A thermally sensitive energy-absorbing composite functionalized by nanoporous carbon

Lu, Weiyi; Punyamurtula, Venkata K.; Han, Aijie; Kim, Tae‐Wan; Qiao, Yu

doi:10.1557/jmr.2009.0408

Cited by 7 publications

(2 citation statements)

References 16 publications

(14 reference statements)

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“…The experimental measured values are summarized in Tables 1, 2, and 3. Recently, interesting experiments were done regarding the infiltration of highly viscous liquids in nanopores comparable with or even smaller than end-to-end distance (Lu et al 2009;Han et al 2008a, b;Chen et al 2008). We hope that the application of our proposed method to these problems could also lead to reveal interesting phenomena.…”

Section: Artificial Neural Networkmentioning

confidence: 83%

Prediction and analysis of flow behavior of a polymer melt through nanochannels using artificial neural network and statistical methods

Ahadian

Mizuseki

2009

Microfluid Nanofluid

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A new methodology, namely, artificial neural network (ANN) approach was proposed for modeling and predicting flow behavior of the polyethylene melt through nanochannels of nanoporous alumina templates. Wetting length of the nanochannels was determined to be a function of time, temperature, diameter of nanochannels, and surface properties of the inner wall of the nanochannels. An ANN was designed to forecast the relationship between the length of wetting as output parameter and other aforementioned parameters as input variables. It was demonstrated that the ANN method is capable of modeling this phenomenon with high accuracy. The designed ANN was then employed to obtain the wetting length of the nanochannels for those cases, which were not reported by the wetting experiments. The results were then analyzed statistically to identify the effect of each independent variable, namely, time, temperature, diameter of nanochannels, and surface properties of the inner wall of nanochannels as well as their combinations on the wetting length of the nanochannels. Interesting results were attained and discussed.

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Section: Artificial Neural Networkmentioning

confidence: 83%

Prediction and analysis of flow behavior of a polymer melt through nanochannels using artificial neural network and statistical methods

Ahadian

Mizuseki

2009

Microfluid Nanofluid

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“…In order to increase the hydrophobicity of the silica gel, a monolayer of silyl groups was grafted onto the nanopore surfaces. 25 First, 1 g of silica gel was dried at 100°C and then mixed with 40 mL of anhydrous toluene. The mixture was stirred for 3 h at 90°C, after which 10 mL of chloro(dimethyl)octylsilane and 1 mL of pyridine were injected into the mixture at room temperature.…”

Section: Methodsmentioning

confidence: 99%

Liquid marble: A novel liquid nanofoam structure for energy absorption

2017

AIP Advances

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The liquid nanofoam (LN), a system composed of liquid and hydrophobic nanoporous particles, is a promising energy absorbing material. Despite its excellent energy absorbing capabilities under quasi-static conditions, the LN’s performance is limited under dynamic impacts due to its heterogeneity. We hypothesize that the energy absorption capacity of the LN can be increased by reconfiguration of the material into a liquid marble form. To test this hypothesis, we have prepared the LN sample in two different configurations, one with the heterogeneous layered structure and the other with a macroscopically homogeneous liquid marble structure. The mechanical behavior of these two types of LN was examined by quasi-static compression tests and dynamic impact tests. We demonstrated that although both types of LN exhibited comparable quasi-static energy absorption capacity, the liquid marble form of LN showed better performance under dynamic impacts. These findings suggest that the liquid marble form is the preferred LN structure under blunt impact and shed lights on the design of next-generation energy absorbing materials and structures.

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Enhanced filler-tube wall interaction in liquid nanofoam-filled thin-walled tubes

Barbat

et al. 2018

Composite Structures

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A thermally sensitive energy-absorbing composite functionalized by nanoporous carbon

Cited by 7 publications

References 16 publications

Prediction and analysis of flow behavior of a polymer melt through nanochannels using artificial neural network and statistical methods

Prediction and analysis of flow behavior of a polymer melt through nanochannels using artificial neural network and statistical methods

Liquid marble: A novel liquid nanofoam structure for energy absorption

Enhanced filler-tube wall interaction in liquid nanofoam-filled thin-walled tubes

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