Showing papers by "Beijing University of Technology published in 2018"

Multicomponent intermetallic nanoparticles and superb mechanical behaviors of complex alloys

Abstract: Alloy design based on single-principal-element systems has approached its limit for performance enhancements. A substantial increase in strength up to gigapascal levels typically causes the premature failure of materials with reduced ductility. Here, we report a strategy to break this trade-off by controllably introducing high-density ductile multicomponent intermetallic nanoparticles (MCINPs) in complex alloy systems. Distinct from the intermetallic-induced embrittlement under conventional wisdom, such MCINP-strengthened alloys exhibit superior strengths of 1.5 gigapascals and ductility as high as 50% in tension at ambient temperature. The plastic instability, a major concern for high-strength materials, can be completely eliminated by generating a distinctive multistage work-hardening behavior, resulting from pronounced dislocation activities and deformation-induced microbands. This MCINP strategy offers a paradigm to develop next-generation materials for structural applications.

Synthesis of Carbon Dots with Multiple Color Emission by Controlled Graphitization and Surface Functionalization.

Abstract: Multiple-color-emissive carbon dots (CDots) have potential applications in various fields such as bioimaging, light-emitting devices, and photocatalysis. The majority of the current CDots to date exhibit excitation-wavelength-dependent emissions with their maximum emission limited at the blue-light region. Here, a synthesis of multiple-color-emission CDots by controlled graphitization and surface function is reported. The CDots are synthesized through controlled thermal pyrolysis of citric acid and urea. By regulating the thermal-pyrolysis temperature and ratio of reactants, the maximum emission of the resulting CDots gradually shifts from blue to red light, covering the entire light spectrum. Specifically, the emission position of the CDots can be tuned from 430 to 630 nm through controlling the extent of graphitization and the amount of surface functional groups, COOH. The relative photoluminescence quantum yields of the CDots with blue, green, and red emission reach up to 52.6%, 35.1%, and 12.9%, respectively. Furthermore, it is demonstrated that the CDots can be uniformly dispersed into epoxy resins and be fabricated as transparent CDots/epoxy composites for multiple-color- and white-light-emitting devices. This research opens a door for developing low-cost CDots as alternative phosphors for light-emitting devices.

Use of Electrochemistry in the Synthesis of Heterocyclic Structures.

Abstract: The preparation and transformation of heterocyclic structures have always been of great interest in organic chemistry. Electrochemical technique provides a versatile and powerful approach to the assembly of various heterocyclic structures. In this review, we examine the advance in relation to the electrochemical construction of heterocyclic compounds published since 2000 via intra- and intermolecular cyclization reactions.

Efficient green light-emitting diodes based on quasi-two-dimensional composition and phase engineered perovskite with surface passivation.

Abstract: Perovskite light-emitting diodes (LEDs) are attracting great attention due to their efficient and narrow emission. Quasi-two-dimensional perovskites with Ruddlesden–Popper-type layered structures can enlarge exciton binding energy and confine charge carriers and are considered good candidate materials for efficient LEDs. However, these materials usually contain a mixture of phases and the phase impurity could cause low emission efficiency. In addition, converting three-dimensional into quasi-two-dimensional perovskite introduces more defects on the surface or at the grain boundaries due to the reduction of crystal sizes. Both factors limit the emission efficiency of LEDs. Here, firstly, through composition and phase engineering, optimal quasi-two-dimensional perovskites are selected. Secondly, surface passivation is carried out by coating organic small molecule trioctylphosphine oxide on the perovskite thin film surface. Accordingly, green LEDs based on quasi-two-dimensional perovskite reach a current efficiency of 62.4 cd A−1 and external quantum efficiency of 14.36%.

Direct observation of noble metal nanoparticles transforming to thermally stable single atoms.

