Ultra-Low-Potential Methanol Oxidation on Single-Ir-Atom Catalyst.

Methanol oxidation plays a central role to implement sustainable energy economy, which is restricted by the sluggish reaction kinetics due to the multi-electron transfer process accompanied by numerous sequential intermediate. In this study, an efficient cascade methanol oxidation reaction is catalyzed by single-Ir-atom catalyst at ultra-low potential (<0.1 V) with the promotion of the thermal and electrochemical integration in a high temperature polymer electrolyte membrane electrolyzer. At the elevated temperature, the electron deficient Ir site with higher methanol affinity could spontaneous catalyze the CH3OH dehydrogenation to CO under the voltage, then the generated CO and H2 was electrochemically oxidized to CO2 and proton. However, the methanol cannot thermally decompose with the voltage absence, which confirm the indispensable of the coupling of thermal and electrochemical integration for the methanol oxidation. By assembling the methanol oxidation reaction with hydrogen evolution reaction with single-Ir-atom catalysts in the anode chamber, a max hydrogen production rate reaches 18 mol gIr -1 h-1, which is much greater than that of Ir nanoparticles and commercial Pt/C. This study also demonstrated the electrochemical methanol oxidation activity of the single atom catalysts, which broadens the renewable energy devices and the catalyst design by an integration concept.

Electrocatalysis Boosts the Methanol Thermocatalytic Dehydrogenation for High-Purity H2 and CO Production.

Hydrogen production from methanol represents an energy-sustainable way to produce ethanol, but it normally results in heavy CO2 emissions. The selective conversion of methanol into H2 and valuable chemical feedstocks offers a promising strategy; however, it is limited by the harsh operating conditions and low conversion efficiency. Herein, we realize efficient high-purity H2 and CO production from methanol by coupling the thermocatalytic methanol dehydrogenation with electrocatalytic hydrogen oxidation on a bifunctional Ru/C catalyst. Electrocatalysis enables the acceleration of C-H cleavage and reduces the partial pressure of hydrogen at the anode, which drives the chemical equilibrium and significantly enhances methanol dehydrogenation. Furthermore, a bilayer Ru/C + Pd/C electrode is designed to mitigate CO poisoning and facilitate hydrogen oxidation. As a result, a high yield of H2 (558.54 mmol h-1 g-1) with high purity (99.9%) was achieved by integrating an applied cell voltage of 0.4 V at 200 °C, superior to the conventional thermal and electrocatalytic processes, and CO is the main product at the anode. This work presents a new avenue for efficient H2 production together with valuable chemical synthesis from methanol.

Efficient Low-temperature Hydrogen Production by Electrochemical-assisted Methanol Steam Reforming.

Methanol steam reforming (MSR) provides an alternative way for efficient production and safe transportation of hydrogen but requires harsh conditions and complicated purification processes. In this work, an efficient electrochemical-assisted MSR reaction for pure H2 production at lower temperature (~140 °C) is developed by coupling the electrocatalysis reaction into the MSR in a polymer electrolyte membrane electrolysis reactor. By electrochemically assisted, the two critical steps including the methanol dehydrogenation and water-gas shift reaction are accelerated, which is attributed to decreasing the methanol dehydrogenation energy and promoting the dissociation of H2 O to OH* by the applied potential. Furthermore, the reduced H2 partial pressure by the hydrogen oxidation and reduction process further promotes MSR. The combination of these advantages not only efficiently decreases the MSR temperature but also achieves the high rate of hydrogen production of 505 mmol H2 g Pt -1 h-1 with exceptionally high H2 selectivity (99 %) at 180 °C and a low voltage (0.4 V), and the productivity is about 30-fold than that of traditional MSR. This study opens up a new avenue to design novel electrolysis cells for hydrogen production.

Electrocatalytic Synthesis of Nylon-6 Precursor at Almost 100 % Yield.

