Room Temperature Gas Sensing Using Pure and Modified Metal Oxide Nanowires

Recently, various quasi 1D metal oxide semiconductor nanostructures (nanorods, nanowires, nanotubes, nanobelts) of various binary oxides have been found to be excellent materials for gas sensing. However, some of the sensitive gas sensors can work only at elevated temperatures. The sensing performance can be further improved when these oxides are doped with noble metal nanoparticles and form hetero-junction with other oxides, especially different types of metal oxide. These modifications can substantially change the surface properties as well as electronic properties due to their enhancement of the depletion layer at the metal nanoparticle-metal oxide nanowire and homo/hetero-interfaces. The objective of this dissertation study is to investigate the sensing performance of WO3, ZnO, NiO and TiO2 nanowires towards various air pollutant gases such as NH3, NO2, H2S and CO at room temperature. The sensing performance of pure metal oxide nanowires are further improved by doping these nanowires with noble metal nanoparticles and through the formation of n-p hetero-junction of two dissimilar oxides. Based on this study, it was found that pure ZnO and NiO nanowires show a high sensitivity and the best selectivity performance towards the ppm level NO2 (1 ppm) with respect to other interfering gases. On the other hand, both WO3/Ag and WO3-NiO gas sensors show enhanced sensing and highly selective performance towards H2S (~10ppm) at room temperature. Additionally, sensor response and recovery become faster with WO3/Ag than pure WO3 nanowires. The plausible reasons for such improvements with these surface modifications are discussed. This study provides a scientific foundation to engineer practical room-temperature gas sensors with enhanced performance

Understanding Heterostructure Chemiresistive Gas Sensing at Room Temperature

Chemiresistive sensors are the most widely investigated gas sensors due to their ease in fabrication, cost-effectiveness, simplicity of operation, and offer advances in miniaturization. Up to date, typical and well-researched resistive-type sensing materials include semiconductor metal oxides, noble metals, carbon-based nanomaterials (e.g., graphene and carbon nanotubes), and conducting polymers. Gas sensors based on a single material were found difficult to meet the practical requirements for multi-sensing properties, including sensitivity, selectivity, speed of response/recovery, stability, limit of detection, and room temperature operation. Rational design through a combination of chemically or electronically dissimilar nanomaterials is an effective route to enhancing gas sensing performance. Because the chemical composition varies with position, especially at the interface between two dissimilar materials, the newly hybridized structure is defined as a heterostructure. During the past decades, there has been significant research effort in exploring the nanocomposite heterostructures for chemiresistive room-temperature gas sensors. However, sensing mechanisms for such heterostructures are still elusive without solid analysis or direct characterization results. The objective of this dissertation study is to understand the sensing mechanisms of heterostructure-based chemiresistive gas sensors through in situ investigation and analysis under real operating conditions. Various novel heterostructures have been developed for specific types of gas sensing, with a variety of in situ/operando techniques applied to investigate the sensing mechanisms toward different gases. Firstly, nickel oxide-tungsten oxide (NiO-WO3) nanowire-based heterostructures with various component ratios were fabricated via a facile, sonication-based solution mixing method. The exhibited heterojunction effect is maximally observed for W3N1 (75 mol% WO3-25 mol% NiO) and confirmed by observation of the increase in resistance due to the formation of a diode-like p-n junction at the NiO-WO3 interface. The excellent hydrogen sulfide (H2S) sensing performance for W3N1 is attributed to the p-n junction effect, sulfurization by H2S (formation of tungsten sulfides (WS2-x), and nickel sulfides (NiS1-x)), and the ideal ratio of the NiO component in the composite. The formation of reactive semi-metallic products due to sulfurization on the sensor surface was confirmed by in situ X-ray diffraction (XRD) analyses. Operando impedance measurements and resistor-capacitor (RC) equivalent circuit analyses during gas sensing experiments were performed to evaluate the effect of grain-grain boundary or the p-n junction on the sensing performance. It was found that for pure WO3 and W3N1 samples, these contributing effects are in the same direction, resulting in a cooperative and highly sensitive performance, whereas, for other compositions, the samples exhibited competing influences, resulting in low sensitivity. Secondly, the gold doped tin oxide/reduced graphene oxide (Au-SnO2/rGO) ternary nanohybrid heterostructure was designed with improved room temperature hydrogen (H2) sensing performance. The sputtered Au nanoparticles enhanced both sensitivity and recovery of the SnO2-rGO platform. Such an enhancement was attributed to the increased surface area and the oxygen ions spillover effect of loaded Au nanoparticles. The catalytic effect of Au nanoparticles for hydrogen adsorption and desorption was then revealed through the temperature-dependent gas sensing test and the Arrhenius analysis. A better balance between sensitivity and recovery can be further achieved in the future by tuning the deposition conditions of Au nanoparticles. A prototype handheld device based on the Au-SnO2/rGO composites was finally developed for hydrogen detection. The prototype device demonstrates the potential for real-time hydrogen monitoring. The availability of such sensors will contribute to promoting a sustainable hydrogen economy, protecting public safety, and enhancing lead-acid battery safety in a wide range of applications. Thirdly, the nickel-doped tin oxide-reduced graphene oxide (Ni/SnO2-rGO) ternary nanohybrid heterostructure was prepared with enhanced room temperature sulfur dioxide (SO2) sensing performance. The Ni additives significantly improved the lower detection limit (ppb level) of the SnO2-rGO platform. The SO2 concentration calibration curve is well fitted by the Langmuir isotherm. The humidity effect on the sensing performance was also investigated. The results suggested that current nanohybrid materials still suffer from the humidity effect. Metal oxide nanocomposite doping enhanced the SO2 sensing and activated the adsorption of water molecules, which diminished the sensor response to sulfur dioxide gas. Finally, the Poly[3-(3carboxypropyl)thiophene-2,5-diyl]regioregular (PT-COOH)-GO binary nanocomposite heterostructure was prepared. The gas sensing properties were investigated toward NO2, NH3, SO2, and CO. The PT-COOH based sensors exhibited tunable sensing performance through the drain voltage modulation. PT-COOH-GO sensors indicated enhanced NO2 sensing performance with good sensitivity, recovery, and stable responses. The statistical signal analysis was conducted to obtain proof-of-concept results for gas discrimination through signal processing. This study reveals the electronic conduction gas sensing model of multi-metal oxide -nanowires-based chemiresistive gas sensors through the combination of direct current (DC) and alternating current (AC) impedance measurements. The research also suggests that two-dimensional (2D) rGO with proper modifications can be efficient gas sensing materials toward various gaseous analytes. Combining in situ characterization and critical sensing factor analyses, results from the study will offer valuable and comprehensive insights for the rational design of superior heterostructure-based chemiresistive gas sensors

