SO2 effect on degradation of MEA and some other amines

AbstractSO2 is the main acidic impurity in flue gas and will affect amine degradation in CO2 capture process. This work introduced SO2/Na2SO3 in various experiment conditions of MEA (monoethanolamine) oxidative degradation and evaluated the SO2 effect on MEA degradation considering both oxidative and thermal degradation. 60ppm SO2 could inhibit MEA oxidative degradation by scavenging oxidative radicals in absorber condition. Higher concentration of SO2 does not enhance the inhibitory effect, but will increase the corrosivity of the solution. NH3 is promoted by sulfite and becomes significant in MEA thermal degradation. Thiosulfate, the disproportionation product of sulfite, is believed to be the catalyst of SN2reaction. Na2SO3 was used to test SO32- effect on thermal degradation of EDA (ethylenediamine), 2-PE (2-piperidineethanol) and PZ/AMP (piperazine/2-amino-2-methyl-1-propanol) solution. Alkyl structure of amines has important effect on the SN2 reactions

Linear-quadratic Mean Field Control with Non-convex Data

In this manuscript, we study a class of linear-quadratic (LQ) mean field control problems with a common noise and their corresponding $N$-particle systems. The mean field control problems considered are not standard LQ mean field control problems in the sense that their dependence on the mean field terms can be non-linear and non-convex. Therefore, all the existing methods to deal with LQ mean field control problems fail. The key idea to solve our LQ mean field control problem is to utilize the common noise. We first prove the global well-posedness of the corresponding Hamilton-Jacobi equations via the non-degeneracy of the common noise. In contrast to the LQ mean field games master equations, the Hamilton-Jacobi equations for the LQ mean field control problems can not be reduced to finite-dimensional PDEs. We then globally solve the Hamilton-Jacobi equations for $N$-particle systems. As byproducts, we derive the optimal quantitative convergence results from the $N$-particle systems to the mean field control problem and the propagation of chaos property for the related optimal trajectories. This paper extends the results in [{\sc M. Li, C. Mou, Z. Wu and C. Zhou}, \emph{Trans. Amer. Math. Soc.}, 376(06) (2023), pp.~4105--4143] to the LQ mean field control problems.Comment: 35 page

Carbon Nanotube Coated Fibrous Tubes for Highly Stretchable Strain Sensors Having High Linearity

Strain sensors are currently limited by an inability to operate over large deformations or to exhibit linear responses to strain. Producing strain sensors meeting these criteria remains a particularly difficult challenge. In this work, the fabrication of a highly flexible strain sensor based on electrospun thermoplastic polyurethane (TPU) fibrous tubes comprising wavy and oriented fibers coated with carboxylated multiwall carbon nanotubes (CNTs) is described. By combining spraying and ultrasonic-assisted deposition, the number of CNTs deposited on the electrospun TPU fibrous tube could reach 12 wt%, which can potentially lead to the formation of an excellent conductive network with high conductivity of 0.01 S/cm. The as-prepared strain sensors exhibited a wide strain sensing range of 0–760% and importantly high linearity over the whole sensing range while maintaining high sensitivity with a GF of 57. Moreover, the strain sensors were capable of detecting a low strain (2%) and achieved a fast response time whilst retaining a high level of durability. The TPU/CNTs fibrous tube-based strain sensors were found capable of accurately monitoring both large and small human body motions. Additionally, the strain sensors exhibited rapid response time, (e.g., 45 ms) combined with reliable long-term stability and durability when subjected to 60 min of water washing. The strain sensors developed in this research had the ability to detect large and subtle human motions, (e.g., bending of the finger, wrist, and knee, and swallowing). Consequently, this work provides an effective method for designing and manufacturing high-performance fiber-based wearable strain sensors, which offer wide strain sensing ranges and high linearity over broad working strain ranges

Printable Dielectric Elastomers of High Electromechanical Properties Based on SEBS Ink Incorporated With Polyphenols Modified Dielectric Particles

In this work, a recently developed 3D additive processing technology termed electrohydrodynamic (EHD) printing was employed to fabricate dielectric elastomer (DE) films by using styrene-ethylene-butylene-styrene (SEBS) inks with the addition of high dielectric titanium dioxide (TiO2) nanoparticles. In order to improve the dispersibility of TiO2 in the SEBS matrix, extracted walnut polyphenols were utilized for surface modification of TiO2 nanoparticles labelled wp-TiO2. The effect of the applied voltage on the ink jet morphology of the obtained SEBS based inks during EHD printing was analyzed. The prepared films had precision patterned shapes and their morphology was studied. It revealed that the dispersibility of TiO2 nanoparticles in the SEBS matrix and their compatibility were greatly improved using this procedure. Furthermore, the printed DE films were found to have excellent mechanical, dielectric and electromechanical properties. For the range of DEs fabricated, the SEBS/10%wp-TiO2 composite exhibited the maximum actuated area strain of 21.5% at an electric field of about 34.0 V/μm without degradation of other properties

