Sulfion oxidation assisting self-powered hydrogen production system based on efficient catalysts from spent lithium-ion batteries.

Converting spent lithium-ion batteries (LIBs) and industrial wastewater into high-value-added substances by advanced electrocatalytic technology is important for sustainable energy development and environmental protection. Here, we propose a self-powered system using a home-made sulfide fuel cell (SFC) to power a two-electrode electrocatalytic sulfion oxidation reaction (SOR)-assisted hydrogen (H2) production electrolyzer (ESHPE), in which the sulfion-containing wastewater is used as the liquid fuel to produce clean water, sulfur, and hydrogen. The catalysts for the self-powered system are mainly prepared from spent LIBs to reduce the cost, such as the bifunctional Co9S8 catalyst was prepared from spent LiCoO2 for SOR and hydrogen evolution reaction (HER). The Fe-N-P codoped coral-like carbon nanotube arrays encapsulated Fe2P (C-ZIF/sLFP) catalyst was prepared from spent LiFePO4 for oxygen reduction reaction. The Co9S8 catalyst shows excellent catalytic activities in both SOR and HER, evidenced by the low cell voltage of 0.426 V at 20 mA cm-2 in ESHPE. The SFC with Co9S8 as anode and C-ZIF/sLFP as cathode exhibits an open-circuit voltage of 0.69 V and long discharge stability for 300 h at 20 mA cm-2. By integrating the SFC and ESHPE, the self-powered system delivers an impressive hydrogen production rate of 0.44 mL cm-2 min-1. This work constructs a self-powered system with high-performance catalysts prepared from spent LIBs to transform sulfion-containing wastewater into purified water and prepare hydrogen, which is promising to achieve high economic efficiency, environmental remediation, and sustainable development.

A Polarized Gel Electrolyte for Wide-Temperature Flexible Zinc-Air Batteries.

The development of flexible zinc-air batteries (FZABs) has attracted broad attention in the field of wearable electronic devices. Gel electrolyte is one of the most important components in FZABs, which is urgent to be optimized to match with Zn anode and adapt to severe climates. In this work, a polarized gel electrolyte of polyacrylamide-sodium citric (PAM-SC) is designed for FZABs, in which the SC molecules contain large amount of polarized -COO- functional groups. The polarized -COO- groups can form an electrical field between gel electrolyte and Zn anode to suppress Zn dendrite growth. Besides, the -COO- groups in PAM-SC can fix H2 O molecules, which prevents water from freezing and evaporating. The polarized PAM-SC hydrogel delivers a high ionic conductivity of 324.68 mS cm-1 and water retention of 96.85 % after being exposed for 96 h. FZABs with the PAM-SC gel electrolyte exhibit long cycling life of 700 cycles at -40 °C, showing the application prospect under extreme conditions.

Gas sensing devices based on two-dimensional materials: a review.

Gas sensors have been widely utilized penetrating every aspect of our daily lives, such as medical industry, environmental safety testing, and the food industry. In recent years, two-dimensional (2D) materials have shown promising potential and prominent advantages in gas sensing technology, due to their unique physical and chemical properties. In addition, the ultra-high surface-to-volume ratio and surface activity of the 2D materials with atomic-level thickness enables enhanced absorption and sensitivity. Till now, different gas sensing techniques have been developed to further boost the performance of 2D materials-based gas sensors, such as various surface functionalization and Van der Waals heterojunction formation. In this article, a comprehensive review of advanced gas sensing devices is provided based on 2D materials, focusing on two sensing principles of charge-exchange and surface oxygen ion adsorption. Six types of typical gas sensor devices based on 2D materials are introduced with discussion of latest research progress and future perspectives.

Copper-Catalyzed Vinylogous Aerobic Oxidation of Unsaturated Compounds with Air.

A mild and operationally simple copper-catalyzed vinylogous aerobic oxidation of ß,γ- and α,ß-unsaturated esters is described. This method features good yields, broad substrate scope, excellent chemo- and regioselectivity, and good functional group tolerance. This method is additionally capable of oxidizing ß,γ- and α,ß-unsaturated aldehydes, ketones, amides, nitriles, and sulfones. Furthermore, the present catalytic system is suitable for bisvinylogous and trisvinylogous oxidation. Tetramethylguanidine (TMG) was found to be crucial in its role as a base, but we also speculate that it serves as a ligand to copper(II) triflate to produce the active copper(II) catalyst. Mechanistic experiments conducted suggest a plausible reaction pathway via an allylcopper(II) species. Finally, the breadth of scope and power of this methodology are demonstrated through its application to complex natural product substrates.

