The colors of photonic crystals are based on their periodic crystalline structure. They show clear advantages over conventional chromophores for many applications, mainly due to their anti-photobleaching and responsiveness to stimuli. More specifically, combining colloidal photonic crystals and invisible patterns is important in steganography and watermarking for anticounterfeiting applications. Here a convenient way to imprint robust invisible patterns in colloidal crystals of hollow silica spheres is presented. While these patterns remain invisible under static environmental humidity, even up to near 100% relative humidity, they are unveiled immediately (≈100 ms) and fully reversibly by dynamic humid flow, e.g., human breath. They reveal themselves due to the extreme wettability of the patterned (etched) regions, as confirmed by contact angle measurements. The liquid surface tension threshold to induce wetting (revealing the imprinted invisible images) is evaluated by thermodynamic predictions and subsequently verified by exposure to various vapors with different surface tension. The color of the patterned regions is furthermore independently tuned by vapors with different refractive indices. Such a system can play a key role in applications such as anticounterfeiting, identification, and vapor sensing.

No abstract

In nature the spontaneous formation of ordered structures from molecules, called self-assembly, is a very common process occurring in inorganic matter and living organisms. It is driven by atoms, molecules, particles, granular matter, etc. trying to reach the lowest possible energy state while interacting with each other. Deeper understanding of the subtleties of such interactions will allow mimicking of this kind of behaviour to build custom structures from synthetic molecules. This review attempts to cover the existing techniques for directed self-assembly that are currently used for colloidal crystal growth with a brief explanation of the interactions involved in each technique. It provides examples of the fundamental phenomena occurring in photonic crystals that, in the future, can be exploited in various applications.

No abstract

Neural network recognition of features of the fluorescence spectrum of a thermosensitive probe is exploited in order to achieve fluorescence-based thermometry with an accuracy of 200 mK with 100 MHz bandwidth, and with high robustness against fluctuations of the probe laser intensity used. The concept is implemented on a rhodamine B dyed mixture of copper chloride and glycerol, and the temperature dependent fluorescence is investigated in the temperature range between 234 K and 311 K. The spatial dependence of the calibrated amplitude and phase of photothermally induced temperature oscillations along the axis of the excitation laser are determined at different modulation frequencies. The spatial and frequency dependence of the extracted temperature signals is well fitted by a 1D multi-layer thermal diffusion model. In a time domain implementation of the approach, the gradual temperature rise due to the accumulation of the DC component of the heat flux supplied by repetitive laser pulses as well the immediate transient temperature evolution after each single pulse is extracted from acquired temporal sequences of fluorescence spectra induced by a CW green laser. A stroboscopic implementation of fluorescence thermometry, using a pulsed fluorescence evoking probe laser, is shown to achieve remote detection of temperature changes with a time resolution of 10 ns. V