Laboratory studies of KOH – induced corrosion under conditions relevant to the air reactor in chemical looping combustion

Due to the gradual increase of the greenhouse effect, the emissions of CO2 need to be limited. In addition, as the concerns for air pollution increase, a clean combustion process becomes important. Chemical looping combustion (CLC) provides efficient power production and enables the efficient capture of CO2. In CLC two interconnected reactors are used, the fuel reactor (FR) and the air reactor (AR). Most of the corrosion studies so far concerning CLC have focused on conditions typical for the fuel reactor. However, little is known about the corrosion in the AR. In this thesis, the behaviour of KOH and its effects on corrosion in the AR was investigated through experiments. Firstly, the melting point of KOH was determined by TGA. Corrosion experiments were carried out at 345 ℃, 380 ℃ and 415 ℃, with air and synthetic air without CO2, both below and above the melting point. Finally, the corrosion rate was determined, and the corrosion products were analysed by SEM-EDX. The melting point of KOH was determined to be 363 °C by TGA measurements and thermodynamic calculations. The work revealed that KOH induces corrosion on both carbon steel and 10CrMo. The extent of the corrosion depended on the temperature and gas composition. The higher the temperature, the higher the corrosion, within a temperature range of 345-415 °C. Synthetic air without CO2 showed higher corrosion than the experiments conducted in air. In the experiments in air, some KOH converted to K2CO3 by the CO2 in the air leading to a less corrosive deposit. The work showed that the presence of KOH on the steel surface in the air reactor can lead to excessive corrosion

Satellite road extraction method based on RFDNet neural network

The road network system is the core foundation of a city. Extracting road information from remote sensing images has become an important research direction in the current traffic information industry. The efficient residual factorized convolutional neural network (ERFNet) is a residual convolutional neural network with good application value in the field of biological information, but it has a weak effect on urban road network extraction. To solve this problem, we developed a road network extraction method for remote sensing images by using an improved ERFNet network. First, the design of the network structure is based on an ERFNet; we added the DoubleConv module and increased the number of dilated convolution operations to build the road network extraction model. Second, in the training process, the strategy of dynamically setting the learning rate is adopted and combined with batch normalization and dropout methods to avoid overfitting and enhance the generalization ability of the model. Finally, the morphological filtering method is used to eliminate the image noise, and the ultimate extraction result of the road network is obtained. The experimental results show that the method proposed in this paper has an average F1 score of 93.37% for five test images, which is superior to the ERFNet (91.31%) and U-net (87.34%). The average value of IoU is 77.35%, which is also better than ERFNet (71.08%) and U-net (65.64%)

A neutralizing bispecific single-chain antibody against SARS-CoV-2 Omicron variant produced based on CR3022

IntroductionThe constantly mutating SARS-CoV-2 has been infected an increasing number of people, hence the safe and efficacious treatment are urgently needed to combat the COVID-19 pandemic. Currently, neutralizing antibodies (Nabs), targeting the receptor-binding domain (RBD) of the SARS-CoV-2 spike protein are potentially effective therapeutics against COVID-19. As a new form of antibody, bispecific single chain antibodies (BscAbs) can be easily expressed in E. coli and exhibits broad-spectrum antiviral activity.MethodsIn this study, we constructed two BscAbs 16-29, 16-3022 and three single chain variable fragments (scFv) S1-16, S2-29 and S3022 as a comparison to explore their antiviral activity against SARS-CoV-2. The affinity of the five antibodies was characterized by ELISA and SPR and the neutralizing activity of them was analyzed using pseudovirus or authentic virus neutralization assay. Bioinformatics and competitive ELISA methods were used to identify different epitopes on RBD.ResultsOur results revealed the potent neutralizing activity of two BscAbs 16-29 and 16-3022 against SARS-CoV-2 original strain and Omicron variant infection. In addition, we also found that SARS-CoV RBD-targeted scFv S3022 could play a synergistic role with other SARS-CoV-2 RBD-targeted antibodies to enhance neutralizing activity in the form of a BscAb or in cocktail therapies.DiscussionThis innovative approach offers a promising avenue for the development of subsequent antibody therapies against SARSCoV-2. Combining the advantages of cocktails and single-molecule strategies, BscAb therapy has the potential to be developed as an effective immunotherapeutic for clinical use to mitigate the ongoing pandemic

