Variations in diesel soot reactivity along the exhaust after-treatment system, based on the morphology and nanostructure of primary soot particles

Liati, Anthi; Eggenschwiler, Panayotis Dimopoulos; Schreiber, Daniel; Zelenay, Veronika; Ammann, Markus

doi:10.1016/j.combustflame.2012.10.024

Cited by 106 publications

(57 citation statements)

References 33 publications

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“…Similarly, discrepancies exist between observed oxidation rates [2] and the rates predicted by the Nagle-Strickland-Constable equation [20], which is applicable to describe the high-temperature oxidation of carbon blacks -these discrepancies are possibly caused by peculiarities of soot structure. The dependence of soot oxidation kinetics upon nanostructure was shown [21,22]. Hurt et al showed, that soot nanostructure can be qualitatively modeled by thermodynamic principles [23].…”

Section: Introductionmentioning

confidence: 97%

The effect of oxidation pressure on the equilibrium nanostructure of soot particles

Tóth

Palotás

Ring

et al. 2015

Combustion and Flame

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Section: Introductionmentioning

confidence: 97%

The effect of oxidation pressure on the equilibrium nanostructure of soot particles

Tóth

Palotás

Ring

et al. 2015

Combustion and Flame

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“…1e,h). As these nuclei are more abundant during the early stage of soot formation [53], the present particles are likely to originate from this phase. Wan Mahmood et al [54] conducted simulations on the in-cylinder formation and distribution of soot.…”

Section: Hrtemmentioning

confidence: 99%

Comparative nanostructure analysis of gasoline turbocharged direct injection and diesel soot-in-oil with carbon black

Pfau

Rocca

Haffner-Staton

et al. 2018

Carbon

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a b s t r a c tTwo gasoline turbocharged direct injection (GTDI) and two diesel soot-in-oil samples were compared with one flame-generated soot sample. High resolution transmission electron microscopy imaging was employed for the initial qualitative assessment of the soot morphology. Carbon black and diesel soot both exhibit core-shell structures, comprising an amorphous core surrounded by graphene layers; only diesel soot has particles with multiple cores. In addition to such particles, GTDI soot also exhibits entirely amorphous structures, of which some contain crystalline particles only a few nanometers in diameter. Subsequent quantification of the nanostructure by fringe analysis indicates differences between the samples in terms of length, tortuosity, and separation of the graphitic fringes. The shortest fringes are exhibited by the GTDI samples, whilst the diesel soot and carbon black fringes are 9.7% and 15.1% longer, respectively. Fringe tortuosity is similar across the internal combustion engine samples, but lower for the carbon black sample. In contrast, fringe separation varies continuously among the samples. Raman spectroscopy further confirms the observed differences. The GTDI soot samples contain the highest fraction of amorphous carbon and defective graphitic structures, followed by diesel soot and carbon black respectively. The A D1 :A G ratios correlate linearly with both the fringe length and fringe separation.

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“…comparing milk treatments; Impoco et al, 2012), mechanics (e.g. investigating combustion byproducts; Coz et al, 2014;Liati et al, 2013;Toth et al, 2013a, b, c), and medicine (e.g. examining wear particles of artificial joint fillers; Tipper et al, 2006;Schröder et al, 2013;Wu & Peng, 2013;Moro et al, 2014;Nine et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

“…Common approaches follow natural human visual inspection when investigating new objects: estimation of size, shape, contour and texture in order to describe the NP samples in terms of length, surface area, circularity, etc. (Tipper et al, 2006;Brun et al, 2010;Impoco et al, 2012;Liati et al, 2013;Toth et al, 2013a, b, c;Coz et al, 2014;Moro et al, 2014;Nine et al, 2014;Schröder et al, 2013;Wu & Peng, 2013). Extracted NP morphology can be later correlated with physicochemical or biological properties.…”

Section: Introductionmentioning

confidence: 99%

Scanning electron microscopy image representativeness: morphological data on nanoparticles

Odziomek

Ushizima

Oberbek

et al. 2016

Journal of Microscopy

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A sample of a nanomaterial contains a distribution of nanoparticles of various shapes and/or sizes. A scanning electron microscopy image of such a sample often captures only a fragment of the morphological variety present in the sample. In order to quantitatively analyse the sample using scanning electron microscope digital images, and, in particular, to derive numerical representations of the sample morphology, image content has to be assessed. In this work, we present a framework for extracting morphological information contained in scanning electron microscopy images using computer vision algorithms, and for converting them into numerical particle descriptors. We explore the concept of image representativeness and provide a set of protocols for selecting optimal scanning electron microscopy images as well as determining the smallest representative image set for each of the morphological features. We demonstrate the practical aspects of our methodology by investigating tricalcium phosphate, Ca (PO ) , and calcium hydroxyphosphate, Ca (PO ) (OH), both naturally occurring minerals with a wide range of biomedical applications.

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Variations in diesel soot reactivity along the exhaust after-treatment system, based on the morphology and nanostructure of primary soot particles

Cited by 106 publications

References 33 publications

The effect of oxidation pressure on the equilibrium nanostructure of soot particles

The effect of oxidation pressure on the equilibrium nanostructure of soot particles

Comparative nanostructure analysis of gasoline turbocharged direct injection and diesel soot-in-oil with carbon black

Scanning electron microscopy image representativeness: morphological data on nanoparticles

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