Ru nanocrystals modified porous FeOOH nanostructures with open 3D interconnected architecture supported on NiFe foam as high-performance electrocatalyst for oxygen evolution reaction and electrocatalytic urea oxidation.

The construction of binder-free electrodes with well-defined three-dimensional (3D) morphology and optimized electronic structure represents an efficient strategy for the design of high-performance electrocatalysts for the development of efficient green hydrogen technologies. Herein, Ru nanocrystals were modified on 3D interconnected porous FeOOH nanostructures with open network-like frameworks on NiFe foam (Ru/FeOOH@NFF), which were used as an efficient electrocatalyst. In this study, a 3D interconnected porous FeOOH with an open network structure was first electrodeposited on NiFe foam and served as the support for the in-situ modification of Ru nanocrystals. Subsequently, the Ru nanocrystals and abundant oxygen vacancies were simultaneously incorporated into the FeOOH matrix via the adsorption-reduction method, which involved NaBH4 reduction. The Ru/FeOOH@NFF electrocatalyst shows a large specific surface area, abundant oxygen vacancies, and modulated electronic structure, which collectively result in a significant enhancement of catalytic properties with respect to the oxygen evolution reaction (OER) and urea oxidation reaction (UOR). The Ru/FeOOH@NFF catalyst exhibits an outstanding OER performance, requiring a low overpotential (360 mV) at 200 mA cm-2 with a small Tafel slope (58 mV dec-1). Meanwhile, the Ru/FeOOH@NFF catalyst demonstrates more efficient UOR activity for achieving 200 mA cm-2 at a lower overpotential of 272 mV. Furthermore, an overall urea electrolysis cell using the Ru/FeOOH@NFF as the anode and Pt as the cathode (Ru/FeOOH@NFF||Pt) reveals a cell voltage of 1.478 V at 10 mA cm-2 and a prominent durability (120 h at 50 mA cm-2). This work will provide a valuable understanding of the construction of high-performance electrocatalysts with 3D microstructure for promoting urea-assisted water electrolysis.

Rapid Preparation Triggered by Visible Light for Tough Hydrogel Sensors with Low Hysteresis and High Elasticity: Mechanism, Use and Recycle-by-Design.

Hydrogels have emerged as promising candidates for flexible devices and water resource management. However, further applications of conventional hydrogels are restricted due to their limited performance and lack of a recycling strategy. Herein, a tough, flexible, and recyclable hydrogel sensor via a visible-light-triggered polymerization is rapidly created. The Zn2+ crosslinked terpolymer is in situ polymerized using g-C3N4 as the sole initiator to form in situ chain entanglements, endowing the hydrogels with low hysteresis and high elasticity. In the use phase, the hydrogel sensor exhibited high ion conductivity, excellent mechanical properties, fast responsiveness, high sensitivity, and remarkable anti-fatigue ability, making it exceptionally effective in accurately monitoring complex human movements. At the end-of-life (EOL), leveraging the synergy between the photodegradation capacity of g-C3N4 and the adsorption function of the hydrogel matrix, the post-consumer hydrogel is converted into water remediation materials, which not only promoted the rapid degradation of organic pollutants, but also facilitated collection and reuse. This innovative strategy combined in situ entangling reinforcement and tailored recycle-by-design that employed g-C3N4 as key blocks in the hydrogel to achieve high performance in the use phase and close the loop through the reutilization at EOL, highlighting the cost-effective synthesis, specialized structure, and life cycle management.

An empirical study of the flag rugby game programme to promote gross motor skills and physical fitness in 5-6 year old preschool children.

In order to investigate the effects of a flag rugby game programs on the development of gross motor skills and physical fitness in 5-6 year old preschool children. An overall random sampling method was adopted to select 56 preschoolers aged 5-6 years from class A and class B,class A was the ExG (28) implemented a flag rugby games intervention program,and class B was the ConG (28) implemented a regular physical education program,with a 12-week intervention period.The content and requirements of the experimental intervention program were determined through a systematic analysis method, and the subjects' gross motor skills level was tested using the TGMD-3, and their physical fitness level was measured using China's fifth national physical fitness monitoring of early childhood (3-6 years) component.Experimental data were statistically analyzed using independent samples t-test, paired samples t-test, and repeated measures ANOVA test.After 12 weeks of practice, 1）We found that the ExG outperformed the ConG in Skip,One hand stationary dribble, Overhand throw, Underhand throw, Forehand strike of self-bounced,Kick a stationary ball, Total object control subject score, Total gross motor score test results with significant differences (p < 0.05).The ExG differed significantly (p < 0.01) in Grip strength, Stand long jump,Sit forward bend, Continuous jump on both feet,15 m obstacle run,Walk the balance beam, while the ConG differed significantly (p < 0.05) only in Continuous jump on both feet,15 m obstacle run and Stand long jump.2）We also found gender differences in gross motor skills and Physical fitness test results, this difference is manifested in boys outperformed girls in Total object control subject Score, Grip strength and 15 m obstacle run with significant difference (p < 0.05), girls outperformed boys in Sit forward bend with significant difference (p < 0.05).The 12-week flag rugby game programs improved gross motor skills and physical fitness levels of 5-6 year old preschoolers more comprehensively than the regular program, and we recommend the purposeful and organized promotion of a flag rugby game programs in the physical activity curriculum for 5-6 year old preschoolers.

