The practical application of the Zn‐metal anode for aqueous batteries is greatly restricted by catastrophic dendrite growth, intricate hydrogen evolution, and parasitic surface passivation. Herein, a ...polyanionic hydrogel film is introduced as a protective layer on the Zn anode with the assistance of a silane coupling agent (denoted as Zn–SHn). The hydrogel framework with zincophilic –SO3− functional groups uniformizes the zinc ions flux and transport. Furthermore, such a hydrogel layer chemically bonded on the Zn surface possesses an anti‐catalysis effect, which effectively suppresses both the hydrogen evolution reaction and formation of Zn dendrites. As a result, stable and reversible Zn stripping/plating at various currents and capacities is achieved. A full cell by pairing the Zn–SHn anode with a NaV3O8·1.5 H2O cathode shows a capacity of around 176 mAh g−1 with a retention around 67% over 4000 cycles at 10 A g−1. This polyanionic hydrogel film protection strategy paves a new way for future Zn‐anode design and safe aqueous batteries construction.
A unique polyanionic hydrogel is employed as an artificial protective layer for reversible Zn‐metal anodes. The polyanions in the hydrogel framework facilitate a homogeneous zinc‐ion flux, and the Zn–O bonding strengthens the interface and suppresses surface corrosion and irregular Zn dendrites growth. This strategy could apply also to other aqueous metal batteries.
The large‐scale deployment of aqueous Zn‐ion batteries is hindered by Zn anode instability including surface corrosion, hydrogen gas evolution, and irregular Zn deposition. To tackle these ...challenges, a polyhydroxylated organic molecular additive, trehalose, is incorporated to refine the solvation structure and promote planar Zn deposition. Within solvation structure regions involving trehalose, the hydroxy groups participate in the reconstruction of hydrogen bond networks, which increases the overpotential for water decomposition reaction. Moreover, at the Zn metal–molecule interface, the chemisorption of trehalose onto the surface of the zinc anode enhances corrosion resistance and facilitates the deposition of zinc in a planar manner. The optimized electrolyte significantly improves Zn striping/plating reversibility and maintains stable potentials over 1600 h at 5 mA cm−2 with a cutoff capacity of 1 mA h cm−2 in symmetric cells. When combined with the MnO2 cathode, the assembled coin cell retains ≈89% of its capacity after 1000 cycles. This organic molecule additive, emphasizing the role of polyhydroxylated organic molecules in fine‐tuning solvation structures and anode/electrolyte interfaces, holds promise for enhancing various aqueous metal batteries.
Trehalose, a widely used moisturizer, preservative, and stabilizer in the food industry, is proven to be an effective electrolyte additive to the sulfite electrolyte for aqueous zinc ion battery. It optimizes the solvation structure by decreasing free water molecules and forming hydrogen bond networks. Trehalose also stabilizes the metal‐electrolyte interface through chemisorption, benefiting planar zinc deposition and suppressing dendrite growth.
This paper proposes a generalized prediction system called a recurrent self-evolving fuzzy neural network (RSEFNN) that employs an on-line gradient descent learning rule to address the ...electroencephalography (EEG) regression problem in brain dynamics for driving fatigue. The cognitive states of drivers significantly affect driving safety; in particular, fatigue driving, or drowsy driving, endangers both the individual and the public. For this reason, the development of brain-computer interfaces (BCIs) that can identify drowsy driving states is a crucial and urgent topic of study. Many EEG-based BCIs have been developed as artificial auxiliary systems for use in various practical applications because of the benefits of measuring EEG signals. In the literature, the efficacy of EEG-based BCIs in recognition tasks has been limited by low resolutions. The system proposed in this paper represents the first attempt to use the recurrent fuzzy neural network (RFNN) architecture to increase adaptability in realistic EEG applications to overcome this bottleneck. This paper further analyzes brain dynamics in a simulated car driving task in a virtual-reality environment. The proposed RSEFNN model is evaluated using the generalized cross-subject approach, and the results indicate that the RSEFNN is superior to competing models regardless of the use of recurrent or nonrecurrent structures.
