We show assembly of functional and reconfigurable three-dimensional photonic crystals aided by opto-thermal effects due to localized optical heating of a thin gold film on a glass substrate. The optical stop bands of the photonic crystals are probed using Fourier plane imaging and angle resolved spectroscopy of locally excited dye molecules present in the solution. Additionally, dark field scattering spectroscopy indicates the structural colors of the assembled structures and changes with the lattice constants. The results have direct implications for low power manipulation and assembly of functional photonic structures.
We investigate the optical properties of opto-thermally assembled reconfigurable three-dimensional photonic crystals through localized optical heating of a thin gold film on a glass substrate. The assembly process is aided by the resulting thermal gradient induced hydrodynamic, thermophoretic as well as depletion effects. The band structure and the corresponding stop bands of the photonic crystals are probed using Fourier plane imaging and angle resolved spectroscopy of locally excited dye molecules present in the fluidic solution. The results hold direct implications for low power manipulation and assembly of functional photonic structures.
Microfluidics is commonly ruled by pressure driven flows enabling the transport of material on large scales incorporating different kinds of functionality for sensing flow control or chemical synthesis. Yet, a local control of fluids and dissolved species is difficult due to the macroscopic nature of the exerted pressure gradients.
Here we present our efforts to control liquids and dissolved species at the microscale using thermo-fluidic approaches. We employ optically controlled thermo-osmotic, thermophoretic, and thermoviscous flows to induce fluid flow to sense, localise, or separate different species in solution. We introduce different spectroscopic and microscopic signals to report on the local properties and composition of the solution with the help of machine learning approaches to track and classify species in real time to provide a feedback to steer the system into desired directions.
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