The effect of cultivation methods on enhancing the methane oxidation capacity of ceramsite

AbstractInoculated with compost from methane oxidation habitats, the differences of methane oxidation capacity were compared and investigated after suspended-growth process and attached-growth process. The result showed that the ceramsite-compost matrix was formed in short time (3d) under the suspended growth condition and its methane oxidation capacity increased remarkably. The peak methane oxidation rate was 52.7g CH4·m-3·h-1 after suspended-growth process. However, the methane oxidation capacity of ceramsite was lower after attached-growth process with the peak methane oxidation rate of 13.5g CH4·m-3·h-1

Facile Preparation of g-C3N4-WO3 Composite Gas Sensing Materials with Enhanced Gas Sensing Selectivity to Acetone

In this paper, g-C3N4-WO3 composite materials were prepared by hydrothermal processing. The composites were characterized by means of X-ray powder diffraction (XRD), scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR), X-ray photoelectron spectroscopy (XPS), and N2 adsorption-desorption, respectively. The gas sensing properties of the composites were investigated. The results indicated that the addition of appropriate amount of g-C3N4 to WO3 could improve the response and selectivity to acetone. The sensor based on 2 wt% g-C3N4-WO3 composite showed the best gas sensing performances. When operating at optimum temperature of 310°C, the responses to 1000 ppm and 0.5 ppm acetone were 58.2 and 1.6, respectively, and the ratio of the S1000 ppm acetone to S1000 ppm ethanol reached 3.7

Izgradnja podatkovne mreže senzora u Internetu stvari

In the Internet of Things, wireless sensor networks (WSN) is in charge of gathering and transferring environment data. It is an essential work to mine data semantic in WSN in the data derived from sensors to improve the WSN. This paper proposes the Data Association Network of sensors (DAN) to organize the mined association semantic relations among sensors into an effective form. Because DAN holds the rich data semantic of WSN, it can improve WSN in some aspects, such as detecting the abnormal sensors, simulating the data of faulty sensors, or optimizing the topology of WSN. Experimental results show that the proposed method can mine the associated relations among sensor nodes effectively, and the DAN is helpful in solving some problems of WSN.Govoreći o Internetu stvari, bežična mreža senzora (WSN) ima ulogu prikupljanja i slanja podataka o okolini. Osnovni je zadatak analizirati semantiku podataka u WSN-u u podacima dobivenim sa senzora u svrhu unaprije.enja bežične mreže senzora. U ovom radu predloženo je mrežno udruženje podataka (DAN) sa senzora u svrhu organiziranja analiziranih udruženja semantičkih relacija izme.u senzora u djelotvorne forme. S obzirom da DAN sadrži dosta semantičkih podataka s WSN-a, može unaprijediti WSN u odre.enim aspektima kao npr. detekcija neispravnih senzora, simuliranje podataka sa senzora u kvaru ili optimiziranje topologije WSN-a. Eksperimentalni rezultati pokazuju da predložena metoda može efektivno analizirati udružene relacije izme.u senzorskih čvorova te da je DAN korisno u rješavanju određenih problema WSN-a

Learning Cooperative Oversubscription for Cloud by Chance-Constrained Multi-Agent Reinforcement Learning

Oversubscription is a common practice for improving cloud resource utilization. It allows the cloud service provider to sell more resources than the physical limit, assuming not all users would fully utilize the resources simultaneously. However, how to design an oversubscription policy that improves utilization while satisfying the some safety constraints remains an open problem. Existing methods and industrial practices are over-conservative, ignoring the coordination of diverse resource usage patterns and probabilistic constraints. To address these two limitations, this paper formulates the oversubscription for cloud as a chance-constrained optimization problem and propose an effective Chance Constrained Multi-Agent Reinforcement Learning (C2MARL) method to solve this problem. Specifically, C2MARL reduces the number of constraints by considering their upper bounds and leverages a multi-agent reinforcement learning paradigm to learn a safe and optimal coordination policy. We evaluate our C2MARL on an internal cloud platform and public cloud datasets. Experiments show that our C2MARL outperforms existing methods in improving utilization ($20\%\sim 86\%$) under different levels of safety constraints

