scholarly journals Seawater Absorption and Adhesion Properties of Hydrophobic and Superhydrophobic Thermoset Epoxy Nanocomposite Coatings

Nanomaterials ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 272
Author(s):  
Ayman M. Atta ◽  
Mohamed H. El-Newehy ◽  
Meera Moydeen Abdulhameed ◽  
Mohamed H. Wahby ◽  
Ahmed I. Hashem
Keyword(s):  
Thermal Stability ◽  
Oleic Acid ◽  
Salt Spray ◽  
Adhesion Properties ◽  
In Situ Modification ◽  
Long Time ◽  
Seawater Environment ◽  
Thermoset Epoxy ◽  
The Cost

The enhancement of both thermal and mechanical properties of epoxy materials using nanomaterials becomes a target in coating of the steel to protect it from aggressive environmental conditions for a long time, with reducing the cost. In this respect, the adhesion properties of the epoxy with the steel surfaces, and its proper superhyrophobicity to repel the seawater humidity, can be optimized via addition of green nanoparticles (NPs). In-situ modification of silver (Ag) and calcium carbonate (CaCO3) NPs with oleic acid (OA) was carried out during the formation of Ag−OA and CaCO3−OA, respectively. The epoxide oleic acid (EOA) was also used as capping for Ca−O3 NPs by in-situ method and epoxidation of Ag−OA NPs, too. The morphology, thermal stability, and the diameters of NPs, as well as their dispersion in organic solvent, were investigated. The effects of the prepared NPs on the exothermic curing of the epoxy resins in the presence of polyamines, flexibility or rigidity of epoxy coatings, wettability, and coatings durability in aggressive seawater environment were studied. The obtained results confirmed that the proper superhyrophobicity, coating adhesion, and thermal stability of the epoxy were improved after exposure to salt spray fog for 2000 h at 36 °C.

scholarly journals Radiation Synthesis and Characterization of Polymeric Wet Adhesives for Attracting and Trapping Insects

2021 ◽  
Author(s):  
Ahmed Awadallah-F ◽  
M. A. Elhady ◽  
I. M. Mousaa
Keyword(s):  
Electron Microscopy ◽  
Oleic Acid ◽  
Water Sorption ◽  
Irradiation Dose ◽  
Synthesis And Characterization ◽  
High Concentration ◽  
Good Efficiency ◽  
Adhesion Properties

Abstract Different blends are fabricated from oleic acid (OA), polyvinyl pyrrolidone (PVP), and cis-1,4-polyisoprene (PI) are prepared at different ratios as wet adhesives for attracting and trapping insects. Different ratios of blends (PVP-PI)/OA are (1:2, 1:1 and 2:1 g/g), respectively. Ratio between PVP and PI is 9 to 1 (g/g). The OA, PVP and PI are dissolved to form homogenous blends in all in-situ compositions. The blends were exposed to dose of 10 to 50 kGy. Adhesion properties, water sorption, Fourier transform infrared, thermogravimetric analysis and scan electron microscopy were conducted. The results showed that adhesion of all blends were augmented by increasing the irradiation dose up to 20 kGy. The best ratio of blend was 2:1 which contained high concentration of oleic acid. Blends (PVP-PI)/OA were tested to attract and trap flies and mosquitoes. Results confirmed good efficiency of (PVP-PI)/OA as wet adhesives for attracting and trapping flies and mosquitoes.

Thermal gravimetric analysis of in-situ crosslinked nanocellulose whiskers • poly(methyl vinyl ether-co-maleic acid) • polyethylene glycol

TAPPI Journal ◽  
2011 ◽  
Vol 10 (4) ◽  
pp. 29-33
Author(s):  
LEE A. GOETZ ◽  
AJI P. MATHEW ◽  
KRISTIINA OKSMAN ◽  
ARTHUR J. RAGAUSKAS
Keyword(s):  
Thermal Stability ◽  
Polyethylene Glycol ◽  
Vinyl Ether ◽  
Thermal Gravimetric Analysis ◽  
Maleic Acid ◽  
Gravimetric Analysis ◽  
Thermal Gravimetric ◽  
Methyl Vinyl ◽  
Methyl Vinyl Ether

The thermal stability and decomposition of in-situ crosslinked nanocellulose whiskers – poly(methyl vinyl ether-co-maleic acid) – polyethylene glycol formulations (PMVEMA-PEG), (25%, 50%, and 75% whiskers) – were investigated using thermal gravimetric analysis (TGA) methods. The thermal degradation behavior of the films varied according to the percent cellulose whiskers in each formulation. The presence of cellulose whiskers increased the thermal stability of the PMVEMA-PEG matrix.

