Applications of Polyoxyalkylene Block Copolymer Surfactants

Edens, Michael W.

doi:10.1201/9780203745656-5

Cited by 4 publications

(2 citation statements)

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“…We demonstrate our new GPC–ANN method through the analysis of poly(ethylene oxide), poly(propylene oxide) homopolymers and poloxamers, i.e., poly(ethylene oxide)- block -poly(propylene oxide)- block -poly(ethylene oxide) ( b -PEO- b -PPO- b -PEO) copolymers and the reversed b -PPO- b -PEO- b -PPO structures. These amphiphilic block copolymers are extensively used as nonionic surfactants in industrial and domestic applications ranging from pharmaceuticals to cosmetics, and so forth. − In the first set of our experiments, we analyzed 61 polymers (12 homopolymers and 49 poloxamers) with various number-average molecular weights ( M n ) ranging from 900 to 12,500 g/mol and with different ethylene oxide (EO) content, i.e., different weight fractions of the EO unit (EO %). The M n values were determined by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS).…”

Section: Resultsmentioning

confidence: 99%

Enhanced Copolymer Characterization for Polyethers Using Gel Permeation Chromatography Combined with Artificial Neural Networks

et al. 2023

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Gel permeation chromatography (GPC) is a generally applied method for the mass analysis of various polymers and copolymers, but it inherently fails to provide additional important information such as the composition of copolymers. However, we will show that GPC measurements using different solvents can yield not just the correct molecular weight but the composition of the copolymer. Accordingly, artificial neural networks (ANNs) have been developed to process the data of GPC measurements and determine the molecular weight and the chemical composition of the copolymers. The target values of the ANNs were obtained by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) and nuclear magnetic resonance (NMR) spectroscopy. Our GPC–ANN method is demonstrated by the analysis of various poloxamers, i.e., poly(ethylene oxide) (PEO)–poly(propylene oxide) (PPO) block copolymers. Two ANNs were constructed. The first one (ANN_1) works in a wider mass range (from 900 to 12,500 dalton), while the second one (ANN_2) produces more output values. ANN_2 can thus predict seven characteristic copolymer parameters, namely, two average molecular weights, the average weight fraction of the EO unit, and four average numbers of the repeat units. The correlation between the experimentally obtained outputs and the predicted ones is high (r > 0.98). The accuracy of the ANNs is very convincing, and both ANNs predict the number-average molecular weight (M n) with an accuracy below 5%. Furthermore, this work is the first step for creating an open database and applications extending the use of the GPC–ANN method for the analysis of copolymers.

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Section: Resultsmentioning

confidence: 99%

Enhanced Copolymer Characterization for Polyethers Using Gel Permeation Chromatography Combined with Artificial Neural Networks

et al. 2023

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“…We demonstrate our new short cut data mining approach for copolymer analysis through the characterization of poly(ethylene oxide)-block-poly(propylene oxide)-blockpoly(ethylene oxide) (PEO−PPO−PEO) copolymers and the reversed PPO−PEO−PPO structures. These block copolymers have extensive industrial applications as surface active agents in detergency, cosmetics, pharmaceuticals and so on [20][21][22].…”

Section: Introductionmentioning

confidence: 99%

A Short-Cut Data Mining Method for the Mass Spectrometric Characterization of Block Copolymers

et al. 2021

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A new data mining approach as a short cut method is given for the determination of the copolymer composition from mass spectra. Our method simplifies the copolymer mass spectra by reduction of the number of mass peaks. The proposed procedure, namely the selection of the mass peaks, which is based on the most abundant peak of the mass spectrum, can be performed manually or more efficiently using our recently invented Mass-remainder analysis (MARA). The considerable reduction of the MS spectra also simplifies the calculation of the copolymer quantities for instance the number- and weight-average molecular weights (Mn and Mw, respectively), polydispersity index (Đ = Mw/Mn), average molar fraction (cA) and weight fraction (wA) of the comonomer A and so on. These copolymer properties are in line with those calculated by a reference method taking into account all the mass peaks of the copolymer distribution. We also suggest a highly efficient method and template for the determination of the composition drift by processing the reduced mass spectra.

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Micellization of block copolymers

Riess¹

2003

Progress in Polymer Science

1,870

1,787

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This article deals with recent progress including the author's work in the field of block copolymer self-assembly in solution and on solid surfaces. The synthesis methods for producing block copolymers with well-defined structures, molecular weights and composition are outlined with emphasis on ionic and controlled free radical polymerization techniques. A general overview of the preparation, characterization and theories of block copolymer micellar systems is presented. Selected examples of micelle formation in aqueous and organic medium are given for di-and triblock copolymers, as well as for block copolymers with more complex architectures. Current and potential application possibilities of block copolymer colloidal assemblies as stabilizers, flocculants, nanoreservoir in, among others, controlled delivery of bioactive agents, catalysis, latex agglomeration and stabilization of non-aqueous emulsion are also discussed.

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Applications of Polyoxyalkylene Block Copolymer Surfactants

Cited by 4 publications

References 0 publications

Enhanced Copolymer Characterization for Polyethers Using Gel Permeation Chromatography Combined with Artificial Neural Networks

Enhanced Copolymer Characterization for Polyethers Using Gel Permeation Chromatography Combined with Artificial Neural Networks

A Short-Cut Data Mining Method for the Mass Spectrometric Characterization of Block Copolymers

Micellization of block copolymers

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