Development of high-throughput electron tomography for 3D morphological characterisation of soot nanoparticles

In this work a methodology has been developed to permit high-throughput, high-quality morphology characterisation of soot nanoparticles in 3D, using transmission electron microscopy (TEM) and electron tomography (ET). Nanoparticle morphology plays an extremely important role in determining soots contribution to global climate change, its impacts on human health, and its relation to friction and wear in engines. Morphology characterisation is thus fundamental to enabling full understanding of soot-induced phenomena and subsequent strategies for their mitigation. Initial work employed ET for the 3D reconstruction and morphology characterisation of a flame-generated soot nanoparticle. Variability in 2D-derived measurements as a function of nanoparticle orientation was measured at up to 45%, which may be an important source of error in 2D-derived morphology measurements. Significant discrepancies of up to 36% between 2D- and 3D-derived measurements were also observed. This type of particle was particularly complex and 3-dimensional, and acted as a useful case-study for the identification of areas for possible optimisation in the 3D reconstruction process. A sample of soot-in-oil from a gasoline turbocharged direct injection (GTDI) engine was chosen as the main focus of study in this work. This is due to both the novelty of study and potential for useful research outputs, and the difficulty of study via TEM due to the presence of lubricant oil. General characteristics of particulate matter in the GTDI soot-in-oil sample were understood through TEM imaging, and revealed an abundance of localised amorphous carbon structures and patches of crystalline material, in addition to typical soot nanoparticles. Optimisation of ET was carried out by assessment of the speed and relative accuracy of a number of well-established procedures for tilt-series acquisition, tomographic reconstruction, and tomogram segmentation. It was found that the computationally efficient WBP algorithm produced data of equivalent quality to that of the theoretically more accurate SIRT algorithm, though was significantly quicker. Optimal tilt-series were acquired over ±60° ranges, in increments as large as 3°. For the final stage of optimisation, 6 nanoparticles from the GTDI soot-in-oil sample were reconstructed in 3D via ET. Linear interpolation was found to be useful for increasing speed while retaining accuracy in volume segmentation for producing the final nanoparticle models. Efforts were also made to reduce the amount of time and human involvement required for nanoparticle identification and image acquisition, resulting in the development of semi-automated process for TEM imaging. Large areas of the TEM grid are automatically imaged, and then screened for structures of interest via an automated image processing algorithm and a manual review process. Nanoparticles locations are then communicated to the electron microscope for subsequent 3D study. Using this semi-automated process over 4000 μm2 of a TEM grid was imaged, resulting in identification of 271 soot nanoparticles. Additional steps were implemented for high-throughput 2D morphology characterisation, and resulted in the measurement of 523 individual nanoparticles from the GTDI soot-in-oil sample. Analysis of this data revealed similar size of soot primary particles and aggregates compared with previous studies of soot-in-oil samples, but modest aggregate sizes in comparison to exhaust and flame-generated soots. Accuracy of ET was assessed via computational models of soot-like aggregates which acted as ‘ground truth’ (i.e. 3D morphology was known exactly). The soot-like models were subjected to the 3D-TEM procedure, and post-reconstruction morphology was compared to that of the original models. The results of our prior optimisation work were confirmed, and absolute accuracy of 3D-TEM for the soot-like models was shown to be extremely high (within 3.5% of original values for a range of parameters). From the pool of 271 nanoparticles identified using the automated TEM imaging procedure, an additional 28 nanoparticles were chosen for study in 3D via ET. These nanoparticles were chosen to express the general extremes of visible structures, e.g. particularly large and complex, or small and simple nanoparticles. This total sample size of 34 nanoparticles represents the largest 3D study of soot-in-oil to date, and one of the largest 3D studies of any soot sample to date. The 3D aspect ratio of nanoparticles revealed a general tendency towards 2-dimensionality rather than strong 3-dimensionality, and overall size of nanoparticles was generally small in comparison to aerosol soot. Direct comparison of 2D- and 3D-TEM characterisation showed deviations on the order of 20-35% for some important morphological parameters (volume, surface area, circularity, aspect-ratio), though 2D-derived radius of gyrations measurements were generally accurate. Strong variability was observed across the sample of nanoparticles, and no clear correlations were drawn between particle morphology and the accuracy of 2D-derived measurements. The 3D models were used to explored the full extent of variability in the 2D appearance due to nanoparticle orientation, with an average variability in 2D area of 59%. Several nanoparticles were observed with prominent features such as rings, cavities, and arches that were not appreciable via typical 2D-TEM study, and may be important for greater understand of soot formation and impacts of morphology

