scholarly journals Fiber-Based Thermoelectric Materials and Devices for Wearable Electronics

Micromachines ◽  
2021 ◽  
Vol 12 (8) ◽  
pp. 869
Author(s):  
Pengxiang Zhang ◽  
Biao Deng ◽  
Wenting Sun ◽  
Zijian Zheng ◽  
Weishu Liu
Keyword(s):  
Thermoelectric Materials ◽  
State Of The Art ◽  
Temperature Management ◽  
Light Weight ◽  
Thermoelectric Generation ◽  
Wearable Electronics ◽  
Challenges And Opportunities ◽  
Potential Applications ◽  
Material Fabrication ◽  
Weight Stability

Fiber-based thermoelectric materials and devices have the characteristics of light-weight, stability, and flexibility, which can be used in wearable electronics, attracting the wide attention of researchers. In this work, we present a review of state-of-the-art fiber-based thermoelectric material fabrication, device assembling, and its potential applications in temperature sensing, thermoelectric generation, and temperature management. In this mini review, we also shine some light on the potential application in the next generation of wearable electronics, and discuss the challenges and opportunities.

scholarly journals New Progress on Fiber-Based Thermoelectric Materials: Performance, Device Structures and Applications

Materials ◽  
2021 ◽  
Vol 14 (21) ◽  
pp. 6306
Author(s):  
Yanan Shen ◽  
Chunyang Wang ◽  
Xiao Yang ◽  
Jian Li ◽  
Rui Lu ◽  
...  
Keyword(s):  
Power Systems ◽  
Carbon Fibers ◽  
Thermoelectric Materials ◽  
Rapid Development ◽  
Wearable Electronics ◽  
Hybrid Fibers ◽  
Surrounding Environment ◽  
Device Structures ◽  
Potential Applications ◽  
Self Powered

With the rapid development of wearable electronics, looking for flexible and wearable generators as their self-power systems has proved an extensive task. Fiber-based thermoelectric generators (FTEGs) are promising candidates for these self-powered systems that collect energy from the surrounding environment or human body to sustain wearable electronics. In this work, we overview performances and device structures of state-of-the-art fiber-based thermoelectric materials, including inorganic fibers (e.g., carbon fibers, oxide fibers, and semiconductor fibers), organic fibers, and hybrid fibers. Moreover, potential applications for related thermoelectric devices are discussed, and future developments in fiber-based thermoelectric materials are also briefly expected.

scholarly journals Ga Based Particles, Alloys and Composites: Fabrication and Applications

Nanomaterials ◽  
2021 ◽  
Vol 11 (9) ◽  
pp. 2246
Author(s):  
Zhi Li ◽  
Yiming Guo ◽  
Yufen Zong ◽  
Kai Li ◽  
Shuang Wang ◽  
...  
Keyword(s):  
Electronic Devices ◽  
Functional Materials ◽  
Inorganic Nanoparticles ◽  
Wearable Electronics ◽  
Practical Applications ◽  
Functional Composites ◽  
Challenges And Opportunities ◽  
Potential Applications ◽  
Storage Batteries ◽  
Energy Harvesting Devices

Liquid metal (LM) materials, including pure gallium (Ga) LM, eutectic alloys and their composites with organic polymers and inorganic nanoparticles, are cutting-edge functional materials owing to their outstanding electrical conductivity, thermal conductivity, extraordinary mechanical compliance, deformability and excellent biocompatibility. The unique properties of LM-based materials at room temperatures can overcome the drawbacks of the conventional electronic devices, particularly high thermal, electrical conductivities and their fluidic property, which would open tremendous opportunities for the fundamental research and practical applications of stretchable and wearable electronic devices. Therefore, research interest has been increasingly devoted to the fabrication methodologies of LM nanoparticles and their functional composites. In this review, we intend to present an overview of the state-of-art protocols for the synthesis of Ga-based materials, to introduce their potential applications in the fields ranging from wearable electronics, energy storage batteries and energy harvesting devices to bio-applications, and to discuss challenges and opportunities in future studies.

scholarly journals Graphene Integrated Hydrogels Based Biomaterials in Photothermal Biomedicine

Nanomaterials ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 906
Author(s):  
Le Minh Tu Phan ◽  
Thuy Anh Thu Vo ◽  
Thi Xoan Hoang ◽  
Sungbo Cho
Keyword(s):  
Photothermal Therapy ◽  
Biomedical Applications ◽  
High Light ◽  
Conversion Materials ◽  
Potential Applications ◽  
Good Biocompatibility ◽  
Biomedical Field ◽  
Hydrogel Composites ◽  
Material Fabrication ◽  
Biomedical Fields

