Abstract:Recently lead halide nanocrystals (quantum dots) have been reported with potential for photovoltaic and optoelectronic applications due to their excellent luminescent properties. Herein excitonic photoluminescence (PL) excited by two-photon absorption in perovskite CsPbBr3 quantum dots (QDs) have been studied across a broad temperature range from 80K to 380K. Twophoton absorption has been investigated with absorption coefficient up to 0.085 cm/GW at room temperature. Moreover, the photoluminescence excited by two-photon absorption shows a linear blue-shift (0.25meV/K) below temperature of ~220K and turned steady with fluctuation below 1nm (4.4meV) for higher temperature up to 380K. These phenomena are distinctly different from general red-shift of semiconductor and can be explained by the competition between lattice expansion and electron−phonon coupling. Our results reveal the strong nonlinear absorption and temperatureindependent chromaticity in a large temperature range from 220K to 380K in the CsPbX3 QDs, which will offer new opportunities in nonlinear photonics, light-harvesting and light-emitting devices.

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The nonlinear properties of black phosphorus (BP) nanoplatelets (NPs) have been characterized with Z-scan measurements under 800-nm femtosecond pulsed laser excitation. A transition from saturable absorption (SA) to reverse saturable absorption (RSA) with the increase of laser intensity was observed in the open-aperture (OA) measurements. Simultaneously, closed-aperture (CA) measurements were carried out to investigate the nonlinear refractive index of BP NPs together, and a value of n(2) ≃(6.8±0.2)×10(-13) m2/W was obtained. The nonlinear absorption properties were analyzed according to the band structure of BP. A theoretical analysis based on SA and two-photon absorption (TPA) was used to determine the nonlinear absorption coefficients from the experimental results, and the TPA coefficient at 800 nm was estimated about (4.5±0.2)×10(-10) m/W.

Intracellular transport is predominantly heterogeneous in both time and space, exhibiting varying non-Brownian behavior. Characterization of this movement through averaging methods over an ensemble of trajectories or over the course of a single trajectory often fails to capture this heterogeneity. Here, we developed a deep learning feedforward neural network trained on fractional Brownian motion, providing a novel, accurate and efficient method for resolving heterogeneous behavior of intracellular transport in space and time. The neural network requires significantly fewer data points compared to established methods. This enables robust estimation of Hurst exponents for very short time series data, making possible direct, dynamic segmentation and analysis of experimental tracks of rapidly moving cellular structures such as endosomes and lysosomes. By using this analysis, fractional Brownian motion with a stochastic Hurst exponent was used to interpret, for the first time, anomalous intracellular dynamics, revealing unexpected differences in behavior between closely related endocytic organelles.