[Plant metabolomics approach for age discrimination of mountain cultivated ginseng using UPLC-Q-TOF/MS].

Growth year is one of the important factors for the quality of mountain cultivated ginseng (MCG). For age differentiation of MCG, rhizome extracts of ginseng aged from 11 to 15 years were analyzed using a non-targeted approach with ultra-performance liquid chromatography/quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF/MS)-based on plant metabolomics technique. Multivariate statistical methods such as principal component analysis (PCA) and orthogonal partial least squared discrimination analysis (OPLS-DA) were used to compare the derived patterns among the samples. The results showed that the chemical constituents of MCG rhizome extracts of ginseng aged from 11 to 15 years were different. The data set was subsequently applied to metabolite selection by variable importance in the projection (VIP) for sophisticated classification with the optimal number of metabolites. The OPLS-DA model of MCG has a high interpretability and predictive capability, which established by selecting metabolites of MCG aged from 11 to 15 years. By this approach, MCG samples aged from 11 to 15 years, which are the most in demand in the Chinese ginseng market, can be precisely differentiated on the basis of selected metabolites. This proposed analytical method is fast, accurate, and reliable for discriminating the growth year of MCG. Moreover, this study supplies a new method for the age discrimination of other Chinese medicinal materials.

Constructing Bipolar Dual-Active Sites through High-Entropy-Induced Electric Dipole Transition for Decoupling Oxygen Redox.

It remains a significant challenge to construct active sites to break the trade-off between oxidation and reduction processes occurring in battery cathodes with conversion mechanism, especially for the oxygen reduction and evolution reactions (ORR/OER) involved in the zinc-air batteries (ZABs). Here, using a high-entropy-driven electric dipole transition strategy to activate and stabilize the tetrahedral sites is proposed, while enhancing the activity of octahedral sites through orbital hybridization in a FeCoNiMnCrO spinel oxide, thus constructing bipolar dual-active sites with high-low valence states, which can effectively decouple ORR/OER. The FeCoNiMnCrO high-entropy spinel oxide with severe lattice distortion, exhibits a strong 1s→4s electric dipole transition and intense t2g(Co)/eg(Ni)-2p(OL) orbital hybridization that regulates the electronic descriptors, eg and t2g, which leads to the formation of low-valence Co tetrahedral sites (Coth) and high-valence Ni octahedral sites (Nioh), resulting in a higher half-wave potential of 0.87 V on Coth sites and a lower overpotential of 0.26 V at 10 mA cm-2 on Nioh sites as well as a superior performance of ZABs compared to low/mild entropy spinel oxides. Therefore, entropy engineering presents a distinctive approach for designing catalytic sites by inducing novel electromagnetic properties in materials across various electrocatalytic reactions, particularly for decoupling systems.

Recycled Tandem Catalysts Promising Ultralow Overpotential Li-CO2 Batteries.

Lithium-carbon dioxide (Li-CO2 ) batteries are regarded as a prospective technology to relieve the pressure of greenhouse emissions but are confronted with sluggish CO2 redox kinetics and low energy efficiency. Developing highly efficient and low-cost catalysts to boost bidirectional activities is craved but remains a huge challenge. Herein, derived from the spent lithium-ion batteries, a tandem catalyst is subtly synthesized and significantly accelerates the CO2 reduction and evolution reactions (CO2 RR and CO2 ER) kinetics with an in-built electric field (BEF). Combining with the theoretical calculations and advanced characterization techniques, this work reveals that the designed interface-induced BEF regulates the adsorption/decomposition of the intermediates during CO2 RR and CO2 ER, endowing the recycled tandem catalyst with excellent bidirectional activities. As a result, the spent electronics-derived tandem catalyst exhibits remarkable bidirectional catalytic performance, such as an ultralow voltage gap of 0.26 V and an ultrahigh energy efficiency of 92.4%. Profoundly, this work affords new opportunities to fabricate low-cost electrocatalysts from recycled spent electronics and inspires fresh perceptions of interfacial regulation including but not limited to BEF to engineer better Li-CO2 batteries.