Recently, photothermal therapy (PTT) has emerged as one of the most promising biomedical strategies for different areas in the biomedical field owing to its superior advantages, such as being noninvasive, target-specific and having fewer side effects. Graphene-based hydrogels (GGels), which have excellent mechanical and optical properties, high light-to-heat conversion efficiency and good biocompatibility, have been intensively exploited as potential photothermal conversion materials. This comprehensive review summarizes the current development of graphene-integrated hydrogel composites and their application in photothermal biomedicine. The latest advances in the synthesis strategies, unique properties and potential applications of photothermal-responsive GGel nanocomposites in biomedical fields are introduced in detail. This review aims to provide a better understanding of the current progress in GGel material fabrication, photothermal properties and potential PTT-based biomedical applications, thereby aiding in more research efforts to facilitate the further advancement of photothermal biomedicine.

scholarly journals In vivo interactome profiling by enzyme‐catalyzed proximity labeling

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yangfan Xu ◽  
Xianqun Fan ◽  
Yang Hu
Keyword(s):  
State Of The Art ◽  
Catalytic Efficiency ◽  
Biological Processes ◽  
Protein Protein Interaction ◽  
Current State ◽  
Protein Interactome ◽  
Potential Applications ◽  
Protein Protein Interaction Networks ◽  
Temporal And Spatial

AbstractEnzyme-catalyzed proximity labeling (PL) combined with mass spectrometry (MS) has emerged as a revolutionary approach to reveal the protein-protein interaction networks, dissect complex biological processes, and characterize the subcellular proteome in a more physiological setting than before. The enzymatic tags are being upgraded to improve temporal and spatial resolution and obtain faster catalytic dynamics and higher catalytic efficiency. In vivo application of PL integrated with other state of the art techniques has recently been adapted in live animals and plants, allowing questions to be addressed that were previously inaccessible. It is timely to summarize the current state of PL-dependent interactome studies and their potential applications. We will focus on in vivo uses of newer versions of PL and highlight critical considerations for successful in vivo PL experiments that will provide novel insights into the protein interactome in the context of human diseases.

scholarly journals Disaster Resilience of Optical Networks: State of the Art, Challenges, and Opportunities

2021 ◽  
pp. 100619
Author(s):  
Jacek Rak ◽  
Rita Girão-Silva ◽  
Teresa Gomes ◽  
Georgios Ellinas ◽  
Burak Kantarci ◽  
...  
Keyword(s):  
Optical Networks ◽  
State Of The Art ◽  
Disaster Resilience ◽  
Challenges And Opportunities

scholarly journals IoT Technologies during and Beyond COVID-19: A Comprehensive Review

2021 ◽  
Vol 13 (5) ◽  
pp. 105
Author(s):  
Mohamed Yousif ◽  
Chaminda Hewage ◽  
Liqaa Nawaf
Keyword(s):  
Social Impact ◽  
Contact Tracing ◽  
Privacy And Security ◽  
Comprehensive Review ◽  
Iot Applications ◽  
Depth Analysis ◽  
Challenges And Opportunities ◽  
Potential Applications ◽  
Readiness Level ◽  
Improved Performance

The COVID-19 pandemic provided a much-needed sanity check for IoT-inspired frameworks and solutions. IoT solutions such as remote health monitoring and contact tracing provided support for authorities to successfully manage the spread of the coronavirus. This article provides the first comprehensive review of key IoT solutions that have had an impact on COVID-19 in healthcare, contact tracing, and transportation during the pandemic. Each sector is investigated in depth; and potential applications, social and economic impact, and barriers for mass adaptation are discussed in detail. Furthermore, it elaborates on the challenges and opportunities for IoT framework solutions in the immediate post-COVID-19 era. To this end, privacy and security concerns of IoT applications are analyzed in depth and emerging standards and code of practices for mass adaptation are also discussed. The main contribution of this review paper is the in-depth analysis and categorization of sector-wise IoT technologies, which have the potential to be prominent applications in the new normal. IoT applications in each selected sector are rated for their potential economic and social impact, timeline for mass adaptation, and Technology Readiness Level (TRL). In addition, this article outlines potential research directions for next-generation IoT applications that would facilitate improved performance with preserved privacy and security, as well as wider adaptation by the population at large.

scholarly journals Towards Scalable Economic Photovoltaic Potential Analysis Using Aerial Images and Deep Learning

Energies ◽  
2021 ◽  
Vol 14 (13) ◽  
pp. 3800
Author(s):  
Sebastian Krapf ◽  
Nils Kemmerzell ◽  
Syed Khawaja Haseeb Khawaja Haseeb Uddin ◽  
Manuel Hack Hack Vázquez ◽  
Fabian Netzler ◽  
...  
Keyword(s):  
Deep Learning ◽  
System Analysis ◽  
State Of The Art ◽  
Critical Role ◽  
Semantic Segmentation ◽  
Energy System ◽  
Aerial Images ◽  
Potential Analysis ◽  
3D Data ◽  
Challenges And Opportunities

