Promoted hydrogen and acetaldehyde production from alcohol dehydrogenation enabled by electrochemical hydrogen pumps.

The dehydrogenation reaction of bioderived ethanol is of particular interest for the synthesis of fuels and value-added chemicals. However, this reaction historically suffered from high energy consumption (>260 °C or >0.8 V) and low efficiency. Herein, the efficient conversion of alcohol to hydrogen and aldehyde is achieved by integrating the thermal dehydrogenation reaction with electrochemical hydrogen transfer at low temperature (120 °C) and low voltage (0.06 V), utilizing a bifunctional catalyst (Ru/C) with both thermal-catalytic and electrocatalytic activities. Specifically, the coupled electrochemical hydrogen separation procedure can serve as electrochemical hydrogen pumps, which effectively promote the equilibrium of ethanol dehydrogenation toward hydrogen and acetaldehyde production and simultaneously purifies hydrogen at the cathode. By utilizing this strategy, we achieved boosted hydrogen and acetaldehyde yields of 1,020 mmol g-1 h-1 and 1,185 mmol g-1 h-1, respectively, which are threefold higher than the exclusive ethanol thermal dehydrogenation. This work opens up a prospective route for the high-efficiency production of hydrogen and acetaldehyde via coupled thermal-electrocatalysis.

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.

A Fault Diagnosis Method for 5G Cellular Networks Based on Knowledge and Data Fusion.

As 5G networks become more complex and heterogeneous, the difficulty of network operation and maintenance forces mobile operators to find new strategies to stay competitive. However, most existing network fault diagnosis methods rely on manual testing and time stacking, which suffer from long optimization cycles and high resource consumption. Therefore, we herein propose a knowledge- and data-fusion-based fault diagnosis algorithm for 5G cellular networks from the perspective of big data and artificial intelligence. The algorithm uses a generative adversarial network (GAN) to expand the data set collected from real network scenarios to balance the number of samples under different network fault categories. In the process of fault diagnosis, a naive Bayesian model (NBM) combined with domain expert knowledge is firstly used to pre-diagnose the expanded data set and generate a topological association graph between the data with solid engineering significance and interpretability. Then, as the pre-diagnostic prior knowledge, the topological association graph is fed into the graph convolutional neural network (GCN) model simultaneously with the training data set for model training. We use a data set collected by Minimization of Drive Tests under real network scenarios in Lu'an City, Anhui Province, in August 2019. The simulation results demonstrate that the algorithm outperforms other traditional models in fault detection and diagnosis tasks, achieving an accuracy of 90.56% and a macro F1 score of 88.41%.

A Universal Approach for Sustainable Urea Synthesis via Intermediate Assembly at the Electrode/Electrolyte Interface.

Electrocatalytic C-N coupling process is indeed a sustainable alternative for direct urea synthesis and co-upgrading of carbon dioxide and nitrate wastes. However, the main challenge lies in the unactivated C-N coupling process. Here, we proposed a strategy of intermediate assembly with alkali metal cations to activate C-N coupling at the electrode/electrolyte interface. Urea synthesis activity follows the trend of Li+

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.

Electrodeposition-Assisted Crystal Growth Regulation of PdBi Clusters on Carbon Cloths for Ethanol Oxidation.

Carbon-supported Pd-based clusters are one of the most promising anodic catalysts for ethanol oxidation reaction (EOR) due to their encouraging activity and practical applications. However, unclear growth mechanism of Pd-based clusters on the carbon-based materials has hindered their extensive applications. Herein, we first introduce multi-void spherical PdBi cluster/carbon cloth (PdBi/CC) composites by an electrodeposition routine. The growth mechanism of PdBi clusters on the CC supports has been systemically investigated by evaluating the selected samples and tuning their compositions, which involve the big difference in standard redox potential between Pd2+/Pd and Bi3+/Bi and easy adsorption of Bi3+ on the surface of Pd-rich seeds. Benefitting from the ensembles of many nanocrystal subunits, multi-void spherical PdBi clusters can present collective properties and novel functionalities. In addition, the outstanding characteristics of CC supports enable PdBi clusters with stable nanostructures. Thanks to the unique structure, Pd20Bi/CC catalysts manifest higher EOR activity and better stability compared to Pd/CC. Systematic characterizations and a series of CO poisoning tests further confirm that the dramatically enhanced EOR activity and stability can be attributed to the incorporation of Bi species and the strong coupling of the structure between PdBi clusters and CC supports.