An extended substrate screening strategy enabling a low lattice mismatch for highly reversible zinc anodes.

Aqueous zinc batteries possess intrinsic safety and cost-effectiveness, but dendrite growth and side reactions of zinc anodes hinder their practical application. Here, we propose the extended substrate screening strategy for stabilizing zinc anodes and verify its availability (dsubstrate: dZn(002) = 1: 1→dsubstrate: dZn(002)=n:1, n = 1, 2). From a series of calculated phyllosilicates satisfying dsubstrate ≈ 2dZn(002), we select vermiculite, which has the lowest lattice mismatch (0.38%) reported so far, as the model to confirm the effectiveness of "2dZn(002)" substrates for zinc anodes protection. Then, we develop a monolayer porous vermiculite through a large-scale and green preparation as a functional coating for zinc electrodes. Unique "planting Zn(002) seeds" mechanism for "2dZn(002)" substrates is revealed to induce the oriented growth of zinc deposits. Additionally, the coating effectively inhibits side reactions and promotes zinc ion transport. Consequently, the modified symmetric cells operate stably for over 300 h at a high current density of 50 mA cm-2. This work extends the substrate screening strategy and advances the understanding of zinc nucleation mechanism, paving the way for realizing high-rate and stable zinc-metal batteries.

A Disease-Prediction Protocol Integrating Triage Priority and BERT-Based Transfer Learning for Intelligent Triage.

Large hospitals can be complex, with numerous discipline and subspecialty settings. Patients may have limited medical knowledge, making it difficult for them to determine which department to visit. As a result, visits to the wrong departments and unnecessary appointments are common. To address this issue, modern hospitals require a remote system capable of performing intelligent triage, enabling patients to perform self-service triage. To address the challenges outlined above, this study presents an intelligent triage system based on transfer learning, capable of processing multilabel neurological medical texts. The system predicts a diagnosis and corresponding department based on the patient's input. It utilizes the triage priority (TP) method to label diagnostic combinations found in medical records, converting a multilabel problem into a single-label one. The system considers disease severity and reduces the "class overlapping" of the dataset. The BERT model classifies the chief complaint text, predicting a primary diagnosis corresponding to the complaint. To address data imbalance, a composite loss function based on cost-sensitive learning is added to the BERT architecture. The study results indicate that the TP method achieves a classification accuracy of 87.47% on medical record text, outperforming other problem transformation methods. By incorporating the composite loss function, the system's accuracy rate improves to 88.38% surpassing other loss functions. Compared to traditional methods, this system does not introduce significant complexity, yet substantially improves triage accuracy, reduces patient input confusion, and enhances hospital triage capabilities, ultimately improving the patient's medical experience. The findings could provide a reference for intelligent triage development.

DECAF: An interpretable deep cascading framework for ICU mortality prediction.

Medical risk detection is an important topic and a challenging task to improve the performance of clinical practices in Intensive Care Units (ICU). Although many bio-statistical learning and deep learning approaches have provided patient-specific mortality predictions, these existing methods lack interpretability that is crucial to gain adequate insight on why such predictions would work. In this paper, we introduce cascading theory to model the physiological domino effect and provide a novel approach to dynamically simulate the deterioration of patients' conditions. We propose a general DEep CAscading Framework (DECAF) to predict the potential risks of all physiological functions at each clinical stage. Compared with other feature-based and/or score-based models, our approach has a range of desirable properties, such as being interpretable, applicable with multi prediction tasks, and learnable from medical common sense and/or clinical experience knowledge. Experiments on a medical dataset (MIMIC-III) of 21,828 ICU patients show that DECAF reaches up to 89.30 % on AUROC, which surpasses the best competing methods for mortality prediction.

Intercalation-induced amorphous hydrated vanadium oxide for boosted aqueous Zn2+ storage.

An amorphous hydrated vanadium oxide induced by Zn2+ intercalation in Mg-ion inserted V2O5·nH2O (MgVOH) is developed as a high-performance cathode for ZIBs. In particular, zinc pyrovanadate as the product of the second phase transition is found to suppress the dissolution issue of the vanadium species for the cathode to facilitate long lifespan.

