Search

Development of solid oxide fuel cell (SOFC) anode with high resistance to coking and sulfur poisoning is highly desirable for the direct application of natural gas in SOFC. Herein, a (Cu, Sm)CeO2 anode with anchored Cu nanoparticles has been prepared. Most of Cu nanoparticles particle size ranges from 20 to 50 nm, which can increase the conductivity and catalytic activity of the anode. The Cu/CSCO10 supported cell exhibits a maximum power density of 404.6 mW/cm2 at 600 ⁰C when dry methane is used as fuel while its ohmic resistance is only 0.39 Ω·cm2. The single SOFC shows good stability when H2S content in the fuel is less than 150 ppm. Up to 900 hours of continuous stable operation with simulated natural gas and commercial natural gas as fuel prove the advantages and application potential of this anode.

Bi-doped La1.5-xBixSr0.5Ni0.5Mn0.5O4+δ (LBSNM-x, x = 0, 0.05, 0.1, 0.15) was investigated as a potential air electrode for solid oxide electrolysis cell (SOEC). The effect of Bi doping on the structure, electrical conductivity, chemical compatibility with GDC electrolyte, electrochemical performance and thermal expansion coefficients (TECs) were investigated. XRD characterization results show that the solid solution content of Bi is less than or equal to 0.1. XPS characterization results indicate that Bi doping increases the oxygen vacancy content of LBSNM-x air electrode and thus greatly benefits its oxygen evolution reaction. Among the Bi-doped samples, LBSNM-0.1 electrode has the best electrochemical performance with its lowest Rp (polarization resistance) of 0.28 Ω cm2 at 800 °C based on LBSNM-0.1/GDC half-cell. LBSNM-0.1 single cell with 70%CO2 + 30%CO fuel gas feed on the fuel electrode has achieved current density of 811 mA cm−2 at 800 °C and 1.4 V, a 62.2% increase relative to that of LSNM single cell. In addition, LBSNM-0.1 single cell exhibits excellent stability at 800 °C and 1.3 V with 70%CO2 + 30%CO feed gas on the fuel electrode. These results prove that Bi-doped LBSNM-0.1 is an efficient air electrode for SOEC.

Electrolysis of CO2 into CO by using solid oxide electrolysis cells (SOECs) is an advanced and sustainable energy conversion technology in the production of chemical energy by electrical power. However, the development of SOECs is limited because of the lack of cathode with high activity and stability. Herein, La0.75Sr0.25-xCaxCr0.5Mn0.5O3/Gd0.1Ce0.9O2-δ (Cax-LSCM/GDC, x = 0, 0.05, 0.1 and 0.15) composites were obtained by introducing Ca into the A-site of LSCM and developed as desirable cathodes for high-temperature electrolysis of CO2. The electrode polarization resistance (Rp) for CO2 electrochemistry reduction of the half-cell with the Ca0.05-LSCM/GDC cathode shows the minimal values within different temperature and voltage ranges, this cathode has a Rp value of 0.98 Ω cm2 at 800 °C under an applied voltage of 1.0 V, which is decreased by 32% compared with that of the LSCM/GDC cathode under the same test condition. The current density of the full cell with the configuration of Ca0.05-LSCM/GDC|ScSZ|LSM/GDC achieves 31% increase at 1.2 V and 800 °C compared with that of LSCM/GDC cell. Moreover, Ca0.05-LSCM full cell remains fairly stable after 100 h of continuous operation in pure CO2 atmosphere. Results indicate that Ca doped LSCM may be a reliable cathode material for CO2 electrolysis in SOECs.

