Though being a promising anode material for sodium-ion batteries (SIBs), MoS with high theoretical capacity shows poor rate capability and rapid capacity decay, especially involving the conversion of MoS to Mo metal and NaS. Here, we report all-in-one MoS nanosheets tailored by porous nitrogen-doped graphene (N-RGO) for the first time to achieve superior structural stability and high cycling reversibility of MoS in SIBs. The all-in-one MoS nanosheets possess desirable structural characteristics by admirably rolling up all good qualities into one, including vertical alignment, an ultrathin layer, vacancy defects, and expanded layer spacing. Thus, the all-in-one MoS@N-RGO composite anode exhibits an improvement in the charge transport kinetics and availability of active materials in SIBs, resulting in outstanding cycling and rate performance. More importantly, the restricted growth of all-in-one MoS by the porous N-RGO via a strong coupling effect dramatically improves the cycling reversibility of conversion reaction. Consequently, the all-in-one MoS@N-RGO composite anode demonstrates excellent reversible capacity, outstanding rate capability, and superior cycling stability. This study strongly suggests that the all-in-one MoS@N-RGO has great potential for practical application in high-performance SIBs.
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http://dx.doi.org/10.1021/acsami.0c15169 | DOI Listing |
Adv Mater
August 2024
Department of Electrical and Electronic Engineering, University of Hong Kong, Hong Kong SAR, 999077, China.
In this era of artificial intelligence and Internet of Things, emerging new computing paradigms such as in-sensor and in-memory computing call for both structurally simple and multifunctional memory devices. Although emerging two-dimensional (2D) memory devices provide promising solutions, the most reported devices either suffer from single functionalities or structural complexity. Here, this work reports a reconfigurable memory device (RMD) based on MoS/CuInPS heterostructure, which integrates the defect engineering-enabled interlayer defects and the ferroelectric polarization in CuInPS, to realize a simplified structure device for all-in-one sensing, memory and computing.
View Article and Find Full Text PDFACS Nano
October 2023
Key Laboratory of Biorheological Science and Technology, Ministry of Education College of Bioengineering, Chongqing University, Chongqing 400044, P. R. China.
Low-temperature photothermal therapy (PTT) is a noninvasive method that harnesses the photothermal effect at low temperatures to selectively eliminate tumor cells, while safeguarding normal tissues, minimizing thermal damage, and enhancing treatment safety. First we evaluated the transcriptome of tumor cells at the gene level following low-temperature treatment and observed significant enrichment of genes involved in cell cycle and heat response-related signaling pathways. To address this challenge, we have developed an engineering multifunctional nanoplatform that offered an all-in-one strategy for efficient sensitization of low-temperature PTT.
View Article and Find Full Text PDFACS Appl Mater Interfaces
June 2023
Engineering Science and Mechanics, Penn State University, University Park, Pennsylvania 16802, United States.
Limitations in cloud-based computing have prompted a paradigm shift toward all-in-one "edge" devices capable of independent data sensing, computing, and storage. Advanced defense and space applications stand to benefit immensely from this due to their need for continual operation in areas where maintaining remote oversight is difficult. However, the extreme environments relevant to these applications necessitate rigorous testing of technologies, with a common requirement being hardness to ionizing radiation.
View Article and Find Full Text PDFACS Nano
December 2022
Department of Engineering Science and Mechanics, Pennsylvania State University, University Park, Pennsylvania16802, United States.
In spite of recent advancements in artificial neural networks (ANNs), the energy efficiency, multifunctionality, adaptability, and integrated nature of biological neural networks remain largely unimitated by hardware neuromorphic computing systems. Here, we exploit optoelectronic, computing, and programmable memory devices based on emerging two-dimensional (2D) layered materials such as MoS to demonstrate a monolithically integrated, multipixel, and "all-in-one" bioinspired neural network (BNN) capable of sensing, encoding, learning, forgetting, and inferring at minuscule energy expenditure. We also demonstrate learning adaptability and simulate learning challenges under specific synaptic conditions to mimic biological learning.
View Article and Find Full Text PDFACS Nano
December 2022
Engineering Science and Mechanics, Penn State University, University Park, Pennsylvania 16802, United States.
Natural intelligence has many dimensions, with some of its most important manifestations being tied to learning about the environment and making behavioral changes. In primates, vision plays a critical role in learning. The underlying biological neural networks contain specialized neurons and synapses which not only sense and process visual stimuli but also learn and adapt with remarkable energy efficiency.
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