Osteoporosis is a highly prevalent disorder characterized by low bone mineral density and an increased risk of fracture, termed osteoporotic fracture. Notably, bone mineral density, osteoporosis and ...osteoporotic fracture are highly heritable; however, determining the genetic architecture, and especially the underlying genomic and molecular mechanisms, of osteoporosis in vivo in humans is still challenging. In addition to susceptibility loci identified in genome-wide association studies, advances in various omics technologies, including genomics, transcriptomics, epigenomics, proteomics and metabolomics, have all been applied to dissect the pathogenesis of osteoporosis. However, each technology individually cannot capture the entire view of the disease pathology and thus fails to comprehensively identify the underlying pathological molecular mechanisms, especially the regulatory and signalling mechanisms. A change to the status quo calls for integrative multi-omics and inter-omics analyses with approaches in 'systems genetics and genomics'. In this Review, we highlight findings from genome-wide association studies and studies using various omics technologies individually to identify mechanisms of osteoporosis. Furthermore, we summarize current studies of data integration to understand, diagnose and inform the treatment of osteoporosis. The integration of multiple technologies will provide a road map to illuminate the complex pathogenesis of osteoporosis, especially from molecular functional aspects, in vivo in humans.
Computerized microscopy image analysis plays an important role in computer aided diagnosis and prognosis. Machine learning techniques have powered many aspects of medical investigation and clinical ...practice. Recently, deep learning is emerging as a leading machine learning tool in computer vision and has attracted considerable attention in biomedical image analysis. In this paper, we provide a snapshot of this fast-growing field, specifically for microscopy image analysis. We briefly introduce the popular deep neural networks and summarize current deep learning achievements in various tasks, such as detection, segmentation, and classification in microscopy image analysis. In particular, we explain the architectures and the principles of convolutional neural networks, fully convolutional networks, recurrent neural networks, stacked autoencoders, and deep belief networks, and interpret their formulations or modelings for specific tasks on various microscopy images. In addition, we discuss the open challenges and the potential trends of future research in microscopy image analysis using deep learning.
Although ether‐based electrolytes have been extensively applied in anode evaluation of batteries, anodic instability arising from solvent oxidability is always a tremendous obstacle to matching with ...high‐voltage cathodes. Herein, by rational design for solvation configuration, the fully coordinated ether‐based electrolyte with strong resistance against oxidation is reported, which remains anodically stable with high‐voltage Na3V2(PO4)2O2F (NVPF) cathode under 4.5 V (versus Na+/Na) protected by an effective interphase. The assembled graphite//NVPF full cells display superior rate performance and unprecedented cycling stability. Beyond that, the constructed full cells coupling the high‐voltage NVPF cathode with hard carbon anode exhibit outstanding electrochemical performances in terms of high average output voltage up to 3.72 V, long‐term cycle life (such as 95 % capacity retention after 700 cycles) and high energy density (247 Wh kg−1). In short, the optimized ether‐based electrolyte enriches systematic options, the ability to maintain oxidative stability and compatibility with various anodes, exhibiting attractive prospects for application.
By rational design of the solvation configuration, a cation–solvent fully coordinated ether‐based electrolyte with strong oxidation resistance up to 4.5 V (versus Na+/Na) was developed and applied in graphite//NVPF and LHC//NVPF full cells which showed superior rate performance and unprecedented cycling stability.
•A high-thixotropy 3D printing concrete has been successfully developed.•Such concrete had lower drying shrinkage, normal elastic modulus and Poisson’s ratio.•The rheological properties and ...anisotropy of such concrete were systematically investigated.•A series of large-scale components industry–scale were applied into public transportation.
3D printing is a promising technology in construction industry. Unlike conventional construction process, 3D printing buildings are extruded by a nozzle layer-over-layer without the requirement of formwork. This paper investigates the rheological and harden properties of the high-thixotropy 3D printing concrete. Flowability, rheological property (viscosity, yield stress, thixotropy) and open time are considered as critical wet properties to control the printable property (pumpability, extrudability and buildability) of such concrete material. Five different mixtures are systematically investigated to obtain the optimum mix and then it’s used to study the harden property, such as anisotropy (compression and flexural), elastic modulus and drying shrinkage. At last, a large-scale components-bus station preliminarily was prepared by using this technology.
