Explore the vast range of titles available.

Copper-based materials can reliably convert carbon dioxide into multi-carbon products but they suffer from poor activity and product selectivity. The atomic structure-activity relationship of electrocatalysts for the selectivity is controversial due to the lacking of systemic multiple dimensions for operando condition study. Herein, we synthesized high-performance CO 2 RR catalyst comprising of CuO clusters supported on N-doped carbon nanosheets, which exhibited high C 2+ products Faradaic efficiency of 73% including decent ethanol selectivity of 51% with a partial current density of 14.4 mA/cm −2 at −1.1 V vs. RHE. We evidenced catalyst restructuring and tracked the variation of the active states under reaction conditions, presenting the atomic structure-activity relationship of this catalyst. Operando XAS, XANES simulations and Quasi-in-situ XPS analyses identified a reversible potential-dependent transformation from dispersed CuO clusters to Cu 2 -CuN 3 clusters which are the optimal sites. This cluster can’t exist without the applied potential. The N-doping dispersed the reduced Cu n clusters uniformly and maintained excellent stability and high activity with adjusting the charge distribution between the Cu atoms and N-doped carbon interface. By combining Operando FTIR and DFT calculations, it was recognized that the Cu 2 -CuN 3 clusters displayed charge-asymmetric sites which were intensified by CH 3 * adsorbing, beneficial to the formation of the high-efficiency asymmetric ethanol. Copper-based materials can convert carbon dioxide into multi-carbon products but suffer from poor activity and selectivity. Here, the authors report CuO clusters supported on nitrogen-doped carbon nanosheets for the reduction CO 2 -to-ethanol, and investigate the change in the catalytic sites while in operation.

While inheriting the exceptional merits of single atom catalysts, diatomic site catalysts (DASCs) utilize two adjacent atomic metal species for their complementary functionalities and synergistic actions. Herein, a DASC consisting of nickel-iron hetero-diatomic pairs anchored on nitrogen-doped graphene is synthesized. It exhibits extraordinary electrocatalytic activities and stability for both CO 2 reduction reaction (CO 2 RR) and oxygen evolution reaction (OER). Furthermore, the rechargeable Zn-CO 2 battery equipped with such bifunctional catalyst shows high Faradaic efficiency and outstanding rechargeability. The in-depth experimental and theoretical analyses reveal the orbital coupling between the catalytic iron center and the adjacent nickel atom, which leads to alteration in orbital energy level, unique electronic states, higher oxidation state of iron, and weakened binding strength to the reaction intermediates, thus boosted CO 2 RR and OER performance. This work provides critical insights to rational design, working mechanism, and application of hetero-DASCs. Diatomic site catalysts utilize two adjacent atomic metal species for their complementary functionalities and synergistic actions. Here, the authors report the orbital coupling of hetero-diatomic nickel-iron site boosts CO 2 reduction reaction and oxygen evolution reaction.

In this work, via engineering the conformation of cobalt active center in cobalt phthalocyanine molecular catalyst, the catalytic efficiency of electrochemical carbon monoxide reduction to methanol can be dramatically tuned. Based on a collection of experimental investigations and density functional theory calculations, it reveals that the electron rearrangement of the Co 3d orbitals of cobalt phthalocyanine from the low-spin state (S = 1/2) to the high-spin state (S = 3/2), induced by molecular conformation change, is responsible for the greatly enhanced CO reduction reaction performance. Operando attenuated total reflectance surface-enhanced infrared absorption spectroscopy measurements disclose accelerated hydrogenation of CORR intermediates, and kinetic isotope effect validates expedited proton-feeding rate over cobalt phthalocyanine with high-spin state. Further natural population analysis and density functional theory calculations demonstrate that the high spin Co 2+ can enhance the electron backdonation via the d xz / d yz −2π* bond and weaken the C-O bonding in *CO, promoting hydrogenation of CORR intermediates. Molecular catalysts provide an ideal model system to investigate the relationship between active site structure and catalytic performance. Here, the authors explore how electrochemical CO reduction to methanol can be controlled through modification of the active cobalt site in cobalt phthalocyanine.