Abstract: Single noble metal atoms and ultrafine metal clusters catalysts tend to sinter into aggregated particles at elevated temperatures, driven by the decrease of metal surface free energy. Herein, we report an unexpected phenomenon that noble metal nanoparticles (Pd, Pt, Au-NPs) can be transformed to thermally stable single atoms (Pd, Pt, Au-SAs) above 900 °C in an inert atmosphere. The atomic dispersion of metal single atoms was confirmed by aberration-corrected scanning transmission electron microscopy and X-ray absorption fine structures. The dynamic process was recorded by in situ environmental transmission electron microscopy, which showed competing sintering and atomization processes during NP-to-SA conversion. Further, density functional theory calculations revealed that high-temperature NP-to-SA conversion was driven by the formation of the more thermodynamically stable Pd-N4 structure when mobile Pd atoms were captured on the defects of nitrogen-doped carbon. The thermally stable single atoms (Pd-SAs) exhibited even better activity and selectivity than nanoparticles (Pd-NPs) for semi-hydrogenation of acetylene.

The influence of the molecular packing on the room temperature phosphorescence of purely organic luminogens.

Abstract: Organic luminogens with persistent room temperature phosphorescence (RTP) have attracted great attention for their wide applications in optoelectronic devices and bioimaging. However, these materials are still very scarce, partially due to the unclear mechanism and lack of designing guidelines. Herein we develop seven 10-phenyl-10H-phenothiazine-5,5-dioxide-based derivatives, reveal their different RTP properties and underlying mechanism, and exploit their potential imaging applications. Coupled with the preliminary theoretical calculations, it is found that strong π-π interactions in solid state can promote the persistent RTP. Particularly, CS-CF3 shows the unique photo-induced phosphorescence in response to the changes in molecular packing, further confirming the key influence of the molecular packing on the RTP property. Furthermore, CS-F with its long RTP lifetime could be utilized for real-time excitation-free phosphorescent imaging in living mice. Thus, our study paves the way for the development of persistent RTP materials, in both the practical applications and the inherent mechanism.

EANN: Event Adversarial Neural Networks for Multi-Modal Fake News Detection

Abstract: As news reading on social media becomes more and more popular, fake news becomes a major issue concerning the public and government. The fake news can take advantage of multimedia content to mislead readers and get dissemination, which can cause negative effects or even manipulate the public events. One of the unique challenges for fake news detection on social media is how to identify fake news on newly emerged events. Unfortunately, most of the existing approaches can hardly handle this challenge, since they tend to learn event-specific features that can not be transferred to unseen events. In order to address this issue, we propose an end-to-end framework named Event Adversarial Neural Network (EANN), which can derive event-invariant features and thus benefit the detection of fake news on newly arrived events. It consists of three main components: the multi-modal feature extractor, the fake news detector, and the event discriminator. The multi-modal feature extractor is responsible for extracting the textual and visual features from posts. It cooperates with the fake news detector to learn the discriminable representation for the detection of fake news. The role of event discriminator is to remove the event-specific features and keep shared features among events. Extensive experiments are conducted on multimedia datasets collected from Weibo and Twitter. The experimental results show our proposed EANN model can outperform the state-of-the-art methods, and learn transferable feature representations.

Enhanced oxygen reduction with single-atomic-site iron catalysts for a zinc-air battery and hydrogen-air fuel cell

Abstract: Efficient, durable and inexpensive electrocatalysts that accelerate sluggish oxygen reduction reaction kinetics and achieve high-performance are highly desirable. Here we develop a strategy to fabricate a catalyst comprised of single iron atomic sites supported on a nitrogen, phosphorus and sulfur co-doped hollow carbon polyhedron from a metal-organic framework@polymer composite. The polymer-based coating facilitates the construction of a hollow structure via the Kirkendall effect and electronic modulation of an active metal center by long-range interaction with sulfur and phosphorus. Benefiting from structure functionalities and electronic control of a single-atom iron active center, the catalyst shows a remarkable performance with enhanced kinetics and activity for oxygen reduction in both alkaline and acid media. Moreover, the catalyst shows promise for substitution of expensive platinum to drive the cathodic oxygen reduction reaction in zinc-air batteries and hydrogen-air fuel cells.