Ensemble learning models have been widely used for wind power forecasting to facilitate efficient dispatching of power systems. However, traditional ensemble methods cannot always function well due to insufficient accuracy and diversity of base learners, ignorance of ensemble pruning, as well as the lack of adaptation capability. Therefore, a novel probabilistic wind power forecasting method is proposed based on selective ensemble of finite mixture Gaussian process regression models (SEFMGPR). First, a set of diverse local Gaussian process regression (GPR) models are constructed through multimodal perturbation mechanism, i.e., perturbing the training data and input attributes simultaneously. Then, a set of finite mixture GPR models (FMGPR) is built by integrating local GPR models through finite mixture mechanism (FMM). Next, the highly influential FMGPR models are selected using genetic algorithm (GA) based ensemble pruning. When a new test sample comes, the component predictions from the selected FMGPR models are adaptively combined by using FMM again and the probabilistic prediction results of the SEFMGPR model are obtained. Besides, an incremental adaptation mechanism is used to alleviate performance degradation of SEFMGPR. The application results from a real wind farm dataset show that SEFMGPR outperforms the traditional global and ensemble wind power prediction methods, and can maintain high prediction accuracy by effectively handling time-varying changes of wind power data.

Carbonaceous materials present the promising anode candidates for sodium-ion batteries (SIBs) and potassium-ion batteries (PIBs), however, the poor rate performance and low capacity due to limited interlayer spacing greatly impact their practical application. Herein, we report soybean roots-derived mesoporous hard carbon constructed by N, P co-doping (NPDCs) with enlarged interlayer distance and more active sites for high-performance SIBs and PIBs. As a result, the optimized NPDC achieves an excellent cyclic stability (197 mA g−1 after 2000 cycles at 2.0 A g−1) for SIBs, superior rate capability (a high reversible capacity of 179 mAh g−1 at 5.0 A g−1) for PIBs. Kinetic and first-principles calculation studies further demonstrate the dual-doping improves the Na (K) ions diffusion and adsorption as well as electrons transport. This work provides a facile and sustainable strategy to realize advanced carbon-based materials with heteratoms doping in application for energy storage devices.

Due to the sodium abundance and availability, sodium-ion batteries (SIBs) have the potential to meet the worldwide growing demand of electrical energy storage. P2-type sodium transition-metal layer oxides with a high energy density are considered as the most promising cathode materials for SIBs. We present here a detailed study of the enhanced rate capability and cyclic stability of the Ti-doped Na0.67Ni0.33Mn0.67O2 cathode material. The combined analysis of ex-situ X-ray absorption fine structure (XAFS) spectroscopy, aberration-corrected high resolution transmission electron microscopy (AB-HRTEM) and X-ray diffraction (XRD) show that the strong Ti–O bond in the transition metal layers stabilizes the local structure, destroy the Na+-vacancy ordering and arrest the irreversible multiphase transformation that occurs during the intercalation/deintercalation process. Actually, Na0.67Ni0.33Mn0.52Ti0.15O2 exhibits a reversible capacity of 89.6 mA h g−1 even at 5 C, an excellent cyclability with 88.78 % capacity retention after 200 cycles at 0.5 C. This study provides a better understanding in optimization of the design of high-energy cathode materials based on titanium doped layered oxides for SIBs.

The SnSe nanoparticles encapsulated in the carbon nanofibers (SnSe@C) with microrod morphology and core–shell structure are prepared by electrospinning and annealing process, and investigated as anode materials for sodium ion batteries. Benefiting from this unique structure, the SnSe@C can deliver a reversible capacity of 283.8 mAh g−1 after 500 cycles at a high current density of 1.0 A g−1. The sodium ion storage mechanisms of SnSe are further characterized by ex-situ X-ray diffraction, high-resolution transmission electron microscope and selected area electron diffraction measurements. Besides, the excellent electrochemical performance of the electrodes is investigated by pseudocapacitance and in situ electrochemical impedance spectroscopy measurements. This work may provide a new avenue for synthesis of metal selenides with core–shell structure and a good idea for studying the kinetics process.

