Zeolitic-imidazolate frameworks-derived Co3S4/NiS@Ni foam heterostructure as highly efficient electrocatalyst for oxygen evolution reaction

Transition metals sulfide-based nanomaterials have recently received significant attention as a promising cathode electrode for the oxygen evolution reaction (OER) due to their easily tunable electronic, chemical, and physical properties. However, the poor electrical conductivity of metal-sulfide materials impedes their practical application in energy devices. Herein, firstly nano-sized crystals of cobalt-based zeolitic-imidazolate framework (Co-ZIF) arrays were fabricated on nickel-form (NF) as the sacrificial template by a facile solution method to enhance the electrical conductivity of the electrocatalyst. Then, the Co₃S₄/NiS@NF heterostructured arrays were synthesized by a simple hydrothermal route. The Co-ZIFs derived Co₃S₄ nanosheets are grown successfully on NiS nanorods during the hydrothermal sulfurization process. The bimetallic sulfide-based Co₃S₄/NiS@NF-12 electrocatalyst demonstrated a very low overpotential of 119 mV at 10 mA cm^?2 for OER, which is much lower than that of mono-metal sulfide NiS@NF (201 mV) and ruthenium-oxide (RuO₂) on NF (440 mV) electrocatalysts. Furthermore, the Co₃S₄/NiS@NF-12 electrocatalyst showed high stability during cyclic voltammetry and chronoamperometry measurements. This research work offers an effective strategy for fabricating high-performance non-precious OER electrocatalysts.     

Lanthanide-organic-frameworks modified ZnIn2S4 for boosting hydrogen generation under UV–Vis and visible light

Novel Ln-MOF with microrods shape were successfully combined with ZnIn₂S₄ (ZIS) microsphere and used for photocatalytic hydrogen generation under UV–Vis and visible light. The Ln-MOFs/ZIS system comprises lanthanide-carboxylate coordination networks (Tm and Gd as metal ions, and 1,3,5-benzenetricarboxylic acid (BTC) as the organic linker) deposited on ZnIn₂S₄ microspheres. Effect of the amount of ((Tm,Gd)-BTC) (1, 5, 10 wt%) on the optical properties and photocatalytic hydrogen evolution performance was investigated. ZIS microsphere shows the marigold flower-like morphology and hexagonal polytopic crystal form. Our results proved that the combination of ZIS microsphere, Ln-MOF and Pt nanoparticles (NPs) caused significant enhancement in hydrogen generation. Amount of formed hydrogen was raised from 196.3 to 7782.1 μmol g^?1 for pristine ZIS and ZIS decorated with 1% (Tm, Gd)-BTC/Pt under UV–Vis light, respectively.     

Porous CoNiOOH nanosheets derived from the well-mixed MOFs as stable and highly efficient electrocatalysts for water oxidation

The development of efficient and stable electrocatalysts is of great significance for improving water splitting. Among them, transition metal oxyhydroxides show excellent performance in oxygen evolution reactions (OER), but there are certain difficulties in direct preparation. Recently, Metal–organic frameworks (MOFs) as precatalysts or precursors have shown promising catalytic performance in OER and can be decomposed under alkaline conditions. Therefore, using a mild and controllable way to convert MOFs into oxyhydroxides and retaining the original structural advantages is crucial for improving the catalytic activity. Herein, a rapid electrochemical strategy is used to activate well-mixed MOFs to prepare Co/Ni oxyhydroxide nanosheets for efficient OER catalysts, and the structural transformation in this process was investigated in detail by using scanning electron microscope, X-ray diffraction, Raman, X-ray photoelectron spectroscopy and electrochemical methods. It is discovered that electrochemical activation can promote ligand substitution of well-mixed MOFs to form porous oxyhydroxide nanosheets and tune the electronic structure of the metal (Co and Ni), which can lead to more active site exposure and accelerate charge transfer. In addition, the change of structure also improves hydrophilicity, as well as benefiting from the strong synergistic effect between multiple species, the optimal a-MCoNi–MOF/NF has excellent OER performance and long-term stability. More obviously, the porous CoNiOOH nanosheets are formed in situ during electrochemical activation process through structural transformation and acts as the active centers. This work provides new insights for mild synthesis of MOFs derivatives and also provides ideas for the preparation of highly efficient catalysts.     

Flexible,Mechanically Stable,Porous Self-Standing Microfiber Network Membranes of Covalent Organic Frameworks: Preparation Method and Characterization

Covalent organic frameworks (COFs) show advantageous characteristics, such as an ordered pore structure and a large surface area for gas storage and separation, energy storage, catalysis, and molecular separation. However, COFs usually exist as difficult-to-process powders, and preparing continuous, robust, flexible, foldable, and rollable COF membranes is still a challenge. Herein, such COF membranes with fiber morphology for the first time prepared via a newly introduced template-assisted framework process are reported. This method uses electrospun porous polymer membranes as a sacrificial large dimension template for making self-standing COF membranes. The porous COF fiber membranes, besides having high crystallinity, also show a large surface area (1153 m² g⁻¹), good mechanical stability, excellent thermal stability, and flexibility. This study opens up the possibility of preparation of large dimension COF membranes and their derivatives in a simple way and hence shows promise in technical applications in separation, catalysis, and energy in the future.     