Broadband nonlinear modulation of incoherent light using a transparent optoelectronic neuron array

Nonlinear optical processing of ambient natural light is highly desired in computational imaging and sensing applications. A strong optical nonlinear response that can work under weak broadband incoherent light is essential for this purpose. Here we introduce an optoelectronic nonlinear filter array that can address this emerging need. By merging 2D transparent phototransistors (TPTs) with liquid crystal (LC) modulators, we create an optoelectronic neuron array that allows self-amplitude modulation of spatially incoherent light, achieving a large nonlinear contrast over a broad spectrum at orders-of-magnitude lower intensity than what is achievable in most optical nonlinear materials. For a proof-of-concept demonstration, we fabricated a 10,000-pixel array of optoelectronic neurons, each serving as a nonlinear filter, and experimentally demonstrated an intelligent imaging system that uses the nonlinear response to instantly reduce input glares while retaining the weaker-intensity objects within the field of view of a cellphone camera. This intelligent glare-reduction capability is important for various imaging applications, including autonomous driving, machine vision, and security cameras. Beyond imaging and sensing, this optoelectronic neuron array, with its rapid nonlinear modulation for processing incoherent broadband light, might also find applications in optical computing, where nonlinear activation functions that can work under ambient light conditions are highly sought.Comment: 20 Pages, 5 Figure

In situ interface engineering for probing the limit of quantum dot photovoltaic devices.

Quantum dot (QD) photovoltaic devices are attractive for their low-cost synthesis, tunable band gap and potentially high power conversion efficiency (PCE). However, the experimentally achieved efficiency to date remains far from ideal. Here, we report an in-situ fabrication and investigation of single TiO2-nanowire/CdSe-QD heterojunction solar cell (QDHSC) using a custom-designed photoelectric transmission electron microscope (TEM) holder. A mobile counter electrode is used to precisely tune the interface area for in situ photoelectrical measurements, which reveals a strong interface area dependent PCE. Theoretical simulations show that the simplified single nanowire solar cell structure can minimize the interface area and associated charge scattering to enable an efficient charge collection. Additionally, the optical antenna effect of nanowire-based QDHSCs can further enhance the absorption and boost the PCE. This study establishes a robust 'nanolab' platform in a TEM for in situ photoelectrical studies and provides valuable insight into the interfacial effects in nanoscale solar cells

Multifunctional Materials: A Case Study of the Effects of Metal Doping on ZnO Tetrapods with Bismuth and Tin Oxides

Hybrid metal oxide nano‐ and microstructures exhibit novel properties, which make them promising candidates for a wide range of applications, including gas sensing. In this work, the characteristics of the hybrid ZnO‐Bi2O3 and ZnO‐Zn2SnO4 tetrapod (T) networks are investigated in detail. The gas sensing studies reveal improved performance of the hybrid networks compared to pure ZnO‐T networks. For the ZnO‐T‐Bi2O3 networks, an enhancement in H2 gas response is obtained, although the observed p‐type sensing behavior is attributed to the formed junctions between the arms of ZnO‐T covered with Bi2O3 and the modulation of the regions where holes accumulate under exposure to H2 gas. In ZnO‐T‐Zn2SnO4 networks, a change in selectivity to CO gas with high response is noted. The devices based on individual ZnO‐T‐Bi2O3 and ZnO‐T‐Zn2SnO4 structures showed an enhanced H2 gas response, which is explained on the basis of interactions (electronic sensitization) between the ZnO‐T arm and Bi2O3 shell layer and single Schottky contact structure, respectively. Density functional theory‐based calculations provide mechanistic insights into the interaction of H2 and CO gas molecules with Bi‐ and Sn‐doped ZnO(0001) surfaces, revealing changes in the Fermi energies, as well as charge transfer between the molecules and surface species, which facilitate gas sensing