Tailoring grain boundary structures and chemistry of Ni-rich layered cathodes for enhanced cycle stability of lithium-ion batteries

Abstract: A critical challenge for the commercialization of layer-structured nickel-rich lithium transition metal oxide cathodes for battery applications is their capacity and voltage fading, which originate from the disintegration and lattice phase transition of the cathode particles. The general approach of cathode particle surface modification could partially alleviate the degradation associated with surface processes, but it still fails to resolve this critical barrier. Here, we report that infusing the grain boundaries of cathode secondary particles with a solid electrolyte dramatically enhances the capacity retention and voltage stability of the cathode. We find that the solid electrolyte infused in the boundaries not only acts as a fast channel for lithium-ion transport, it also, more importantly, prevents penetration of the liquid electrolyte into the boundaries, and consequently eliminates the detrimental factors, which include cathode–liquid electrolyte interfacial reactions, intergranular cracking and layered-to-spinel phase transformation. This grain-boundary engineering approach provides design ideas for advanced cathodes for batteries. The development of Ni-rich layered lithium transition metal oxides is plagued by their voltage and capacity fading on battery cycling. Here, the authors demonstrate an effective approach to treat these problems by infusing a solid electrolyte into the grain boundaries of the secondary particles of these layered materials.

A review of data-driven approaches for prediction and classification of building energy consumption

Abstract: A recent surge of interest in building energy consumption has generated a tremendous amount of energy data, which boosts the data-driven algorithms for broad application throughout the building industry. This article reviews the prevailing data-driven approaches used in building energy analysis under different archetypes and granularities, including those methods for prediction (artificial neural networks, support vector machines, statistical regression, decision tree and genetic algorithm) and those methods for classification (K-mean clustering, self-organizing map and hierarchy clustering). The review results demonstrate that the data-driven approaches have well addressed a large variety of building energy related applications, such as load forecasting and prediction, energy pattern profiling, regional energy-consumption mapping, benchmarking for building stocks, global retrofit strategies and guideline making etc. Significantly, this review refines a few key tasks for modification of the data-driven approaches in the context of application to building energy analysis. The conclusions drawn in this review could facilitate future micro-scale changes of energy use for a particular building through the appropriate retrofit and the inclusion of renewable energy technologies. It also paves an avenue to explore potential in macro-scale energy-reduction with consideration of customer demands. All these will be useful to establish a better long-term strategy for urban sustainability.

Food Packaging: A Comprehensive Review and Future Trends

Abstract: Innovations in food packaging systems will help meet the evolving needs of the market, such as consumer preference for "healthy" and high-quality food products and reduction of the negative environmental impacts of food packaging. Emerging concepts of active and intelligent packaging technologies provide numerous innovative solutions for prolonging shelf-life and improving the quality and safety of food products. There are also new approaches to improving the passive characteristics of food packaging, such as mechanical strength, barrier performance, and thermal stability. The development of sustainable or green packaging has the potential to reduce the environmental impacts of food packaging through the use of edible or biodegradable materials, plant extracts, and nanomaterials. Active, intelligent, and green packaging technologies can work synergistically to yield a multipurpose food-packaging system with no negative interactions between components, and this aim can be seen as the ultimate future goal for food packaging technology. This article reviews the principles of food packaging and recent developments in different types of food packaging technologies. Global patents and future research trends are also discussed.