The degree and the origins of quantitative variability of most human plasma proteins are largely unknown. Because the twin study design provides a natural opportunity to estimate the relative ...contribution of heritability and environment to different traits in human population, we applied here the highly accurate and reproducible SWATH mass spectrometry technique to quantify 1,904 peptides defining 342 unique plasma proteins in 232 plasma samples collected longitudinally from pairs of monozygotic and dizygotic twins at intervals of 2–7 years, and proportioned the observed total quantitative variability to its root causes, genes, and environmental and longitudinal factors. The data indicate that different proteins show vastly different patterns of abundance variability among humans and that genetic control and longitudinal variation affect protein levels and biological processes to different degrees. The data further strongly suggest that the plasma concentrations of clinical biomarkers need to be calibrated against genetic and temporal factors. Moreover, we identified 13 cis‐SNPs significantly influencing the level of specific plasma proteins. These results therefore have immediate implications for the effective design of blood‐based biomarker studies.
Synopsis
The degree and origins of the abundance variability of 342 human plasma proteins are analyzed by a longitudinal twin design and SWATH mass spectrometry. The results suggest genetic control and longitudinal variation affect protein levels and biological processes to different degrees.
We used the highly accurate and reproducible SWATH mass spectrometry technique to quantify 342 unique plasma proteins in 232 plasma samples collected longitudinally from pairs of monozygotic and dizygotic twins at intervals of 2–7 years.
The observed total quantitative variability of human plasma proteome is dissected to its root causes, genes, environment and longitudinal factors.
The roles of the heritable, environmental and longitudinal determinants in controlling plasma protein levels are different for different proteins and functional clusters, strongly suggesting that the plasma concentrations of clinical biomarkers need to be calibrated against genetic and temporal factors.
We further identified 13 cis‐SNPs significantly influencing the level of specific plasma proteins as protein quantitative trait loci (pQTLs), and five of them are associated with gene expression QTLs (eQTLs) in human tissues.
The degree and origins of the abundance variability of 342 human plasma proteins are analyzed by a longitudinal twin design and SWATH mass spectrometry. The results suggest genetic control and longitudinal variation affect protein levels and biological processes to different degrees.
Over the past decade, the surging interest for higher‐energy‐density, cheaper, and safer battery technology has spurred tremendous research efforts in the development of improved rechargeable ...zinc–air batteries. Current zinc–air batteries suffer from poor energy efficiency and cycle life, owing mainly to the poor rechargeability of zinc and air electrodes. To achieve high utilization and cyclability in the zinc anode, construction of conductive porous framework through elegant optimization strategies and adaptation of alternate active material are employed. Equally, there is a need to design new and improved bifunctional oxygen catalysts with high activity and stability to increase battery energy efficiency and lifetime. Efforts to engineer catalyst materials to increase the reactivity and/or number of bifunctional active sites are effective for improving air electrode performance. Here, recent key advances in material development for rechargeable zinc–air batteries are described. By improving fundamental understanding of materials properties relevant to the rechargeable zinc and air electrodes, zinc–air batteries will be able to make a significant impact on the future energy storage for electric vehicle application. To conclude, a brief discussion on noteworthy concepts of advanced electrode and electrolyte systems that are beyond the current state‐of‐the‐art zinc–air battery chemistry, is presented.
Benefiting from their high theoretical energy density, electrically rechargeable zinc–air batteries lie at the heart of emerging energy storage technology for electric vehicles. Recent key advances in material development for rechargeable zinc–air batteries are described, followed by a brief discussion on novel concepts that are beyond the current state‐of‐the‐art zinc–air battery chemistry.
Developing high-safety Li-metal anodes (LMAs) is extremely important for the application of high-energy Li-metal batteries (LMBs), especially Li-S and Li-O
2
battery systems. However, the notorious ...Li-dendrite growth problem results in serious safety concerns for any energy storage application. Through a recent combination of interface-based science, nanotechnology-based solutions and characterization methods, the LMA is now primed for a technological boom. In this review, the recent emerging strategies and perspectives on LMAs are summarized, following which the current huge evolution in interfacial chemistry regulation, optimizing electrolyte components, designing a rational 'host' for lithium metal, optimizing "solid-state electrolytes" and other emerging strategies for developing high-safety LMAs is highlighted. Furthermore, several state-of-the-art
in situ
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operando
synchrotron-based X-ray techniques for high safety LMB research are introduced. With the further development of LMAs in the future, subsequent application in high energy LMBs is to be expected.
Developing high-safety Li-metal anodes (LMAs) are extremely important for the application of high-energy Li-metal batteries. The recently state-of-the-art technologies, strategies and perspectives for developing LMAs are comprehensively summarized in this review.
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