The efficient separation of acetylene (CH) and ethylene (CH) is an important and complex process in the industry. Herein, we report a new family of -topologic coordination frameworks (termed to ) with CuMF (M = Si, Ti, and Zr) nodes. These charged frameworks are compensated by different counterbalanced ions (MF, BF, and Cl), yielding changes in the size of the window apertures. Among these frameworks, (activated ) shows good adsorption selectivity of CH/CH and also significant ability in recovering both highly pure CH (99.95%) and CH (99.98%). Our work not only presents a potential alternative for energy-saving purification of C2 hydrocarbons but also provides a new approach for tuning the function of charged porous materials.
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http://dx.doi.org/10.1021/acs.inorgchem.3c03182 | DOI Listing |
ACS Appl Mater Interfaces
December 2024
Department of Applied Bioengineering, Graduate School of Convergence Science and Technology, Seoul National University, 1, Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea.
A machine learning (ML) strategy is suggested to optimize dual-layer oxide thin film transistor (TFTs) performance. In this study, Bayesian optimization (BO), an algorithm recognized for its efficiency in optimizing material design, is applied to guide the design of a channel layer composed of IZO and IGZO. The sputtering fabrication process, which has attracted attention as an oxide semiconductor channel layer deposition method, is fine-tuned using ML to enhance multiple electrical characteristics of transistors: field-effect mobility, threshold voltage, and subthreshold swing.
View Article and Find Full Text PDFCommun Med (Lond)
December 2024
Shanghai Jiao Tong University, Shanghai, China.
Background: Medical Visual Question Answering (MedVQA) enhances diagnostic accuracy and healthcare delivery by leveraging artificial intelligence to interpret medical images. This study aims to redefine MedVQA as a generation task that mirrors human-machine interaction and to develop a model capable of integrating complex visual and textual information.
Methods: We constructed a large-scale medical visual-question answering dataset, PMC-VQA, containing 227,000 VQA pairs across 149,000 images that span various modalities and diseases.
ISA Trans
December 2024
Amity Centre for Artificial Intelligence, Amity University, Noida, UP, India. Electronic address:
Inverse kinematics, crucial in robotics, involves computing joint configurations to achieve specific end-effector positions and orientations. This task is particularly complex for six-degree-of-freedom (six-DoF) anthropomorphic robots due to complicated mathematical equations, nonlinear behaviours, multiple valid solutions, physical constraints, non-generalizability and computational demands. The primary contribution of this work is to address the complex inverse kinematics problem for six-DoF anthropomorphic robots through the systematic exploration of AI models.
View Article and Find Full Text PDFEur Radiol
December 2024
Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Objectives: To develop and validate deep learning (DL)-models that denoise late iodine enhancement (LIE) images and enable accurate extracellular volume (ECV) quantification.
Methods: This study retrospectively included patients with chest discomfort who underwent CT myocardial perfusion + CT angiography + LIE from two hospitals. Two DL models, residual dense network (RDN) and conditional generative adversarial network (cGAN), were developed and validated.
ACS Chem Biol
December 2024
Department of Chemistry, Scripps Research, 10550 N Torrey Pines Rd, La Jolla, California 92037, United States.
Fibroblast growth factor 2 (FGF2) is a multipotent growth factor and signaling protein that exhibits broad functions across multiple cell types. These functions are often initiated by binding to growth factor receptors and fine-tuned by glycosaminoglycan (GAG)-modified proteins called proteoglycans. The various outputs of FGF2 signaling and functions arise from a dynamic and cell type-specific set of binding partners.
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