Synthesis of cyclohexanone oxime via the cyclohexanone-hydroxylamine process is widespread in the caprolactam industry, which is an upstream industry for nylon-6 production. However, there are two shortcomings in this process, harsh reaction conditions and the potential danger posed by explosive hydroxylamine. In this study, we presented a direct electrosynthesis of cyclohexanone oxime using nitrogen oxides and cyclohexanone, which eliminated the usage of hydroxylamine and demonstrated a green production of caprolactam. With the Fe electrocatalysts, a production rate of 55.9 g h-1 gcat -1 can be achieved in a flow cell with almost 100 % yield of cyclohexanone oxime. The high efficiency was attributed to their ability of accumulating adsorbed hydroxylamine and cyclohexanone. This study provides a theoretical basis for electrocatalyst design for C-N coupling reactions and illuminates the tantalizing possibility to upgrade the caprolactam industry towards safety and sustainability.

Pt-Based Rare Earth Alloy as Efficient and Robust Electrocatalyst for High-Temperature Proton Exchange Membrane Fuel Cells.

High-temperature proton exchange membrane fuel cells (HT-PEMFCs) are crucial in future energy systems. However, the activity and stability of the electrocatalysts are severely restricted by high temperature and phosphoric acid poisoning. Herein, PtCe alloy as oxygen reduction reaction (ORR) electrocatalyst for HT-PEMFCs exhibits fantastic performance. Ce can increase the electronic density of Pt, weakening phosphoric acid poisoning and improving ORR activity. The optimized electronic structure can also reduce the dipole effect between Pt and O, which suppresses the irreversible oxidation of Pt. Additionally, the dramatically negative heat of formation in PtCe catalyst brings high kinetic barrier of metal diffusion and dissolution. With this electrocatalyst, the HT-PEMFCs show a preeminent peak power of 605 mW cm-2 with 0.3 mgPt cm-2 . After 30000 cycles of accelerated stability test, the peak power density only decreases by 31.6%, achieving the goal of Department of Energy in 2020 (<40% loss).

Identification of MISO Hammerstein system using sparse multiple kernel-based hierarchical mixture prior and variational Bayesian inference.

The Hammerstein model is a cascade composition of a static memoryless nonlinear function followed by a linear time-invariant dynamical subsystem, which is capable of modeling a wide range of nonlinear dynamical systems. Model structural parameter selection (including the model order and the nonlinearity order) and sparse representation of the static nonlinear function are two issues that receive increasing interests in Hammerstein system identification. In this paper, a novel Bayesian sparse multiple kernel-based identification method (BSMKM) for multiple-input single-output (MISO) Hammerstein system is proposed to handle those issues, where the basis-function model and the finite impulse response model are used to represent the nonlinear part and the linear part respectively. Firstly, in order to jointly realize the model parameter estimation, the sparse representation of static nonlinear function (the nonlinearity order selection can also be realized indirectly) and the model order selection of linear dynamical system, a hierarchical prior distribution is constructed based on Gaussian scale mixture model and sparse multiple kernel, which can characterize both inter-group sparsity and intra-group correlation structure. Then, a full Bayesian method based on variational Bayesian inference is proposed to estimate all unknown model parameters, including finite impulse response coefficients, hyperparameters and noise variance. Finally, the performance of the proposed BSMKM identification method is evaluated by numerical experiments using both simulation and real data.

Durable High-Temperature Proton Exchange Membrane Fuel Cells Enabled by the Working-Temperature-Matching Palladium-Hydrogen Buffer Layer.

The durability degradation during stack-operating conditions seriously deteriorates the lifetime and performance of the fuel cell. To alleviate the rapid potential rise and performance degradation, an anode design is proposed to match the working temperature of high-temperature proton exchange membrane fuel cells (HT-PEMFCs) with the release temperature of hydrogen from palladium. The result is significantly enhanced hydrogen oxidation reaction (HOR) activity of Pd and superior performance of the Pd anode. Furthermore, Pd as hydrogen buffer and oxygen absorbent layer in the anode can provide additional in situ hydrogen and absorb infiltrated oxygen during local fuel starvation to maintain HOR and suppress reverse-current degradation. Compared with the traditional Pt/C anode, the Pd/C also greatly improved HT-PEMFCs durability during start-up/shut-down and current mutation. The storage/release of hydrogen provides innovative guidance for improving the durability of PEMFCs.

Identification of Gene Regulatory Networks Using Variational Bayesian Inference in the Presence of Missing Data.