Thiol-Functionalized Covalent Organic Frameworks as Thermal History Indictor for Temperature and Time History Monitoring

Various products, including foods and pharmaceuticals, are sensitive to temperature fluctuations. Thus, temperature monitoring during production, transportation, and storage is critical. Facile indicators are required to monitor temperature conditions via color changes in real time. This study aimed to prepare and apply thiol-functionalized covalent organic frameworks (COFs) as a novel indicator for monitoring thermal history and temperature abuse. The COFs underwent obvious color changes from bright yellow to purple after exposure to different temperatures for varying durations. The reaction kinetics are analyzed under isothermal conditions, which reveal that the order of reaction rates is k_−20°C < k_4°C < k_20°C < k_35°C < k_55°C. The activation energy (E_a) of the COFs is calculated using the Arrhenius equation as 50.71 kJ moL⁻¹. The COFs are capable of sensitive color changes and offer a broad temperature tracking range, thereby demonstrating their application potential for the monitoring of temperature and time exposure history during production, transportation, and storage. This excellent performance thermal history indicator also shows promise for expanding the application field of COFs.     

Trajectories of knowledge economy in SSA and MENA countries

In the first critical assessment of knowledge economy dynamic paths in Africa and the Middle East, but for a few exceptions, we find overwhelming support for diminishing cross-country disparities in knowledge-based economy dimensions. The paper employs all the four components of the World Bank's Knowledge Economy Index (KEI): economic incentives, innovation, education, and information infrastructure. The main finding suggests that sub-Saharan African (SSA) and the Middle East and North African (MENA) countries with low levels of KE dynamics and catching-up their counterparts of higher KE levels. We provide the speeds of integration and time necessary to achieve full (100%) integration. Policy implications are also discussed.     

Application of higher order spectral features and support vector machines for bearing faults classification

Condition monitoring and fault diagnosis of rolling element bearings timely and accurately are very important to ensure the reliability of rotating machinery. This paper presents a novel pattern classification approach for bearings diagnostics, which combines the higher order spectra analysis features and support vector machine classifier. The use of non-linear features motivated by the higher order spectra has been reported to be a promising approach to analyze the non-linear and non-Gaussian characteristics of the mechanical vibration signals. The vibration bi-spectrum (third order spectrum) patterns are extracted as the feature vectors presenting different bearing faults. The extracted bi-spectrum features are subjected to principal component analysis for dimensionality reduction. These principal components were fed to support vector machine to distinguish four kinds of bearing faults covering different levels of severity for each fault type, which were measured in the experimental test bench running under different working conditions. In order to find the optimal parameters for the multi-class support vector machine model, a grid-search method in combination with 10-fold cross-validation has been used. Based on the correct classification of bearing patterns in the test set, in each fold the performance measures are computed. The average of these performance measures is computed to report the overall performance of the support vector machine classifier. In addition, in fault detection problems, the performance of a detection algorithm usually depends on the trade-off between robustness and sensitivity. The sensitivity and robustness of the proposed method are explored by running a series of experiments. A receiver operating characteristic (ROC) curve made the results more convincing. The results indicated that the proposed method can reliably identify different fault patterns of rolling element bearings based on vibration signals.     

Operational time-series data modeling via LSTM network integrating principal component analysis based on human experience

Today’s information technologies involve increasingly intelligent systems, which come at the cost of increasingly complex equipment. Modern monitoring systems collect multi-measuring-point and long-term data which make equipment health prediction a “big data” problem. It is difficult to extract information from such condition monitoring data to accurately estimate or predict health statuses. Deep learning is a powerful tool for big data processing that is widely utilized in image and speech recognition applications, and can also provide effective predictions in industrial processes. This paper proposes the Long Short-term Memory Integrating Principal Component Analysis based on Human Experience (HEPCA-LSTM), which uses operational time-series data for equipment health prognostics. Principal component analysis based on human experience is first conducted to extract condition parameters from the condition monitoring system. The long short-term memory (LSTM) framework is then constructed to predict the target status. Finally, a dynamic update of the prediction model with incoming data is performed at a certain interval to prevent any model misalignment caused by the drifting of relevant variables. The proposed model is validated on a practical case and found to outperform other prediction methods. It utilizes a powerful deep learning analysis method, the LSTM, to fully process big condition monitoring series data; it effectively extracts the features involved with human experience and takes dynamic updates into consideration.