The maintenance of buildings, underpinned by the digital twin technique, becomes integral to heritage conservation efforts. To achieve efficient modelling with minimal manual intervention, automated component recognition based on semantic segmentation of point clouds is imperative. Confronted by the challenges of the paucity of requisite datasets and the inherent geometric diversity of historical buildings, a two-step strategy including feature extraction and classification is proposed. First, an improved SHOT descriptor is proposed to extract discriminative features by defining a specific local reference system and concatenating support fields at different scales. The extracted features are then classified with a learning-based network, avoiding a feature learning process that relies on sufficient data. Experiments on real-world heritage point clouds yield 93.7% accuracy and an 80.0% mean-intersection-over-union (mIoU) when descriptors with radii of 0.3 m and 0.9 m are combined, surpassing computationally expensive deep learning networks and data-intensive unsupervised learning. A slight decrease in segmentation performance with random removal of points indicates the high robustness of the proposed method against data missing and sampling density changes. Additionally, a geometric modelling process with an error of less than 10% is introduced to achieve a direct transition from point cloud to model, contributing to the establishment of digital twins for heritage structures.@en

Elliptic Cu-Ag nanoflakes were syntheszied via facile in situ galvanic replacement between prepared Cu particles and Ag ions. Alloy nanoflakes with high purity and uniformity present a size of 700 × 500 nm, with a thinness of 30 nm. Nontoxic and low-cost polyvinyl pyrrolidone was used as a dispersant and structure-directing agent, promoting the formation of the remarkable structure. Synthesized nanoflakes were utilized as a filler for conductive paste in an epoxy resin matrix. Conductive patterns on flexible substrates with a resistivity of 3.75 × 10 ^-5 ηcm could be achieved after curing at 150 °C for 2 h. Compared with traditional silver microflakes, smart alloy nanoflakes provide much improved conductive interconnection, whose advantage could be attributed to their nanoscale thicknesses. It is also noteworthy that the conductive patterns are able to tolerate multiple bendings at different angles, having good conductivity even after 200 repeated bendings. Therefore, alloy nanoflakes could be a promising candidate conductive filler for flexible printing electronics, electronic packaging, and other conductive applications.

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Recently, the application of phosphorene structure analogues in gas sensors has been a hot research topic since the appearance of phosphorene. SnSe monolayer as one of them has been proved to be much more stable properties than phosphorene. Based on the density-functional theory, the interaction between gas molecules (CO, CO ₂ , O ₂ , NO, NH ₃ , SO ₂ and NO ₂ ) and SnSe monolayer are theoretically investigated by first-principles calculation. Macroscopically, gas molecules selective adsorption of SnSe monolayer is analyzed by molecular dynamics. Compared to CO, CO ₂ , O ₂ , SnSe monolayer performs stronger affinity for SO ₂ and NO ₂ , which possesses appropriate adsorption energies (−6.000 eV and −0.759 eV) and elevated charge transfers (−0.239 e and −0.328 e). SnSe monolayer chemical adsorption of NO ₂ , while physically adsorbing SO ₂ , is more suitable for the adsorption mode of SO ₂ sensors. Surprisingly, the adsorption amount of SO ₂ is 6 times that of NO ₂ . Therefore, the adsorption of SO ₂ is more likely to occur compared to other gas molecules. For a mixed environment of SO ₂ and NO ₂ , the adsorption quantity of SO ₂ is not significantly affected, while the adsorption of NO ₂ is inhibited. Therefore, the SnSe monolayer could be a promising candidate as SO ₂ sensors with high selectivity and sensitivity.

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Sensitive materials for formaldehyde (HCHO) sensor need high sensitivity and selectivity. The research on two dimensional (2D) sensitive material is growing, and most studies focus on the pristine or modified graphene. So it is essential to introduce other 2D materials into HCHO gas sensor. In this report, the adsorption behaviors of organic gas molecules including C₂H₆, C₂H₄, C₂H₂, C₆H₆, C₂H₅OH and HCHO over indium triphosphide (InP₃) monolayer were studied by using first-principle atomistic simulations. The calculation results demonstrate that InP₃ monolayer has a high sensitivity and selectivity to HCHO than others. By comparing the structures and adsorption results of InP₃ monolayer, graphene and single-layered MoS₂, it was found that the polarity bonds and steric effect of the site on monolayer play an important role in the detection of HCHO. The effect of strain on the gas/substrate adsorption systems was also studied, implying that the stained InP₃ monolayer could enhance the sensitivity and selectivity to HCHO. This study provides useful insights into the gas-surface interaction that may assist future experimental development of 2D material for HCHO sensing and performance optimization based on strain.

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