The identification of gene regulatory networks (GRN) from gene expression time series data is a challenge and open problem in system biology. This paper considers the structure inference of GRN from the incomplete and noisy gene expression data, which is a not well-studied issue for GRN inference. In this paper, the dynamical behavior of the gene expression process is described by a stochastic nonlinear state-space model with unknown noise information. A variational Bayesian (VB) framework are proposed to estimate the parameters and gene expression levels simultaneously. One of the advantages of this method is that it can easily handle the missing observations by generating the prediction values. Considering the sparsity of GRN, the smoothed gene data are modeled by the extreme gradient boosting tree, and the regulatory interactions among genes are identified by the importance scores based on the tree model. The proposed method is tested on the artificial DREAM4 datasets and one real gene expression dataset of yeast. The comparative results show that the proposed method can effectively recover the regulatory interactions of GRN in the presence of missing observations and outperforms the existing methods for GRN identification.

Design of unscented Kalman filter based on the adjustments of the number and placements of the sampling points.

This paper addresses a general sampling method of the unscented Kalman filter (UKF) for nonlinear state estimation. The sampling method for standard UKF is analyzed, and we propose a theorem to address the conditions that UKF provides a third order accuracy in terms of Taylor series expansion for expectation estimation by changing the number and placements of the sampling points. This theorem can be used to develop new UKF. Based on this theorem, we propose a method to design the placements of the sampling points, including the directions and lengths by optimization strategies. Simulation studies demonstrate that the proposed UKF is effective and can significantly improve the filter performance.

Cognition of Nurses in Neurology Department on Rehabilitation Nursing.

Stroke has become a major disease seriously threating human health due to its high morbidity, mortality and disability. Rehabilitation nursing care for stroke patients has always been a key part of clinical care. The neurological nursing managers should pay high attention to the issue about how to more effectively improve the level of nurses' rehabilitation nursing on stroke patients. Therefore, this paper investigates the current cognition of neurological nurses about stroke knowledge, attitude and behaviour, and then analyses the factors affecting the knowledge, attitude and behaviour of stroke in the nurses, in order to provide better nursing services for stroke patients, and improve their nursing quality. The findings show that the different cognitions of nurses about their role have different effects on the knowledge, attitudes and behavioural levels of the neurological nurses; the nurses with more types of roles have better knowledge and behavioural levels of stroke.

Strong/Weak Feature Recognition of Promoters Based on Position Weight Matrix and Ensemble Set-Valued Models.

In this article, we propose a method to recognize the strong/weak property of the promoters based on the nucleotide sequence. To the best of our knowledge, it is the first time to predict the strong/weak property of the promoters. First, position weight matrix (PWM) is used to evaluate the contributions of the nucleotides to the promoter strength. Then, the set-valued model is used to describe the relation between the nucleotide sequence and the strength. Considering the small-sample and imbalance features of the promoter data, we propose an ensemble approach to predict the strong/weak property of the promoters. The proposed method is used to recognize 60 [Formula: see text] promoters of Escherichia coli. The results show the effectiveness of the proposed method. This article provides a simple way for a biologist to evaluate the strong/weak feature of promoters from the nucleotide sequence.

A novel optimization algorithm for MIMO Hammerstein model identification under heavy-tailed noise.

In this paper, we study the system identification of multi-input multi-output (MIMO) Hammerstein processes under the typical heavy-tailed noise. To the best of our knowledge, there is no general analytical method to solve this identification problem. Motivated by this, we propose a general identification method to solve this problem based on a Gaussian-Mixture Distribution intelligent optimization algorithm (GMDA). The nonlinear part of Hammerstein process is modeled by a Radial Basis Function (RBF) neural network, and the identification problem is converted to an optimization problem. To overcome the drawbacks of analytical identification method in the presence of heavy-tailed noise, a meta-heuristic optimization algorithm, Cuckoo search (CS) algorithm is used. To improve its performance for this identification problem, the Gaussian-mixture Distribution (GMD) and the GMD sequences are introduced to improve the performance of the standard CS algorithm. Numerical simulations for different MIMO Hammerstein models are carried out, and the simulation results verify the effectiveness of the proposed GMDA.