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Abstract The timescale of eyewall replacement cycle (ERC) is critical for the prediction of intensity and structure changes of tropical cyclones (TCs) with concentric eyewall (CE) structures. Previous studies have indicated that the moat width can regulate the interaction between the inner and outer eyewalls and has a salient relationship with the ERC timescale. In this study, a series of sensitivity experiments are carried out to investigate the essential mechanisms resulting in the diversity of the duration of CEs using both simple and full‐physics models. Results reveal that a larger moat can induce stronger inflow under the same inner eyewall intensity by providing a longer distance for air parcels to accelerate in the boundary layer. Thus, there is greater inward absolute vorticity flux to sustain the inner eyewall. Besides, the equivalent potential temperature (θ_e) budget indicates that the vertical advection and surface flux of moist entropy can overbalance the negative contribution from the horizontal advection and lead to an increasing trend ofθ_ein the inner eyewall. This suggests that the thermodynamic process in the boundary layer is not indispensable to the inner eyewall weakening. It is also found that the contraction rate of the secondary eyewall, which directly influences the moat width, is subject to the activity of outer spiral rainbands. By directly introducing positive wind tendency outside the eyewall and indirectly promoting a vertically tilted eyewall structure, active convection in the outer region will impede or even suspend the contraction of the outer eyewall and hence extend the ERC timescale. 

We studied the use of deep neural networks (DNNs) in the numerical solution of the oscillatory Fredholm integral equation of the second kind. It is known that the solution of the equation exhibits certain oscillatory behaviors due to the oscillation of the kernel. It was pointed out recently that standard DNNs favor low frequency functions, and as a result, they often produce poor approximation for functions containing high frequency components. We addressed this issue in this study. We first developed a numerical method for solving the equation with DNNs as an approximate solution by designing a numerical quadrature that tailors to computing oscillatory integrals involving DNNs. We proved that the error of the DNN approximate solution of the equation is bounded by the training loss and the quadrature error. We then proposed a multigrade deep learning (MGDL) model to overcome the spectral bias issue of neural networks. Numerical experiments demonstrate that the MGDL model is effective in extracting multiscale information of the oscillatory solution and overcoming the spectral bias issue from which a standard DNN model suffers. 

Chiral semiconductors have been recently suggested as the basic building blocks for the design of chiral optoelectronic and electronic devices for chiral emission and spintronics. Herein, we report that through the formation of a chiral/achiral heterostructure, one can develop a chiral system that integrates the merits of both chiral and achiral components for developing a demanded chiral emitter. In the R-(+)-(or S-(−)-)1-(1-naphthyl)-ethylammonium lead bromide/CsPbBr3 heterostructure, we show that the photoluminescence of CsPbBr3 carries a degree of circular polarization of around 1% at room temperature. It is explained that such chiral emission is enabled through the chiral self-trapped exitonic absorption of R-(+)- (or S-(−)-)1-(1-naphthyl)-ethylammonium lead bromide. This work may provide an alternative way to generate bright circularly polarized light from achiral materials, which has potential applications in spintronics, biosensing, and signal encryption. 

Nanostructuring photocatalytic and catalytic materials substantially increases the surface‐to‐volume ratio, thereby exposing a greater number of active sites essential for enhanced catalytic efficiency. However, optimizing these efficiencies requires the non‐destructive,operandointerrogation of individual nanocrystals under realistic catalytic conditions—a capability that has long remained elusive. Here, this challenge is addressed by reporting three‐dimensional imaging of defects, crystal morphology, and strain dynamics in individual Bi₂WO₆(BWO) nanoflakes using Bragg coherent diffractive imaging (BCDI) underoperandotemperature, gas, and light‐driven conditions. It is demonstrated that maintaining a constant temperature of 40°C thermally activates charge carriers, likely enhancing their mobility and reducing recombination rates. Furthermore, an Argon (Ar) gas flow stabilizes the reaction environment, while a mixed Hydrogen–Nitrogen (H₂+ N₂) flow induces a hydrogen‐triggered semiconducting‐to‐metallic (SM) electronic phase transition accompanied by a structural transformation, as supported by density functional theory (DFT) calculations. Both DFT and BCDI analyses reveal that during the SM phase transition, a new structural phase nucleates near defects and propagates inhomogeneously. Notably, the onset of nanoscale cracking is observed, driven by localized strain accumulation and environmental cycling, which increases surface area and potentially introduces new reactive sites. These findings illustrate that combining advanced nanostructuring withoperandoimaging techniques can provide critical insights into the local structural features that govern photocatalytic performance, paving the way for the rational design of next‐generation photocatalytic materials.