Confinement Effects in Carbonized ZIF-Confined Hollow PtCo Nanospheres Enable the Methanol Oxidation Reaction.

Confinement effects in highly porous nanostructures can effectively adjust the selectivity and kinetics of electrochemical reactions, which can boost the methanol oxidation reaction (MOR). In this work, carbonized ZIF-8-confined hollow PtCo nanospheres (PtCo@carbonized ZIF-8) were fabricated using a facile strategy. A monodisperse confined region was successfully prepared, and the dispersion of the PtCo nanoparticles (NPs) could be precisely regulated, allowing for the effective tuning of the confined region. Thus, the precise regulation of the catalytic reaction was achieved. Importantly, hollow PtCo NPs were prepared using a method based on the Kirkendall effect, and their forming mechanism was systematically investigated. Because of the confinement effects of carbonized zeolitic imidazolate framework-8 (ZIF-8), the crystal and electronic structures of the PtCo NPs were able to be effectively tuned. Our electrochemical results show that PtCo@carbonized ZIF-8 composites manifest a higher mass activity (1.4 A mgPt-1) and better stability compared to commercial Pt/C.

Intensity modulated proton arc therapy via geometry-based energy selection for ependymoma.

PURPOSE: We developed and tested a novel method of creating intensity modulated proton arc therapy (IMPAT) plans that uses computing resources similar to those for regular intensity-modulated proton therapy (IMPT) plans and may offer a dosimetric benefit for patients with ependymoma or similar tumor geometries. METHODS: Our IMPAT planning method consists of a geometry-based energy selection step with major scanning spot contributions as inputs computed using ray-tracing and single-Gaussian approximation of lateral spot profiles. Based on the geometric relation of scanning spots and dose voxels, our energy selection module selects a minimum set of energy layers at each gantry angle such that each target voxel is covered by sufficient scanning spots as specified by the planner, with dose contributions above the specified threshold. Finally, IMPAT plans are generated by robustly optimizing scanning spots of the selected energy layers using a commercial proton treatment planning system (TPS). The IMPAT plan quality was assessed for four ependymoma patients. Reference three-field IMPT plans were created with similar planning objective functions and compared with the IMPAT plans. RESULTS: In all plans, the prescribed dose covered 95% of the clinical target volume (CTV) while maintaining similar maximum doses for the brainstem. While IMPAT and IMPT achieved comparable plan robustness, the IMPAT plans achieved better homogeneity and conformity than the IMPT plans. The IMPAT plans also exhibited higher relative biological effectiveness (RBE) enhancement than did the corresponding reference IMPT plans for the CTV in all four patients and brainstem in three of them. CONCLUSIONS: The proposed method demonstrated potential as an efficient technique for IMPAT planning and may offer a dosimetric benefit for patients with ependymoma or tumors in close proximity to critical organs. IMPAT plans created using this method had elevated RBE enhancement associated with increased linear energy transfer (LET) in both targets and abutting critical organs.

Cellular Network Fault Diagnosis Method Based on a Graph Convolutional Neural Network.

The efficient and accurate diagnosis of faults in cellular networks is crucial for ensuring smooth and uninterrupted communication services. In this paper, we propose an improved 4G/5G network fault diagnosis with a few effective labeled samples. Our solution is a heterogeneous wireless network fault diagnosis algorithm based on Graph Convolutional Neural Network (GCN). First, the common failure types of 4G/5G networks are analyzed, and then the graph structure is constructed with the data in the network parameter, given data sets as nodes and similarities as edges. GCN is used to extract features from the graph data, complete the classification task for nodes, and finally predict the fault types of cells. A large number of experiments are carried out based on the real data set, which is achieved by driving tests. The results show that, compared with a variety of traditional algorithms, the proposed method can effectively improve the performance of network fault diagnosis with a small number of labeled samples.

Reconfigurable Digital Satellite-Borne Base Station Design and Virtual Function Fast Migration Algorithm.

