Effects of Acoustic Modulation and Mixed Fuel on Flame Synthesis of Carbon Nanomaterials in an Atmospheric Environment

Hu, Wei Chieh; Sari, Shanti Kartika; Hou, Shuhn-Shyurng; Lin, Ta Hui

doi:10.3390/ma9110939

Cited by 6 publications

(6 citation statements)

References 40 publications

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“…Further discussion onto the effect of acoustic perturbation whether direct or indirect interaction toward the jet flame behavior are discuss in this paper. Figure 1 shows the interaction between acoustic responses and flame [53]. This interaction provides linear change with the increasing of number of frequency (f) detest to the flame.…”

Section: Effect Of Acoustic Excitation On Flame Characteristicsmentioning

confidence: 98%

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A Brief Review of Acoustic Excitation Toward Jet Flame Characteristics

Samsubaha¹,

Razali²,

Madon³

et al. 2020

ARFMTS

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Acoustic interaction toward jet flame in a combustion system is essential to understand the characteristic of combustion instability. Combustion device such as turbine, ramjets, industrial processor, jet rocket and other mostly occurrence for selfexcited due to mechanical part and coupling. The combustion driven oscillation can affect the combustion system life and performance. This paper is mainly focused on the characteristic of the flame, which having interacted with the acoustic excitation. This paper is organized into three parts. First part is the introduction, follows by the literature review, which mainly focused on the physical characteristic of the flame, the structure, the length, the shape, and the color of the flame. Then the last part consists of the subject goes toward the application of acoustic perturbation-utilizing acoustic excitation toward good intention, the formation of soot and nitride oxide reduction.

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Section: Effect Of Acoustic Excitation On Flame Characteristicsmentioning

confidence: 98%

“…A liftoff height is a point measured from the centerline distance of the duct exit to the plane of flame stabilization [52][53][54][55][56][57][58][59][60][61][62][63][64][65]. Oh et al, [30] proved the correlation of interaction between flame length and liftoff height during acoustic excitation.…”

Section: Flame Length and Liftoff Heightmentioning

confidence: 99%

A Brief Review of Acoustic Excitation Toward Jet Flame Characteristics

Samsubaha¹,

Razali²,

Madon³

et al. 2020

ARFMTS

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“…Taking carbon monoxide, methane and ethylene as examples, this paper studies a mixed gas detection algorithm based on extreme random trees to improve the classification accuracy rate. It can provide mixed gas detection and concentration ratio technical support for industrial production, chemical material production, and the light industry [35][36][37]. This study provides a theoretical reference for the simulation of olfactory algorithms.…”

Section: Introductionmentioning

confidence: 99%

Research on a Mixed Gas Classification Algorithm Based on Extreme Random Tree

Zhao

Chen

et al. 2019

Applied Sciences

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Because of the low accuracy of the current machine olfactory algorithms in detecting two mixed gases, this study proposes a hybrid gas detection algorithm based on an extreme random tree to greatly improve the classification accuracy and time efficiency. The method mainly uses the dynamic time warping algorithm (DTW) to perform data pre-processing and then extracts the gas characteristics from gas signals at different concentrations by applying a principal component analysis (PCA). Finally, the model is established by using a new extreme random tree algorithm to achieve the target gas classification. The sample data collected by the experiment was verified by comparison experiments with the proposed algorithm. The analysis results show that the proposed DTW algorithm improves the gas classification accuracy by 26.87%. Compared with the random forest algorithm, extreme gradient boosting (XGBoost) algorithm and gradient boosting decision tree (GBDT) algorithm, the accuracy rate increased by 4.53%, 5.11% and 8.10%, respectively, reaching 99.28%. In terms of the time efficiency of the algorithms, the actual runtime of the extreme random tree algorithm is 66.85%, 90.27%, and 81.61% lower than that of the random forest algorithm, XGBoost algorithm, and GBDT algorithm, respectively, reaching 103.2568 s.

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“…Previous studies showed that CNTs produced by different methods (including chemical vapour deposition and laser ablation) contain metal impurities, mainly Fe, Ni and Co, inside the tubes, which are covered with layers of graphene sheets [19, 20]. Today, most studies reporting preparation of MCNTs ( S1 Table ), seem to have overlooked three important facts.…”

Section: Introductionmentioning

confidence: 99%

“…MCNTs can be either endohedral, i.e., made by encapsulating magnetic nanoparticles, such as iron oxides, into the carbon nanotubes, or exohedral, i.e., made with methods such as electrochemical deposition to attach magnetic nanoparticles on the surface of carbon nanotubes [ 1 , 17 , 18 ]. Previous studies showed that CNTs produced by different methods (including chemical vapour deposition and laser ablation) contain metal impurities, mainly Fe, Ni and Co, inside the tubes, which are covered with layers of graphene sheets [ 19 , 20 ]. Today, most studies reporting preparation of MCNTs ( S1 Table ), seem to have overlooked three important facts.…”

Section: Introductionmentioning

confidence: 99%

Green and facile approach for enhancing the inherent magnetic properties of carbon nanotubes for water treatment applications

et al. 2017

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Current methods for preparing magnetic composites with carbon nanotubes (MCNT) commonly include extensive use of treatment with strong acids and result in massive losses of carbon nanotubes (CNTs). In this study we explore the potential of taking advantage of the inherent magnetic properties associated with the metal (alloy or oxide) incorporated in CNTs during their production. The as-received CNTs are refined by applying a permanent magnet to a suspension of CNTs to separate the high-magnetic fraction; the low-magnetic fraction is discarded with the solvent. The collected MCNTs were characterized by a suite of 10 diffraction and spectroscopic techniques. A key discovery is that metallic nano-clusters of Fe and/or Ni located in the interior cavities of the nanotubes give MCNTs their ferromagnetic character. After refinement using our method, the MCNTs show saturation magnetizations up to 10 times that of the as-received materials. In addition, we demonstrate the ability of these MCNTs to repeatedly remove atrazine from water in a cycle of dispersion into a water sample, adsorption of the atrazine onto the MCNTs, collection by magnetic attraction and regeneration by ethanol. The resulting MCNTs show high adsorption capacities (> 40 mg-atrazine/g), high magnetic response, and straightforward regeneration. The method presented here is simpler, faster, and substantially reduces chemical waste relative to current techniques and the resulting MCNTs are promising adsorbents for organic/chemical contaminants in environmental waters.

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Effects of Acoustic Modulation and Mixed Fuel on Flame Synthesis of Carbon Nanomaterials in an Atmospheric Environment

Cited by 6 publications

References 40 publications

A Brief Review of Acoustic Excitation Toward Jet Flame Characteristics

A Brief Review of Acoustic Excitation Toward Jet Flame Characteristics

Research on a Mixed Gas Classification Algorithm Based on Extreme Random Tree

Green and facile approach for enhancing the inherent magnetic properties of carbon nanotubes for water treatment applications

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