In-situ Modification of Graphene Oxide by Insoluble Sulfur and Application for Nitrile Butadiene Rubber

2020 ◽  
Vol 35 (2) ◽  
pp. 221-228
Author(s):  
S.-B. Chen ◽  
T.-X. Li ◽  
S.-H. Wan ◽  
X. Huang ◽  
S.-W. Cai ◽  
...  
Keyword(s):  
Graphene Oxide ◽  
Butadiene Rubber ◽  
Nitrile Butadiene Rubber ◽  
In Situ Modification

Bionanomaterials: Thermal Stability of the Oleic Acid / alpha- and beta-cyclodextrin Complexes

2008 ◽  
Vol 59 (9) ◽  
Author(s):  
Daniel I. Hadaruga ◽  
Nicoleta G. Hadaruga ◽  
Anca Hermenean ◽  
Adrian Rivis ◽  
Vasile Paslaru ◽  
...  
Keyword(s):  
Thermal Stability ◽  
Oleic Acid ◽  
Solution Method ◽  
Degradation Products ◽  
Water Solution ◽  
Fatty Acid Esters ◽  
Cyclodextrin Complexes ◽  
Free Oleic Acid ◽  
Thermal Stability Of ◽  
Acid Esters

This paper presents the thermal stability of the oleic acid encapsulated in a- and b - cyclodextrin. The complexation of the oleic acid was achieved by the ethanol-water solution method and the nanoparticles were analyzed by DSC. The free oleic acid and the encapsulated one were subjected to the thermal degradation in the range of 50-150�C and the degradation products were identified and quantified by GC-MS analysis of the fatty acid esters obtained by deriving with methanol/boron trifluoride, both for free compounds and for the encapsulated ones. The oleic acid complexes were very stable in this range of temperature.

scholarly journals Snow Depth Fusion Based on Machine Learning Methods for the Northern Hemisphere

2021 ◽  
Vol 13 (7) ◽  
pp. 1250
Author(s):  
Yanxing Hu ◽  
Tao Che ◽  
Liyun Dai ◽  
Lin Xiao
Keyword(s):  
Machine Learning ◽  
Northern Hemisphere ◽  
Snow Depth ◽  
Learning Algorithm ◽  
Random Forest Regression ◽  
Machine Learning Methods ◽  
Long Time ◽  
In Situ Observations ◽  
Input Variables

In this study, a machine learning algorithm was introduced to fuse gridded snow depth datasets. The input variables of the machine learning method included geolocation (latitude and longitude), topographic data (elevation), gridded snow depth datasets and in situ observations. A total of 29,565 in situ observations were used to train and optimize the machine learning algorithm. A total of five gridded snow depth datasets—Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) snow depth, Global Snow Monitoring for Climate Research (GlobSnow) snow depth, Long time series of daily snow depth over the Northern Hemisphere (NHSD) snow depth, ERA-Interim snow depth and Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2) snow depth—were used as input variables. The first three snow depth datasets are retrieved from passive microwave brightness temperature or assimilation with in situ observations, while the last two are snow depth datasets obtained from meteorological reanalysis data with a land surface model and data assimilation system. Then, three machine learning methods, i.e., Artificial Neural Networks (ANN), Support Vector Regression (SVR), and Random Forest Regression (RFR), were used to produce a fused snow depth dataset from 2002 to 2004. The RFR model performed best and was thus used to produce a new snow depth product from the fusion of the five snow depth datasets and auxiliary data over the Northern Hemisphere from 2002 to 2011. The fused snow-depth product was verified at five well-known snow observation sites. The R2 of Sodankylä, Old Aspen, and Reynolds Mountains East were 0.88, 0.69, and 0.63, respectively. At the Swamp Angel Study Plot and Weissfluhjoch observation sites, which have an average snow depth exceeding 200 cm, the fused snow depth did not perform well. The spatial patterns of the average snow depth were analyzed seasonally, and the average snow depths of autumn, winter, and spring were 5.7, 25.8, and 21.5 cm, respectively. In the future, random forest regression will be used to produce a long time series of a fused snow depth dataset over the Northern Hemisphere or other specific regions.