Neural topic modeling with bidirectional adversarial training

Recent years have witnessed a surge of interests of using neural topic models for automatic topic extraction from text, since they avoid the complicated mathematical derivations for model inference as in traditional topic models such as Latent Dirichlet Allocation (LDA). However, these models either typically assume improper prior (e.g. Gaussian or Logistic Normal) over latent topic space or could not infer topic distribution for a given document. To address these limitations, we propose a neural topic modeling approach, called Bidirectional Adversarial Topic (BAT) model, which represents the first attempt of applying bidirectional adversarial training for neural topic modeling. The proposed BAT builds a two-way projection between the document-topic distribution and the document-word distribution. It uses a generator to capture the semantic patterns from texts and an encoder for topic inference. Furthermore, to incorporate word relatedness information, the Bidirectional Adversarial Topic model with Gaussian (Gaussian-BAT) is extended from BAT. To verify the effectiveness of BAT and Gaussian-BAT, three benchmark corpora are used in our experiments. The experimental results show that BAT and Gaussian-BAT obtain more coherent topics, outperforming several competitive baselines. Moreover, when performing text clustering based on the extracted topics, our models outperform all the baselines, with more significant improvements achieved by Gaussian-BAT where an increase of near 6% is observed in accuracy

One Step Quick Detection of Cancer Cell Surface Marker by Integrated NiFe-based Magnetic Biosensing Cell Cultural Chip

RGD peptides has been used to detect cell surface integrin and direct clinical effective therapeutic drug selection. Herein we report that a quick one step detection of cell surface marker that was realized by a specially designed NiFe-based magnetic biosensing cell chip combined with functionalized magnetic nanoparticles. Magnetic nanoparticles with 20-30 nm in diameter were prepared by coprecipitation and modified with RGD-4C, and the resultant RGD-functionalized magnetic nanoparticles were used for targeting cancer cells cultured on the NiFe-based magnetic biosensing chip and distinguish the amount of cell surface receptor-integrin. Cell lines such as Calu3, Hela, A549, CaFbr, HEK293 and HUVEC exhibiting different integrin expression were chosen as test samples. Calu3, Hela, HEK293 and HUVEC cells were successfully identified. This approach has advantages in the qualitative screening test. Compared with traditional method, it is fast, sensitive, low cost, easy-operative, and needs very little human intervention. The novel method has great potential in applications such as fast clinical cell surface marker detection, and diagnosis of early cancer, and can be easily extended to other biomedical applications based on molecular recognition

Different Bacterial Communities Involved in Peptide Decomposition between Normoxic and Hypoxic Coastal Waters

RGD peptides has been used to detect cell surface integrin and direct clinical effective therapeutic drug selection. Herein we report that a quick one step detection of cell surface marker that was realized by a specially designed NiFe-based magnetic biosensing cell chip combined with functionalized magnetic nanoparti- cles. Magnetic nanoparticles with 20-30 nm in diameter were prepared by coprecipitation and modified with RGD-4C, and the resultant RGD-functionalized magnetic nanoparticles were used for targeting cancer cells cul- tured on the NiFe-based magnetic biosensing chip and distinguish the amount of cell surface receptor-integrin. Cell lines such as Calu3, Hela, A549, CaFbr, HEK293 and HUVEC exhibiting different integrin expression were chosen as test samples. Calu3, Hela, HEK293 and HUVEC cells were successfully identified. This approach has advantages in the qualitative screening test. Compared with traditional method, it is fast, sensitive, low cost, easy-operative, and needs very little human intervention. The novel method has great potential in applications such as fast clinical cell surface marker detection, and diagnosis of early cancer, and can be easily extended to other biomedical applications based on molecular recognition

Alpha-linolenic acid pretreatment alleviates NETs-induced alveolar macrophage pyroptosis by inhibiting pyrin inflammasome activation in a mouse model of sepsis-induced ALI/ARDS

