Surface barriers to mass transfer in nanoporous materials for catalysis and separations

Surface barriers to mass transfer in various nanoporous materials have been increasingly identified. These past few years especially, a significant impact on catalysis and separations has come to light. Broadly speaking, there are two types of barriers: internal barriers, which affect intraparticle diffusion, and external barriers, which determine the uptake and release rates of molecules into and out of the material. Here, we review the literature on surface barriers to mass transfer in nanoporous materials and describe how the existence and influence of surface barriers has been characterized, aided by molecular simulations and experimental measurements. As this is a complex, evolving research topic, without consensus from the scientific community at the time of writing, we present various current viewpoints, not always in agreement, on the origin, nature, and function of such barriers in catalysis and separation. We also emphasize the need for considering all the elementary steps of the mass transfer process in optimally designing new nanoporous and hierarchically structured adsorbents and catalysts

Time to flashover of a vinyl based lining material: Cone calorimeter experiments

Fire behaviour of a vinyl based lining material with and without anti-corrosion painting has been evaluated through 35 and 50kW/m2 cone calorimeter tests. The minimum heat flux requited for surface ignition was estimated. The data were compared by those provided by a revised Kokkala-Thomas’s classification index prediction model, the Östman-Tsantaridis empirical linear regression model and the Hansen-Hovde multiple discriminant function analysis (MDA). All results collected allowed to predict the material flashover time and to classify the lining material. The results illustrate some differences in the classification of the material due to different approaches of the models used

Adaptive Reversible 3D Model Hiding Method Based on Convolutional Neural Network Prediction Error Expansion

Although reversible data hiding technology is widely used, it still faces several challenges and issues. These include ensuring the security and reliability of embedded secret data, improving the embedding capacity, and maintaining the quality of media data. Additionally, irregular data types, such as three-dimensional point clouds and triangle mesh-represented 3D models, lack an ordered structure in their representation. As a result, embedding these irregular data into digital media does not provide sufficient information for the complete recovery of the original data during extraction. To address this issue, this paper proposes a method based on convolutional neural network prediction error expansion to enhance the embedding capacity of carrier images while maintaining acceptable visual quality. The triangle mesh representation of the 3D model is regularized in a two-dimensional parameterization domain, and the regularized 3D model is reversibly embedded into the image. The process of embedding and extracting confidential information in carrier images is symmetrical, and the regularization and restoration of 3D models are also symmetrical. Experiments show that the proposed method increases the reversible embedding capacity, and the triangle mesh can be conveniently subjected to reversible hiding

Land Cover Classification of Remote Sensing Images Based on Hierarchical Convolutional Recurrent Neural Network

Convolutional neural networks (CNNs) and recurrent neural networks (RNNs) have gained improved results in remote sensing image data classification. Multispectral image classification can benefit from the rich spectral information extracted by these models for land cover classification. This paper proposes a classification model called a hierarchical convolutional recurrent neural network (HCRNN) to combine the CNN and RNN modules for pixel-level classification of multispectral remote sensing images. In the HCRNN model, the original 13-band information from Sentinel-2 is transformed into a 1D multispectral sequence using a fully connected layer. It is then reshaped into a 3D multispectral feature matrix. The 2D-CNN features are extracted and used as inputs to the corresponding hierarchical RNN. The feature information at each level is adapted to the same convolution size. This network structure fully leverages the advantages of CNNs and RNNs to extract temporal and spatial features from the spectral data, leading to high-precision pixel-level multispectral remote sensing image classification. The experimental results demonstrate that the overall accuracy of the HCRNN model on the Sentinel-2 dataset reaches 97.62%, which improves the performance by 1.78% compared to the RNN model. Furthermore, this study focused on the changes in forest cover in the study area of Laibin City, Guangxi Zhuang Autonomous Region, which was 7997.1016 km2, 8990.4149 km2, and 8103.0020 km2 in 2017, 2019, and 2021, respectively, with an overall trend of a small increase in the area covered

Highly sensitive and rapid determination of Mycobacterium leprae based on real-time multiple cross displacement amplification

Abstract Background Mycobacterium leprae (ML) is the pathogen that causes leprosy, which has a long history and still exists today. ML is an intracellular mycobacterium that dominantly induces leprosy by causing permanent damage to the skin, nerves, limbs and eyes as well as deformities and disabilities. Moreover, ML grows slowly and is nonculturable in vitro. Given the prevalence of leprosy, a highly sensitive and rapid method for the early diagnosis of leprosy is urgently needed. Results In this study, we devised a novel tool for the diagnosis of leprosy by combining restriction endonuclease, real-time fluorescence analysis and multiple cross displacement amplification (E-RT-MCDA). To establish the system, primers for the target gene RLEP were designed, and the optimal conditions for E-RT-MCDA at 67 °C for 36 min were determined. Genomic DNA from ML, various pathogens and clinical samples was used to evaluate and optimize the E-RT-MCDA assay. The limit of detection (LoD) was 48.6 fg per vessel for pure ML genomic DNA, and the specificity of detection was as high as 100%. In addition, the detection process could be completed in 36 min by using a real-time monitor. Conclusion The E-RT-MCDA method devised in the current study is a reliable, sensitive and rapid technique for leprosy diagnosis and could be used as a potential tool in clinical settings

Biosensor-Based Multiple Cross Displacement Amplification for the Rapid Detection of Mycobacterium leprae

Leprosy is an ancient disease caused by Mycobacterium leprae (ML) that remains a public health problem in poverty-stricken areas worldwide. Although many ML detection techniques have been used, a rapid and sensitive tool is essential for the early detection and treatment of leprosy. Herein, we developed a rapid ML detection technique by combining multiple cross displacement amplification (MCDA) with a nanoparticle-based lateral flow biosensor (LFB), termed ML-MCDA-LFB. MCDA induced a rapid isothermal reaction using specific primers targeting the RLEP gene, and the LFB enabled instant visual amplicon detection. The pure genomic DNA of ML and nucleic acids from various pathogens were employed to evaluate and optimize the ML-MCDA-LFB assay. The optimal conditions for ML-MCDA-LFB were 68 °C and 35 min, respectively. The limit of detection for pure ML genomic DNA was 150 fg per vessel, and the specificity of detection was 100% for the experimental strains. Additionally, the entire detection process could be performed within 40 min, including the isothermal amplification (35 min) and result confirmation (1–2 min). Hence, the ML-MCDA-LFB assay was shown to be a rapid, sensitive, and visual method for detecting ML and could be used as a potential tool for early clinical diagnosis and field screening of leprosy

Additional file 1 of Highly sensitive and rapid determination of Mycobacterium leprae based on real-time multiple cross displacement amplification

Additional file 1