Stability of Y/MCM-48 composite molecular sieve with mesoporous and microporous structures

AbstractY/MCM-48 composite molecular sieve was hydrothermally synthesized at different crystallization temperatures and crystallization times using ethyl orthosilicate as Si source and cetyltrimethyl ammonium bromide as template with the aid of fluoride ions and was characterized by X-ray diffraction, N2 physical adsorption technique, scanning electron microscopy and transmission electron microscopy. The thermal, hydrothermal, acidic, and basic stabilities of the Y/MCM-48 composite were investigated. The results show that Y/MCM-48 composite molecular sieve with meso- and microporous structures was synthesized successfully at 120°C for 36h. The Y/MCM-48 composite has the surface area of 864m2/g and the average pore size is ca. 2.48nm. The bi-porous structure in composite molecular sieve still maintains its stability even after thermal treatment at 800°C for 4h or hydrothermal treatment at 100°C for 48h. After treatment in 1mol/L hydrochloric acid solution or 1mol/L sodium hydroxide solution for 48h, the Y/MCM-48 composite exhibits good acidic stability. The acidic stability is superior to the basic stability at the same treatment time

Glance and Focus Networks for Dynamic Visual Recognition

Spatial redundancy widely exists in visual recognition tasks, i.e., discriminative features in an image or video frame usually correspond to only a subset of pixels, while the remaining regions are irrelevant to the task at hand. Therefore, static models which process all the pixels with an equal amount of computation result in considerable redundancy in terms of time and space consumption. In this paper, we formulate the image recognition problem as a sequential coarse-to-fine feature learning process, mimicking the human visual system. Specifically, the proposed Glance and Focus Network (GFNet) first extracts a quick global representation of the input image at a low resolution scale, and then strategically attends to a series of salient (small) regions to learn finer features. The sequential process naturally facilitates adaptive inference at test time, as it can be terminated once the model is sufficiently confident about its prediction, avoiding further redundant computation. It is worth noting that the problem of locating discriminant regions in our model is formulated as a reinforcement learning task, thus requiring no additional manual annotations other than classification labels. GFNet is general and flexible as it is compatible with any off-the-shelf backbone models (such as MobileNets, EfficientNets and TSM), which can be conveniently deployed as the feature extractor. Extensive experiments on a variety of image classification and video recognition tasks and with various backbone models demonstrate the remarkable efficiency of our method. For example, it reduces the average latency of the highly efficient MobileNet-V3 on an iPhone XS Max by 1.3x without sacrificing accuracy. Code and pre-trained models are available at https://github.com/blackfeather-wang/GFNet-Pytorch.Comment: Accepted by IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI). Journal version of arXiv:2010.05300 (NeurIPS 2020). The first two authors contributed equall

Multi-optimized reconfiguration of hybrid photovoltaic-thermoelectric generation (PV-TEG) system for performance enhancement

This study designs a hybrid photovoltaic-thermoelectric generation (PV-TEG) system, aimed at achieving an efficient photo-thermal-electrical energy conversion. However, dynamically varying real-world environmental conditions frequently lead to partial shading conditions (PSCs) and non-uniform thermal distribution (NTD), which adversely affect the system's output characteristics, resulting in power loss and compromised operation reliability. To alleviate the mismatch effects caused by PSCs and enhance the output power of hybrid PV-TEG systems while maintaining the electrical connections intact, array reconfiguration is a desirable solution. However, existing research on PV-TEG system reconfiguration only focuses on maximizing power output, neglecting the importance of service life and control complexity of switching devices. To bridge this gap, this study proposes a multi-optimized PV-TEG reconfiguration strategy based on the devised chaos-driven dynamical interactive whale optimization algorithm (CDI-WOA), aiming at simultaneously improving the power output while minimizing unnecessary switching actions to prolong the service life of switching devices. Case studies are carried out using two commonly utilized scales of array configuration in engineering, i.e., 6x6 and 6x10 arrays under ten different PSC patterns for systematic validation. Five evaluation indicators are employed for a more comprehensive performance evaluation than prior work, the results demonstrate that CDI-WOA based reconfiguration can increase the maximum power output by 28.24% and 32.02% for 6x6 and 6x10 arrays compared with that without reconfiguration, respectively

Visualization of the electronic phase separation in superconducting K x Fe 2-y Se 2

