Natural gas sweetening using tailored ionic liquid-methanol mixed solvent with selective removal of H₂S and CO₂

Natural gas is often preferred in various energy applications due to its many advantages over conventional fossil fuels such as oil and coal. However, the removal of pollutants from natural gas, particularly hydrogen sulfide (H2S) and carbon dioxide (CO2), requires complex treatment strategies, significantly impacting the cost of natural gas production. In this work, we propose a mixed solvent combining ionic liquid (IL) and methanol, which can selectively and simultaneously remove H2S and CO2 by customizing the IL structure and its ratio in the solvent. This purification process offers improved efficiency and energy savings compared to traditional methods. To determine the optimal IL structure in the mixed solvent, a computer-aided design method was employed. Through solving the formulated MINLP problem, the IL 1-methyl pyridinium trifluoroacetate ([C1OHPy][TFA]) was identified as having the highest affinity for H2S, making it suitable for use in the IL-methanol mixed solvent. Furthermore, the upgrading process of high-sulfur natural gas using the IL-methanol mixed solvent was simulated and evaluated, comparing it to the benchmark natural gas upgrading (Rectisol) process. The results demonstrate that the IL-methanol mixed solvent natural gas upgrading process achieved a 55.57 % power savings and reduced the annual total cost (TAC) by 23.90 % compared to the Rectisol process. These findings highlight the significant potential of our tailored IL-methanol mixed solvent in natural gas production.</p

Exploring the key factors affecting the seasonal variation of phytoplankton in the coastal Yellow Sea

Marine phytoplankton play crucial roles in the ocean’s biological pump and have great impacts on global biogeochemical cycles, yet the knowledge of environmental variables controlling their seasonal dynamics needs to be improved further, especially in the coastal ecosystems. In order to explore the determinants affecting the seasonal variation of phytoplankton, here we conducted three surveys during spring, summer and autumn along the coastal Yellow Sea. Among the phytoplankton community, 49 species of diatoms and 9 species of dinoflagellates were observed in spring, 63 species of diatoms and 10 species of dinoflagellates in summer, and 62 species of diatoms and 11 species of dinoflagellates in autumn. These results thus suggested that there were obvious differences in the number of species across the three seasons, of which diatoms were the most diverse group, followed by dinoflagellates. Additionally, diatoms were the most dominant species of the phytoplankton community and varied largely during different seasons. According to the redundancy analysis, the abundance of phytoplankton community was mainly related to water temperature and dissolved inorganic nitrogen (DIN) during the three seasons, indicating that water temperature and DIN could be the key factors controlling the seasonal variability of phytoplankton community along the coastal Yellow Sea. Also, significant correlations were observed between phytoplankton abundance and heavy metals Zn, As, and Hg during the three seasons, suggesting that these metals also had potential influences on the seasonal dynamics of phytoplankton community in the coastal Yellow Sea

SIAD: Self-supervised Image Anomaly Detection System

Recent trends in AIGC effectively boosted the application of visual inspection. However, most of the available systems work in a human-in-the-loop manner and can not provide long-term support to the online application. To make a step forward, this paper outlines an automatic annotation system called SsaA, working in a self-supervised learning manner, for continuously making the online visual inspection in the manufacturing automation scenarios. Benefit from the self-supervised learning, SsaA is effective to establish a visual inspection application for the whole life-cycle of manufacturing. In the early stage, with only the anomaly-free data, the unsupervised algorithms are adopted to process the pretext task and generate coarse labels for the following data. Then supervised algorithms are trained for the downstream task. With user-friendly web-based interfaces, SsaA is very convenient to integrate and deploy both of the unsupervised and supervised algorithms. So far, the SsaA system has been adopted for some real-life industrial applications.Comment: 4 pages, 3 figures, ICCV 2023 Demo Trac

An effective tool for predicting survival in breast cancer patients with de novo lung metastasis: Nomograms constructed based on SEER

Background &amp; objectivesAn effective tool for forecasting the survival of BCLM is lacking. This study aims to construct nomograms to predict overall survival (OS) and breast cancer-specific survival (BCSS) in breast cancer patients with de novo lung metastasis, and to help clinicians develop appropriate treatment regimens for breast cancer lung metastasis (BCLM) individuals.MethodsWe gathered clinical data of 2,537 patients with BCLM between 2010 and 2015 from the Surveillance, Epidemiology, and End Results (SEER) database. Cox regression analysis was employed to identify independent prognostic parameters for BCLM, which were integrated to establish nomograms by R software. The discriminative ability and predictive accuracy of the nomograms were assessed using the concordance index (C-index), receiver operating characteristic (ROC) curves, and calibration plots. Kaplan–Meier analyses were applied to evaluate the clinical utility of the risk stratification system and investigate the survival benefit of primary site surgery, chemotherapy, and radiotherapy for BCLM patients.ResultsTwo nomograms shared common prognostic indicators including age, marital status, race, laterality, grade, AJCC T stage, subtype, bone metastasis, brain metastasis, liver metastasis, surgery, and chemotherapy. The results of the C-index, ROC curves, and calibration curves demonstrated that the nomograms exhibited an outstanding performance in predicting the prognosis of BCLM patients. Significant differences in the Kaplan–Meier curves of various risk groups corroborated the nomograms' excellent stratification. Primary site surgery and chemotherapy remarkably improved OS and BCSS of BCLM patients whether the patients were at low-risk or high-risk, but radiotherapy did not.ConclusionsWe successfully developed prognostic stratification nomograms to forecast prognosis in BCLM patients, which provide important information for indicating prognosis and facilitating individualized treatment regimens for BCLM patients

