## 2 Materials and Methods  
The droplet combustion apparatus used in this work is shown in Figure 1, which is the same as that used in our previous work [7]. Therefore, only a brief introduction is provided here. Two quartz fibers with the diameter of 100 \(\upmu\)m were crossed to suspend the droplet. The diameters of the initially suspended droplets were maintained at 1.2-1.6 mm, which is over an order of magnitude thicker than the fiber diameter. During each experiment, the droplet was ignited by an electric spark generated by two electrodes that were synchronized with a high-speed camera (Phantom VEO1310L). The ignition energy was controlled at 100 mJ, and the ignition duration time was set as 100 ms in all experiments. An acrylic chamber was used to avoid disturbance from any surrounding airflow. The pressure, temperature, and relative humidity in the chamber are 1 atm, 25 \({}^{\circ}\)C, and 25%, respectively. During the experiment, the droplet is surrounded by a flame.  
TiO\({}_{2}\) particles with diameters around 1 \(\upmu\)m were seeded in the binary droplets. The droplets were sonicated to ensure uniform dispersion of the tracer particles. In the micro-PIV system, a high-speed camera (Phantom VEO 1310L) and a 527 nm dual-cavity Nd:YLF pulsed laser (Beamtech Optronics, Vilte-Hi-527-40) were operated at 800 Hz and synchronized by a LabVIEW delay generator program based on a digital I/O module (NI PCIe-7856). Tracer particles in the droplets were illuminated through the thin laser sheet. The video of the droplet combustion constituted by particle images was recorded by the high-speed camera operated in dual mode with a macro lens (Navitar Zoom 6000) attached  
Figure 1: Schematic diagram of the droplet combustion apparatus combined with a micro-PIV system.  
with a short bandpass filter (Edmund Optics 527/20 nm). The time interval of two consecutive frames can be regulated. The field of view of this camera was set as 80 mm \(\times\) 60 mm, corresponding to a spatial resolution of \(\sim\)3 \(\upmu\)m/pixel. The frame rate was set as 800 fps, and the maximum recorded duration was set as 3 s to capture the whole lifetime of the droplet.  
The data processing procedure of particle images is shown in Figure 2. All steps were accomplished with DaVis 8.4 and MATLAB. First, the raw particle image was calibrated and scaled to the global coordinate system. Second, the image was preprocessed and a sliding background was subtracted from the particle image to reduce the fluctuation of background intensity. Then, an algorithm mask based on a count threshold was applied to separate the target domain from the whole image. Subsequently, a multi-pass cross-correlation algorithm was applied to calculate the velocity vectors and some basic post-processing operations were used to remove obviously erroneous velocity vectors. Therefore, the flow field inside a binary droplet can be acquired, which has the velocity field overlapped with vector arrows and the vorticity field overlapped with streamlines. Using a statistical analysis of how each pixel contributes to the cross-correlation peak shape, the uncertainty of the velocity field is derived [21]. For the following results in this work, the maximum uncertainty of the velocity is 0.2 mm/s.  
In this work, the two fuel components, i.e., \(\mathrm{PODE}_{2}\) and \(\mathrm{PODE}_{4}\), are mixed at different content ratios (volume fractions). Table 1 lists the composition, content ratios, boiling point difference (\(\Delta T_{\mathrm{bp}}\)), and nomenclatures of the investigated binary solution droplets in this work. The two components are perfectly miscible, and the binary mixtures have been ultrasonicated for at least 30 min. Therefore, the binary droplets are uniformly mixed. For better comparison, pure \(\mathrm{PODE}_{2}\) and \(\mathrm{PODE}_{4}\) droplets are also investigated.  
Figure 2: Data processing procedure from raw particle image to flow field.