Ruthenium-bearing intermetallics (Ru-IMCs) featured with well-defined structures and variable compositions offer new opportunities to develop ammonia synthesis catalysts under mild conditions. However, their complex phase nature and the numerous controlling parameters pose major challenges for catalyst design and exploration. Herein, we demonstrate that a combination of machine learning (ML) and model mining techniques can effectively address these challenges. Based on the combination techniques, we generate a two-dimensional activity volcano plot with adsorption energies of N 2 and N, and identify the radius of atom coordinating to Ru as a key parameter. The well-designed Sc 1/8 Nd 7/8 Ru 2 reaches as high as 8.18 mmol m −2 h −1 at 0.1 MPa and 400 °C. Density functional theory (DFT) calculations combined with N 2 -TPD and FT-IR studies reveal that Ru‒N interaction is controlled by the d - p orbital hybridization between Ru and N. These findings underscore the importance of ML towards material design on exploring IMCs for ammonia synthesis. Developing Ru-intermetallic catalysts for mild ammonia synthesis faces structural complexity. Here, machine learning identified Sc 1/8 Nd 7/8 Ru 2 , optimizing Ru–N bonding and orbital hybridization, enhancing catalytic activity under mild conditions.

Traffic safety among taxi drivers is a critical concern, particularly in high-density urban environments. This study proposes a quantitative model to predict crash risk by examining the symmetry and asymmetry of various influencing factors, including personal characteristics, workload, risky driving behaviors, and crash history. An anonymous survey was conducted among taxi drivers in four Chinese cities, collecting 1010 valid responses. Using an ordered logistic regression model, we identified 14 key risk indicators, such as severe sleep problems, high income dissatisfaction, and 12 types of risky driving behaviors. The results indicate asymmetric effects, where some risk factors disproportionately contribute to crash risk, revealing an imbalance in workload distribution and behavioral tendencies. Additionally, descriptive analysis shows that Chinese taxi drivers face significant workload pressures, with an average of 12-hour workdays, which further disrupts driving stability and safety. To address these asymmetries, the study recommends government regulations to limit working hours and encourages taxi companies to provide targeted safety education. The findings provide valuable insights for policymakers and industry stakeholders to design interventions that restore balance in driver workload and risk exposure, ultimately contributing to a safer urban transportation system.

With the continuous advancements in electrification, connectivity, and intelligence in the automotive industry, the mixed traffic of vehicles with different levels of driving automation is changing the carbon emission characteristics in the impact areas of off-ramps on highways. Considering the insufficient research on the carbon emission characteristics of heterogeneous traffic flow in the downstream influence areas of highway off-ramps, this study applied a scenario analysis method. Furthermore, considering factors such as vehicle composition, road control, and platoon management, it establishes and calibrates measurement models for carbon emissions from conventional vehicles, intelligent vehicles, the platoon driving of electric vehicles, and the mixed platoon driving of conventional vehicles and electric vehicles. This study also provides a simulation scenario for a three-lane highway off-ramp based on the actual conditions of the Xi’an Ring Expressway. Finally, by applying the constructed carbon emission calculation models for heterogeneous traffic flow in the intelligent vehicle mixed traffic scenario, a quantitative analysis was conducted to assess the impacts of the intelligent vehicle infiltration rate, off-ramp vehicle proportion, smart-vehicle-dedicated lanes, and platoon driving on carbon emissions in the downstream influence area of off-ramps. The results revealed the impact of intelligent vehicle integration and platoon driving on carbon emission characteristics in the downstream influence areas of highway off-ramps.

This study presents insights into the influence of the characteristics of tourism itineraries on tourist purchasing behavior. We adopted data between 1 August 2019 and 30 November 2019 from the Qunar, the biggest online tourism platform in China and 4366 samples on travel itineraries were obtained. The ordinary least square regression (OLS) method was used. Controlling for product-related and channel-related factors, we demonstrate that in terms of tourism destination choice, outbound tourism products attract an increased number of tourists; in terms of the types of travel, private travel has replaced group travel to become the majority of the tourism market; in terms of the length of travel, mid-term travel (4–6 days) is the first choice, outnumbering short-term and long-term ones; price promotions such as discount for early decision, multi-person price reduction and membership prices significantly lead to increased sales; online reviews also have great impact on tourist purchasing behavior. In sum, this study uses a unique data set to reveal the influence of online tourism product characteristics on sales and provide potential guidance of the marketing strategy in response to consumer behavior for the online tourism industry.