Towards Environment Independent Device Free Human Activity Recognition

Abstract: Driven by a wide range of real-world applications, significant efforts have recently been made to explore device-free human activity recognition techniques that utilize the information collected by various wireless infrastructures to infer human activities without the need for the monitored subject to carry a dedicated device. Existing device free human activity recognition approaches and systems, though yielding reasonably good performance in certain cases, are faced with a major challenge. The wireless signals arriving at the receiving devices usually carry substantial information that is specific to the environment where the activities are recorded and the human subject who conducts the activities. Due to this reason, an activity recognition model that is trained on a specific subject in a specific environment typically does not work well when being applied to predict another subject's activities that are recorded in a different environment. To address this challenge, in this paper, we propose EI, a deep-learning based device free activity recognition framework that can remove the environment and subject specific information contained in the activity data and extract environment/subject-independent features shared by the data collected on different subjects under different environments. We conduct extensive experiments on four different device free activity recognition testbeds: WiFi, ultrasound, 60 GHz mmWave, and visible light. The experimental results demonstrate the superior effectiveness and generalizability of the proposed EI framework.

Direct Observation of Structural Evolution of Metal Chalcogenide in Electrocatalytic Water Oxidation

Abstract: As one of the most remarkable oxygen evolution reaction (OER) electrocatalysts, metal chalcogenides have been intensively reported during the past few decades because of their high OER activities. It has been reported that electron-chemical conversion of metal chalcogenides into oxides/hydroxides would take place after the OER. However, the transition mechanism of such unstable structures, as well as the real active sites and catalytic activity during the OER for these electrocatalysts, has not been understood yet; therefore a direct observation for the electrocatalytic water oxidation process, especially at nano or even angstrom scale, is urgently needed. In this research, by employing advanced Cs-corrected transmission electron microscopy (TEM), a step by step oxidational evolution of amorphous electrocatalyst CoS x into crystallized CoOOH in the OER has been in situ captured: irreversible conversion of CoS x to crystallized CoOOH is initiated on the surface of the electrocatalysts with a morphology change via Co(OH)2 intermediate during the OER measurement, where CoOOH is confirmed as the real active species. Besides, this transition process has also been confirmed by multiple applications of X-ray photoelectron spectroscopy (XPS), in situ Fourier-transform infrared spectroscopy (FTIR), and other ex situ technologies. Moreover, on the basis of this discovery, a high-efficiency electrocatalyst of a nitrogen-doped graphene foam (NGF) coated by CoS x has been explored through a thorough structure transformation of CoOOH. We believe this in situ and in-depth observation of structural evolution in the OER measurement can provide insights into the fundamental understanding of the mechanism for the OER catalysts, thus enabling the more rational design of low-cost and high-efficient electrocatalysts for water splitting.

Highly selective oxidation of methane to methanol at ambient conditions by titanium dioxide-supported iron species

Abstract: Methane activation under moderate conditions and with good selectivity for value-added chemicals still remains a huge challenge. Here, we present a highly selective catalyst for the transformation of methane to methanol composed of highly dispersed iron species on titanium dioxide. The catalyst operates under moderate light irradiation (close to one Sun) and at ambient conditions. The optimized sample shows a 15% conversion rate for methane with an alcohol selectivity of over 97% (methanol selectivity over 90%) and a yield of 18 moles of alcohol per mole of iron active site in just 3 hours. X-ray photoelectron spectroscopy measurements with and without xenon lamp irradiation, light-intensity-modulated spectroscopies, photoelectrochemical measurements, X-ray absorption near-edge structure and extended X-ray absorption fine structure spectra, as well as isotopic analysis confirm the function of the major iron-containing species—namely, FeOOH and Fe2O3, which enhance charge transfer and separation, decrease the overpotential of the reduction reaction and improve selectivity towards methanol over carbon dioxide production. Methanol synthesis from methane is a promising route to valorize this abundant natural gas, but existing thermal processes require harsh reaction conditions. Now, a photocatalytic approach based on TiO2-supported iron oxide species is described, which affords methanol in high yield and selectivity at ambient conditions.

