Facile Synthesis of the Amorphous Carbon Coated Fe-N-C Nanocatalyst with Efficient Activity for Oxygen Reduction Reaction in Acidic and Alkaline Media

With the assistance of surfactant, Fe nanoparticles are supported on g-C3N4 nanosheets by a simple one-step calcination strategy. Meanwhile, a layer of amorphous carbon is coated on the surface of Fe nanoparticles during calcination. Transmission electron microscopy (TEM), scanning electron microscope (SEM), X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), and inductively coupled plasma (ICP) were used to characterize the morphology, structure, and composition of the catalysts. By electrochemical evaluate methods, such as linear sweep voltammetry (LSV) and cyclic voltammetry (CV), it can be found that Fe25-N-C-800 (calcinated in 800 °C, Fe loading content is 5.35 wt.%) exhibits excellent oxygen reduction reaction (ORR) activity and selectivity. In 0.1 M KOH (potassium hydroxide solution), compared with the 20 wt.% Pt/C, Fe25-N-C-800 performs larger onset potential (0.925 V versus the reversible hydrogen electrode (RHE)) and half-wave potential (0.864 V vs. RHE) and limits current density (2.90 mA cm−2, at 400 rpm). In 0.1 M HClO4, it also exhibits comparable activity. Furthermore, the Fe25-N-C-800 displays more excellent stability and methanol tolerance than Pt/C. Therefore, due to convenience synthesis strategy and excellent catalytic activity, the Fe25-N-C-800 will adapt to a suitable candidate for non-noble metal ORR catalyst in fuel cells

Two-dimensional nanosheets for electrocatalysis in energy generation and conversion

The 2D structures and tunable properties of nanosheets make them intriguing catalytic materials. This research area is being driven by a need to replace scarce noble metal-based catalysts in energy technologies. We describe recent advances in nanosheet electrocatalysis of oxygen reduction, oxygen evolution, hydrogen evolution, and CO2 reduction reactions. We find at this early stage of development that nanosheet catalysis has surpassed classical noble metal catalysts in several of these applications and is showing high potential in others. CO2 reduction to methane is now catalyzed best by metal-free carbon nanosheets. These trends will likely transform heterogeneous catalysis

Multivariate Transfer Passenger Flow Forecasting with Data Imputation by Joint Deep Learning and Matrix Factorization

Accurate forecasting of the future transfer passenger flow from historical data is essential for helping travelers to adjust their trips, optimal resource allocation and alleviating traffic congestion. However, current studies have mainly emphasized predicting traffic parameters for a single type of transport, while lacking research into transfer passenger flow influenced by multiple factors across different transport modes. Additionally, efficient traffic prediction relies on high-quality traffic data, yet data loss issues are inevitable but often ignored. To fill these gaps, we present for the first time a reliable joint long short-term memory with matrix factorization deep learning model (i.e., Joint-IF) for accurate imputation and forecasting of transfer passenger flow between metro and bus. This hybrid Joint-IF model uses a repair-before-prediction strategy to deliver the final high-quality outputs. In particular, we simulate a variety of missing combinations under the natural conditions and apply a low-rank matrix factorization to infer those lost values. In addition, we investigate the effects of crucial parameters and spatiotemporal features on transfer flow prediction. To validate the effectiveness of Joint-IF, a large series of experiments are carried out for models’ comparison and validation on the real-world transfer passenger flow dataset of the Shenzhen public transport system, and the results show that the proposed Joint-IF performs better for both imputation and forecasting of transfer passenger flow relative to the baseline models in terms of accuracy and stability

Effects of Chlorella vulgaris Enhancement on Endogenous Microbial Degradation of Marine Oil Spills and Community Diversity

Biofortification could improve the bioremediation efficiency of microbes in the reparation of marine environmental damage caused by oil spills. In this paper, Chlorella vulgaris LH-1 was used as a fortifier to enhance the degradation of a marine oil spill by endogenous microorganisms. The addition of C. vulgaris LH-1 increased the degradation efficiency of crude oil by 11.09–42.41% and considerably accelerated oil degradation efficiency. Adding C. vulgaris LH-1 to a crude oil environment can improve the activity of endogenous seawater microorganisms. The results of high-throughput sequencing showed that the main bacterial genera were Oceanicola, Roseibacillus, and Rhodovulum when exotrophic C. vulgaris LH-1 and seawater endogenous microorganisms degraded low-concentration crude oil together. However, the addition of high-concentration nutrient salts changed the main bacterial genera in seawater to unclassified Microbacterium, Erythrobacter, and Phaeodactylibacter. The addition of C. vulgaris LH-1 increased the abundance of marine bacteria, Rhodococcus, and Methylophaga and decreased the abundance of Pseudomonas, Cladosporium, and Aspergillus. The functional prediction results of phylogenetic investigation of communities by reconstruction of unobserved states indicated that C. vulgaris LH-1 could improve the metabolic ability of seawater endogenous microorganisms to degrade endogenous bacteria and fungi in crude oil

Two-dimensional materials for energy conversion and storage

© 2020 Elsevier Ltd. Two-dimensional (2D) materials with varied structured features are showing promise for diverse processes. We focus on their energy applications in electrocatalysis of the oxygen reduction reaction, the oxygen evolution reaction, the hydrogen evolution reaction, CO2 reduction reactions, photocatalytic water splitting and CO2 reduction, electrical double layer capacitors, pseudocapacitors, and batteries. Effects of synthesis parameters and surface modification are examined as a means to tune conductivity, catalytic activity, and other performance-related properties. Activity parameters of leading 2D materials and their hybrids are discussed and compared with more classical benchmark materials to provide an evolutionary perspective of performance progress. Doped graphenes are currently producing about half their theoretical electrostatic maximum energy storage in electrical double layer capacitors at about 260 F g−1. Nanosheet pseudocapacitors have yielded significant early advances in hybrids of graphene with layered double hydroxides and with metal oxide nanosheets to store energy at about 3000 F g−1. These pseudocapacitor results also have enabled promising early developments in using similar electrodes in batteries. Nanosheet hybrid structures are also yielding improved electrodes for lithium and sodium ion batteries. High electrical conductivity, robustly porous nanosheet assemblies, and facile ionic and molecular diffusion pathways are design criteria important for nanosheet-based energy conversion and storage materials. Development opportunities and challenges are summarized