A breakthrough in the technology for virtualizing satellite-borne networks and computing and storage resources can significantly increase the processing capacity and resource utilization efficiency of satellite-borne base stations in response to the development trend in multi-star and multi-system converged satellite internet iterative systems. The protocol processing function of traditional satellite communication systems is generally placed in the ground station system for processing, with poor flexibility and low efficiency. As a result, a reconfigurable digital satellite-borne base station architecture design is suggested, allowing for separation of the hardware and software of the satellite-borne base station and flexible programming and dynamic loading of the satellite-borne base station's functions by software. Meanwhile, a fast adaptive migration algorithm based on multi-dimensional environment awareness is proposed on top of the reconfigurable digital base station, and migration precomputation and real-time computation are added in order to realize rapid deployment of the digital base station system network. Simulation results demonstrate the effectiveness of the proposed algorithm in enhancing system stability and decreasing real-time calculation costs associated with system network migration under conditions of high dynamic changes for each network element in a star-loaded environment. In conclusion, a digital satellite-borne base station system that effectively addresses the issues of low flexibility and high dynamic changes of nodes in the resource-constrained satellite environment can be created by combining the adaptive migration algorithm and the reconfigurable digitized satellite-borne base station architecture.

Multi-UAV Data Collection and Path Planning Method for Large-Scale Terminal Access.

In the context of the relentless evolution of network and communication technologies, the need for enhanced communication content and quality continues to escalate. Addressing the demands of data collection from the abundance of terminals within Internet of Things (IoT) scenarios, this paper presents an advanced approach to multi-Unmanned Aerial Vehicle (UAV) data collection and path planning tailored for extensive terminal accessibility. This paper focuses on optimizing the complex interplay between task completion time and task volume equilibrium. To this end, a novel strategy is devised that integrates sensor area partitioning and flight trajectory planning for multiple UAVs, forming an optimization framework geared towards minimizing task completion duration. The core idea of this work involves designing an innovative k-means algorithm capable of balancing data quantities within each cluster, thereby achieving balanced sensor node partitioning based on data volume. Then, the UAV flight trajectory paths are discretely modeled, and a grouped, improved genetic algorithm is used to solve the Multiple Traveling Salesman Problem (MTSP). The algorithm introduces a 2-opt optimization operator to improve the computational efficiency of the genetic algorithm. Empirical validation through comprehensive simulations clearly underscores the efficacy of the proposed approach. In particular, the method demonstrates a remarkable capacity to rectify the historical issue of diverse task volumes among multiple UAVs, all the while significantly reducing task completion times. Moreover, its convergence rate substantially outperforms that of the conventional genetic algorithm, attesting to its computational efficiency. This paper contributes an innovative and efficient paradigm to improve the problem of data collection from IoT terminals through the use of multiple UAVs. As a result, it not only augments the efficiency and balance of task distribution but also showcases the potential of tailored algorithm solutions for realizing optimal outcomes in complex engineering scenarios.

Electrocatalytic Urea Synthesis with 63.5 % Faradaic Efficiency and 100 % N-Selectivity via One-step C-N coupling.

Electrocatalytic urea synthesis via coupling N2 and CO2 provides an effective route to mitigate energy crisis and close carbon footprint. However, the difficulty on breaking N≡N is the main reason that caused low efficiencies for both electrocatalytic NH3 and urea synthesis, which is the bottleneck restricting their industrial applications. Herein, a new mechanism to overcome the inert of the nitrogen molecule was proposed by elongating N≡N instead of breaking N≡N to realize one-step C-N coupling in the process for urea production. We constructed a Zn-Mn diatomic catalyst with axial chloride coordination, Zn-Mn sites display high tolerance to CO poisoning and the Faradaic efficiency can even be increased to 63.5 %, which is the highest value that has ever been reported. More importantly, negligible N≡N bond breakage effectively avoids the generation of ammonia as intermediates, therefore, the N-selectivity in the co-electrocatalytic system reaches100 % for urea synthesis. The previous cognition that electrocatalysts for urea synthesis must possess ammonia synthesis activity has been broken. Isotope-labelled measurements and Operando synchrotron-radiation Fourier transform infrared spectroscopy validate that activation of N-N triple bond and nitrogen fixation activity arise from the one-step C-N coupling process of CO species with adsorbed N2 molecules.

Study on the Structure-Activity Relationship Between Single-Atom, Cluster and Nanoparticle Catalysts in a Hierarchical Structure for the Oxygen Reduction Reaction.