Copper(I)-catalyzed asymmetric 1,3-dipolar cycloaddition of 1,3-enynes and azomethine ylides.

Herein, we report a copper(I)-catalyzed asymmetric 1,3-dipolar cycloaddition of azomethine ylides and 1,3-enynes, which provides a series of chiral poly-substituted pyrrolidines in high regio-, diastereo-, and enantioselectivities. Both 4-aryl-1,3-enynes and 4-silyl-1,3-enynes serve as suitable dipolarophiles while 4-alkyl-1,3-enynes are inert. Moreover, the method is successfully applied in the construction of both tetrasubstituted stereogenic carbon centers and chiral spiro pyrrolidines. The DFT calculations are also conducted, which imply a concerted mechanism rather than a stepwise mechanism. Finally, various transformations started from the pyrrolidine bearing a triethylsilylethynyl group and centered on the alkyne group are achieved, which compensates for the inertness of 4-alkyl-1,3-enynes in the present reaction.

The Influence of Microstructure on the Flexural Properties of 3D Printed Zirconia Part via Digital Light Processing Technology.

In recent years, additive manufacturing of ceramics is becoming of increasing interest due to the possibility of the fabrication of complex shaped parts. However, the fabrication of a fully dense bulk ceramic part without cracks and defects is still challenging. In the presented work, the digital light processing method was introduced for fabricating zirconia parts. The flexural properties of the printed zirconia were systematically investigated via a three-point bending test with the digital image correlation method, scanning electron microscopy observation and fractography analysis. Due to the anisotropy of the sample, the bending deformation behaviors of the zirconia samples in the parallel and vertical printing directions were significantly different. The flexural strength and the related elastic modulus of the samples under vertical loading were higher than that of the parallel loading, as the in-plane strength is higher than that of the interlayer strength. The maximum horizontal strain always appeared at the bottom center before the failure for the parallel loading case; while the maximum horizontal strain for the vertical loading moved upward from the bottom center to the top center. There was a clear dividing line between the minimum perpendicular strain and the maximum perpendicular strain of the samples under parallel loading; however, under vertical loading, the perpendicular strain declined from the bottom to the top along the crack path. The surrounding dense part of the sintered sample (a few hundred microns) was mainly composed of large and straight cracks between printing layers, whereas the interior contained numerous small winding cracks. The intense cracks inside the sample led to a low flexural property compared to other well-prepared zirconia samples, which the inadequate additive formulations would be the main reason for the generation of cracks. A better understanding of the additive formulation (particularly the dispersant) and the debinding-sintering process are necessary for future improvement.

Effects of NaCl Concentration on the Behavior of Vibrio brasiliensis and Transcriptome Analysis.

The growth of Vibrio bacteria is affected by environmental conditions, and unfavorable conditions will produce different degrees of stress on Vibrio. The cells respond to the stress on the bacteria through changes in biological characteristics and transcriptomes. To study the effect of NaCl concentration on Vibrio brasiliensis, we have determined the biological characteristics of the 0%, 1%, 2%, 3%, 5%, and 7% NaCl concentrations cultured V. brasiliensis to research the salt stress to bacteria. We found that the biological properties of V. brasiliensis cultured with different NaCl concentrations were different, and the expression of outer membrane proteins of V. brasiliensis changed when it was grown under different NaCl concentrations. When bacteria cultured in higher NaCl concentrations (3%, 5% and 7% NaCl), the sodium-type flagellar protein MotY was found. Finally, the transcriptome analysis of V. brasiliensis cultured with 0% NaCl and 7% NaCl was carried out to find out the differentially expressed genes. We found that the same gene have opposite up-regulated and down-regulated expression in two treatments, indicating that these types of genes are regulated different in low and high osmotic stress.

Gated Tree-based Graph Attention Network (GTGAT) for medical knowledge graph reasoning.

Knowledge graph (KG) is a multi-relational data that has proven valuable for many tasks including decision making and semantic search. In this paper, we present GTGAT (Gated Tree-based Graph Attention), a method for tackling the problems of transductive and inductive reasoning in generalized KGs. Based on recent advancement of graph attention network (GAT), we develop a gated tree-based method to distill valuable information in neighborhood via hierarchical-aware and semantic-aware attention mechanism. Our approach not only addresses several key challenges of GAT but is also capable of undertaking multiple downstream tasks. Experimental results have revealed that our proposed GTGAT has matched state-of-the-art approaches across transductive benchmarks on the Cora, Citeseer, and electronic medical record networks (EMRNet). Meanwhile, the inductive experiments on medical knowledge graphs show that GTGAT surpasses the best competing methods for personalized disease diagnosis.