AbstractType-II iron-based superconductors (Fe-SCs), the alkali-metal-intercalated iron selenide AxFe2−ySe2 (A = K, Tl, Rb, etc.) with a superconducting transition temperature of 32 K, exhibit unique properties such as high Néel temperature, Fe-vacancies ordering, antiferromagnetically ordered insulating state in the phase diagram, and mesoscopic phase separation in the superconducting materials. In particular, the electronic and structural phase separation in these systems has attracted intensive attention since it provides a platform to unveil the insulating parent phase of type-II Fe-SCs that mimics the Mott parent phase in cuprates. In this work, we use spatial- and angle-resolved photoemission spectroscopy to study the electronic structure of superconducting KxFe2−ySe2. We observe clear electronic phase separation of KxFe2−ySe2 into metallic islands and insulating matrix, showing different K and Fe concentrations. While the metallic islands show strongly dispersive bands near the Fermi level, the insulating phase shows an energy gap up to 700 meV and a nearly flat band around 700 meV below the Fermi energy, consistent with previous experimental and theoretical results on the superconducting K1−xFe2Se2 (122 phase) and Fe-vacancy ordered K0.8Fe1.6Se2 (245 phase), respectively. Our results not only provide important insights into the mysterious composition of phase-separated superconducting and insulating phases of KxFe2−ySe2, but also present their intrinsic electronic structures, which will shed light on the comprehension of the unique physics in type-II Fe-SCs

Emergency materials management of petrochemical accidents considering the randomness and uncertainty base on stochastic programming

Petroleum is the pillar industry of the national economy, but safety accidents are frequent all over the world. The government attaches more importance to the safety production management of enterprises to reduce the occurrence of accidents that infringe on personal safety. The management of emergency supplies, which can effectively respond to the occurrence of safety production accidents, is a key measure for handling emergency accidents. Rapid response to accidents means reducing accident rescue costs and protecting personal and property safety. This paper proposes a material stochastic model with the randomness of accident demand for materials. The enterprise and the government can obtain the material management scheme and the quantitative evaluation standard of accident preventive measures from the model results respectively. The model covers as many accident scenarios as possible through multi-scenario modeling to reduce the impact of accident uncertainty. Finally, the feasibility is proved by an example of a petroleum enterprise in Zhoushan City. When the accident demand fluctuates randomly between 80% and 120%, the model proposes a material management scheme that the dispatching time of materials and the cost in rescue work do not exceed 31.33 min and 11.68 million CNY respectively. With the assistance of the model, the enterprise saves the cost of safe production and improves the efficiency of rescue. The government has strengthened the supervision and evaluation of enterprise safety production management. Finally, the mission of protecting the property and life safety of the people will be realized

Long-term network structure evolution investigation for sustainability improvement: an empirical analysis on global top full-service carriers

The continuous and strategic planning of full-service carriers plays a prominent role in transferring and adapting them into resilient full-service carrier network structures. The exploration of full-service carrier network structures using the latest long-term empirical data facilitates enhancing cognitive capabilities in aspects of identifying network development tendencies, readjusting network structures, and supporting determinations of strategic business routes. Aiming at providing sustainable transport network solutions with historical long-term network structure analysis, this paper researches the global top 10 full-service carriers’ air transport networks from 2007 to 2022, applied using social network analysis (SNA). The static metrics from local to path-based perspectives are adopted to explore the global network evolution trend, along with competitiveness characteristics over critical airports. The cascading failure model is applied as a key indicator to analyze the dynamic robustness capability for the network. The similarity changing feature among the selected networks over the past years from 2007 to 2022 is measured using the autocorrelation function (ACF). The results indicate that, from 2011 to 2019, the majority of full-service carrier networks belong to the network types of closed, structural symmetry and two-way transitivity. The critical airports in North America present superiority in terms of network efficiency over those in Europe, Asia, and Oceania. The 10 full-service carriers’ air transport networks all show the trend of being more destruction-resistant. During the COVID-19 pandemic period, the merger with other airlines and the signing of a joint venture agreement led to higher temporal variability in the network structure

Chitosan/Silver Nanoparticle/Graphene Oxide Nanocomposites with Multi-Drug Release, Antimicrobial, and Photothermal Conversion Functions

In this work, we designed and fabricated a multifunctional nanocomposite system that consists of chitosan, raspberry-like silver nanoparticles, and graphene oxide. The room temperature atmospheric pressure microplasma (RT-APM) process provides a rapid, facile, and environmentally-friendly method for introducing silver nanoparticles into the composite system. Our composite can achieve a pH controlled single and/or dual drug release. Under pH 7.4 for methyl blue loaded on chitosan, the drug release profile features a burst release during the first 10 h, followed by a more stabilized release of 70–80% after 40–50 h. For fluorescein sodium loaded on graphene oxide, the drug release only reached 45% towards the end of 240 h. When the composite acted as a dual drug release system, the interaction of fluorescein sodium and methyl blue slowed down the methyl blue release rate. Under pH 4, both single and dual drug systems showed a much higher release rate. In addition, our composite system demonstrated strong antibacterial abilities against E. coli and S. aureus, as well as an excellent photothermal conversion effect under irradiation of near infrared lasers. The photothermal conversion efficiency can be controlled by the laser power. These unique functionalities of our nanocomposite point to its potential application in multiple areas, such as multimodal therapeutics in healthcare, water treatment, and anti-microbials, among others