The low efficiency and instability of electron emission from two-dimensional (2D) materials such as WS2 and NiS hamper application to vacuum microelectronic devices. Although nickel-based alloys have many favorable physicochemical properties suitable for nanoelectronics, electron field emission (FE) from heterostructures comprising tungsten-nickel sulfide alloys and graphite carbon (WNi–S/C) has been seldom studied because it is difficult to fabricate the nanostructures in situ. In this work, field emitters composed of the WNi–S/C nanobelt composite are produced on Si microchannel plates (WNi–S/C@Si) hydrothermally. The structure has a porous and vertically aligned 3D morphology in addition to excellent adhesion strength with the substrate. Geometrical modulation of the WNi–S/C@Si is performed by changing the hydrothermal reaction temperature and an ultralow turn-on electric field of 0.51 V μm−1 for a current density 0.1 mA cm−2, threshold electric field of 0.65 V μm−1 for a current density of 1 mA cm−2, and stability of 97.1% for 8 h are accomplished. The related mechanisms are investigated and discussed. Moreover, finite element simulation shows that not only the shape of the nanostructures but also the high aspect plays a key role in reducing screen effects. These outstanding properties suggest large potential in high-performance FE and vacuum nanodevices.

Poor electrical conductivity and large volume changes are two disadvantages of metal selenides. In this work, MnSe nanoparticles encapsulated into N-carbon fibers are designed and prepared using electrospinning and calcination methods. When calcined at 700 °C, the composite has a high reversible capacity of 684 mAh g−1 after 100 cycles at 0.1 A g−1, owing to the improved conductivity and protection provided by carbon fibers. Furthermore, the in situ electrochemical impedance measurements reveal the process kinetics of the composites. This work will provide the basis for the further development of the application of metal selenides in the field of flexible electrodes.

Solid oxide electrolysis cell (SOEC) is a promising electrochemical device with high efficiency for energy conversion and storage. In this work, La1.5Sr0.5Ni1-xMnxO4+δ (LSNMx) (x = 0, 0.1, 0.25, 0.5, 0.75) Ruddlesden-Popper oxides are synthesized via the conventional solid-state method and evaluated as potential air electrodes for SOEC. The effects of substituting Mn for Ni on the crystal structure, oxygen content, thermal expansion behavior, electrical conductivity, and electrochemical performance are comprehensively investigated. XRD results show that the solid-solution concentration of Mn in LSNMx cannot exceed 0.5. The X-ray photoelectron spectroscopy of O1s suggest that Mn doping increases the concentration of oxygen vacancy. The polarization resistance (Rp) of LSNMx (x = 0, 0.1, 0.25, 0.5) exhibits a significantly decreasing tendency with increasing Mn content. LSNM0.5 shows the lowest Rp value of 0.488 Ω cm2 at 800 °C and is reduced by as much as 81.1% in comparison with that of the LSN electrode. A current density of 500 mA cm−2 at 1.4 V is obtained for the LSNM0.5 air electrode with 70% CO2 + 30% CO feed gas on the fuel electrode in a fuel electrode-supported single cell at 800 °C. This current density shows an increase of approximately 85.2% compared with that of the un-doped LSN electrode. The stability test of the half-cell shows that the LSNM0.5 air electrode is relatively stable after anodic polarization at 500 mA cm−2 and 800 °C in air for 145 h. Thus, the Mn-doped LSNM0.5 Ruddlesden-Popper material is a promising air electrode for SOEC.

A facile method is designed to synthesize CeO2 nanoparticles embedded one dimensional N doped carbon matrix by the electrospinning and calcination process. The electrochemical tests reveal that the CeO2@NC composite delivers excellent electrochemical performance with good cycling stability and rate capability. CeO2@NC composite exhibits a high reversible capacity of 724.8 mAh g−1 after 100 cycles at a current density of 100 mA g−1, owing to that the one dimensional N doped carbon matrix can effectively improve the electron conductivity of composite and accelerate the ion transfer with short diffusion length. This work can be extended to construct one dimensional other metal oxides/carbon composite and applied in a new generation of anode materials.