Mass Balance-Based Method for Quantifying the Oil Moveable Threshold and Oil Content Evaluation of Lacustrine Shale in the Paleogene Shahejie Formation, Nanpu Sag, Bohai Bay Basin.

Substantial heterogeneity in lacustrine shale brings significant challenges to oil exploration. Therefore, a clear and effective resource evaluation standard can significantly reduce the exploration risk and cost, thus further guiding the prediction in productive areas. However, due to the lack of consideration of the thermal maturity and kerogen type, the present evaluation standards may result in the misjudgment of the resource quality of shale oil reservoirs. In this study, a method based on mass balance involving a hydrocarbon generation statistical model was proposed to calculate oil movable threshold (OMT) values. The OMT values for different types of kerogens are determined from simple and easily obtained pyrolysis parameters. Based on the OMT values, a three-dimensional resource quality evaluation model is constructed and applied to the source rocks in Member (Mbr) 1 of the Shahejie Formation (Fm) Nanpu Sag, Bohai Bay Basin, eastern China. The results show that the Mbr 1 of Shahejie Fm shale is a set of high-quality source rocks with high total organic matter (TOC) and S1c (calibrated free hydrocarbons) content. Meanwhile, the hydrocarbon generation potential of the studied lacustrine shales are in the order of type I > type II1 > type II2 > type III, whereas the OMT values show a similar order. From type I to type III, the hydrocarbon expulsion threshold (HET) values for the four types of shales correspond to pyrolysis peak temperatures (T max) at 438, 426, 428, and 432 °C with the maximum OMT values being 143, 128, 127, and 122 mg HC/g TOC, respectively. The movable and favorable shale oil accumulations are mainly associated with type II1 and II2 shales. Our work provides a novel method for distinguishing the resource quality and locating a favorable exploration target for lacustrine shale, improving efficiency and reducing exploration risks.

Coupled high and low-frequency ultrasound remediation of PFAS-contaminated soils.

Solids such as soils and sediments contaminated with per- and polyfluorinated alkyl substances (PFAS) from exposure to impacted media, e.g., landfill leachate or biosolids, direct contaminated discharge, and contaminant transport from atmospheric deposition, have caused significant environmental pollution. Such solids can act as secondary sources of PFAS for groundwater and surface water contamination. There are currently no proven technologies that can degrade PFAS in soil and sediments in a cost-effective, environmentally-friendly, and energy-efficient manner. This study examines the use of coupled high and low-frequency ultrasound in desorbing and degrading PFAS in soil, thereby achieving concurrent treatment and destruction of PFAS in soil. Two common PFAS, namely perfluorooctanoic acid (PFOA) and perfluorooctane sulfonic acid (PFOS), were used to evaluate treatment performance in soils with both low and high organic matter contents. The test results showed that the ultrasound treatment could significantly reduce PFAS concentrations in artificially contaminated soil; however, no significant degradation was achieved. Ultrasound treatment did improve desorption of PFAS from solid particles, particularly from the highly absorbent organic soil; 68.8 ± 1.8% of PFOA and 45.4 ± 4.1% of PFOS were leached from the soil after ultrasound treatment compared to only 28 ± 0.2% of PFOA and 1 ± 3.1% of PFOSafter desorption in water. This work shows that sonication treatment is an effective technology for the removal of PFAS from solids, however, the presence of solids in the solid-liquid slurry can negatively impact ultrasonic cavitation, inhibiting the sonolytic degradation of desorbed PFAS.

Precise CO2 Reduction for Bilayer Graphene.

