## 3 Results and Discussion
The two oils with higher Noack volatility had higher soot emissions, except in the <30 nm region, where the low volatility oil had more particles in steady-state driving cycles. This overall trend of higher volatility increasing emissions could be due to an increase in oil consumption--a relationship supported by past research [47,48]. The way volatility can affect consumption can be due to its direct effect on oil evaporation. The oil evaporation mechanism from the cylinder wall occurs as the piston rings deposit during the downward stroke a thin oil film on the surface. Over the combustion, high temperatures cause the oil to vaporise and mix with the combustion gases, particularly in the power and exhaust strokes [49]. In modern engines with low oil consumption, only a few molecular layers of oil are lost per cycle, influenced by pressure, temperature, gas velocity, and oil composition. Lubricating oil with higher volatility enables more oil vapour into the combustion chamber. One of the ways in which oil volatility can affect particle formation is with the volatile elements getting condensed or adsorbed onto the particle surface. Volatile hydrocarbon vapours may also nucleate and form new particles. As also found by [PERSON] et al. [62], this depends on the level of volatility, local cooling degree, and concentration. The relative impact also depends on running conditions and fuel mixture [45,49]. Therefore, the outcome of this investigation is consistent with previous research, though the baseline is not exactly in the middle of the spectrum, being even higher than HiHi under some conditions. This would need further investigation of why it occurred. Since higher ash results in even  
Figure 4: F1-PN > 23 and F3-PN > 10 HiHi to LoLo comparison plots. smaller particles, it may be that these were grown by condensation of more oil on them due to higher volatility, remaining at the end in the same nucleation order. However, it is not clear why at some matrix points HiHi PN is lower than baseline even without the catalytic stripper. This notably shows that the running condition is pivotal in causing fewer or more TPN emissions. To further verify this, a three-way ANOVA was performed (Tables 4 and 5). This is a robust method for determining what has the most statistically significant impact on the emissions. The observed data were F1 PN > 23 (Table 4) and F1 PN > 10 (Table 5) for all 15 conditions. Below are the summary tables, where \"Source\" is the factor or interaction being analysed and \"DoF\" are the degrees of freedom from the number of independent variables contributing to the variance. Oil type, load, and speed were chosen as independent variables. The tables show that for the overall 15 steady-state conditions, load and speed are the dominant factors impacting the TPN emissions. The same was repeated for raw TPN, obtaining the same result. However, individual test points still show a significant impact of oil formulation as per Figures 3 and 4. Another important aspect to highlight is observing how the F3-PN > 10 difference (3.6%) decreased with respect to the F1-PN > 23 (9.7%) from HiHi to LoLo oil in Figure 4. This could indicate that LoLo is emitting a higher number of <23 nm particles than HiHi. A reasonable explanation may be that high volatility shifts particle size towards a larger diameter as there is a larger quantity of oil vapour that can condense on particles [63] or more simply, the catalytic stripper used in F3-PN > 10 has eliminated more volatiles being formed in HiHi. This can be interpreted by looking at the PSDs plots in Figure 5.  
They are equally extracted from the DMS500 for each test condition and oil utilised. The sets of charts from lowest to highest speed (Figure 5) are represented in a grid to visualise the results and compare them to each other more easily. Mean raw PSDs with (w) and without (w/o) CS are plotted together. Confidence intervals are not included for more clarity, but are shown in Appendix A (Figures A1-A3).