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.

Articles you may be interested in Fluorescence resonance energy transfer measured by spatial photon migration in CdSe-ZnS quantum dots colloidal systems as a function of concentration Appl. Phys. Lett. 105, 203108 (2014) As a system of interest gets small, due to the influence of the sensor mass and heat leaks through the sensor contacts, thermal characterization by means of contact temperature measurements becomes cumbersome. Non-contact temperature measurement offers a suitable alternative, provided a reliable relationship between the temperature and the detected signal is available. In this work, exploiting the temperature dependence of their fluorescence spectrum, the use of quantum dots as thermomarkers on the surface of a fiber of interest is demonstrated. The performance is assessed of a series of neural networks that use different spectral shape characteristics as inputs (peak-based-peak intensity, peak wavelength; shape-based-integrated intensity, their ratio, full-width half maximum, peak normalized intensity at certain wavelengths, and summation of intensity over several spectral bands) and that yield at their output the fiber temperature in the optically probed area on a spider silk fiber. Starting from neural networks trained on fluorescence spectra acquired in steady state temperature conditions, numerical simulations are performed to assess the quality of the reconstruction of dynamical temperature changes that are photothermally induced by illuminating the fiber with periodically intensitymodulated light. Comparison of the five neural networks investigated to multiple types of curve fits showed that using neural networks trained on a combination of the spectral characteristics improves the accuracy over use of a single independent input, with the greatest accuracy observed for inputs that included both intensity-based measurements (peak intensity) and shape-based measurements (normalized intensity at multiple wavelengths), with an ultimate accuracy of 0.29 K via numerical simulation based on experimental observations. The implications are that quantum dots can be used as a more stable and accurate fluorescence thermometer for solid materials and that use of neural networks for temperature reconstruction improves the accuracy of the measurement. Published by AIP Publishing.

Ionic liquids with an ether-functionalised cation and the bis(2-ethylhexyl)phosphate anion show thermomorphic behaviour in water, with a lower critical solution temperature. These ionic liquids are useful for homogeneous liquid-liquid extraction of first-row (3d) transition metals.

Statistics of the bleaching number and the bleaching time in single-molecule fluorescence spectroscopy

In the presence of disorder, superconductivity exhibits short-range characteristics linked to localized Cooper pairs which are responsible for anomalous phase transitions and the emergence of quantum states such as the bosonic insulating state. Complementary to well-studied homogeneously disordered superconductors, superconductor-normal hybrid arrays provide tunable realizations of the degree of granular disorder for studying anomalous quantum phase transitions. Here, we investigate the superconductor-bosonic dirty metal transition in disordered nanodiamond arrays as a function of the dispersion of intergrain spacing, which ranges from angstroms to micrometers. By monitoring the evolved superconducting gaps and diminished coherence peaks in the single-quasiparticle density of states, we link the destruction of the superconducting state and the emergence of bosonic dirty metallic state to breaking of the global phase coherence and persistence of the localized Cooper pairs. The observed resistive bosonic phase transitions are well modeled using a series-parallel circuit in the framework of bosonic confinement and coherence.