In recent years, flexible light-emitting devices (LEDs) have become the main focus in the field of display technology. Graphene, a two-dimensional layered material, has attracted great interest in LEDs due to its excellent properties. However, there are many problems such as efficiency, lifetime, and flexibility not well solved. Herein, we have successfully prepared a flexible LED using laser-induced reduced graphene oxide (LIRGO). The LIRGO LED achieves a luminescence lifetime of over 60 hours and a wall plug efficiency of up to 1.4% in a vacuum environment of 0.02 Pa. There are many small luminescent spots randomly distributed on 3.5 Â 5 mm 2 of LIRGO. LIRGO's luminous behavior can be controlled by modifying the supply voltage and laser reduction intensity. We also explore LIRGO's applications by testing it in different packages and customizable bulbs. Furthermore, as an interesting demo, the LIRGO device can be used to mimic constellations with visual shapes. This work demonstrates LIRGO's great potential in many fields, such as flexible and miniature light sources and displays. Fig. 6 Packaging and applications of LIRGO. (A) The lifetime of emission vs. the package methods of LIRGO. (B) Structure of the LIRGO bulb. (C) LIRGO bulb emits bright light. (D) Constellations of Cassiopeia, Big Dipper and Columba mimicked with LIRGO devices. 4752 | Nanoscale Adv., 2019, 1, 4745-4754 This journal is

Plant-originated triterpenes are important insecticidal molecules. The research on insecticidal activity of molecules from Meliaceae plants has always received attention due to the molecules from this family showing a variety of insecticidal activities with diverse mechanisms of action. In this paper, we discuss 102 triterpenoid molecules with insecticidal activity of plants of eight genera (Aglaia, Aphanamixis, Azadirachta, Cabralea, Carapa, Cedrela, Chisocheton, and Chukrasia) in Meliaceae. In total, 19 insecticidal plant species are presented. Among these species, Azadirachta indica A. Juss is the most well-known insecticidal plant and azadirachtin is the active molecule most widely recognized and highly effective botanical insecticide. However, it is noteworthy that six species from Cedrela were reported to show insecticidal activity and deserve future study. In this paper, a total of 102 insecticidal molecules are summarized, including 96 nortriterpenes, 4 tetracyclic triterpenes, and 2 pentacyclic triterpenes. Results showed antifeedant activity, growth inhibition activity, poisonous activity, or other activities. Among them, 43 molecules from 15 plant species showed antifeedant activity against 16 insect species, 49 molecules from 14 plant species exhibited poisonous activity on 10 insect species, and 19 molecules from 11 plant species possessed growth regulatory activity on 12 insect species. Among these molecules, azadirachtins were found to be the most successful botanical insecticides. Still, other molecules possessed more than one type of obvious activity, including 7-deacetylgedunin, salannin, gedunin, azadirone, salannol, azadiradione, and methyl angolensate. Most of these molecules are only in the primary stage of study activity; their mechanism of action and structure–activity relationship warrant further study.

Exploiting both RGB (2D appearance) and Depth (3D geometry) information can improve the performance of semantic segmentation. However, due to the inherent difference between the RGB and Depth information, it remains a challenging problem in how to integrate RGB-D features effectively. In this letter, to address this issue, we propose a Nonlocal Aggregation Network (NANet), with a well-designed Multimodality Non-local Aggregation Module (MNAM), to better exploit the non-local context of RGB-D features at multi-stage. Compared with most existing RGB-D semantic segmentation schemes, which only exploit local RGB-D features, the MNAM enables the aggregation of non-local RGB-D information along both spatial and channel dimensions. The proposed NANet achieves comparable performances with state-of-the-art methods on popular RGB-D benchmarks, NYUDv2 and SUN-RGBD.