The operation safety of high-speed railways has received great concern globally in recent years. The power supply of the electronic monitoring devices alongside high-speed railway tunnels and their maintenance are becoming increasingly important. In this paper, a portable renewable wind energy harvesting system integrated with an S-rotor and H-rotor has been designed to provide electricity to the monitoring sensors alongside railway tunnels in remote areas with shortages in the electricity supply. The proposed renewable wind energy harvesting system can harvest the wind energy inside the tunnel and convert it into electricity for self-powered applications. The renewable wind energy harvesting system scheme mainly consists of three components: a portable wind harvester mechanism, a generator module and a power storage module. The S-rotor and H-rotor are integrated as the wind harvester mechanisms in this system. The S-rotor harvests natural wind energy, and the H-rotor harvests piston wind energy when a high-speed train passes through the tunnel. Meanwhile, the S-rotor and H-rotor rotate the generator module separately through the one-way bearing. Finally, the supercapacitors in the power storage module are used to store the electrical energy. Experiments were conducted to study the performance of the system. A maximum electrical power output of 107.76 mW and a maximum efficiency of 23.2% are demonstrated in the test. The experimental data indicate that the wind energy harvesting system is beneficial and is a promising method to generate energy used for renewable self-powered applications in high-speed railway tunnels.

Transition metal phosphides (TMPs) have attracted much considerable interest for electrochemical energy storage, due to their high theoretical capacity and earth-abundant. In this work, we report ultrafine carbon-coated bimetal phosphide nanoparticles embedded into the nitrogen-doped graphene network (NGN) through a solution-phase self-assembly strategy. The unique carbon-coated Ni1.4Co0.6P nanoparticles and strongly coupled with NGN can not only provide more active sites for lithium-ions reaction and enhance the conductivity of electrode, but also restrain the volume expansion during the charge/discharge process. When evaluated as anode material for lithium-ion batteries, the as-prepared hybrid electrode exhibits high specific capacities (1320 mAh g−1 at 120 mA g−1), superior rate capability (227 mAh g−1 at a current density of 3000 mA g−1) and excellent cycling stability (350 mAh g−1 at 1200 mA g−1 after 1000 cycles). This work can be extended to develop advanced electrode materials for next-generation energy storage systems.

Cobalt sulfides (including CoS, CoS2, Co3S4, Co9S8 and nonstoichiometric Co1−xS) are deemed to a kind of novel anode material for lithium ion batteries due to their large specific capacities. However, cobalt sulfides suffer from poor cycling stability with the large volume expansion. In this work, we designed and synthesized a novel Co-metal organic frameworks (MOFs) material by using carbazolyl carboxylates ligand H4DCDC. The Co1−xS nanoparticles embedded in N-doped carbon sheets (Co1−xS/NCS) composites are obtained via vulcanizing the Co-MOFs precursor. As a result, the Co1−xS/NCS exhibits outstanding cycling stability and maintains a high capacity of 796.3 mA h g−1 at the current density of 200 mA g−1 after 100 cycles. The N-doped carbon sheets can not only enhance the conductivity of materials but also buffer the volume expansion during charge-discharge process.

In this work, the effect of Ga substitution on the crystal structure, phase transitions, magnetocaloric effect, and exchange bias properties, was systematically investigated in Ni43Mn46Sn11−xGax alloys. With increasing Ga content, the Curie temperature of austenite slightly decreases, while the martensitic transformation temperature shifts to a higher temperature and finally disappears, due to the emergence of γ phase. Large low-field magnetic entropy changes around the martensitic transformation were observed as the transition from a paramagnetic martensitic phase to a ferromagnetic austenitic phase took place. Ni43Mn46Sn11 has a reentrant-spin-glass ground state, and Ga doping can enhance the antiferromagnetic interaction arising from lattice contraction, leading to close of intermediate ferromagnetic windows and establishment of canonical spin-glass for x > 2. Prominently, the zero-field-cooled exchange was achieved in the spin-glass region with 5 ⩽ x ⩽ 10 .

