Dissipative particle dynamics simulation study on ATRP-brush modification of variably shaped surfaces and biopolymer adsorption

Samiksha, Shrivastava,; Mirza, Ifra; Saha, Sampa; Singh, Awaneesh

doi:10.1039/d2cp01749k

Cited by 10 publications

(13 citation statements)

References 89 publications

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“…38,80,81 However, the maximum value of | x ij |, | y ij |, and | z ij | is considered to be less than or equal to half of the box length for a system periodic in all the three directions. 80,81 The central peak position of g AB ( r ) provides information on the average cluster size. The peak position moves to higher r values as the domain sizes increase with time.…”

Section: Resultsmentioning

confidence: 99%

“…One can also estimate the range of domain sizes by examining the breadth of the peaks. 78,79 To obtain g AB (r), we evaluate the probability of an A-bead being at a distance 38,80,81 However, the maximum value of |x ij |, |y ij |, and |z ij | is considered to be less than or equal to half of the box length for a system periodic in all the three directions. 80,81 The central peak position of g AB (r) provides information on the average cluster size.…”

Section: Blob Morphology Versus Coated Morphologymentioning

confidence: 99%

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Segregation of fluids with polymer additives at domain interfaces: a dissipative particle dynamics study

Gogoi¹,

Chauhan²,

Puri³

et al. 2023

Soft Matter

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Section: Resultsmentioning

confidence: 99%

Section: Blob Morphology Versus Coated Morphologymentioning

confidence: 99%

Segregation of fluids with polymer additives at domain interfaces: a dissipative particle dynamics study

Gogoi¹,

Chauhan²,

Puri³

et al. 2023

Soft Matter

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“…32,58 Thus, with a given simulation time increment of Δt = 0.02τ, these reactions occur every ten discrete time steps. 29,31,32,56,[58][59][60]67 The monomer and polymer-brush beads exhibit hydrophilic characteristics in water (solvent beads). Consequently, we set their mutual interaction, a 25 MS = and a 27 BS = .…”

Section: Table 2 Details Of the Number Of Beads Constituting The Mps ...mentioning

confidence: 99%

“…, and the corresponding mass m 180 = Da for the given number density . 29,31,32,58,68,69 . 69−71 This is an intrinsic time scale that accelerates the system's dynamics due to the softcore potential.…”

mentioning

confidence: 99%

Evolution Kinetics of Stabilizing Pickering Emulsion by Brush-Modified Janus Particles: DPD Simulation and Experimental Insights

Shrivastava,

Upadhyay,

Pradhan

et al. 2024

Langmuir

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In the present study, we report the evolution of stabilizing Pickering emulsions using brush-modified Janus particles (JPs), utilizing the dissipative particle dynamics (DPD) simulation technique. Our results are subsequently corroborated with experimental findings. Each JP has one-half of the hydrophobic surface, with the other half embedded with hydrophilic polymer brushes grown via atom transfer radical polymerization (ATRP). Our generic simulation model analyzes the chemical kinetics of polymer brush growth on one-half of the initiator-embedded microparticle (MP) surface, resulting in the formation of JP. This involves evaluating monomer conversion and reaction rates. Our results exhibit a substantial influence of the number of JPs, grafted brush density, and brush length on oil-inwater emulsion stability. We studied the evolution kinetics and stability of emulsion formation by analyzing the growth of average domain size and corresponding scaling functions up to a late time limit. This study aims to clarify the connection between the size, quantity, and functionality of JPs and the stability of Pickering emulsions.

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“…[20,21] The use of simulation models to study polymerization reactions has also developed over the last decade. [22][23][24] When coupled to external electric field control, the complexity of the reaction is intensified. From an operational point of view, this has been addressed through the development of simplified electrochemical atom transfer radical polymerization (seATRP) [25] which employs sacrificial electrodes to enable 3-electrode (potential controlled) or 2-electrode (current controlled) configurations in undivided electrochemical cells.…”

Section: Introductionmentioning

confidence: 99%

Active Learning as a Tool for Optimizing “Plug‐n‐Play” Electrochemical Atom Transfer Radical Polymerization

Zhao

Cheng

Gao

et al. 2023

Macro Chemistry & Physics

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A recently reported “plug‐n‐play” approach to simplified electrochemical atom transfer radical polymerization (seATRP) is investigated using machine learning. It is shown that Bayesian optimization via an active learning (AL) algorithm accelerates optimization of the polymerization of oligo(ethylene glycol methyl ether acrylate)480 (OEGA480) in water. Molecular weight distribution (Mw/Mn; dispersity; Ɖm) is the output selected for optimization targeting poly(oligo[ethylene glycol methyl ether acrylate]) (POEGA480) with low dispersity (Ɖm < 1.30). Input variables included applied potential (Eapp), [M] and [M]/[I], which led to a potential space of 275 possible reaction conditions. From a training data set of seven reactions, selected to yield uncontrolled POEGA480 with higher dispersities (Ɖm > 1.5), ten iteration loops are performed. During each iteration the algorithm suggests the next reaction conditions. The reactions are then performed and the conversion, number average molecular weight (Mn) and Ɖm values are recorded and the Ɖm values fed back into the algorithm. Overall, 80% of the experiments yield POEGA with Ɖm < 1.30. Conversely, only 30% of experiments performed using reaction conditions selected at random from the possible reaction space yield POEGA with Ɖm < 1.30. This study suggests that adopting AL methods can improve the efficiency of optimizing a given seATRP reaction.

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Dissipative particle dynamics simulation study on ATRP-brush modification of variably shaped surfaces and biopolymer adsorption

Abstract: We present a dissipative particle dynamics (DPD) simulation study on the surface modification of initiator embedded microparticles (MPs) of different shapes via atom transfer radical polymerization (ATRP) brush growth. The...

Cited by 10 publications

References 89 publications

Segregation of fluids with polymer additives at domain interfaces: a dissipative particle dynamics study

Segregation of fluids with polymer additives at domain interfaces: a dissipative particle dynamics study

Evolution Kinetics of Stabilizing Pickering Emulsion by Brush-Modified Janus Particles: DPD Simulation and Experimental Insights

Active Learning as a Tool for Optimizing “Plug‐n‐Play” Electrochemical Atom Transfer Radical Polymerization

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