On the prediction of critical micelle concentration for sugar-based non-ionic surfactants

Baghban, Alireza Akbarzadeh; Sasanipour, Jafar; Sarafbidabad, Mohsen; Piri, Amin; Razavi, Razieh

doi:10.1016/j.chemphyslip.2018.05.008

Cited by 24 publications

(6 citation statements)

References 72 publications

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“…Among the factors known to affect the cmc value in aqueous solution is the structure of the surfactant [1]. Therefore, as shown in many articles [2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18], the critical micelle concentration can be predicted directly from the structure of the molecule by quantitative structure-property relationship analysis, in short QSPR. The first correlation was given by Klevens [19] who empirically found that the logarithm of cmc linearly decreases with the increase in the number of carbon atoms in the surfactant alkyl chain.…”

Section: Introductionmentioning

confidence: 99%

On the hydrophobic chains effect on critical micelle concentration of cationic gemini surfactants using molecular connectivity indices

Mozrzymas

2020

Monatsh Chem

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The influence of the structure of hydrophobic tail chains on the critical micelle concentration of cationic gemini surfactants, using only the molecular connectivity indices, has been investigated in this work. The best model obtained shows that the relationship between the logarithm of critical micelle concentration and the alkyl chains length is parabolic. The formula has been derived for compounds with the same head groups and the same, medium length, spacer but with various hydrocarbon tail chains. Good-quality QSPR model obtained can be used to predict the critical micelle concentration value of structurally similar gemini surfactants as well as to design the structure of the hydrophobic tail chains to obtain new molecules more active in micelle formation. Graphic abstract

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Section: Introductionmentioning

confidence: 99%

On the hydrophobic chains effect on critical micelle concentration of cationic gemini surfactants using molecular connectivity indices

Mozrzymas

2020

Monatsh Chem

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“…Micelles only form above CMT. The micelle process is mainly caused by the hydrophobic-block PPO, which is proven by the inverse ratio between the CMC value and PPO unit number [ 21 ]. Owing to the difference in properties between PPO and PEO, the CMC of poloxamers will decrease with an increase in temperature to minimize the effect of poloxamers on drugs [ 22 ].…”

Section: Subjectsmentioning

confidence: 99%

An Overview on Thermosensitive Oral Gel Based on Poloxamer 407

et al. 2021

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In this review, we describe the application of thermosensitive hydrogels composed of poloxamer in medicine, especially for oral cavities. Thermosensitive hydrogels remain fluid at room temperature; at body temperature, they become more viscous gels. In this manner, the gelling system can remain localized for considerable durations and control and prolong drug release. The chemical structure of the poloxamer triblock copolymer leads to an amphiphilic aqueous solution and an active surface. Moreover, the poloxamer can gel by forming micelles in an aqueous solution, depending on its critical micelle concentration and critical micelle temperature. Owing to its controlled-release effect, a thermosensitive gel based on poloxamer 407 (P407) is used to deliver drugs with different characteristics. As demonstrated in studies on poloxamer formulations, an increase in gelling viscosity decreases the drug release rate and gel dissolution time to the extent that it prolongs the drug’s duration of action in disease treatment. This property is used for drug delivery and different therapeutic applications. Its unique route of administration, for many oral diseases, is advantageous over traditional routes of administration, such as direct application and systemic treatment. In conclusion, thermosensitive gels based on poloxamers are suitable and have great potential for oral disease treatment.

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“…If the termination criterion has not been met, return to Step 2. Following the Adam algorithm, the objective function equation 20is applied in an elementwise manner to achieve the optimization of the network parameters [44].…”

Section: Overall Optimization Of the Rbfnnmentioning

confidence: 99%

Radial Basis Function Neural Network Model for Dissolved Oxygen Concentration Prediction Based on an Enhanced Clustering Algorithm and Adam

Sun

Feng

2021

IEEE Access

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In fishery aquaculture, water quality directly determines the economic benefits of aquatic products, and dissolved oxygen is an important factor affecting water quality. To accurately grasp the trends of variation in dissolved oxygen, a dissolved oxygen concentration forecasting model based on an enhanced clustering algorithm and Adam with a radial basis function neural network (ECA-Adam-RBFNN) is proposed. An enhanced clustering algorithm (ECA) combining K-means with ant colony optimization is introduced in place of random selection to determine the center positions of the neural network hidden layer units. If the number of center points is too high, the neural network will be overfit, whereas if it is too low, sudden changes will appear in the results. Once the hidden layer centers have been determined, the radial basis function (RBF) width is calculated from the maximum center distance and the number of center points to avoid the two extreme cases of RBF that are too peaked or flat. The recursive least squares (RLS) algorithm is introduced to obtain the connection weights from the hidden layer to the output layer. The Adam algorithm is introduced to iteratively differentiate the objective function to adjust the center values, weights and width while adaptively varying the learning rates for these three types of parameters. Finally, the improved forecasting algorithm is applied for the prediction of the dissolved oxygen concentration in fishery aquaculture. The experimental results show that under identical conditions, compared with a long short-term memory (LSTM) network, a backpropagation neural network (BPNN), a traditional RBF neural network, a support vector regression (SVR) model, an autoregressive integrated moving average (ARIMA), K-MLPNN (K-means muhilayer perceptron neural networks), and SC-K-means-RBF model, the improved algorithm achieves significant reductions in the mean absolute error (MAE), mean absolute percentage error (MAPE) and root mean square error (RMSE) as model evaluation indicators.

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On the prediction of critical micelle concentration for sugar-based non-ionic surfactants

Cited by 24 publications

References 72 publications

On the hydrophobic chains effect on critical micelle concentration of cationic gemini surfactants using molecular connectivity indices

On the hydrophobic chains effect on critical micelle concentration of cationic gemini surfactants using molecular connectivity indices

An Overview on Thermosensitive Oral Gel Based on Poloxamer 407

Radial Basis Function Neural Network Model for Dissolved Oxygen Concentration Prediction Based on an Enhanced Clustering Algorithm and Adam

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