BackgroundNeutrophil extracellular traps (NETs) can cause acute lung injury (ALI)/acute respiratory distress syndrome (ARDS) by inducing macrophage pyroptosis. The purpose of this study was to find out whether pretreatment of alpha-linolenic acid (ALA) could inhibit NETs-induced macrophage pyroptosis in sepsis-induced ALI/ARDS, as well as to identify which inflammasome is involved in this process.MethodsLPS was instilled into the trachea to establish sepsis-induced ALI/ARDS in a mouse model. ​Lung injury was assessed by microscopic examination of lung tissue after hematoxylin and eosin staining, pathology score, and bronchoalveolar lavage fluid (BALF) total protein concentration. The level of NETs in lung tissue was detected by MPO-DNA ELISA. Purified NETs, extracted from peritoneal neutrophils, induced macrophage pyroptosis in vitro. Expression of pyroptosis-related proteins (Cl-caspase-1, Cl-GSDMD, ASC) and IL-1β in the lung tissue and bone marrow-derived macrophages (BMDMs) were determined by western blotting or ELISA. Specks of Pyrin/ASC were examined by confocal immunofluorescence microscopy. Mefv (Pyrin)-/- mice were used to study the role of Pyrin in the process of sepsis-induced ALI/ARDS.ResultsALA alleviated LPS-induced lung injury. ALA reduced the level of NETs, pyroptosis-related proteins (Cl-caspase-1, Cl-GSDMD, ASC), and IL-1β in the lung tissue of sepsis mice. In vitro, NETs increased the expression of pyroptosis-related proteins (Cl-caspase-1, Cl-GSDMD, ASC) and IL-1β significantly in BMDMs. Pyrin protein was found to be higher and form the inflammasome with ASC in NETs challenged-BMDMs. Knockout of Mefv (Pyrin) gene fully restored the increased expression of pyroptosis-related proteins (Cl-caspase-1, Cl-GSDMD, ASC) and IL-1β in vitro and in vivo. Lung injury was alleviated significantly in Mefv (Pyrin)-/- mice as well.​ ALA suppresses all the NETs-induced changes as mentioned above.ConclusionOur study is the first to demonstrate Pyrin inflammasome driving NETs-induced macrophage pyroptosis, and ALA may reduce ALI/ARDS by inhibiting the activation of the Pyrin inflammasome-driven macrophage pyroptosis

Feasibility study on quantifying retinal vascular features for predicting preeclampsia based on artificial intelligence models

Objective·To explore the predictive capability of retinal vascular features in preeclampsia (PE) based on artificial intelligence (AI) models.Methods·This retrospective study enrolled 789 pregnant women who registered from June 2020 to January 2021 at Shanghai First Maternity and Infant Hospital of Tongji University in the first 16 weeks of gestation. These women underwent regular prenatal examinations, had retinal fundus images captured, and delivered singleton live births at the hospital. According to whether they developed hypertensive disorders of pregnancy (HDP), they were divided into unaffected group (n=685) and HDP group (n=104). Within the HDP group, pregnancies were further categorized into gestational hypertension (GH) group (n=36) and PE group (n=68) based on the occurrence of PE. Based on the gestational age at onset, the PE group was further divided into early-onset PE group (gestational age<34 weeks) and late-onset PE group (gestational age≥34 weeks). Fundus images of the pregnant women were obtained, and an AI algorithm was utilized to diagnose retinal lesions and quantify retinal vascular features. Comparative analyses were conducted on fundus features and retinal vascular features. Univariate Logistic regression model was employed to analyze the influencing factors of PE occurrence, and multivariate Logistic regression model was further utilized to assess the correlation between retinal vascular features and the occurrence of PE. The predictive capability of retinal vascular features for PE (both early- and late-onset PE) was analyzed by using area under the curve (AUC) of receiver operator characteristic curve (ROC curve).Results·The comparative analysis of fundus features and retinal vascular features demonstrated statistically significant differences between the unaffected group and PE group in terms of central retinal artery equivalent (CRAE), central retinal vein equivalent (CRVE), arteriole-to-venular ratio (AVR), retinal artery tortuosity and retinal artery fractal dimension (all P<0.05). Univariate Logistic regression analysis indicated that second-trimester mean arterial pressure (MAP), second-trimester estimated fetal weight (EFW), CRAE, CRVE, AVR, retinal artery tortuosity and retinal artery fractal dimension were the influencing factors for PE occurrence (all P<0.05). Multivariate Logistic regression analysis revealed that second-trimester EFW, CRAE, CRVE, AVR, retinal artery tortuosity and retinal artery fractal dimension were the protective factors for the occurrence of PE, while second-trimester MAP was the risk factor for PE (all P<0.05). The analysis of ROC curves revealed that maternal risk factors along with second-trimester prenatal examination data (including MAP and EFW) and retinal vascular features model had good predictive ability for PE [AUC (95% CI)=0.784 (0.725－0.843), and this model exhibited better predictive capability for early-onset PE, with an AUC (95% CI) of 0.840 (0.756－0.924).Conclusion·The integration of quantified retinal vascular features based on AI models with maternal risk factors and second-trimester prenatal examination data (including MAP and EFW) enables a more effective prediction of PE occurrence, particularly early-onset PE