scholarly journals In-situ synthesis of Fe2O3/rGO using different hydrothermal methods as anode materials for lithium-ion batteries

2020 ◽  
Vol 59 (1) ◽  
pp. 477-487 ◽  
Author(s):  
Zhuang Liu ◽  
Haiyang Fu ◽  
Bo Gao ◽  
Yixuan Wang ◽  
Kui Li ◽  
...  
Keyword(s):  
Graphene Oxide ◽  
Oleic Acid ◽  
Lithium Ion Batteries ◽  
Reduced Graphene Oxide ◽  
Anode Materials ◽  
Lithium Ion ◽  
In Situ Synthesis ◽  
Cycle Performance ◽  
Reduced Graphene

AbstractThis paper studies in-situ synthesis of Fe2O3/reduced graphene oxide (rGO) anode materials by different hydrothermal process.Scanning Electron Microscopy (SEM) analysis has found that different processes can control the morphology of graphene and Fe2O3. The morphologies of Fe2O3 prepared by the hydrothermal in-situ and oleic acid-assisted hydrothermal in-situ methods are mainly composed of fine spheres, while PVP assists The thermal in-situ law presents porous ellipsoids. Graphene exhibits typical folds and small lumps. X-ray diffraction analysis (XRD) analysis results show that Fe2O3/reduced graphene oxide (rGO) is generated in different ways. Also, the material has good crystallinity, and the crystal form of the iron oxide has not been changed after adding GO. It has been reduced, and a characteristic peak appears around 25°, indicating that a large amount of reduced graphene exists. The results of the electrochemical performance tests have found that the active materials prepared in different processes have different effects on the cycle performance of lithium ion batteries. By comprehensive comparison for these three processes, the electro-chemical performance of the Fe2O3/rGO prepared by the oleic acid-assisted hydrothermal method is best.

scholarly journals Black Soldier Fly Larval Valorization Benefitting from Ex-Situ Fungal Fermentation in Reducing Coconut Endosperm Waste

Processes ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 275
Author(s):  
Chung Yiin Wong ◽  
Kunlanan Kiatkittipong ◽  
Worapon Kiatkittipong ◽  
Seteno K. O. Ntwampe ◽  
Man Kee Lam ◽  
...  
Keyword(s):  
Principal Component ◽  
Correlation Study ◽  
Statistical Correlation ◽  
Waste Reduction ◽  
Ex Situ ◽  
Larval Feeding ◽  
Rhizopus Oligosporus ◽  
Black Soldier Fly ◽  
The Cost

Oftentimes, the employment of entomoremediation to reduce organic wastes encounters ubiquitous shortcomings, i.e., ineffectiveness to valorize recalcitrant organics in wastes. Considering the cost-favorability, a fermentation process can be employed to facilitate the degradation of biopolymers into smaller organics, easing the subsequent entomoremediation process. However, the efficacy of in situ fermentation was found impeded by the black soldier fly larvae (BSFL) in the current study to reduce coconut endosperm waste (CEW). Indeed, by changing into ex situ fermentation, in which the fungal Rhizopus oligosporus was permitted to execute fermentation on CEW prior to the larval feeding, the reduction of CEW was significantly enhanced. In this regard, the waste reduction index of CEW by BSFL was almost doubled as opposed to in situ fermentation, even with the inoculation of merely 0.5 wt % of Rhizopus oligosporus. Moreover, with only 0.02 wt % of fungal inoculation size to execute the ex situ fermentation on CEW, it could spur BSFL growth by about 50%. Finally, from the statistical correlation study using principal component analysis, the presence of Rhizopus oligosporus in a range of 0.5–1.0 wt % was regarded as optimum to ferment CEW via ex situ mode, prior to the valorization by BSFL in reducing the CEW.

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