Electrochemical nitrate reduction reaction (NO 3 RR) has great potential for ammonia (NH 3 ) synthesis benefiting from its environmental friendliness and sustainability. Cu-based alloys with elemental diversity and adsorption tunability are widely used as electrocatalyst to lower the reaction overpotential for NO 3 RR catalysis. However, phase separation commonly found in alloys leads to uneven distribution of elements, which limits the possibility of further optimizing the catalytic activity. Herein, an electro-triggered Joule heating method, possessing unique superiority of flash heating and cooling that lead to well-dispersed nanoparticles and uniform mixing of various elements, was adopted to synthesize a single-phase CuNi nano-alloy catalyst evenly dispersed on carbon fiber paper, CFP-Cu 1 Ni 1 , which exhibited a more positive NO 3 RR initial potential of 0.1 V versus reversible hydrogen electrode (vs. RHE) than that of pure copper nanoparticles at 10 mA·cm −2 in 0.5 mol·L −1 Na 2 SO 4 + 0.1 mol·L −1 KNO 3 solution. Importantly, CFP-Cu 1 Ni 1 presented high electrocatalytic activity with a Faradaic efficiency of 95.7% and NH 3 yield rate of 180.58 µmol·h −1 ·cm −2 (2550 µmol·h −1 ·mg cat −1 ) at −0.22 V vs. RHE. Theoretical calculations showed that alloying Cu with Ni into single-phase caused an upshift of its d-band center, which promoted the adsorption of NO 3 − and weakened the adsorption of NH 3 . Moreover, the competitive adsorption of hydrogen ions was restrained until −0.24 V. This work offers a rational design concept with clear guidance for rapid synthesis of uniformly dispersed single-phase nano-alloy catalyst for efficient electrochemical NO 3 RR toward ammonia.

Estimated Time of Arrival (ETA) is a crucial task in the logistics and transportation industry, aiding businesses and individuals in optimizing time management and improving operational efficiency. This study proposes a novel Graph Recurrent Neural Network (GRNN) model that integrates external factor data. The model first employs a Multilayer Perceptron (MLP)-based external factor data embedding layer to categorize and combine influencing factors into a vector representation. A Graph Recurrent Neural Network, combining Long Short-Term Memory (LSTM) and GNN models, is then used to predict ETA based on historical data. The model undergoes both offline and online evaluation experiments. Specifically, the offline experiments demonstrate a 5.3% reduction in RMSE on the BikeNYC dataset and a 6.1% reduction on the DidiShenzhen dataset, compared to baseline models. Online evaluation using Baidu Maps data further validates the model's effectiveness in real-time scenarios. These results underscore the model's potential in improving ETA predictions for urban traffic systems.

Copper-based materials can reliably convert carbon dioxide into multi-carbon products but they suffer from poor activity and product selectivity. The atomic structure-activity relationship of electrocatalysts for the selectivity is controversial due to the lacking of systemic multiple dimensions for operando condition study. Herein, we synthesized high-performance CO RR catalyst comprising of CuO clusters supported on N-doped carbon nanosheets, which exhibited high C products Faradaic efficiency of 73% including decent ethanol selectivity of 51% with a partial current density of 14.4 mA/cm at -1.1 V vs. RHE. We evidenced catalyst restructuring and tracked the variation of the active states under reaction conditions, presenting the atomic structure-activity relationship of this catalyst. Operando XAS, XANES simulations and Quasi-in-situ XPS analyses identified a reversible potential-dependent transformation from dispersed CuO clusters to Cu -CuN clusters which are the optimal sites. This cluster can't exist without the applied potential. The N-doping dispersed the reduced Cu clusters uniformly and maintained excellent stability and high activity with adjusting the charge distribution between the Cu atoms and N-doped carbon interface. By combining Operando FTIR and DFT calculations, it was recognized that the Cu -CuN clusters displayed charge-asymmetric sites which were intensified by CH adsorbing, beneficial to the formation of the high-efficiency asymmetric ethanol.