Artificial Neural Networks: Applications in Nanotechnology

Amani, Amir; Mohammadyani, Dariush

doi:10.5772/15551

Cited by 19 publications

(13 citation statements)

References 62 publications

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“…18,19 ANNs are established methods to study nonlinear phenomena (as commonly observed in preparation of NPs). [20][21][22] For instance, ANNs have been used to determine the effect of independent variables on size of triblock poly (lactide)e poly (ethylene glycol)epoly (lactide) (PLAePEGePLA) 23 and silver 24 NPs.…”

Section: Introductionmentioning

confidence: 99%

Size, Loading Efficiency, and Cytotoxicity of Albumin-Loaded Chitosan Nanoparticles: An Artificial Neural Networks Study

Baharifar

Amani

2017

Journal of Pharmaceutical Sciences

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When designing nanoparticles for drug delivery, many variables such as size, loading efficiency, and cytotoxicity should be considered. Usually, smaller particles are preferred in drug delivery because of longer blood circulation time and their ability to escape from immune system, whereas smaller nanoparticles often show increased toxicity. Determination of parameters which affect size of particles and factors such as loading efficiency and cytotoxicity could be very helpful in designing drug delivery systems. In this work, albumin (as a protein drug model)-loaded chitosan nanoparticles were prepared by polyelectrolyte complexation method. Simultaneously, effects of 4 independent variables including chitosan and albumin concentrations, pH, and reaction time were determined on 3 dependent variables (i.e., size, loading efficiency, and cytotoxicity) by artificial neural networks. Results showed that concentrations of initial materials are the most important factors which may affect the dependent variables. A drop in the concentrations decreases the size directly, but they simultaneously decrease loading efficiency and increase cytotoxicity. Therefore, an optimization of the independent variables is required to obtain the most useful preparation.

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Section: Introductionmentioning

confidence: 99%

Size, Loading Efficiency, and Cytotoxicity of Albumin-Loaded Chitosan Nanoparticles: An Artificial Neural Networks Study

Baharifar

Amani

2017

Journal of Pharmaceutical Sciences

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“…ANNs can learn and recognize relations between independent variables (i.e., input data set) and corresponding dependent variable(s) (i.e., output parameter(s)) (14). In recent years, ANNs have been successfully used in various areas of applications such as image processing, medicine, pharmaceutics (14,15), and nanotechnology (16), where statistical methods may not be efficient due to complex relations commonly observed between the data.…”

Section: Introductionmentioning

confidence: 99%

The Use of Artificial Neural Networks for Optimizing Polydispersity Index (PDI) in Nanoprecipitation Process of Acetaminophen in Microfluidic Devices

2012

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Abstract. Artificial neural networks (ANNs) were used in this study to determine factors that control the polydispersity index (PDI) in an acetaminophen nanosuspension which was prepared using nanoprecipitation in microfluidic devices. The PDI of prepared formulations was measured by dynamic light scattering. Afterwards, the ANNs were applied to model the data. Four independent variables, namely, surfactant concentration, solvent temperature, and flow rate of solvent and antisolvent were considered as input variables, and the PDI of acetaminophen nanosuspension was taken as the output variable. The response surfaces, generated as 3D graphs after modeling, were used to survey the interactions happening between the input variables and the output variable. Comparison of the response surfaces indicated that the antisolvent flow rate and the solvent temperature have reverse effect on the PDI, whereas solvent flow rate has direct relation with PDI. Also, the effect of the concentration of the surfactant on the PDI was found to be indirect and less influential. Overall, it was found that minimum PDI may be obtained at high values of antisolvent flow rate and solvent temperature, while the solvent flow rate should be kept to a minimum.

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“…Neurons compute the weighted sum of all of the inputs yielding an output . In recent years, ANNs have been quite complimentary to response surface methodologies (RSMs) and have successfully been used in various applications including image processing, medicine, environmental science, pharmaceutics, water resources, and nanotechnology, where statistical methods may not be efficient due to complex relations commonly observed between the data .…”

Section: Introductionmentioning

confidence: 99%

Thread/paper‐ and paper‐based microfluidic devices for glucose assays employing artificial neural networks

et al. 2018

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This paper describes the fabrication of and data collection from two microfluidic devices: a microfluidic thread/paper based analytical device (μTPAD) and 3D microfluidic paper-based analytical device (μPAD). Flowing solutions of glucose oxidase (GOx), horseradish peroxidase (HRP), and potassium iodide (KI), through each device, on contact with glucose, generated a calibration curve for each platform. The resultant yellow-brown color from the reaction indicates oxidation of iodide to iodine. The devices were dried, scanned, and analyzed yielding a correlation between yellow intensity and glucose concentration. A similar procedure, using an unknown concentration of glucose in artificial urine, is conducted and compared to the calibration curve to obtain the unknown value. Studies to quantify glucose in artificial urine showed good correlation between the theoretical and actual concentrations, as percent differences were ≤13.0%. An ANN was trained on the four-channel CMYK color data from 54 μTPAD and 160 μPAD analysis sites and Pearson correlation coefficients of R = 0.96491 and 0.9739, respectively, were obtained. The ANN was able to correctly classify 94.4% (51 of 54 samples) and 91.2% (146 of 160 samples) of the μTPAD and μPAD analysis sites, respectively. The development of this technology combined with ANN should further facilitate the use of these platforms for colorimetric analysis of other analytes.

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Artificial Neural Networks: Applications in Nanotechnology

Cited by 19 publications

References 62 publications

Size, Loading Efficiency, and Cytotoxicity of Albumin-Loaded Chitosan Nanoparticles: An Artificial Neural Networks Study

Size, Loading Efficiency, and Cytotoxicity of Albumin-Loaded Chitosan Nanoparticles: An Artificial Neural Networks Study

The Use of Artificial Neural Networks for Optimizing Polydispersity Index (PDI) in Nanoprecipitation Process of Acetaminophen in Microfluidic Devices

Thread/paper‐ and paper‐based microfluidic devices for glucose assays employing artificial neural networks

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