Learning a No-Reference Quality Assessment Model of Enhanced Images With Big Data

Abstract: In this paper, we investigate into the problem of image quality assessment (IQA) and enhancement via machine learning. This issue has long attracted a wide range of attention in computational intelligence and image processing communities, since, for many practical applications, e.g., object detection and recognition, raw images are usually needed to be appropriately enhanced to raise the visual quality (e.g., visibility and contrast). In fact, proper enhancement can noticeably improve the quality of input images, even better than originally captured images, which are generally thought to be of the best quality. In this paper, we present two most important contributions. The first contribution is to develop a new no-reference (NR) IQA model. Given an image, our quality measure first extracts 17 features through analysis of contrast, sharpness, brightness and more, and then yields a measure of visual quality using a regression module, which is learned with big-data training samples that are much bigger than the size of relevant image data sets. The results of experiments on nine data sets validate the superiority and efficiency of our blind metric compared with typical state-of-the-art full-reference, reduced-reference and NA IQA methods. The second contribution is that a robust image enhancement framework is established based on quality optimization. For an input image, by the guidance of the proposed NR-IQA measure, we conduct histogram modification to successively rectify image brightness and contrast to a proper level. Thorough tests demonstrate that our framework can well enhance natural images, low-contrast images, low-light images, and dehazed images. The source code will be released at https://sites.google.com/site/guke198701/publications .

Single-atomic cobalt sites embedded in hierarchically ordered porous nitrogen-doped carbon as a superior bifunctional electrocatalyst.

Abstract: Exploring efficient and cost-effective catalysts to replace precious metal catalysts, such as Pt, for electrocatalytic oxygen reduction reaction (ORR) and hydrogen evolution reaction (HER) holds great promise for renewable energy technologies. Herein, we prepare a type of Co catalyst with single-atomic Co sites embedded in hierarchically ordered porous N-doped carbon (Co-SAS/HOPNC) through a facile dual-template cooperative pyrolysis approach. The desirable combination of highly dispersed isolated atomic Co-N4 active sites, large surface area, high porosity, and good conductivity gives rise to an excellent catalytic performance. The catalyst exhibits outstanding performance for ORR in alkaline medium with a half-wave potential (E1/2) of 0.892 V, which is 53 mV more positive than that of Pt/C, as well as a high tolerance of methanol and great stability. The catalyst also shows a remarkable catalytic performance for HER with distinctly high turnover frequencies of 0.41 and 3.8 s−1 at an overpotential of 100 and 200 mV, respectively, together with a long-term durability in acidic condition. Experiments and density functional theory (DFT) calculations reveal that the atomically isolated single Co sites and the structural advantages of the unique 3D hierarchical porous architecture synergistically contribute to the high catalytic activity.

Emerging Nonaqueous Aluminum-Ion Batteries: Challenges, Status, and Perspectives.

Abstract: Aluminum-ion batteries (AIBs) are regarded as viable alternatives to lithium-ion technology because of their high volumetric capacity, their low cost, and the rich abundance of aluminum. However, several serious drawbacks of aqueous systems (passive film formation, hydrogen evolution, anode corrosion, etc.) hinder the large-scale application of these systems. Thus, nonaqueous AIBs show incomparable advantages for progress in large-scale electrical energy storage. However, nonaqueous aluminum battery systems are still nascent, and various technical and scientific obstacles to designing AIBs with high capacity and long cycling life have not been resolved until now. Moreover, the aluminum cell is a complex device whose energy density is determined by various parameters, most of which are often ignored, resulting in failure to achieve the maximum performance of the cell. The purpose here is to discuss how to further develop reliable nonaqueous AIBs. First, the current status of nonaqueous AIBs is reviewed based on statistical data from the literature. The influence of parameters on energy density is analyzed, and the current situation and existing problems are summarized. Furthermore, possible solutions and concerns regarding the construction of reliable nonaqueous AIBs are comprehensively discussed. Finally, future research directions and prospects in the aluminum battery field are proposed.

