Electroless Ni-based coatings for biodiesel containers

Sukkasi, Sittha; Sahapatsombut, Ukrit; Sukjamsri, Chamaiporn; Saenapitak, Sawalee; Boonyongmaneerat, Yuttanant

doi:10.1007/s11998-010-9286-x

Cited by 5 publications

(2 citation statements)

References 24 publications

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“…Another source of acidity in biodiesel is associated with the oxidation that it can suffer. As it has been established, one characteristic of biodiesel is the poor oxidative stability of the unsaturated fatty acids that comprise the methyl ester [ 12 , 13 ]. The presence of radical initiators, such as trace metals, peroxides, heat, and light, can conduce to the oxidation of the unsaturated methyl esters.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of the Stability of Polymeric Materials Exposed to Palm Biodiesel and Biodiesel–Organic Acid Blends

2018

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Abstract:The aim of the present work is to evaluate the impact of pure palm biodiesel fuel (B100) and biodiesel blends with 0.32% oleic, palmitic, acetic, myristic, and stearic acids on the properties of some polymeric materials used commonly in the manufacture of auto parts such as the polyamide 66 (PA66), polyoxymethylene (POM), and high-density polyethylene (HDPE). The effects of the B100 and B100-acid blends on polymeric materials were examined by comparing changes in the gain/loss of mass and by measuring the hardness, the impact strength, and the tensile strength of the materials at the end of the exposure. The characterization of the polymers was carried out before and after exposure by using differential scanning calorimetry (DSC), thermogravimetric analysis (TGA), and Fourier transform infrared spectroscopy (FTIR). After the immersion in B100-acids blends, the HDPE exhibited an increase in mass of 5%, which was very similar in all blends. The PA66 showed a small decrease in weight (2% approx.) in all mixtures. The POM presented an increase in the percentage of weight in the mixture of B100 with acetic acid of 0.3%. A decrease was observed in the crystallinity of the HDPE when exposed to blends of B100-acids. This behavior may be associated with a plasticizing effect in the HDPE exposed to the blends. The mechanical properties of POM and HDPE showed no significant changes after immersion in the fuels. On the other hand, PA66 exhibited a significant decrease in maximum stress value after immersion in B100, B100-oleic acid and B100-palmitic acid blends. The variation of the mechanical properties of the PA66 after exposure to B100 was potentiated by addition of organic acids. The assessed polymers did not undergo appreciable changes in the chemical structure of the samples after immersion in the fuels, so the variation in the mechanical properties could be explained by physical absorption of the fuel into the polymers.

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Section: Introductionmentioning

confidence: 99%

Evaluation of the Stability of Polymeric Materials Exposed to Palm Biodiesel and Biodiesel–Organic Acid Blends

2018

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“…Electroless Ni-P coatings have different applications in different industries such as microelectronic [11], oil and gas [12], aviation and aerospace, medicine [10], and automotive. Different applications require electroless Ni-P coatings of different phosphorus content.…”

Section: Applicationmentioning

confidence: 99%

Prediction of phosphorus content of electroless nickel–phosphorous coatings using artificial neural network modeling

Vaghefi

2010

Neural Comput & Applic

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A multilayer feedforward neural network with two hidden layers was designed and developed for prediction of the phosphorus content of electroless Ni-P coatings. The input parameters of the network were the pH, metal turnover, and loading of an electroless bath. The output parameter was the phosphorus content of the electroless Ni-P coatings. The temperature and molar rate of the bath were constant (91 C; 0:4 Ni þþ =H 2 PO ÀÀ 2 ). The network was trained and tested using the data gathered from our own experiments. The goal of the study was to estimate the accuracy of this type of neural network in prediction of the phosphorus content. The study result shows that this type of network has high accuracy even when the number of hidden neurons is very low. Some comparison between the network's predictions and own experimental data are given.

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Reverse Engineering in Electroless Coatings: An Application on Bath Parameter Optimization for User-Defined Ni-B-P Coating Properties

Kökçam,

Taşkın,

Uygun

et al. 2023

Lecture Notes in Mechanical Engineering

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Electroless Ni-based coatings for biodiesel containers

Cited by 5 publications

References 24 publications

Evaluation of the Stability of Polymeric Materials Exposed to Palm Biodiesel and Biodiesel–Organic Acid Blends

Evaluation of the Stability of Polymeric Materials Exposed to Palm Biodiesel and Biodiesel–Organic Acid Blends

Prediction of phosphorus content of electroless nickel–phosphorous coatings using artificial neural network modeling

Reverse Engineering in Electroless Coatings: An Application on Bath Parameter Optimization for User-Defined Ni-B-P Coating Properties

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