Literature reports have shown that in primary structures, single-atom catalysts exhibit better performance than cluster and nanoparticles due to their maximum atom utilization and the fine-tuning of the electronic structure of the active sites. Hierarchical structures have recently been extensively studied because of increased active sites and orderliness of channels significantly improves the catalytic performance compare to primary structures especially in nanoparticles, however, the different sized effect of catalysts research has not been reported. Herein, a unique hollow double-shell structure (a distinct cavity-containing) is used as a hierarchical model to study the possible difference between single atom, cluster, and nanoparticle and to establish the corresponding structure-activity relationship. Three Co catalysts are prepared: single atoms (Co-Catalyst-1), clusters (Co-Catalyst-2, 0.5-1 nm), and nanoparticles (Co-Catalyst-3, ≈5 nm) and their oxygen-reduction capacity is evaluated. The unique electronic interactions, the strong electron-withdrawing ability of N in Co-N4 (Co-Catalyst-1), attract electrons from the electrode to Co, specifically by expediting the generation and transformation of the rate-determining step intermediates *OOH. The variant spatial structure which is caused by Co atom aggregation, and led to surface area, pore size, and carbon disorder, is a distinct, therefore significant variation in mass and charge transport efficiency, and activities.

Lithium Vacancy-Tuned [CuO4 ] Sites for Selective CO2 Electroreduction to C2+ Products.

Electrochemical CO2 reduction to valuable multi-carbon (C2+ ) products is attractive but with poor selectivity and activity due to the low-efficient CC coupling. Herein, a lithium vacancy-tuned Li2 CuO2 with square-planar [CuO4 ] layers is developed via an electrochemical delithiation strategy. Density functional theory calculations reveal that the lithium vacancies (VLi ) lead to a shorter distance between adjacent [CuO4 ] layers and reduce the coordination number of Li+ around each Cu, featuring with a lower energy barrier for COCO coupling than pristine Li2 CuO2 without VLi . With the VLi percentage of ≈1.6%, the Li2- x CuO2 catalyst exhibits a high Faradaic efficiency of 90.6 ± 7.6% for C2+ at -0.85 V versus reversible hydrogen electrode without iR correction, and an outstanding partial current density of -706 ± 32 mA cm-2 . This work suggests an attractive approach to create controllable alkali metal vacancy-tuned Cu catalytic sites toward C2+ products in electrochemical CO2 reduction.

Optical performance monitoring using lifelong learning with confrontational knowledge distillation in 7-core fiber for elastic optical networks.

We propose a novel optical performance monitoring (OPM) scheme, including modulation format recognition (MFR) and optical signal-to-noise ratio (OSNR) estimation, for 7-core fiber in elastic optical networks (EONs) by using the specific Stokes sectional images of the received signals. Meanwhile, MFR and OSNR estimation in all channels can be utilized by using a lightweight neural network via lifelong learning. In addition, the proposed scheme saves the computational resources for real implementation through confrontational knowledge distillation, making it easy to deploy the proposed neural network in the receiving end and intermediate node. Five modulation formats, including BPSK, QPSK, 8PSK, 8QAM, and 16QAM, were recognized by the proposed scheme within the OSNR of 10-30 dB over 2 km weakly coupled 7-core fiber. Experimental results show that 100% recognition accuracy of all these five modulation formats can be achieved while the RMSE of the estimation is below 0.1 dB. Compared with conventional neural network architectures, the proposed neural network achieves better performance, whose runtime is merely 20.2 ms, saving the computational resource of the optical network.

High-security and low-complexity OCDM transmission scheme based on GAN enhanced chaotic encryption.

We report a low-complexity and high-security orthogonal chirp division multiplexing (OCDM) transmission scheme based on generative adversarial networks (GAN) enhanced chaotic encryption. Our investigation focuses on the security and efficiency of the communication system. To successfully apply GAN for the encryption scheme, we design our networks with new network architectures and modify the loss functions to improve the adversarial training performance of the networks. In the experiment, a weakly coupled seven cores fiber of 2 km was applied to achieve a 70 Gb/s transmission system. The results reveal that our proposed scheme has a maximum receiver sensitivity gain of about 1.26dB than traditional OFDM transmission system, and our encryption scheme has a large keyspace at about 1 × 10202 against brute force cracking by illegal optical network units with only 0.63% running time compared with the traditional chaotic scheme. The results highlight that the proposed encryption scheme has a remarkable reduction in complexity and improvement in security, which is a promising candidate for next-generation PONs.

