Geometrical structures of titanium oxide cluster cations and anions have been investigated by ion mobility mass spectrometry and quantum chemical calculations based on density functional theory. Stable cluster compositions with respect to collision induced dissociation were also determined by changing ion injection energy to an ion drift cell for mobility measurements. The TinO2n-1 (+) cations and TinO2n (-) anions were predominantly observed at high injection energies, in addition to TinO2n (+) for cations and TinO2n+1 (-) for anions. Collision cross sections of TinO2n (+) and TinO2n+1 (-) for n = 1-7, determined by ion mobility mass spectrometry, were compared with those obtained theoretically as orientation-averaged cross sections for the optimized structures by quantum chemical calculations. All of the geometrical structures thus assigned have three-dimensional structures, which are in marked contrast with other oxides of late transition metals. One-oxygen atom dissociation processes from TinO2n (+) and TinO2n+1 (-) by collisions were also explained by analysis of spin density distributions.
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Sci Rep
January 2025
Advanced Manufacturing Lab, ETH Zürich, Leonhardstrasse 21, 8092, Zurich, Switzerland.
The rapid advancements in additive manufacturing (AM) across different scales and material classes have enabled the creation of architected materials with highly tailored properties. Beyond geometric flexibility, multi-material AM further expands design possibilities by combining materials with distinct characteristics. While machine learning has recently shown great potential for the fast inverse design of lattice structures, its application has largely been limited to single-material systems.
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January 2025
Airworthiness Division, Air Force Institute of Technology, 01-494 Warsaw, Poland.
The range of sensor technologies for structural health monitoring (SHM) systems is expanding as the need for ongoing structural monitoring increases. In such a case, damage to the monitored structure elements is detected using an integrated network of sensors operating in real-time or periodically in frequent time stamps. This paper briefly introduces a new type of sensor, called a Customized Crack Propagation Sensor (CCPS), which is an alternative for crack gauges, but with enhanced functional features and customizability.
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January 2025
Shanghai Film Academy, Shanghai University, Shanghai 200072, China.
The advancement of neural radiance fields (NeRFs) has facilitated the high-quality 3D reconstruction of complex scenes. However, for most NeRFs, reconstructing 3D tissues from endoscopy images poses significant challenges due to the occlusion of soft tissue regions by invalid pixels, deformations in soft tissue, and poor image quality, which severely limits their application in endoscopic scenarios. To address the above issues, we propose a novel framework to reconstruct high-fidelity soft tissue scenes from low-quality endoscopic images.
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January 2025
Peking University Yangtze River Delta Institute of Optoelectronics, Nantong 100871, China.
To improve the performance of Radio Frequency Identification (RFID) multi-label systems, the multi-label network structure needs to be quickly located and optimized. A multi-label location measurement method based on the NLM-Harris algorithm is proposed in this paper. Firstly, multi-label geometric distribution images are obtained through a label image acquisition system of a multi-label semi-physical simulation platform with two vertical Charge-Coupled Device (CCD) cameras, and Gaussian noise is added to the image to simulate thermoelectric interference.
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January 2025
Faculty of Land and Resources Engineering, Kunming University of Science and Technology, Kunming 650093, China.
Scarce feature points are a critical limitation affecting the accuracy and stability of incremental structure from motion (SfM) in small-scale scenes. In this paper, we propose an incremental SfM method for small-scale scenes, combined with an auxiliary calibration plate. This approach increases the number of feature points in sparse regions, and we randomly generate feature points within those areas.
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