Progress towards a methodology for high throughput 3D reconstruction of soot nanoparticles via electron tomography

The aim of this work is to make progress towards the development of 3D reconstruction as a legitimate alternative to traditional 2D characterisation of soot. Time constraints are the greatest opposition to its implementation, as currently reconstruction of a single soot particle takes around 5-6 hours to complete. As such, the accuracy and detail gains are currently insufficient to challenge 2D characterisation of a representative sample (e.g. 200 particles). This work is a consideration of the optimisation of the steps included within the computational reconstruction and manual segmentation of soot particles. Our optimal process reduced the time required by over 70% in comparison to a typical procedure, whilst producing models with no appreciable decrease in quality

Assessing the Accuracy of Soot Nanoparticle Morphology Measurements Using Three- Dimensional Electron Tomography

Morphology plays an important role in determining behaviour and impact of soot nanoparticles, including effect on human health, atmospheric optical properties, contribution to engine wear, and role in marine ecology. However, its nanoscopic size has limited the ability to directly measure useful morphological parameters such as surface area and effective volume. Recently, 3D morphology characterization of soot nanoparticles via electron tomography has been the subject of several introductory studies. So-called '3D-TEM' has been posited as an improvement over traditional 2D-TEM characterization due to the elimination of the error-inducing information gap that exists between 3-dimensional soot structures and 2-dimensional TEM projections. Little follow-up work has been performed due to difficulties with developing methodologies into robust high-throughput techniques. Recent work by the authors has exhibited significant improvements in efficiency, though as yet due consideration has not been given to assessing fidelity of the technique. This is vital to confirm significant and tangible improvements in soot-characterization accuracy that will establish 3D-TEM as a legitimate tool. Synthetic ground-truth data was developed to closely mimic real soot structures and the 3D-TEM volume-reconstruction process. A variety of procedures were tested to assess the magnitude and nuances of deviations from ground-truth values. Results showed average Z-elongation due to the 'missing-wedge' at 3.5% for the previously developed optimized procedure. Mean deviations from ground-truth in volume and surface area were 2.0% and-0.1% respectively. Results indicate highly accurate 3D-reconstruction can be achieved with an optimized procedure that can bridge the gap to permit high-throughput 3D morphology characterization of soot

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

Two 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 AD1:AG ratios correlate linearly with both the fringe length and fringe separation

Soot in the Lubricating Oil: An Overlooked Concern for the Gasoline Direct Injection Engine?

Formation of soot is a known phenomenon for diesel engines, however, only recently emerged for gasoline engines with the introduction of direct injection systems. Soot-in-oil samples from a three-cylinder turbo-charged gasoline direct injection (GDI) engine have been analysed. The samples were collected from the oil sump after periods of use in predominantly urban driving conditions with start-stop mode activated. Thermogravimetric analysis (TGA) was performed to measure the soot content in the drained oils. Soot deposition rates were similar to previously reported rates for diesel engines, i.e. 1 wt% per 15,000 km, thus indicating a similar importance. Morphology was assessed by transmission electron microscopy (TEM). Images showed fractal agglomerates comprising multiple primary particles with characteristic core-shell nanostructure. Furthermore, large amorphous structures were observed. Primary particle sizes ranged from 12 to 55 nm, with a mean diameter of 30 nm and mode at 31 nm. Particle agglomerates were measured by nanoparticle tracking analysis (NTA). The agglomerates were found to range between 42 and 475 nm, with a mean size of 132 nm and mode at 100 nm. The distribution was shifted towards larger sizes with a minor concentration of very large agglomerates observed around 382 nm. While deposition rate and agglomerate morphology were similar to diesel engines, distinctive amorphous carbon and smaller particles were observed. Hence, existing knowledge for diesel applications might not be directly transferrable

