Adsorption properties and mechanism of Cu(II) on virgin and aged microplastics in the aquatic environment.

Microplastics (MPs) migrate by adsorbing heavy metals in aquatic environments and act as their carriers. However, the aging mechanisms of MPs in the environment and the interactions between MPs and heavy metals in aquatic environments require further study. In this study, two kinds of materials, polyamide (PA) and polylactic acid (PLA) were used as target MPs, and the effects of UV irradiation on the physical and chemical properties of the MPs and the adsorption behavior of Cu(II) were investigated. The results showed that after UV irradiation, pits, folds and pores appeared on the surface of aged MPs, the specific surface area (SSA) increased, the content of oxygen-containing functional groups increased, and the crystallinity decreased. These changes enhanced the adsorption capacity of aged MPs for Cu(II) pollutants. The adsorption behavior of the PA and PLA MPs for Cu(II) conformed to the pseudo-second-order model and Langmuir isotherm model, indicating that the monolayer chemical adsorption was dominant. The maximum amounts of aged PA and PLA reached 1.415 and 1.398 mg/g, respectively, which were 1.59 and 1.76 times of virgin MPs, respectively. The effects of pH and salinity on the adsorption of Cu(II) by the MPs were significant. Moreover, factors such as pH, salinity and dosage had significant effects on the adsorption of Cu(II) by MPs. Oxidative complexation between the oxygen-containing groups of the MPs and Cu(II) is an important adsorption mechanism. These findings reveal that the UV irradiation aging of MPs can enhance the adsorption of Cu(II) and increase their role as pollutant carriers, which is crucial for assessing the ecological risk of MPs and heavy metals coexisting in aquatic environments.

Machine learning predictors of risk of death within 7 days in patients with non-traumatic subarachnoid hemorrhage in the intensive care unit: A multicenter retrospective study.

Non-traumatic subarachnoid hemorrhage (SAH) is a critical neurosurgical emergency with a high mortality rate, imposing a significant burden on both society and families. Accurate prediction of the risk of death within 7 days in SAH patients can provide valuable information for clinicians, enabling them to make better-informed medical decisions. In this study, we developed six machine learning models using the MIMIC III database and data collected at our institution. These models include Logistic Regression (LR), AdaBoosting (AB), Multilayer Perceptron (MLP), Bagging (BAG), Gradient Boosting Machines (GBM), and Extreme Gradient Boosting (XGB). The primary objective was to identify predictors of death within 7 days in SAH patients admitted to intensive care units. We employed univariate and multivariate logistic regression as well as Pearson correlation analysis to screen the clinical variables of the patients. The initially screened variables were then incorporated into the machine learning models, and the performance of these models was evaluated. Furthermore, we compared the performance differences among the six models and found that the MLP model exhibited the highest performance with an AUC of 0.913. In this study, we conducted risk factor analysis using Shapley values to identify the factors associated with death within 7 days in patients with SAH. The risk factors we identified include Gcsmotor, bicarbonate, wbc, spo2, heartrate, age, nely, glucose, aniongap, GCS, rbc, sysbp, sodium, and gcseys. To provide clinicians with a useful tool for assessing the risk of death within 7 days in SAH patients, we developed a web calculator based on the MLP machine learning model.

MDACl2-Modified SnO2 Film for Efficient Planar Perovskite Solar Cells.

The electron transport layer (ETL) with excellent charge extraction and transport ability is one of the key components of high-performance perovskite solar cells (PSCs). SnO2 has been considered as a more promising ETL for the future commercialization of PSCs due to its excellent photoelectric properties and easy processing. Herein, we propose a facile and effective ETL modification strategy based on the incorporation of methylenediammonium dichloride (MDACl2) into the SnO2 precursor colloidal solution. The effects of MDACl2 incorporation on charge transport, defect passivation, perovskite crystallization, and PSC performance are systematically investigated. First, the surface defects of the SnO2 film are effectively passivated, resulting in the increased conductivity of the SnO2 film, which is conducive to electron extraction and transport. Second, the MDACl2 modification contributes to the formation of high-quality perovskite films with improved crystallinity and reduced defect density. Furthermore, a more suitable energy level alignment is achieved at the ETL/perovskite interface, which facilitates the charge transport due to the lower energy barrier. Consequently, the MDACl2-modified PSCs exhibit a champion efficiency of 22.30% compared with 19.62% of the control device, and the device stability is also significantly improved.

Analysis of regional agricultural carbon emission efficiency and influencing factors: Case study of Hubei Province in China.

In recent years, China's industrial economy has grown rapidly and steadily. Concurrently, carbon emissions have gradually increased, among which agricultural production is an important source of greenhouse gas emissions. It is necessary to reduce agricultural carbon emissions by improving their efficiency to achieve the global goal of peak carbon dioxide emissions in 2030. From a dynamic and static point of view, this study puts agricultural carbon emissions into the evaluation index system of agricultural carbon emission efficiency and analyzes the agricultural carbon emission efficiency and its influencing factors in Hubei Province. First, the unexpected output Slacks-based measure (SBM) model in data envelopment analysis was used to evaluate the agricultural carbon emission efficiency of Hubei Province in 2018 and compared it with other provinces horizontally. Second, the Malmquist-Luenberger index was used to analyze the comprehensive efficiency of agricultural carbon emissions in Hubei Province from 2004 to 2018. The role of technological progress and technical efficiency change in the development of low-carbon agriculture in Hubei Province was analyzed. The results showed that agricultural production efficiency in Hubei Province improved from 2004 to 2018, and the overall level was slightly higher than the average level in China. However, agriculture has not eliminated the extensive development modes of high input, low efficiency, high emission, and high pollution. The efficiency of technological progress in agricultural resource utilization in Hubei Province was close to the optimal level. The improvement space was small. Hence, the low efficiency of agricultural technology is a key factor restricting the improvement of agricultural production efficiency. The results provide a reference for low-carbon agricultural policy formulation and expand the policy choice path. This has practical significance.