MONAI: An open-source framework for deep learning in healthcare

Artificial Intelligence (AI) is having a tremendous impact across most areas of science. Applications of AI in healthcare have the potential to improve our ability to detect, diagnose, prognose, and intervene on human disease. For AI models to be used clinically, they need to be made safe, reproducible and robust, and the underlying software framework must be aware of the particularities (e.g. geometry, physiology, physics) of medical data being processed. This work introduces MONAI, a freely available, community-supported, and consortium-led PyTorch-based framework for deep learning in healthcare. MONAI extends PyTorch to support medical data, with a particular focus on imaging, and provide purpose-specific AI model architectures, transformations and utilities that streamline the development and deployment of medical AI models. MONAI follows best practices for software-development, providing an easy-to-use, robust, well-documented, and well-tested software framework. MONAI preserves the simple, additive, and compositional approach of its underlying PyTorch libraries. MONAI is being used by and receiving contributions from research, clinical and industrial teams from around the world, who are pursuing applications spanning nearly every aspect of healthcare.Comment: www.monai.i

Utilize buried sewage treatment equipment to treat municipal sewage in northern China

At present, environmental changes and resource shortage are some of the hot issues in the world, and how to make better use of resources is a problem those are now eager to solve. Better use of resources is equivalent to saving resources. So they make some brief introduction for buried sewage treatment equipment. Give them a new understanding of the method of wastewater treatment, the gradual rise of SBR process and MBR process, slowly, the advantages and disadvantages have emerged. those improved the two processes by many methods, such as chemical methods to strengthen or solve some pollution problems brought by the processes, and after that, those analyzed the municipal wastewater composition in northern China, and showed the possibility of using the two processes in northern China. They also predict and extrapolate the application of these two processes in china and worldwide

A COMPUTATIONAL STUDY OF THERMAL CONDUCTIVITY OF FREESTANDING H-BN STRUCTURES

The fact that hexagonal boron nitride (h-BN) has remarkable thermal transport property, mechanical property and chemical stability provides endless possibilities in nanoscale thermal device designing. In this study, we investigated the thermal conductivity of different h-BN structures. We first gave a brief literature review of former experimental and simulation results, the development of MD simulations, and thermal transport theory based on Fourier's law and Green-Kubo formalism. We then applied equilibrium molecular dynamic (EMD) approach. Tersoff potential and LJ potential are applied as the in-plane/interlayer force field, respectively. Results showed that the in-plane thermal conductivity of bulk h-BN is around 170W/mK, while the interlayer thermal conductivity is reduced to 5W/mK due to interlayer phonon scattering. Thermal conductivity of pristine monolayer is around 300W/mK on average. Different phonon vibration modes could be speculated from the heat flux auto-correlation function (HCACF). We also applied non-equilibrium molecular dynamics (NEMD) methods and compared the result with the result given by Green-Kubo formalism. Both methods could give reasonable values of thermal conductivity, yet for NEMD methods the local stability should be taken into consideration

A COMPUTATIONAL STUDY OF THERMAL CONDUCTIVITY OF FREESTANDING H-BN STRUCTURES

The fact that hexagonal boron nitride (h-BN) has remarkable thermal transport property, mechanical property and chemical stability provides endless possibilities in nanoscale thermal device designing. In this study, we investigated the thermal conductivity of different h-BN structures. We first gave a brief literature review of former experimental and simulation results, the development of MD simulations, and thermal transport theory based on Fourier's law and Green-Kubo formalism. We then applied equilibrium molecular dynamic (EMD) approach. Tersoff potential and LJ potential are applied as the in-plane/interlayer force field, respectively. Results showed that the in-plane thermal conductivity of bulk h-BN is around 170W/mK, while the interlayer thermal conductivity is reduced to 5W/mK due to interlayer phonon scattering. Thermal conductivity of pristine monolayer is around 300W/mK on average. Different phonon vibration modes could be speculated from the heat flux auto-correlation function (HCACF). We also applied non-equilibrium molecular dynamics (NEMD) methods and compared the result with the result given by Green-Kubo formalism. Both methods could give reasonable values of thermal conductivity, yet for NEMD methods the local stability should be taken into consideration