It is of great significance to explore unique and diverse chemical pathways to convert CO2 into high-value-added products. Bilayer graphene (BLG), with a tunable twist angle and band structure, holds tremendous promise in both fundamental physics and next-generation high-performance devices. However, the π-conjugation and precise two-atom thickness are hindering the selective pathway, through an uncontrolled CO2 reduction and perplexing growth mechanism. Here, we developed a chemical vapor deposition method to catalytically convert CO2 into a high-quality BLG single crystal with a room temperature mobility of 2346 cm2 V-1 s-1. In a finely controlled growth window, the CO2 molecule works as both the carbon source and the oxygen etchant, helping to precisely define the BLG nucleus and set a record growth rate of 300 µm h-1.

The use of immersive virtual reality for cancer-related cognitive impairment assessment and rehabilitation: A clinical feasibility study.

Objective: This brief study aimed to examine the potential effects of virtual reality (VR)-assisted cognitive rehabilitation intervention on the health outcomes of patients with cancer. Methods: A single group of pre-test and post-test study designs were used. An innovative VR system was developed to assess cancer-related cognitive impairment and provide cognitive rehabilitation. The potential effects of the system were determined by measuring changes in cognitive function (learning and memory, information processing speed, executive function, and verbal fluency) and the severity of depression, anxiety, and insomnia. Results: Nine subjects completed the entire VR intervention and were included in the analysis. The participants' mean age was 43.3 years (standard deviation, 8.9 years). The VR-based cognitive intervention significantly improved the subjective cognitive measures of perceived cognitive impairment and perceived cognitive ability (P â€‹= â€‹0.01 and P â€‹< â€‹0.01, respectively). The intervention also improved the objective cognitive measures of verbal learning memory as measured using the Auditory Verbal Learning Test (eg., P â€‹< â€‹0.01 for 5-min delay recall), information processing speed as measured using the trail-making test-A (P â€‹= â€‹0.02) and executive function as measured using the trail-making test-B (P â€‹= â€‹0.03). Only the subtest of delayed recall showed no statistically significant difference after the intervention (P â€‹= â€‹0.69). The VR-based psychological intervention significantly reduced the severity of sleep disorders (P â€‹< â€‹0.01). Conclusions: The use of immersive VR was shown to have potential effects on improving cognitive function for patients with cancer. Future studies will require a larger sample size to examine the effects of immersive VR-assisted cognitive rehabilitation on the health outcomes of patients with cancer.

Comparison of machine learning models for predicting the risk of breast cancer-related lymphedema in Chinese women.

Objective: Predictive models for the occurrence of cancer symptoms by using machine learning (ML) algorithms could be used to aid clinical decision-making in order to enhance the quality of cancer care. This study aimed to develop and validate a selection of classification models that used ML algorithms to predict the occurrence of breast cancer-related lymphedema (BCRL) among Chinese women. Methods: This was a retrospective cohort study of consecutive cases that had been diagnosed with breast cancer, stages I-IV. Forty-eight variables were grouped into five feature sets. Five classification models with ML algorithms were developed, and the models' performance and the variables' relative importance were assessed accordingly. Results: Of 370 eligible female participants, 91 had BCRL (24.6%). The mean age of this study sample was 49.89 (SD â€‹= â€‹7.45). All participants had had breast cancer surgery, and more than half of them had had a modified radical mastectomy (n â€‹= â€‹206, 55.5%). The mean follow-up time after breast cancer surgery was 28.73 months (SD â€‹= â€‹11.71). Most of the tumors were either stage I (n â€‹= â€‹49, 31.2%) or stage II (n â€‹= â€‹252, 68.1%). More than half of the sample had had postoperative chemotherapy (n â€‹= â€‹227, 61.4%). Overall, the logistic regression model achieved the best performance in terms of accuracy (91.6%), precision (82.1%), and recall (91.4%) for BCRL. Although this study included 48 predicting variables, we found that the five models required only 22 variables to achieve predictive performance. The most important variable was the number of positive lymph nodes, followed in descending order by the BCRL occurring on the same side as the surgery, a history of sentinel lymph node biopsy, a dietary preference for meat and fried food, and an exercise frequency of less than three times per week. These factors were the most influential predictors for enhancing the ML models' performance. Conclusions: This study found that in the ML training dataset, the multilayer perceptron model and the logistic regression model were the best discrimination models for predicting the outcome of BCRL, and the k-nearest neighbors and support vector machine models demonstrated good calibration performance in the ML validation dataset. Future research will need to use large-sample datasets to establish a more robust ML model for predicting BCRL deeply and reliably.