient distribution of pendant double bonds along the primary chains on the simulated portion of gel molecules is investigated. Primary chain results are compared with MOM and found to be in perfect agreement. Further investigations are done on primary chain microstructures to better understand multiple phenomena going on in these systems. It has been found that a gradient in PDB distribution along the primary chains can introduce heterogeneities in gel molecules in surface-bound type polymerizations where primary chains within gels are aligned in the same direction but these heterogeneities seem to be disappearing in bulk polymerizations where the chain alignments are random.

ISBN: 9781339922706Subjects--Topical Terms:

560457
Chemical engineering.

Chain by chain Monte Carlo simulations for polymerization processes.
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500 $a Source: Dissertation Abstracts International, Volume: 77-11(E), Section: B.
500 $a Adviser: Fouad Teymour.
502 $a Thesis (Ph.D.)--Illinois Institute of Technology, 2016.
520 $a Predicting chain microstructures is an important task for polymer scientists, that is made more interesting and challenging by the polydisperse nature of polymer molecules. In this work, a new method, called "Chain-by-Chain Monte Carlo Method", is presented for simulating chain microstructures one-by-one or chain-by-chain. CBC-MC is a new hybrid method that uses the mean-field background information as concentrations of polymer populations and small molecules from the deterministic solver to provide an environment in which we stochastically simulate chains one-by-one with kinetic Monte Carlo. The deterministic solver in this work uses method of moments. The main advantage of CBC-MC is that the use of the deterministic solver allows the elimination of the computational load associated with simulation of the whole ensemble. Method is suited for chemistries, or situations in which chain architecture develops slowly with respect to the background environment, such as controlled reversible-deactivation radical polymerizations. In this thesis, CBC-MC is applied to two case studies for synthesis of gradient copolymers. Gradient distribution of chain properties is analyzed in all cases since it is relatively more challenging and interesting. Chain properties are compared to results from method of moments and kinetic Monte Carlo method for different sample sizes and are found to be in perfect agreement. Results confirm that if applicable, full information regarding the microstructure of chains can be obtained using this method with reduced simulation times and smaller sample sizes. This method is also applied to a non-linear copolymerization leading to gelation. Effect of a gradient distribution of pendant double bonds along the primary chains on the simulated portion of gel molecules is investigated. Primary chain results are compared with MOM and found to be in perfect agreement. Further investigations are done on primary chain microstructures to better understand multiple phenomena going on in these systems. It has been found that a gradient in PDB distribution along the primary chains can introduce heterogeneities in gel molecules in surface-bound type polymerizations where primary chains within gels are aligned in the same direction but these heterogeneities seem to be disappearing in bulk polymerizations where the chain alignments are random.
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