Non-photochemical
laser-induced nucleation (NPLIN) has
emerged
as a promising primary nucleation control technique offering spatiotemporal
control over crystallization with potential for polymorph control.
So far, NPLIN was mostly investigated in milliliter vials, through
laborious manual counting of the crystallized vials by visual inspection.
Microfluidics represents an alternative to acquiring automated and
statistically reliable data. Thus we designed a droplet-based microfluidic
platform capable of identifying the droplets with crystals emerging
upon Nd:YAG laser irradiation using the deep learning method. In our
experiments, we used supersaturated solutions of KCl in water, and
the effect of laser intensity, wavelength (1064, 532, and 355 nm),
solution supersaturation (S), solution filtration,
and intentional doping with nanoparticles on the nucleation probability
is quantified and compared to control cooling crystallization experiments.
Ability of dielectric polarization and the nanoparticle heating mechanisms
proposed for NPLIN to explain the acquired results is tested. Solutions
with lower supersaturation (S = 1.05) exhibit significantly
higher NPLIN probabilities than those in the control experiments for
all laser wavelengths above a threshold intensity (50 MW/cm2). At higher supersaturation studied (S = 1.10),
irradiation was already effective at lower laser intensities (10 MW/cm2). No significant wavelength effect was observed besides irradiation
with 355 nm light at higher laser intensities (≥50 MW/cm2). Solution filtration and intentional doping experiments
showed that nanoimpurities might play a significant role in explaining
NPLIN phenomena.