Consciously Constructing Heterojunction or Direct Z-Scheme Photocatalysts by Regulating Electron Flow Direction

Abstract: Heterojunction and direct Z-scheme nanostructures are two typical representatives of an efficient photocatalyst, which is composed of two semiconductors. However, it is a great challenge to construct each of them on purpose. The photodeposition technique can be a potentially powerful tool to regulate the electron flow direction for constructing these nanostructures. In this report, CdS nanoparticles were deposited on the g-C3N4 nanosheets by photodeposition and chemical deposition methods for comparison. In the photodeposition case, PL and charge flow tracking demonstrate that a type II heterojunction is constructed because CdS is selectively deposited at the electron transfer site of g-C3N4, which leads to the photoexcited electron from g–C3N4 tending to transfer to CdS in the composites. In the latter, the CdS is randomly deposited onto the g-C3N4 nanosheets through chemical deposition. There is no preferred site for deposition or charge transfer in the composite. The results illustrate that the electro...

Constructing NiCo/Fe3O4 Heteroparticles within MOF-74 for Efficient Oxygen Evolution Reactions

Abstract: Metal–organic frameworks (MOF) have recently emerged as versatile precursors to fabricate functional MOF derivatives for oxygen evolution reactions (OER). Herein, we developed a controlled partial pyrolysis strategy to construct robust NiCo/Fe3O4 heteroparticles within MOF-74 for efficient OER using trimetallic NiCoFe-MOF-74 as precursor. The partial pyrolysis method preserves the framework structure of MOF for effective substrates diffusion while producing highly active nanoparticles. The as-prepared NiCo/Fe3O4/MOF-74 delivered remarkably stable OER current with an overpotential as low as 238 mV at 10.0 mA cm–2 and an Tafel slop of 29 mV/dec, outperforming those of pristine NiCoFe-MOF-74, totally decomposed MOF derivatives, and most reported non-noble metal based electrocatalysts. The key for the formation of NiCo/Fe3O4/MOF-74 nanostructures is that the metals can be decomposed from NiCoFe-MOF-74 in the order of Ni, Co, and Fe under controlled heat treatment. Density functional theory calculations reveal...

Predicting station-level hourly demand in a large-scale bike-sharing network: A graph convolutional neural network approach

Abstract: This study proposes a novel Graph Convolutional Neural Network with Data-driven Graph Filter (GCNN-DDGF) model that can learn hidden heterogeneous pairwise correlations between stations to predict station-level hourly demand in a large-scale bike-sharing network. Two architectures of the GCNN-DDGF model are explored; GCNNreg-DDGF is a regular GCNN-DDGF model which contains the convolution and feedforward blocks, and GCNNrec-DDGF additionally contains a recurrent block from the Long Short-term Memory neural network architecture to capture temporal dependencies in the bike-sharing demand series. Furthermore, four types of GCNN models are proposed whose adjacency matrices are based on various bike-sharing system data, including Spatial Distance matrix (SD), Demand matrix (DE), Average Trip Duration matrix (ATD), and Demand Correlation matrix (DC). These six types of GCNN models and seven other benchmark models are built and compared on a Citi Bike dataset from New York City which includes 272 stations and over 28 million transactions from 2013 to 2016. Results show that the GCNNrec-DDGF performs the best in terms of the Root Mean Square Error, the Mean Absolute Error and the coefficient of determination (R2), followed by the GCNNreg-DDGF. They outperform the other models. Through a more detailed graph network analysis based on the learned DDGF, insights are obtained on the “black box” of the GCNN-DDGF model. It is found to capture some information similar to details embedded in the SD, DE and DC matrices. More importantly, it also uncovers hidden heterogeneous pairwise correlations between stations that are not revealed by any of those matrices.

