Soot-in-Oil 3D Volume Reconstruction Through the Use of Electron Tomography: An Introductory Study


Understanding soot nanoparticle interaction with oil additives and the causes of soot-induced thickening would assist in lubricant formulation, prolonging engine life and improving engine efficiency. Three-dimensional measurement of soot structures is currently not undertaken as established techniques are limited to two dimensions. While they give valuable information on the structure and reactivity of soot nanoparticles, it is not easy to correlate this to geometry of primary particles and agglomerates. In this work, we investigate the development and application of 3D-TEM for characterisation of soot agglomerates as a new capability to yield information on the volumetric character of fractal nanoparticles. This investigation looks at the feasibility for volume reconstruction of nanometric soot particles in used engine oil from multiple imaging at different tilt angles. Bright-field TEM was used to capture two-dimensional images of soot. Heptane and diethyl ether washes were used to remove volatile contaminants and allowed for images from −60° to +60° tilt with no sign of carbon build-up to be acquired. Tomographic reconstruction from the aligned tilt-series images based on weighted back-projection algorithm has yielded useful information about complex soot nanoparticle size. Estimation of soot mass in oil by nanoparticle tracking analysis (NTA) can be considerably improved by taking into account the three-dimensional shape of the soot agglomerate including the shape factor in the calculations. 3D-TEM measurements were compared with values calculated by using a single-sphere approach when tracking nanoparticles moving under Brownian motion. A shape factor was calculated, dividing the surface area and volume calculated using spherical geometrical estimates, by the respective values calculated using the 3D models. The spherical model of the particle is found on average to overestimate the surface area by sevenfold, and the volume to the actual soot agglomerate by 23 times. Applying the calculated shape factor as a correction reduces the NTA overestimation by one order of magnitude.

This is a preview of subscription content, log in to check access.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7


  1. 1.

    Suhre, B., Foster, D.: In: Cylinder Soot Deposition Rates Due to Thermophoresis in a Direct Injection Diesel Engine. SAE Technical Paper 921629 (1992). doi: 10.4271/921629

  2. 2.

    Kittelson, D., Ambs, J., Hadjkacem, H.: In: Particulate Emissions from Diesel Engines: Influence of In-Cylinder Surface. SAE Technical Paper 900645 (1990). doi: 10.4271/900645

  3. 3.

    Esangbedo, C., Boehman, A.L., Perez, J.M.: Characteristics of diesel engine soot that lead to excessive oil thickening. Tribol. Int. 47, 194–203 (2012)

    Article  Google Scholar 

  4. 4.

    Green, D.A., Lewis, R.: The effects of soot-contaminated engine oil on wear and friction: a review. Proc. Inst. Mech. Eng. Part D J. Automob. Eng. 222(9), 1669–1689 (2008)

    Article  Google Scholar 

  5. 5.

    Li, S., Csontos, A., Gable, B., Passut, C., et al.: In: Wear in Cummins M-11/EGR Test Engines. SAE Technical Paper 2002-01-1672, (2002). doi: 10.4271/2002-01-1672

  6. 6.

    Gautama, M., Chitoora, K., Durbhaa, M., Summers, J.C.: Effect of diesel soot contaminated oil on engine wear investigation of novel oil formulations. Tribol. Int. 32, 687–699 (1999)

    Article  Google Scholar 

  7. 7.

    Achombili, A., La Rocca, A., Shayler, P.J.: In: Investigating the Effect of Carbon Nanoparticles on the Viscosity of Lubricant Oil from Light Duty Automotive Diesel Engines. SAE 2014 World Congress. 2014-04-01 (2014)

  8. 8.

    Rogak, N.R., Flagan, R.C.: Characterization of the structure of agglomerate particles. Part. Part. Syst. Charact. 9, 19–27 (1992)

    Article  Google Scholar 

  9. 9.

    Van Poppel, L.H., Friedrich, H., Spinsby, J., Chung, S.H., Seinfield, J.H., Buseck, P.R.: Electron tomography of nanoparticle clusters: implications for atmospheric lifetimes and radiative forcing of soot. Geophys. Res. Pap. (2005). doi:10.1029/2005GL024461

    Google Scholar 

  10. 10.

    La Rocca, A., Di Liberto, G., Shayler, P., Parmenter, C., et al.: A novel diagnostics tool for measuring soot agglomerates size distribution in used automotive lubricant oils. SAE Int. J. Fuels Lubr. 7(1), 301–306 (2014). doi:10.4271/2014-01-1479

    Article  Google Scholar 

  11. 11.