Scaling Novel Object Detection with Weakly Supervised Detection Transformers

Weakly supervised object detection (WSOD) enables object detectors to be trained using image-level class labels. However, the practical application of current WSOD models is limited, as they operate at small scales and require extensive training and refinement. We propose the Weakly Supervised Detection Transformer, which enables efficient knowledge transfer from a large-scale pretraining dataset to WSOD finetuning on hundreds of novel objects. We leverage pretrained knowledge to improve the multiple instance learning framework used in WSOD, and experiments show our approach outperforms the state-of-the-art on datasets with twice the novel classes than previously shown.Comment: CVPR 2022 Workshop on Attention and Transformers in Visio

2,3,4-Tri-O-acetyl-β-d-xylosyl 2,4-dichloro­phenoxy­acetate

In the title compound, C19H20Cl2O10, the hexopyranosyl ring adopts a chair conformation. The four substituents are in equatorial positions. The mol­ecules arelinked via C—H⋯O contacts along the a axis

Economic order quantity under retailer partial trade credit in two-echelon supply chain

In this paper, we want to investigate the retailer’s inventory policy when the retailer maintains a powerful position in two-echelon supply chain. That is, we assumed that the retailer can obtain the full trade credit offered by the supplier yet the retailer just offers the partial trade credit to their customers under two-level trade credit situation. Then, we investigate the retailer’s inventory system as a cost minimization problem to determine the retailer’s optimal inventory policy in two-echelon supply chain. Finally, numerical examples are given to illustrate the results and to obtain managerial insights

Distributed Multi-agent Video Fast-forwarding

In many intelligent systems, a network of agents collaboratively perceives the environment for better and more efficient situation awareness. As these agents often have limited resources, it could be greatly beneficial to identify the content overlapping among camera views from different agents and leverage it for reducing the processing, transmission and storage of redundant/unimportant video frames. This paper presents a consensus-based distributed multi-agent video fast-forwarding framework, named DMVF, that fast-forwards multi-view video streams collaboratively and adaptively. In our framework, each camera view is addressed by a reinforcement learning based fast-forwarding agent, which periodically chooses from multiple strategies to selectively process video frames and transmits the selected frames at adjustable paces. During every adaptation period, each agent communicates with a number of neighboring agents, evaluates the importance of the selected frames from itself and those from its neighbors, refines such evaluation together with other agents via a system-wide consensus algorithm, and uses such evaluation to decide their strategy for the next period. Compared with approaches in the literature on a real-world surveillance video dataset VideoWeb, our method significantly improves the coverage of important frames and also reduces the number of frames processed in the system.Comment: To appear at ACM Multimedia 202

Exact Hawking Radiation of Scalars, Fermions, and Bosons Using the Tunneling Method Without Back-Reaction

Hawking radiation is studied for arbitrary scalars, fermions, and spin-1 bosons, using a tunneling approach, to every order in $\hbar$ but ignoring back-reaction effects. It is shown that the additional quantum terms yield no new contribution to the Hawking temperature. Indeed, it is found that the limit of small $\hbar$ in the standard quantum WKB approximation is replaced by the near-horizon limit in the gravitational WKB approach.Comment: 8 pages, no figures. v3: Introduction updated. Version to appear in PL