All-Carbon-Electrode-Based Endurable Flexible Perovskite Solar Cells

Abstract: Endured, low-cost, and high-performance flexible perovskite solar cells (PSCs) featuring lightweight and mechanical flexibility have attracted tremendous attention for portable power source applications. However, flexible PSCs typically use expensive and fragile indium–tin oxide as transparent anode and high-vacuum processed noble metal as cathode, resulting in dramatic performance degradation after continuous bending or thermal stress. Here, all-carbon-electrode-based flexible PSCs are fabricated employing graphene as transparent anode and carbon nanotubes as cathode. All-carbon-electrodebased flexible devices with and without spiro-OMeTAD (2,2′,7,7′-tetrakis(N,N-di-p-methoxyphenylamine)-9,9′-spirobifluorene) hole conductor achieve power conversion efficiencies (PCEs) of 11.9% and 8.4%, respectively. The flexible carbon-electrode-based solar cells demonstrate superior robustness against mechanical deformation in comparison with their counterparts fabricated on flexible indium–tin oxide substrates. Moreover, all carbon-electrodebased flexible PSCs also show significantly enhanced stability compared to the flexible devices with gold and silver cathodes under continuous light soaking or 60 °C thermal stress in air, retaining over 90% of their original PCEs after 1000 h. The promising durability and stability highlight that flexible PSCs are fully compatible with carbon materials and pave the way toward the realization of rollable and low-cost flexible perovskite photovoltaic devices.

Hollow-core conjoined-tube negative-curvature fibre with ultralow loss.

Abstract: Countering the optical network ‘capacity crunch’ calls for a radical development in optical fibres that could simultaneously minimize nonlinearity penalties, chromatic dispersion and maximize signal launch power. Hollow-core fibres (HCF) can break the nonlinear Shannon limit of solid-core fibre and fulfil all above requirements, but its optical performance need to be significantly upgraded before they can be considered for high-capacity telecommunication systems. Here, we report a new HCF with conjoined-tubes in the cladding and a negative-curvature core shape. It exhibits a minimum transmission loss of 2 dB km−1 at 1512 nm and a <16 dB km−1 bandwidth spanning across the O, E, S, C, L telecom bands (1302–1637 nm). The debut of this conjoined-tube HCF, with combined merits of ultralow loss, broad bandwidth, low bending loss, high mode quality and simple structure heralds a new opportunity to fully unleash the potential of HCF in telecommunication applications. Countering the optical network ‘capacity crunch’ requires developments in optical fibres. Here, the authors report a hollow-core fibre with conjoined tubes in the cladding and a negative-curvature core shape. It exhibits a transmission loss of 2 dB/km at 1512 nm and less than 16 dB/km bandwidth in the 1302–1637 nm range.

Inverted Design for High-Performance Supercapacitor Via Co(OH)2-Derived Highly Oriented MOF Electrodes

Abstract: Metal organic frameworks (MOFs) are considered as promising candidates for supercapacitors because of high specific area and potential redox sites. However, their shuffled orientations and low conductivity nature lead to severely-degraded performance. Designing an accessibly-manipulated and efficient method to address those issues is of outmost significance for MOF application in supercapacitors. It is the common way that MOFs scarify themselves as templates or precursors to prepare target products. But to reversely think it, using target products to prepare MOF could be the way to unlock the bottleneck of MOFs' performance in supercapacitors. Herein, a novel strategy using Co(OH)2 as both the template and precursor to fabricate vertically-oriented MOF electrode is proposed. The electrode shows a double high specific capacitance of 1044 Fg−1 and excellent rate capability compared to MOF in powder form. An asymmetric supercapacitor was also fabricated, which delivers a maximum energy density of 28.5 W h kg−1 at a power density of 1500 W kg−1, and the maximum of 24000 W kg−1 can be obtained with a remaining energy density of 13.3 W h kg−1. Therefore, the proposed strategy paves the way to unlock the inherent advantages of MOFs and also inspires for advanced MOF synthesis with optimum performance.