Microbial metabolites indole derivatives sensitize mice to D-GalN/LPS induced-acute liver failure via the Tlr2/NF-κB pathway

IntroductionAcute liver failure (ALF) is a clinical condition with many causes, fast progression, and a poor prognosis. Previous research has indicated that microbial factors have a role in ALF, but a clear picture has yet to emerge.MethodsTo investigate the specific involvement of microbial metabolites in ALF development, we pretreated D-GalN/LPS-induced ALF mice with indole derivatives, an influential class of gut microbial metabolites.ResultsContrary to their typical role as anti-inflammatory agents in the host, indole-3-acetic acid (IAA), indole-3-lactic acid (ILA), and indolepropionic acid (IPA) gavage sensitize mice to D-GalN/LPS-induced-ALF with a rapid rise in serum transaminases and histologic lesion. For a clearer picture, we performed comprehensive analysis for the IAA therapy. IAA markedly amplified inflammatory response and cellular damage. The transcriptome analysis indicated the participation of the TNF-α/NF-κB signaling pathway. The structure of gut microbiota in ileum and the expression of Toll-like receptor 2 (Tlr2) in the liver were also significantly changed.DiscussionIn conclusion, IAA pretreatment can exacerbate D-GalN/LPS-induced ALF via probable Tlr2/NF-κB pathway involvement and ileac dysbiosis characterized by enriched gram-positive genus with potential pathogenesis. Microbial metabolites IAA may aggravate individual susceptibility to D-GalN/LPS-induced ALF. Further investigation of the underlying mechanism is needed, and intervention with indole derivatives and related commensal species should be undertaken with caution

Phytoplankton community structure in the Western Subarctic Gyre of the Pacific Ocean during summer determined by a combined approach of HPLC-pigment CHEMTAX and metabarcoding sequencing

The Western Subarctic Gyre (WSG) is a cyclonic upwelling gyre in the northwest subarctic Pacific, which is a region with a high concentration of nutrients but low chlorophyll. We investigated the community structure and spatial distribution of phytoplankton in this area by using HPLC-pigment CHEMTAX (a chemotaxonomy program) and metabarcoding sequencing during the summer of 2021. The phytoplankton community showed significant differences between the two methods. The CHEMTAX analyses identified eight major marine phytoplankton assemblages. Cryptophytes were the major contributors (24.96%) to the total Chl a, followed by pelagophytes, prymnesiophytes, diatoms, and chlorophytes. The eukaryotic phytoplankton OTUs obtained by metabarcoding were categorized into 149 species in 96 genera of 6 major groups (diatoms, prymnesiophytes, pelagophytes, chlorophytes, cryptophytes, and dinoflagellates). Dinoflagellates were the most abundant group, accounting for 44.74% of the total OTUs obtained, followed by cryptophytes and pelagophytes. Sixteen out of the 97 identified species were annotated as harmful algal species, and Heterocapsa rotundata, Karlodinium veneficum, and Aureococcus anophagefferens were assigned to the abundant group (i.e., at least 0.1% of the total reads). Nutrients were more important in shaping the phytoplankton community than temperature and salinity. The 24 stations were divided into southern and northern regions along 44°N according to the k-means method, with the former being dominated by high Chl a and low nutrients. Although different phytoplankton assemblages analyzed by the two methods showed various relationships with environmental factors, a common feature was that the dinoflagellate proportion showed a significantly negative correlation with low nutrients and a positive correlation with Chl a

Anomalous stopping of laser-accelerated intense proton beam in dense ionized matter

Ultrahigh-intensity lasers (10$^{18}$-10$^{22}$W/cm$^{2}$) have opened up new perspectives in many fields of research and application [1-5]. By irradiating a thin foil, an ultrahigh accelerating field (10$^{12}$ V/m) can be formed and multi-MeV ions with unprecedentedly high intensity (10$^{10}$A/cm$^2$) in short time scale ($\sim$ps) are produced [6-14]. Such beams provide new options in radiography [15], high-yield neutron sources [16], high-energy-density-matter generation [17], and ion fast ignition [18,19]. An accurate understanding of the nonlinear behavior of beam transport in matter is crucial for all these applications. We report here the first experimental evidence of anomalous stopping of a laser-generated high-current proton beam in well-characterized dense ionized matter. The observed stopping power is one order of magnitude higher than single-particle slowing-down theory predictions. We attribute this phenomenon to collective effects where the intense beam drives an decelerating electric field approaching 1GV/m in the dense ionized matter. This finding will have considerable impact on the future path to inertial fusion energy.Comment: 8 pages, 4 figure

Energy loss enhancement of very intense proton beams in dense matter due to the beam-density effect

Thoroughly understanding the transport and energy loss of intense ion beams in dense matter is essential for high-energy-density physics and inertial confinement fusion. Here, we report a stopping power experiment with a high-intensity laser-driven proton beam in cold, dense matter. The measured energy loss is one order of magnitude higher than the expectation of individual particle stopping models. We attribute this finding to the proximity of beam ions to each other, which is usually insignificant for relatively-low-current beams from classical accelerators. The ionization of the cold target by the intense ion beam is important for the stopping power calculation and has been considered using proper ionization cross section data. Final theoretical values agree well with the experimental results. Additionally, we extend the stopping power calculation for intense ion beams to plasma scenario based on Ohm's law. Both the proximity- and the Ohmic effect can enhance the energy loss of intense beams in dense matter, which are also summarized as the beam-density effect. This finding is useful for the stopping power estimation of intense beams and significant to fast ignition fusion driven by intense ion beams