Roof-mounted photovoltaic systems play a critical role in the global transition to renewable energy generation. An analysis of roof photovoltaic potential is an important tool for supporting decision-making and for accelerating new installations. State of the art uses 3D data to conduct potential analyses with high spatial resolution, limiting the study area to places with available 3D data. Recent advances in deep learning allow the required roof information from aerial images to be extracted. Furthermore, most publications consider the technical photovoltaic potential, and only a few publications determine the photovoltaic economic potential. Therefore, this paper extends state of the art by proposing and applying a methodology for scalable economic photovoltaic potential analysis using aerial images and deep learning. Two convolutional neural networks are trained for semantic segmentation of roof segments and superstructures and achieve an Intersection over Union values of 0.84 and 0.64, respectively. We calculated the internal rate of return of each roof segment for 71 buildings in a small study area. A comparison of this paper’s methodology with a 3D-based analysis discusses its benefits and disadvantages. The proposed methodology uses only publicly available data and is potentially scalable to the global level. However, this poses a variety of research challenges and opportunities, which are summarized with a focus on the application of deep learning, economic photovoltaic potential analysis, and energy system analysis.

scholarly journals Efficient Graph Collaborative Filtering via Contrastive Learning

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4666
Author(s):  
Zhiqiang Pan ◽  
Honghui Chen
Keyword(s):  
Collaborative Filtering ◽  
State Of The Art ◽  
Representation Learning ◽  
First Order ◽  
Satisfactory Performance ◽  
Potential Applications ◽  
Benchmark Datasets ◽  
Bayesian Personalized Ranking ◽  
Graph Neural Networks ◽  
Training Efficiency

Collaborative filtering (CF) aims to make recommendations for users by detecting user’s preference from the historical user–item interactions. Existing graph neural networks (GNN) based methods achieve satisfactory performance by exploiting the high-order connectivity between users and items, however they suffer from the poor training efficiency problem and easily introduce bias for information propagation. Moreover, the widely applied Bayesian personalized ranking (BPR) loss is insufficient to provide supervision signals for training due to the extremely sparse observed interactions. To deal with the above issues, we propose the Efficient Graph Collaborative Filtering (EGCF) method. Specifically, EGCF adopts merely one-layer graph convolution to model the collaborative signal for users and items from the first-order neighbors in the user–item interactions. Moreover, we introduce contrastive learning to enhance the representation learning of users and items by deriving the self-supervisions, which is jointly trained with the supervised learning. Extensive experiments are conducted on two benchmark datasets, i.e., Yelp2018 and Amazon-book, and the experimental results demonstrate that EGCF can achieve the state-of-the-art performance in terms of Recall and normalized discounted cumulative gain (NDCG), especially on ranking the target items at right positions. In addition, EGCF shows obvious advantages in the training efficiency compared with the competitive baselines, making it practicable for potential applications.

scholarly journals Foldable and washable textile-based OLEDs with a multi-functional near-room-temperature encapsulation layer for smart e-textiles

2021 ◽  
Vol 5 (1) ◽  
Author(s):  
So Yeong Jeong ◽  
Hye Rin Shim ◽  
Yunha Na ◽  
Ki Suk Kang ◽  
Yongmin Jeon ◽  
...  
Keyword(s):  
Room Temperature ◽  
Wearable Electronics ◽  
Ambient Conditions ◽  
Light Emitting ◽  
Stable Operating ◽  
Mechanical Durability ◽  
Potential Applications ◽  
Wearable Electronic ◽  
Data Signal ◽  
Wearable Electronic Devices

AbstractWearable electronic devices are being developed because of their wide potential applications and user convenience. Among them, wearable organic light emitting diodes (OLEDs) play an important role in visualizing the data signal processed in wearable electronics to humans. In this study, textile-based OLEDs were fabricated and their practical utility was demonstrated. The textile-based OLEDs exhibited a stable operating lifetime under ambient conditions, enough mechanical durability to endure the deformation by the movement of humans, and washability for maintaining its optoelectronic properties even in water condition such as rain, sweat, or washing. In this study, the main technology used to realize this textile-based OLED was multi-functional near-room-temperature encapsulation. The outstanding impermeability of TiO2 film deposited at near-room-temperature was demonstrated. The internal residual stress in the encapsulation layer was controlled, and the device was capped by highly cross-linked hydrophobic polymer film, providing a highly impermeable, mechanically flexible, and waterproof encapsulation.

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