    Bardasz, E., Cowling, S., Ebeling, V., George, H. et al.: In: Understanding Soot Mediated Oil Thickening Through Designed Experimentation—Part 1: Mack EM6-287, GM 6.2L. SAE Technical Paper 952527, (1995). doi: 10.4271/952527

  12. 12.

    Leong, K.C., Murshed, S.M.S., Yang, C.: Investigations of thermal conductivity and viscosity of nanofluids. Int. J. Therm. Sci. 47(5), 560–568 (2008)

    Article  Google Scholar 

  13. 13.

    Hussein, A.M., Sharma, K.V., Bakar, R.A., Kadirgama, K.: The effect of nanofluid volume concentration on heat transfer and friction factor inside a horizontal tube. J. Nanomater. (2013). doi:10.1155/2013/859563

    Google Scholar 

  14. 14.

    Chavalier, J., Tillement, O., Ayela, F.: Rheological properties of nanofluids flowing through microchannels. Appl. Phys. Lett. 91, 233103 (2007)

    Article  Google Scholar 

  15. 15.

    Corcione, M.: Empirical correlating equations for predicting the effective thermal conductivity and dynamic viscosity of nanofluids. Energy Convers. Manag. 52(1), 789–793 (2011)

    Article  Google Scholar 

  16. 16.

    Tao, R., Xu, X.: Viscosity reduction in liquid suspensions by electric or magnetic fields. Int. J. Mod. Phys. B 19(7&9), 1283–1289 (2005)

    Article  Google Scholar 

  17. 17.

    Toda, K., Hisamoto, F.: Extension of Einstein’s viscosity equation to that for concentrated dispersions of solutes and particles. J. Biosci. Bioeng. 102(6), 524–528 (2006)

    Article  Google Scholar 

  18. 18.

    Covitch, M., Humphrey, B., Ripple, D.: In: Oil Thickening in the Mack T-7 Engine Test—Fuel Effects and the Influence of Lubricant Additives on Soot Aggregation. SAE Technical Paper 852126 (1985). doi: 10.4271/852126

  19. 19.

    Clague, A.D.H., Donnet, J.B., Wang, T.K., Peng, J.C.M.: A comparison of diesel engine soot with carbon black. Carbon 37(10), 1553–1565 (1999)

    Article  Google Scholar 

  20. 20.

    Enzhu, Hu, Xianguo, Hu, Liu, Tianxia, Fang, Ling, Dearn, Karl D., Hongming, Xu: The role of soot particles in the tribological behavior of engine lubricating oils. Wear 304(1–2), 152–161 (2013)

    Google Scholar 

  21. 21.

    Lahouij, I., Dassenoy, F., Vacher, B., Sinha, K., Devine, D.A., Brass, M.: Understanding the deformation of soot particles/agglomerates in a dynamic contact: TEM in situ compression and shear experiments. J. Tribol. Lett. 53(1), 91–99 (2014)

    Article  Google Scholar 

  22. 22.

    La Rocca, A., Di Liberto, G., Shayler, P.J., Fay, M.W., Parmenter, C.D.J.: Application of nanoparticle tracking analysis platform for the measurement of soot-in-oil agglomerates from automotive engines. Tribol. Int. 70, 142–147 (2014)

    Article  Google Scholar 

  23. 23.

    Saghi, Z., Midgley, P.A.: Electron tomography in the (S)TEM: from nanoscale morphological analysis to 3D atomic imaging. Annu. Rev. Mater. Res. 42, 59–79 (2012)

    Article  Google Scholar 

  24. 24.

    Saghi, Z., et al.: Three-dimensional morphology of iron oxide nanoparticles with reactive concave surfaces. A compressed sensing-electron tomography (CS-ET) approach. Nano Lett. 11(11), 4666–4673 (2011)

    Article  Google Scholar 

  25. 25.

    Florea, I., et al.: 3D analysis of the morphology and spatial distribution of nitrogen in nitrogen-doped carbon nanotubes by energy-filtered transmission electron microscopy tomography. J. Am. Chem. Soc. 134(23), 9672–9680 (2012)

    Article  Google Scholar 

  26. 26.

    De Jonge, N., Sourgat, R., Northan, B.M., Pennycook, S.J.: Three-dimensional scanning transmission electron microscopy of biological specimens. Microsc. Microanal. 16, 54–63 (2010)

    Article  Google Scholar 

  27. 27.

    Midgley, P.A., Dunin-Borkowski, R.: Electron tomography and holography in materials science. Nat. Mater. 8, 271–280 (2009)

    Article  Google Scholar 

  28. 28.

