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Metallophthalocyanine (MPc)-linked conductive two-dimensional (2D) metal−organic frameworks (MOFs) hold tremendous promise as modular 2D materials in sensing, catalysis, and energy-related applications due to their combinatory bimetallic system from the MPc core and bridging metal nodes, endowing them with high electrical conductivity and multifunctionality. Despite significant advances, there is a gap in fundamental understanding regarding the periodic effects of metal nodes on the structural properties of MP-linked 2D MOFs. Herein, we report a series of highly crystalline MOFs wherein copper phthalocyanine (CuPc) is linked with Ni, Cu, and Zn nodes (CuPc-O-M, M: Ni, Cu, Zn). The prepared CuPc-O-M MOFs exhibit p-type semiconducting properties with an exceptionally high range of electrical conductivity. Notably, the differences in the 3d orbital configurations of the Ni, Cu, and Zn nodes in CuPc-O-M MOFs lead to perturbations of the interlayer stacking patterns of the 2D framework materials, which ultimately affect material properties, such as semiconducting band gaps and charge transport within the framework. The Cu2+ (3d9) metal node within the eclipsed interlayer stacking of CuPc-O-Cu MOF demonstrates excellent charge transport, which results in the smallest band gap of 1.14 eV and the highest electrical conductivity of 9.3 S m−1, while the Zn2+ (3d10) metal node within CuPc-O-Zn results in a slightly inclined interlayer stacking, leading to the largest band gap of 1.27 eV and the lowest electrical conductivity of 2.9 S m−1. These findings form an important foundation in the strategic molecular design of this class of materials for multifaceted functionality that builds upon the electronic properties of these materials. 

Product disassembly is essential for remanufacturing operations and recovery of end-of-use devices. However, disassembly has often been performed manually with significant safety issues for human workers. Recently, human-robot collaboration has become popular to reduce the human workload and handle hazardous materials. However, due to the current limitations of robots, they are not fully capable of performing every disassembly task. It is critical to determine whether a robot can accomplish a specific disassembly task. This study develops a disassembly score which represents how easy is to disassemble a component by robots, considering the attributes of the component along with the robotic capability. Five factors, including component weight, shape, size, accessibility, and positioning, are considered when developing the disassembly score. Further, the relationship between the five factors and robotic capabilities, such as grabbing and placing, is discussed. The MaxViT (Multi-Axis Vision Transformer) model is used to determine component sizes through image processing of the XPS 8700 desktop, demonstrating the potential for automating disassembly score generation. Moreover, the proposed disassembly score is discussed in terms of determining the appropriate work setting for disassembly operations, under three main categories: human-robot collaboration (HRC), semi-HRC, and worker-only settings. A framework for calculating disassembly time, considering human-robot collaboration, is also proposed. 

Under the $\mathrm{AdS}/\mathrm{CFT}$correspondence, asymptotically anti–de Sitter geometries with backreaction can be viewed as conformal field theory states subject to a renormalization group (RG) flow from an ultraviolet (UV) description toward an infrared (IR) sector. For black holes, however, the IR point is the horizon, so one way to interpret the interior is as an analytic continuation to a “trans-IR” imaginary-energy regime. In this paper, we demonstrate that this analytic continuation preserves some imprints of the UV physics, particularly near its “end point” at the classical singularity. We focus on holographic phase transitions of geometric objects in round black holes. We first assert the consistency of interpreting such black holes, including their interiors, as RG flows by constructing a monotonic $a$function. We then explore how UV phase transitions of entanglement entropy and scalar two-point functions, each of which are encoded by bulk geometry under the holographic mapping, are related to the structure of the near-singularity geometry, which is quantified by Kasner exponents. Using 2D holographic flows triggered by relevant scalar deformations as test beds, we find that the 3D bulk’s near-singularity Kasner exponents can be viewed as functions of the UV physics precisely when the deformation is nonzero. Published by the American Physical Society2024 

