Nanjing University

About: Nanjing University is a education organization based out in Nanjing, China. It is known for research contribution in the topics: Catalysis & Population. The organization has 85961 authors who have published 105504 publications receiving 2289036 citations. The organization is also known as: NJU & Nanking University.

Accelerated discovery of CO 2 electrocatalysts using active machine learning

Abstract: The rapid increase in global energy demand and the need to replace carbon dioxide (CO2)-emitting fossil fuels with renewable sources have driven interest in chemical storage of intermittent solar and wind energy1,2. Particularly attractive is the electrochemical reduction of CO2 to chemical feedstocks, which uses both CO2 and renewable energy3–8. Copper has been the predominant electrocatalyst for this reaction when aiming for more valuable multi-carbon products9–16, and process improvements have been particularly notable when targeting ethylene. However, the energy efficiency and productivity (current density) achieved so far still fall below the values required to produce ethylene at cost-competitive prices. Here we describe Cu-Al electrocatalysts, identified using density functional theory calculations in combination with active machine learning, that efficiently reduce CO2 to ethylene with the highest Faradaic efficiency reported so far. This Faradaic efficiency of over 80 per cent (compared to about 66 per cent for pure Cu) is achieved at a current density of 400 milliamperes per square centimetre (at 1.5 volts versus a reversible hydrogen electrode) and a cathodic-side (half-cell) ethylene power conversion efficiency of 55 ± 2 per cent at 150 milliamperes per square centimetre. We perform computational studies that suggest that the Cu-Al alloys provide multiple sites and surface orientations with near-optimal CO binding for both efficient and selective CO2 reduction17. Furthermore, in situ X-ray absorption measurements reveal that Cu and Al enable a favourable Cu coordination environment that enhances C–C dimerization. These findings illustrate the value of computation and machine learning in guiding the experimental exploration of multi-metallic systems that go beyond the limitations of conventional single-metal electrocatalysts. Machine learning predicts Cu-Al electrocatalysts provide better efficiency and productivity than copper when using intermittent renewable electricity to convert carbon dioxide to useful chemicals and fuels.

Glucose sensing by POMC neurons regulates glucose homeostasis and is impaired in obesity

Abstract: A subset of neurons in the brain, known as 'glucose-excited' neurons, depolarize and increase their firing rate in response to increases in extracellular glucose. Similar to insulin secretion by pancreatic beta-cells, glucose excitation of neurons is driven by ATP-mediated closure of ATP-sensitive potassium (K(ATP)) channels. Although beta-cell-like glucose sensing in neurons is well established, its physiological relevance and contribution to disease states such as type 2 diabetes remain unknown. To address these issues, we disrupted glucose sensing in glucose-excited pro-opiomelanocortin (POMC) neurons via transgenic expression of a mutant Kir6.2 subunit (encoded by the Kcnj11 gene) that prevents ATP-mediated closure of K(ATP) channels. Here we show that this genetic manipulation impaired the whole-body response to a systemic glucose load, demonstrating a role for glucose sensing by POMC neurons in the overall physiological control of blood glucose. We also found that glucose sensing by POMC neurons became defective in obese mice on a high-fat diet, suggesting that loss of glucose sensing by neurons has a role in the development of type 2 diabetes. The mechanism for obesity-induced loss of glucose sensing in POMC neurons involves uncoupling protein 2 (UCP2), a mitochondrial protein that impairs glucose-stimulated ATP production. UCP2 negatively regulates glucose sensing in POMC neurons. We found that genetic deletion of Ucp2 prevents obesity-induced loss of glucose sensing, and that acute pharmacological inhibition of UCP2 reverses loss of glucose sensing. We conclude that obesity-induced, UCP2-mediated loss of glucose sensing in glucose-excited neurons might have a pathogenic role in the development of type 2 diabetes.

An energy-efficient unequal clustering mechanism for wireless sensor networks

Abstract: Clustering provides an effective way for prolonging the lifetime of a wireless sensor network. Current clustering algorithms usually utilize two techniques, selecting cluster heads with more residual energy and rotating cluster heads periodically, to distribute the energy consumption among nodes in each cluster and extend the network lifetime. However, they rarely consider the hot spots problem in multihop wireless sensor networks. When cluster heads cooperate with each other to forward their data to the base station, the cluster heads closer to the base station are burdened with heavy relay traffic and tend to die early, leaving areas of the network uncovered and causing network partition. To address the problem, we propose an energy-efficient unequal clustering (EEUC) mechanism for periodical data gathering in wireless sensor networks. It partitions the nodes into clusters of unequal size, and clusters closer to the base station have smaller sizes than those farther away from the base station. Thus cluster heads closer to the base station can preserve some energy for the inter-cluster data forwarding. We also propose an energy-aware multihop routing protocol for the inter-cluster communication. Simulation results show that our unequal clustering mechanism balances the energy consumption well among all sensor nodes and achieves an obvious improvement on the network lifetime

Energy Level Engineering of MoS2 by Transition-Metal Doping for Accelerating Hydrogen Evolution Reaction

Abstract: Water-splitting devices for hydrogen generation through electrolysis (hydrogen evolution reaction, HER) hold great promise for clean energy. However, their practical application relies on the development of inexpensive and efficient catalysts to replace precious platinum catalysts. We previously reported that HER can be largely enhanced through finely tuning the energy level of molybdenum sulfide (MoS2) by hot electron injection from plasmonic gold nanoparticles. Under this inspiration, herein, we propose a strategy to improve the HER performance of MoS2 by engineering its energy level via direct transition-metal doping. We find that zinc-doped MoS2 (Zn-MoS2) exhibits superior electrochemical activity toward HER as evidenced by the positively shifted onset potential to -0.13 V vs RHE. A turnover of 15.44 s-1 at 300 mV overpotential is achieved, which by far exceeds the activity of MoS2 catalysts reported. The large enhancement can be attributed to the synergistic effect of electronic effect (energy level matching) and morphological effect (rich active sites) via thermodynamic and kinetic acceleration, respectively. This design opens up further opportunities for improving electrocatalysts by incorporating promoters, which broadens the understanding toward the optimization of electrocatalytic activity of these unique materials.

Highly Sensitive Glucose Sensor Based on Pt Nanoparticle/Polyaniline Hydrogel Heterostructures

Abstract: Glucose enzyme biosensors have been shown useful for a range of applications from medical diagnosis, bioprocess monitoring, to beverage industry and environmental monitoring. We present here a highly sensitive glucose enzyme sensor based on Pt nanoparticles (PtNPs)-polyaniline (PAni) hydrogel heterostructures. High-density PtNPs were homogeneously loaded onto the three-dimensional (3D) nanostructured matrix of the PAni hydrogel. The PtNP/PAni hydrogel heterostructure-based glucose sensor synergizes the advantages of both the conducting hydrogel and the nanoparticle catalyst. The porous structure of the PAni hydrogel favored the high density immobilization of the enzyme and the penetration of water-soluble molecules, which helped efficiently catalyze the oxidation of glucose. In addition, the PtNPs catalyzed the decomposition of hydrogen peroxide that was generated during the enzymatic reaction. The transferred charges from these electrochemical processes were efficiently collected by the highly conducting...