A new chiral sulfide family, Ln(4)InSbS(9) (Ln = La, Pr, Nd), with its own structure type in space group P4(1)2(1)2 or its enantiomorph P4(3)2(1)2 has been synthesized by solid-state reaction. Remarkably, the La member shows the strongest Kleinman-forbidden second harmonic generation to date, with an intensity 1.5 times that of commercial AgGaS(2) at a laser wavelength of 2.05 μm, and exhibits type-I phase-matchable behavior. Density functional theory calculations and ab initio molecular dynamics simulations suggest that lattice vibrations may be responsible for the origin and magnitude of the strong SHG effect.

Interest on the nonlinear optical (NLO) switches that turn on/off the second-harmonic generation (SHG) triggered by the external stimulus (such as heat) have continuously grown, especially on the solid-state NLO switches showing superior stability, reversibility, and reproducibility. Herein, we discover (NH 4 ) 2 PO 3 F, as an entirely new solid-state NLO switch showing outstanding switch contrast and reversibility as well as strong SHG intensity (1.1 × KH 2 PO 4 (KDP)) and high laser-induced damage threshold (2.0 × KDP), undergoes a unique first-order phase transition that originates from a reversible hydrogen-bond rearrangement and needs to overcome an energy barrier. Accordingly, we put forward a strategy to continuously modify such an energy barrier by reducing the number of hydrogen bonds per unit cell via an isoelectronic replacement of NH 4 + by K + with a similar size yet incapability of providing any hydrogen bond. Consequently, K x (NH 4 ) 2−x PO 3 F (x = 0−0.3) exhibiting excellent switching performance are obtained. Remarkably, K x (NH 4 ) 2−x PO 3 F not only realizes a continuously tunable T c spanning from 270 to 150 K, representing the widest NLO switching temperature range ever known but also indicates the first solid-state NLO switch example with continuous T c . Intrinsically, such a T c decline depends on the weakening degree of the hydrogen-bonding interactions in the unit cell. These new insights will shed useful light on the future material design and open new application possibilities.

As one of the fundamental tasks in remote sensing (RS) image understanding, multi-label remote sensing image scene classification (MLRSSC) is attracting increasing research interest. Human beings can easily perform MLRSSC by examining the visual elements contained in the scene and the spatio-topological relationships of these visual elements. However, most of existing methods are limited by only perceiving visual elements but disregarding the spatio-topological relationships of visual elements. With this consideration, this paper proposes a novel deep learning-based MLRSSC framework by combining convolutional neural network (CNN) and graph neural network (GNN), which is termed the MLRSSC-CNN-GNN. Specifically, the CNN is employed to learn the perception ability of visual elements in the scene and generate the high-level appearance features. Based on the trained CNN, one scene graph for each scene is further constructed, where nodes of the graph are represented by superpixel regions of the scene. To fully mine the spatio-topological relationships of the scene graph, the multi-layer-integration graph attention network (GAT) model is proposed to address MLRSSC, where the GAT is one of the latest developments in GNN. Extensive experiments on two public MLRSSC datasets show that the proposed MLRSSC-CNN-GNN can obtain superior performance compared with the state-of-the-art methods.

Direct white-light emission and further a dual-channel readable barcode module in both visible and NIR region was established by single-component homo-metallic Pr(iii)-MOF crystals for the first time.