Abstract Despite the importance of product repairability, current methods for assessing and grading repairability are limited, which hampers the efforts of designers, remanufacturers, original equipment manufacturers (OEMs), and repair shops. To improve the efficiency of assessing product repairability, this study introduces two artificial intelligence (AI) based approaches. The first approach is a supervised learning framework that utilizes object detection on product teardown images to measure repairability. Transfer learning is employed with machine learning architectures such as ConvNeXt, GoogLeNet, ResNet50, and VGG16 to evaluate repairability scores. The second approach is an unsupervised learning framework that combines feature extraction and cluster learning to identify product design features and group devices with similar designs. It utilizes an oriented FAST and rotated BRIEF feature extractor (ORB) along with k-means clustering to extract features from teardown images and categorize products with similar designs. To demonstrate the application of these assessment approaches, smartphones are used as a case study. The results highlight the potential of artificial intelligence in developing an automated system for assessing and rating product repairability. 

Contact engineering on monolayer layer (ML) semiconducting transition metal dichalcogenides (TMDs) is considered the most challenging problem towards using these materials as a transistor channel in future advanced technology nodes. The typically observed strong Femi level pinning induced in part by the reaction of the source/drain contact metal and the ML TMD frequently results in a large Schottky barrier height, which limits the electrical performance of ML TMD field-effect transistors (FETs). However, at a microscopic level, little is known about how interface defects or reaction sites impact the electrical performance of ML TMD FETs. In this work, we have performed statistically meaningful electrical measurements on at least 120 FETs combined with careful surface analysis to unveil contact resistance dependencies on the interface chemistry. In particular, we achieved a low contact resistance for ML MoS2 FETs with ultra-high vacuum (UHV, 3×10-11 mbar) deposited Ni contacts, ~500 ohm·μm, which is 5 times lower than the contact resistance achieved when deposited at high vacuum (HV, 3×10-6 mbar) conditions. These electrical results strongly correlate with our surface analysis observations. X-ray photoelectron spectroscopy (XPS) revealed significant bonding species between Ni and MoS2 under UHV conditions compared to HV. We also studied the Bi/MoS2 interface under UHV and HV deposition conditions. Different from the case of Ni, we do not observe a difference in contact resistance or interface chemistry between contacts deposited under UHV and HV. Finally, this article also explores the thermal stability and reliability of the two contact metals employed here. 

People with blindness and low vision (pBLV) encounter substantial challenges when it comes to comprehensive scene recognition and precise object identification in unfamiliar environments. Additionally, due to the vision loss, pBLV have difficulty in accessing and identifying potential tripping hazards independently. Previous assistive technologies for the visually impaired often struggle in real-world scenarios due to the need for constant training and lack of robustness, which limits their effectiveness, especially in dynamic and unfamiliar environments, where accurate and efficient perception is crucial. Therefore, we frame our research question in this paper as: How can we assist pBLV in recognizing scenes, identifying objects, and detecting potential tripping hazards in unfamiliar environments, where existing assistive technologies often falter due to their lack of robustness? We hypothesize that by leveraging large pretrained foundation models and prompt engineering, we can create a system that effectively addresses the challenges faced by pBLV in unfamiliar environments. Motivated by the prevalence of large pretrained foundation models, particularly in assistive robotics applications, due to their accurate perception and robust contextual understanding in real-world scenarios induced by extensive pretraining, we present a pioneering approach that leverages foundation models to enhance visual perception for pBLV, offering detailed and comprehensive descriptions of the surrounding environment and providing warnings about potential risks. Specifically, our method begins by leveraging a large-image tagging model (i.e., Recognize Anything Model (RAM)) to identify all common objects present in the captured images. The recognition results and user query are then integrated into a prompt, tailored specifically for pBLV, using prompt engineering. By combining the prompt and input image, a vision-language foundation model (i.e., InstructBLIP) generates detailed and comprehensive descriptions of the environment and identifies potential risks in the environment by analyzing environmental objects and scenic landmarks, relevant to the prompt. We evaluate our approach through experiments conducted on both indoor and outdoor datasets. Our results demonstrate that our method can recognize objects accurately and provide insightful descriptions and analysis of the environment for pBLV.