Just-in-time (JIT) learning based soft sensors have been widely used for predicting product quality variables of nonlinear processes. They dynamically build online local models by selecting the samples most relevant to the query data from a historical database whenever an estimate is requested. However, building high-performance JIT soft sensors remains challenging due to difficulties defining similarity measures and the selection of input variables for facilitating efficient relevant sample selection and model building. In this study, we propose a novel soft sensing framework, referred to as JIT learning with variable selection and weighting (JIT-VSW). In this framework, a mixture weighted similarity (MWS) measure is defined by combining multiple weighted Euclidean distance (WED) based similarity measures. The MWS measure enables variable weighting embedded in WED measures to account for the relevance between input and output variables and facilitates the handling of highly complex process characteristics through the mixture-type similarity measure. Meanwhile, a wrapper optimization approach using evolutional algorithms is proposed for input variable selection. Further, the selection of input variables and the determination of MWS parameters, i.e., weights assigned to input variables and mixture coefficients of WED similarity measures, are formulated as a mixed integer optimization problem and solved simultaneously by using the mixed integer genetic algorithms (MIGA). The effectiveness and superiority of JIT-VSW are verified through three real-world applications.

The interesting phenomena induced by the interaction between local moments and itinerant electrons in 4f compounds have attracted extensive attention for many years. Here, we report the synthesis of a new 4f compound, i.e. NdPd1-δBi2 (δ∼0.23) which is superconducting with a transition temperature Tc around 3.2 K. A comprehensive investigation of the thermodynamic properties suggests NdPd1-δBi2 is also an anti-ferromagnet with the Neel temperature TN ∼4 K. Moreover, its high-temperature paramagnetic state is characterized by an enhanced heat capacity with a Sommerfeld coefficient γ over 300 mJ mol−1 K−2, which presumably results from Nd3+ crystal-electric-field splitting rather than the Kondo effect. In the superconducting state, the suppression of superconductivity with field increasing for H ||c is faster in comparison with H || ab, resulting in a moderate upper critical field anisotropy with Γ = H c 2 ( 0 K , ‖ a b ) / H c 2 ( 0 K , ‖ c ) ∼2.6. Collectively, NdPd1-δBi2 may serve as an intriguing system to study the interplay among magnetic order, Kondo effect, and superconductivity in 4f compounds.

High-performance transition metal carbonates are considered as promising electrode materials in lithium-ion batteries. However, the poor conductivity limits their further application. Herein, graphene modified CdCO3 cubic particles composite (CdCO3@rGO) are prepared using a facile hydrothermal method and their electrochemical performance are discussed. CdCO3@rGO can maintain a reversible capacity of 423.1 mAh g−1 at 200 mA g−1 after 100 cycles while CdCO3 only delivers a capacity of 142.0 mAh g−1. The excellent cycling stability and rate capability of CdCO3@rGO can be attributed to graphene modification, which not only decreases the particle size with large surface area but also improves the electronic conductivity and effectively buffers volume changes during the lithium insertion/extraction process.

Just-in-time learning (JIT) has recently gained growing popularity for soft sensor development of nonlinear processes. However, traditional JIT methods aim to pursue a globally optimal learning configuration while ignoring the diversity of JIT learning. Since strong JIT learners are desirable yet difficult to get while weak JIT learners are easy to obtain in real practice, a novel soft sensor modeling framework, referred to as evolutional multi-objective optimization (EMO) based ensemble JIT learning (EMO-EJIT), is proposed. Within this framework, the diversity of JIT learning is created by perturbing the weighted Euclidean distance (WED) based similarity measure. Then diverse and accurate JIT learners equipped with diverse WED similarity measures are generated using an EMO approach. Finally, a stacking strategy is used to achieve ensemble prediction. By employing the proposed modeling framework, EMO based ensemble locally weighted partial least squares (EMO-ELWPLS) and EMO based ensemble JIT extreme learning machine (EMO-EJITELM) soft sensors are developed. The application results from a simulated fed-batch penicillin fermentation process and an industrial fed-batch chlortetracycline fermentation process have demonstrated the effectiveness and superiority of the proposed method.