Mode division multiplexing chaotic encryption scheme based on key intertwining and accompanying transmission.

A mode division multiplexing (MDM) chaotic encryption scheme based on key intertwining and accompanying transmission is proposed in this paper. Based on the weakly coupled few-mode fiber (FMF), data and time-varying keys can be accompanied by transmission in two modes, LP01 and LP11, respectively. In order to generate a new key, the current key is XORed with all of the keys from all the preceding moments, one by one. To implement chaotic masking in the digital domain, the three chaotic sequences corresponding to the new key are adopted to encrypt the data at the constellation phase, data symbol block, and subcarrier levels. An 8.89 Gb/s encrypted 16QAM-OFDM signal transmission over 1 km weakly-coupled FMF is experimentally demonstrated. The receiver with the correct key can recover the data normally, while the BER of the illegal receiver remains around 0.5. In the case of the key transmission bit rate of 1 Gb/s, the cracking efficiency threshold of the time-varying key encryption scheme is 5.21 × 106 times that of the time-invariant key encryption scheme, which suggests that the proposed work is a promising candidate for future physical layer security.

Chaotic physical security strategy based on manifold learning-assisted GANs for SDM-OFDM-PONs.

In this paper, we propose a high-security spatial division multiplexing orthogonal frequency division multiplexing passive optical network (SDM-OFDM-PON) encryption scheme based on manifold learning-assisted generative adversarial networks (MFGANs). The chaotic sequences generated by MFGANs are applied to produce the masking vectors to encrypt the constellation and frequency. With the help of manifold learning, the proposed scheme can learn the complex structures from various chaotic models and makes use of more parameters than a single traditional model to achieve the large key space of 1 × 10183. A 70 Gb/s encrypted OFDM signal transmission over 2 km 7-core fiber was experimentally demonstrated. In addition, due to the capacity of parallel computing of graphics processing units (GPUs), the encryption time by the proposed scheme is around 1.38% of the conventional encryption scheme. It is therefore shown that the proposed encryption scheme can ensure both efficiency and security in SDM-OFDM-PON systems.

Mo3(C6O6)2 monolayer as a promising electrocatalyst for the CO2 reduction reaction: a first-principles study.

Designing electrocatalysts with good electrical conductivity, low cost, and abundant surface active sites to actively and selectively catalyze the CO2 reduction reaction (CRR) is crucial for mitigating the impact of high carbon emissions. By performing first principles calculations, the potential of Mo3(C6O6)2 monolayers as CRR electrocatalysts was explored by systematically examining the thermodynamic processes of all possible elementary steps. The Mo centers turn out to be the active sites that can selectively promote CRR and produce methane as the main product. The limiting potential for the potential-determining step (PDS) of the first reaction cycle is -0.58 V, less negative than that of the widely studied Cu(211) surface (-0.74 V). For subsequent reaction cycles, the Mo sites tend to coordinate with hydroxyl, which can further promote the CRR and lower the thermodynamic barrier of the PDS to 0.39 eV and suppress the side reaction of hydrogen evolution. With good conductivity and high catalytic activity and selectivity, the hydroxyl terminated Mo3(C6O6)2 monolayer is predicted to be an effective electrocatalyst for CRR.

A Large-Scalable, Surfactant-Free, and Ultrastable Ru-Doped Pt3Co Oxygen Reduction Catalyst.

Developing a large-scale method to produce platinum (Pt)-based electrocatalysts for the oxygen reduction reaction (ORR) is highly desirable to propel the commercialization of the membrane electrode assembly (MEA). Here, we successfully report the large-scale production of surfactant-free ruthenium-doped Pt-cobalt octahedra grown on carbon (Ru-Pt3Co/C), which display a much higher ORR activity and stability and MEA stability than Pt3Co/C and Pt/C. Significantly, the in-situ X-ray absorption fine structure result reveals that Ru can drive the reduced Pt atoms to reverse to their initial state after the ORR by transferring a redundant electron from Pt to Ru, preventing the over-reduction of Pt active sites and boosting the chemical stability. Theory investigations further confirm that the doped Ru can accelerate the breach and desorption of oxygen intermediates, making it active and durable for the ORR. The present work sheds light on the exploration of a large-scale strategy for producing advanced Pt-based nanocatalysts, which may offer significant advantages for practical fuel cell applications in the future.