@article{VanpouckeDEPVerdingP:AdvMaterTechEnerSusRes2026,
   title = {Characterization of Droplet Formation in Ultrasonic Spray Coating: 
            Influence of Ink Formulation Using Phase Doppler Anemometry and Machine Learning},
   author = {Verding, Pieter and Vanpoucke, Danny E. P. and Aksoy, Yunus T. and Corthouts, Tobias and Vetrano, Maria R. and Deferme, Wim},
   journal = {Adv. Mater. Tech.},
   year = {2026},
   volume = {11},
   number = {15},
   pages = {e02104},
   keywords = {droplet formation, machine learning modeling, phase doppler anemometry, ultrasonic spray coating},
   doi = {10.1002/admt.202502104},
   url = {https://doi.org/10.1002/admt.202502104},
   abstract = {ABSTRACT This study examines droplet formation in ultrasonic spray coating (USSC) as a 
              function of ink formulation (solvent, polymer, nanoparticles). First, acetone with polyvinylidene 
			  fluoride (PVDF) at concentrations from 0 to 4.5 wt.\% is used to examine the effect of polymer 
			  additions. Additionally, acetone-based SiO2 nanofluids (0–10 g/L), are explored. Finally, the 
			  combination of both polymer (PVDF) and nanoparticles (SiO2) in acetone is studied. Droplet sizes 
			  are measured using Phase Doppler Anemometry under varying atomization power and flow rates. Machine 
			  Learning (ML) algorithms are employed to develop droplet size models from key spray parameters, 
			  including atomization power, flow rate, polymer concentration, and nanoparticle concentration. 
			  The model shows significantly higher accuracy than existing empirical models. The model is further 
			  validated on IPA-based inks with polyethylenimine (PEIE) or ZnO nanoparticles, and on acetone–cellulose 
			  acetate formulations, confirming its robustness across diverse ink systems. In addition to revealing 
			  the influence of coating parameters on the droplet formation and distribution, obtained both via 
			  experimental validation and ML, this study demonstrates that machine learning (ML) can be effectively 
			  applied to small experimental datasets, offering a robust framework for optimizing droplet formation 
			  and understanding key spray parameters in USSC for complex, unexplored inks enabling novel coating 
			  applications.},
}