Investigating the impact of copper leaching on combustion characteristics and particulate emissions in HPCR diesel engines

© 2019 Elsevier Ltd Copper leaching in diesel fuel and its impact on combustion and emission characteristics of a Direct Injection High Pressure Common Rail (DI HPCR) diesel engine was investigated. This work was performed using a single cylinder Ricardo Hydra research engine fitted with a cylinder head, piston assembly, and crankshaft from a production 2.2 L DI diesel engine. A fuel conditioning device consisting of a helicoidally shaped copper duct and electromagnetic coils powered from the battery was installed along the fuel line just before the high pressure pump. A diesel fuel with a copper content of less than 0.2 ppm was used. Inductively coupled plasma mass spectrometry (ICP-MS) analysis showed an increase of copper content to 1 ppm when fuel flowed through the conditioning device prior to the injection and returned from the engine back to the fuel tank. Copper leaching from the conditioning device was confirmed using a bespoke test rig. Combustion characteristics were analysed via post-processing pressure measurements, while an AVL Smoke Meter was used to monitor particulate emissions. A pilot plus main strategy was used to achieve a target Brake Mean Effect Pressure (BMEP) typical of medium load. Soot reduction in the range of 7–14% was measured when the device was connected to the fuel line, compared to the baseline. The initiation and early development of combustion was also investigated using an unstirred, quiescent combustion chamber with optical access. High-speed photography showed that ignition probability was enhanced in presence of the fuel conditioning device

Development of high-throughput electron tomography for 3D morphological characterisation of soot nanoparticles

