ABSTRACT
The development of optical humidity detection has been of considerable interest in highly integrated wearable electronics and packaged equipment. However, improving their capacities for color recognition at ultralow humidity and response-recovery rate remains a significant challenge. Herein, we propose a type of hybrid water-harvesting channel to construct brand-new passive fluorescence humidity sensors (PFHSs). Specifically, the hybrid water-harvesting channels involve porous metal-organic frameworks and a hydrophilic poly(acrylic acid) network that can capture water vapors from the ambient environment even at ultralow humidity, into which polar-responsive aggregation-induced emission molecules are doped to impart humidity-sensitive luminescence colors. As a result, the PFHSs exhibit clearly defined fluorescence signals within 0-98% RH coupling with desirable performances such as a fast response rate, precise quantitative feedback, and durable reversibility. Given the flexible processability of this system, we further upgrade the porous structure via electrostatic spinning to furnish a kind of Nano-PFHSs, demonstrating an impressive response time (<100 ms). Finally, we validate the promising applications of these sensors in electronic humidity monitoring and successfully fabricate a portable and rapid humidity indicator card.
ABSTRACT
The synthesis of lightweight yet strong-ductile materials has been an imperative challenge in alloy design. In this study, the CoCrNi-based medium-entropy alloys (MEAs) with added Al and Si were manufactured by vacuum arc melting furnace subsequently followed by cool rolling and anneal process. The mechanical responses of CoCrNiAl0.1Si0.1 MEAs under quasi-static (1 × 10-3 s-1) tensile strength showed that MEAs had an outstanding balance of yield strength, ultimate tensile strength, and elongation. The yield strength, ultimate tensile strength, and elongation were increased from 480 MPa, 900 MPa, and 58% at 298 K to 700 MPa, 1250 MPa, and 72% at 77 K, respectively. Temperature dependencies of the yield strength and strain hardening were investigated to understand the excellent mechanical performance, considering the contribution of lattice distortions, deformation twins, and microbands. Severe lattice distortions were determined to play a predominant role in the temperature-dependent yield stress. The Peierls barrier height increased with decreasing temperature, owing to thermal vibrations causing the effective width of a dislocation core to decrease. Through the thermodynamic formula, the stacking fault energies were calculated to be 14.12 mJ/m2 and 8.32 mJ/m2 at 298 K and 77 K, respectively. In conclusion, the enhanced strength and ductility at cryogenic temperature can be attributed to multiple deformation mechanisms including dislocations, extensive deformation twins, and microbands. The synergistic effect of multiple deformation mechanisms lead to the outstanding mechanical properties of the alloy at room and cryogenic temperature.
ABSTRACT
In this paper, we present a novel appearance model using sparse representation and online dictionary learning techniques for visual tracking. In our approach, the visual appearance is represented by sparse representation, and the online dictionary learning strategy is used to adapt the appearance variations during tracking. We unify the sparse representation and online dictionary learning by defining a sparsity consistency constraint that facilitates the generative and discriminative capabilities of the appearance model. An elastic-net constraint is enforced during the dictionary learning stage to capture the characteristics of the local appearances that are insensitive to partial occlusions. Hence, the target appearance is effectively recovered from the corruptions using the sparse coefficients with respect to the learned sparse bases containing local appearances. In the proposed method, the dictionary is undercomplete and can thus be efficiently implemented for tracking. Moreover, we employ a median absolute deviation based robust similarity metric to eliminate the outliers and evaluate the likelihood between the observations and the model. Finally, we integrate the proposed appearance model with the particle filter framework to form a robust visual tracking algorithm. Experiments on benchmark video sequences show that the proposed appearance model outperforms the other state-of-the-art approaches in tracking performance.