    Koster, A.J., Ziese, U., Verkleij, A.J., Janssen, A.H., de Graaf, J., Geus, J.W., de Jong, K.P.: Development and application of 3-dimensional transmission electron microscopy (3D-TEM) for the characterization of metal-zeolite catalyst systems. Stud. Surf. Sci. Catal. 130, 329–334 (2000)

    Article  Google Scholar 

  29. 29.

    Koster, A.J., Ziese, U., Verkleij, A.J., Janssen, A.H., de Jong, K.P.: Three-dimensional electron microscopy: a novel imaging and characterization technique with nanometer scale resolution for materials science. J. Phys. Chem. B 104, 9368–9370 (2000)

    Article  Google Scholar 

  30. 30.

    Janssen, A.H., Koster, A.J., de Jong, K.P.: On the shape of the mesopores in zeolite Y: a three dimensional transmission electron microscopic study combined with texture analysis. J. Phys. Chem. B 106, 11905–11909 (2002)

    Article  Google Scholar 

  31. 31.

    Ziese, U., de Jong, K.P., Koster, A.J.: Electron tomography: a tool for 3D structural probing of heterogeneous catalysts at the nanometer scale. Appl. Catal. A 260(1), 71–77 (2004)

    Article  Google Scholar 

  32. 32.

    Kaneko, K., Inoke, K., Sato, K., Kitawaki, K., Higashida, H., Arslan, I., Midgley, P.A.: TEM characterization of Ge precipitates in an Al–1.6 at.% Ge alloy. Ultramicroscopy 108(3), 210–220 (2008)

    Article  Google Scholar 

  33. 33.

    Feng, Z.Q., Yang, Y.Q., Huang, B., Luo, X., Li, M.H., Chen, Y.X., Han, M., Fu, M.S., Ru, J.G.: HRTEM and HAADF-STEM tomography investigation of the heterogeneously formed S(Al2CuMg) precipitates in Al–Cu–Mg alloy. Phil. Mag. 93(15), 1843–1858 (2013)

    Article  Google Scholar 

  34. 34.

    Li, M.H., Yang, Y.Q., Huang, B., Luo, X., Zhang, W., Han, M., Ru, J.G.: Development of advanced electron tomography in materials science based on TEM and STEM Trans. Nonferrous Met. Soc. China 24, 3031–3050 (2014)

    Article  Google Scholar 

  35. 35.

    Arslan, I., Tong, J.R., Midgley, P.A.: Reducing the missing wedge: high-resolution dual axis tomography of inorganic materials. Ultramicroscopy 106(11–12), 994–1000 (2006)

    Article  Google Scholar 

  36. 36.

    Midgley, P.A., Dunin-Borkowski, R.E.: Electron tomography and holography in materials science. Nat. Mater. 8(4), 271–280 (2009)

    Article  Google Scholar 

  37. 37.

    Biermans, E., Molina, L., Batenburg, K.J., Bals, S., van Tendeloo, G.: Measuring porosity at the nanoscale by quantitative electron tomography. Nano Lett. 10(12), 5014–5019 (2010)

    Article  Google Scholar 

  38. 38.

    Bals, S., Batenburg, K.J., Verbeeck, J., Sijbers, J., van Tendeloo, G.: Quantitative three-dimensional reconstruction of catalyst particles for bamboo-like carbon nanotubes. Nano Lett. 7(12), 3669–3674 (2007)

    Article  Google Scholar 

  39. 39.

    Leary, R., Saghi, Z., Midgley, P.A., Holland, D.J.: Compressed sensing electron tomography. Ultramicroscopy 131, 70–91 (2013)

    Article  Google Scholar 

  40. 40.

    Miao, J.W., Forster, F., Levi, O.: Equally sloped tomography with oversampling reconstruction. Phys. Rev. B 72(5), 052103 (2005)

    Article  Google Scholar 

  41. 41.

    Lee, E., Fahimian, B.P., Iancu, C.V., Murphy, G.E., Wright, E.R., Castano-Diez, D., Jensen, G.J., Miao, J.W.: Radiation dose reduction and image enhancement in biological imaging through equally-sloped tomography. J. Struct. Biol. 164(2), 221–227 (2008)

    Article  Google Scholar 

  42. 42.

    La Rocca, A., Bonatesta, F., Fay, M.W., Campanella, F.: Characterisation of soot in oil from a gasoline direct injection engine using transmission electron microscopy. Tribol. Int. 86(June), 77–84 (2015)

    Article  Google Scholar 

  43. 43.