In this work a methodology has been developed to permit high-throughput, high-quality morphology characterisation of soot nanoparticles in 3D, using transmission electron microscopy (TEM) and electron tomography (ET). Nanoparticle morphology plays an extremely important role in determining soots contribution to global climate change, its impacts on human health, and its relation to friction and wear in engines. Morphology characterisation is thus fundamental to enabling full understanding of soot-induced phenomena and subsequent strategies for their mitigation. Initial work employed ET for the 3D reconstruction and morphology characterisation of a flame-generated soot nanoparticle. Variability in 2D-derived measurements as a function of nanoparticle orientation was measured at up to 45%, which may be an important source of error in 2D-derived morphology measurements. Significant discrepancies of up to 36% between 2D- and 3D-derived measurements were also observed. This type of particle was particularly complex and 3-dimensional, and acted as a useful case-study for the identification of areas for possible optimisation in the 3D reconstruction process. A sample of soot-in-oil from a gasoline turbocharged direct injection (GTDI) engine was chosen as the main focus of study in this work. This is due to both the novelty of study and potential for useful research outputs, and the difficulty of study via TEM due to the presence of lubricant oil. General characteristics of particulate matter in the GTDI soot-in-oil sample were understood through TEM imaging, and revealed an abundance of localised amorphous carbon structures and patches of crystalline material, in addition to typical soot nanoparticles. Optimisation of ET was carried out by assessment of the speed and relative accuracy of a number of well-established procedures for tilt-series acquisition, tomographic reconstruction, and tomogram segmentation. It was found that the computationally efficient WBP algorithm produced data of equivalent quality to that of the theoretically more accurate SIRT algorithm, though was significantly quicker. Optimal tilt-series were acquired over ±60° ranges, in increments as large as 3°. For the final stage of optimisation, 6 nanoparticles from the GTDI soot-in-oil sample were reconstructed in 3D via ET. Linear interpolation was found to be useful for increasing speed while retaining accuracy in volume segmentation for producing the final nanoparticle models. Efforts were also made to reduce the amount of time and human involvement required for nanoparticle identification and image acquisition, resulting in the development of semi-automated process for TEM imaging. Large areas of the TEM grid are automatically imaged, and then screened for structures of interest via an automated image processing algorithm and a manual review process. Nanoparticles locations are then communicated to the electron microscope for subsequent 3D study. Using this semi-automated process over 4000 μm2 of a TEM grid was imaged, resulting in identification of 271 soot nanoparticles. Additional steps were implemented for high-throughput 2D morphology characterisation, and resulted in the measurement of 523 individual nanoparticles from the GTDI soot-in-oil sample. Analysis of this data revealed similar size of soot primary particles and aggregates compared with previous studies of soot-in-oil samples, but modest aggregate sizes in comparison to exhaust and flame-generated soots. Accuracy of ET was assessed via computational models of soot-like aggregates which acted as ‘ground truth’ (i.e. 3D morphology was known exactly). The soot-like models were subjected to the 3D-TEM procedure, and post-reconstruction morphology was compared to that of the original models. The results of our prior optimisation work were confirmed, and absolute accuracy of 3D-TEM for the soot-like models was shown to be extremely high (within 3.5% of original values for a range of parameters). From the pool of 271 nanoparticles identified using the automated TEM imaging procedure, an additional 28 nanoparticles were chosen for study in 3D via ET. These nanoparticles were chosen to express the general extremes of visible structures, e.g. particularly large and complex, or small and simple nanoparticles. This total sample size of 34 nanoparticles represents the largest 3D study of soot-in-oil to date, and one of the largest 3D studies of any soot sample to date. The 3D aspect ratio of nanoparticles revealed a general tendency towards 2-dimensionality rather than strong 3-dimensionality, and overall size of nanoparticles was generally small in comparison to aerosol soot. Direct comparison of 2D- and 3D-TEM characterisation showed deviations on the order of 20-35% for some important morphological parameters (volume, surface area, circularity, aspect-ratio), though 2D-derived radius of gyrations measurements were generally accurate. Strong variability was observed across the sample of nanoparticles, and no clear correlations were drawn between particle morphology and the accuracy of 2D-derived measurements. The 3D models were used to explored the full extent of variability in the 2D appearance due to nanoparticle orientation, with an average variability in 2D area of 59%. Several nanoparticles were observed with prominent features such as rings, cavities, and arches that were not appreciable via typical 2D-TEM study, and may be important for greater understand of soot formation and impacts of morphology

Investigating the Effect of Volatiles on Sub-23 nm Particle Number Measurements for a Downsized GDI Engine with a Catalytic Stripper and Digital Filtering

Recent efforts of both researchers and regulators regarding particulate emissions have focused on the contribution and presence of sub-23 nm particulates. Despite being previously excluded from emissions legislation with the particle measurement programme (PMP), the latest regulatory proposals suggest lowering the cut-off sizes for counting efficiencies and the use of catalytic strippers to include solid particles in this size range. This work investigated particulate emissions of a 1.0 L gasoline direct injection (GDI) engine using a differential mobility spectrometer (DMS) in combination with a catalytic stripper. Direct comparison of measurements taken with and without the catalytic stripper reveals that the catalytic stripper noticeably reduced variability in sub-23 nm particle concentration measurements. A significant portion of particles in this size regime remained (58–92%), suggesting a non-volatile nature for these particles. Digital filtering functions for imposing defined counting efficiencies were assessed with datasets acquired with the catalytic stripper; i.e., particle size distributions (PSDs) with removed volatiles. An updated filtering function for counting efficiency thresholds of d65 = 10 nm and d90 = 15 nm showed an increase in particulate numbers between 1.5% and up to 11.2%, compared to the closest previous digital filtering function. However, this increase is highly dependent on the underlying PSD. For a matrix of operating conditions (1250 to 2250 rpm and fast-idle to 40 Nm brake torque), the highest emissions occurred at fast-idle 1250 rpm with 1.93 × 108 #/cm3 using the updated filtering function and catalytic stripper. This setup showed an increase in particulate number of +27% to +390% over the test matrix when compared to DMS measurements without the catalytic stripper and applied counting efficiency thresholds of d50 = 23 nm and d90 = 41