Morphological Characterization of Gasoline Soot-in-Oil: Development of Semi-Automated 2D-TEM and Comparison with Novel High-Throughput 3D-TEM

Haffner-Staton, Ephraim; Rocca, A. La; Cairns, Alasdair; Fay, Michael W.

doi:10.4271/2019-24-0042

Cited by 3 publications

(12 citation statements)

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“…Electron tomography (ET) has been used to create 3D reconstructions of soot nanoparticles from a series of 2D TEM images acquired over a wide range of angles (e.g. ±60°) 17,20–25 . ET can produce highly accurate 3D models of soot nanoparticles but is significantly slower due to each particle requiring multiple images, and suitable particles being more difficult to locate on a TEM grid.…”

Section: Introductionmentioning

confidence: 99%

“…ET can produce highly accurate 3D models of soot nanoparticles but is significantly slower due to each particle requiring multiple images, and suitable particles being more difficult to locate on a TEM grid. Work by our research group has aimed to optimise the ET process for soot by implementing automated image acquisition and processing to identify suitable particles over large areas (∼40 μm 2 ) of the TEM grid 17,19,23 . This procedure also permits rapid measurement of particles in 2D, allowing greatly increased sample sizes and thus increased reliability of TEM‐derived measurements 26 .…”

Section: Introductionmentioning

confidence: 99%

“…This procedure also permits rapid measurement of particles in 2D, allowing greatly increased sample sizes and thus increased reliability of TEM‐derived measurements 26 . However, due to low contrast of soot against the TEM grid substrate, and high concentrations of non‐soot particles in samples, a manual review process was required to separate real soot structures from false positives 17,23 …”

Section: Introductionmentioning

confidence: 99%

“…Nanoparticles were extracted from a sample of soot‐laden oil taken from a passenger GDI vehicle, via a solvent dilution process using heptane and diethyl ether (further details can be found in a previous study) 40 . Nanoparticles were prepared for TEM by deposition on 300‐mesh copper TEM grids coated with graphene oxide on lacey carbon.…”

Section: Introductionmentioning

confidence: 99%

“…±60 • ). 17,[20][21][22][23][24][25] ET can produce highly accurate 3D models of soot nanoparticles but is significantly slower due to each particle requiring multiple images, and suitable particles being more difficult to locate on a TEM grid.…”

Section: Introductionmentioning

confidence: 99%

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Automated particle recognition for engine soot nanoparticles

Haffner-Staton

Avanzini

Rocca

et al. 2022

Journal of Microscopy

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A pre-trained convolution neural network based on residual error functions (ResNet) was applied to the classification of soot and non-soot carbon nanoparticles in TEM images. Two depths of ResNet, one 18 layers deep and the other 50 layers deep, were trained using training-validation sets of increasing size (containing 100, 400 and 1400 images) and were assessed using an independent test set of 200 images. Network training was optimised in terms of mini-batch size, learning rate and training length. In all tests, ResNet18 and ResNet50 had statistically similar performances, though ResNet18 required only 25-35% of the training time of ResNet50. Training using the 100-, 400-and 1400-image trainingvalidation sets led to classification accuracies of 84%, 88% and 95%, respectively.ResNet18 and ResNet50 were also compared for their ability to categorise soot and non-soot nanoparticles via a fivefold cross-validation experiment using the entire set of 800 images of soot and 800 images of non-soot. Cross-validation was repeated 3 times with different training durations. For all cross-validation experiments, classification accuracy exceeded 91%, with no statistical differences between any of the network trainings. The most efficient network was ResNet18 trained for 5 epochs, which reached 91.2% classification after only 84 s of training on 1600 images. Use of ResNet for classification of 1000 images, the amount suggested for reliable characterisation of soot sample, requires <4 s, compared with >30 min for a skilled operator classifying images manually. Use of convolution neural networks for classification of soot and non-soot nanoparticles in TEM images is highly promising, particularly when manually classified data sets have already been established.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Automated particle recognition for engine soot nanoparticles

Haffner-Staton

Avanzini

Rocca

et al. 2022

Journal of Microscopy

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Hydrogen, sp² Carbon Hybridization, and sp² Clustering as Pieces of the Puzzling Nanostructure of Soot: A Closer Look

Russo,

Apicella,

Ciajolo

2023

Energy Fuels

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This paper reviews recent results concerning specific structure-sensitive parameters of soot such as the content of hydrogen and the size and shape of sp 2 -hybridized carbon featuring the structure of amorphous carbons as soot. UV−visible, Fourier-transformed infrared (FTIR), and Raman spectra are described along with the methods used for obtaining the hydrogen content, the sp 2 aromatic carbon content, and the size of sp 2 clusters featuring soot structure. The outputs coming from spectroscopic analysis are discussed in connection to transmission electron microscopy (TEM) and high resolution TEM (HRTEM) and mass spectrometric analysis. Soot spectroscopic data were compared to those obtained from organic carbon (OC) constituting as-sampled carbon particulate matter in order to get evidence for the peculiar spectroscopic and compositional signatures, which bring together and differentiate OC and soot.

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Comparing Physical and Chemical Properties of Soot from Laboratory Tests and Heavy-Duty Engines Used in Field Operations

Pacino,

La Rocca,

Cairns

et al. 2024

SAE Int. J. Fuels Lubr.

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<div>Morphology, nanostructure, and composition of soot extracted from the oil sump of different heavy-duty engines operated under dynamometer and field conditions were investigated. Soot characteristics were then compared to a carbon black sample. Soot was extracted from used oil for transmission electron microscopy (TEM) analysis. Energy-dispersive X-ray (EDX) and X-ray photoelectron spectroscopy (XPS) analyses were also performed to assess soot composition. Two soot classes, I and II, can be identified based on their appearance under the TEM. Carbon black and class I particles have graphitic structures, while class II samples have a more sludge-like appearance. Similar aggregate sizes were observed among the samples. In all samples, the primary particle size distribution ranges from 16 nm to 22 nm in terms of mean diameter. Differences in the length and tortuosity of the graphitic fringes between the samples were observed. The findings suggest a greater degree of interaction between class II samples and the lubricating oil, and consequently, a different wear behavior may be expected depending on the specific soot characteristics.</div>

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Morphological Characterization of Gasoline Soot-in-Oil: Development of Semi-Automated 2D-TEM and Comparison with Novel High-Throughput 3D-TEM

Cited by 3 publications

References 75 publications

Automated particle recognition for engine soot nanoparticles

Automated particle recognition for engine soot nanoparticles

Hydrogen, sp² Carbon Hybridization, and sp² Clustering as Pieces of the Puzzling Nanostructure of Soot: A Closer Look

Comparing Physical and Chemical Properties of Soot from Laboratory Tests and Heavy-Duty Engines Used in Field Operations

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Morphological Characterization of Gasoline Soot-in-Oil: Development of Semi-Automated 2D-TEM and Comparison with Novel High-Throughput 3D-TEM

Cited by 3 publications

References 75 publications

Automated particle recognition for engine soot nanoparticles

Automated particle recognition for engine soot nanoparticles

Hydrogen, sp2 Carbon Hybridization, and sp2 Clustering as Pieces of the Puzzling Nanostructure of Soot: A Closer Look

Comparing Physical and Chemical Properties of Soot from Laboratory Tests and Heavy-Duty Engines Used in Field Operations

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Hydrogen, sp² Carbon Hybridization, and sp² Clustering as Pieces of the Puzzling Nanostructure of Soot: A Closer Look