    Bonatesta, F., Chiappetta, E., La Rocca, A.: Part-load particulate matter from a GDI engine and the connection with combustion characteristics. Appl. Energy 124(1), 366–376 (2014)

    Article  Google Scholar 

  44. 44.

    Fernandez, J.: Computational methods for electron tomography. Micron 43(10), 1010–1030 (2012)

    Article  Google Scholar 

  45. 45.

    Radermacher, M.: Weighted back-projection methods. In: Frank, J. (ed.) Electron Tomography, pp. 91–115. Plenum Press, NewYork (1992)

    Google Scholar 

  46. 46.

    Radermacher, M.: Weighted back-projection methods. In: Frank, J. (ed.) Electron Tomography: Methods for Three-Dimensional Visualisation of Structures in the Cell, 2nd edn, pp. 245–273. Springer, New York (2006)

    Google Scholar 

  47. 47.

    Fernández, J.J., Agulleiro, J.I., Bilbao-Castro, J.R., Martínez, A., García, I., Chichón, F.J., Martín-Benito, J., Carrascosa, J.L.: Image processing in electron tomography. In: Méndez-Vilas A., Díaz J. (eds.) Microscopy: Science, Technology, Applications and Education, vol. 3. Microscopy series no. 4. Formatex (2010)

  48. 48.

    Fernandez, J.J.: Computational methods for materials characterization by electron tomography. Curr. Opin. Solid State Mater. Sci. 17(3): 93–106, ISSN 1359-0286,

  49. 49.

    La Rocca, A., Di Liberto, G., Shayler, P.J., Fay, M.W.: The nanostructure of soot-in-oil particles and agglomerates from an automotive diesel engine. Tribol. Int. 61, 80–87 (2013)

    Article  Google Scholar 

  50. 50.

    Mastronarde, D.N.: Automated electron microscope tomography using robust prediction of specimen movements. J. Struct. Biol. 152, 36–51 (2005)

    Article  Google Scholar 

  51. 51.

    Kremer, J.R., Mastronarde, D.N., McIntosh, J.R.: Computer visualization of three-dimensional image data using IMOD. J. Struct. Biol. 116, 71–76 (1996)

    Article  Google Scholar 

  52. 52.

    Florea, I., et al.: 3D analysis of the morphology and spatial distribution of nitrogen in nitrogen-doped carbon nanotubes by energy-filtered transmission electron microscopy tomography. J. Am. Chem. Soc. 134, 9672–9680 (2012)

    Article  Google Scholar 

  53. 53.

    Yoshizawa, N., Tanaike, O., Hatori, H., Yoshikawa, K., Kondo, A., Abe, T.: TEM and electron tomography studies of carbon nanospheres for lithium secondary batteries. Carbon 44, 2558–2564 (2006)

    Article  Google Scholar 

  54. 54.

    Medalia, A.I.: Morphology of aggregates VI. Effective volume of aggregates of carbon black from electron microscopy; application to vehicle absorption and to die swell of filled rubber. J. Colloid Interface Sci. 32(1), 115–131 (1970)

    Article  Google Scholar 

  55. 55.

    Volkmann, N.: Methods for segmentation and interpretation of electron tomographic reconstructions chapter 2 In Cryo-EM, Part C: Analyses, Interpretation, and Case Studies: 483 (Methods in Enzymology) (2010)

  56. 56.

    Roelandts, T., Batenburg, K.J., den Dekker, A.J., Sijbers, J.: The reconstructed residual error: a novel segmentation evaluation measure for reconstructed images in tomography. Comput. Vis. Image Underst. 126, 28–37 (2014). doi:10.1016/j.cviu.2014.05.007

    Article  Google Scholar 

  57. 57.

    Lanzavecchia, S., Cantele, F., Bellon, P.L., Kreman, M., Wright, E., Zampighi, G.A.: Conical tomography of freeze-fracture replicas: a method for the study of integral membrane proteins inserted in phospholipid bilayers. J. Struct. Biol. 149(1), 87–98 (2005)

    Article  Google Scholar 

Download references


The authors would like to thank Dr C.D.J. Parmenter and Dr M. Payne for their valuable insights and discussion.

Author information



Corresponding author

Correspondence to A. La Rocca.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

La Rocca, A., Campbell, J., Fay, M.W. et al. Soot-in-Oil 3D Volume Reconstruction Through the Use of Electron Tomography: An Introductory Study. Tribol Lett 61, 8 (2016).

Download citation


  • Soot
  • Nanoparticles
  • Tomography
  • Transmission electron microscopy
  • Lubricant oil
  • Nanoparticle tracking analysis