SAGL: A self-attention-based graph learning framework for predicting survival of colorectal cancer patients.

BACKGROUND AND OBJECTIVE: Colorectal cancer (CRC) is one of the most commonly diagnosed cancers worldwide. The accurate survival prediction for CRC patients plays a significant role in the formulation of treatment strategies. Recently, machine learning and deep learning approaches have been increasingly applied in cancer survival prediction. However, most existing methods inadequately represent and leverage the dependencies among features and fail to sufficiently mine and utilize the comorbidity patterns of CRC. To address these issues, we propose a self-attention-based graph learning (SAGL) framework to improve the postoperative cancer-specific survival prediction for CRC patients. METHODS: We present a novel method for constructing dependency graph (DG) to reflect two types of dependencies including comorbidity-comorbidity dependencies and the dependencies between features related to patient characteristics and cancer treatments. This graph is subsequently refined by a disease comorbidity network, which offers a holistic view of comorbidity patterns of CRC. A DG-guided self-attention mechanism is proposed to unearth novel dependencies beyond what DG offers, thus augmenting CRC survival prediction. Finally, each patient will be represented, and these representations will be used for survival prediction. RESULTS: The experimental results show that SAGL outperforms state-of-the-art methods on a real-world dataset, with the receiver operating characteristic curve for 3- and 5-year survival prediction achieving 0.849±0.002 and 0.895±0.005, respectively. In addition, the comparison results with different graph neural network-based variants demonstrate the advantages of our DG-guided self-attention graph learning framework. CONCLUSIONS: Our study reveals that the potential of the DG-guided self-attention in optimizing feature graph learning which can improve the performance of CRC survival prediction.

High-performance flexible asymmetric supercapacitor based on hierarchical MnO2/PPy nanocomposites covered MnOOH nanowire arrays cathode and 3D network-like Fe2O3/PPy hybrid nanosheets anode.

The configuration of asymmetric supercapacitors (ASCs) has proven to be an effective approach to increase the energy storage properties due to the expanded working voltage resulting from the well-separated potential windows of the cathode and anode. However, carbonaceous anode materials generally suffer from relatively low capacitance, which restricts the enhancement of the energy storage performance of the full device in a traditional asymmetrical design. Herein, a rational design of all-pseudocapacitive ASCs (APASCs) using pseudocapacitive materials with a novel hierarchical nanostructure on both electrodes was developed to optimize the electrochemical properties for high-performance ASC devices. The assembled APASC employed the MnO2/PPy nanocomposites covered MnOOH nanowire arrays with core-shell hierarchical architecture as the cathode and Fe2O3/PPy hybrid nanosheets with 3D porous network-like structure as the anode. Owing to the coordinated pseudocapacitive properties and unique hierarchical nanostructures, this assembled APASC exhibited an exceptional volumetric capacitance of 4.92F cm-3 in a stable voltage window of 2 V, a maximum volumetric energy density of 2.66 mWh cm-3 at 19.72 mW cm-3, and excellent cyclic stability over 10,000 cycles (90.6 % capacitance retention), as well as remarkable flexibility and mechanical stability, providing insights for the design of flexible energy storage systems with enhanced performance.

Understanding the Thermal Degradation Mechanism of High-Temperature-Resistant Phthalonitrile Foam at Macroscopic and Molecular Levels.

Polymer foam, a special form of polymer, usually demonstrates some unexpected properties that rarely prevail in the bulky polymer. Studying the thermal degradation behavior of a specific polymer foam is important for its rational design, quick identification, objective evaluation, and industrial application. The present study aimed to discover the thermal degradation mechanism of high-temperature-resistant phthalonitrile (PN) foam under an inert gas atmosphere. The macroscopic thermal decomposition of PN foam was carried out at the cost of size/weight loss, resulting in an increasing number of open cells with pyrolyzation debris. Using the TGA/DTG/FTIR/MS technique, it was found that PN foam involves a three-stage thermal degradation mechanism: (I) releasing gases such as H2O, CO2, and NH3 generated from azo-containing intermediate decomposition and these trapped in the closed cells during the foaming process; (II) backbone decomposition from C-N, C-O, and C-C cleavage in the PN aliphatic chain with the generation of H2O, CO2, NH3, CO, CH4, RNH2, HCN, and aromatic gases; and (III) carbonization into a final N-hybrid graphite. The thermal degradation of PN foam was different from that of bulky PN resin. During the entire pyrolysis of PN foam, there was a gas superposition phenomenon since the release of the decomposition volatile was retarded by the closed cells in the PN foam. This research will contribute to the general understanding of the thermal degradation behavior of PN foam at the macroscopic and molecular levels and provide a reference for the identification, determination, and design of PN material.

Predicting Colorectal Cancer Survival Using Time-to-Event Machine Learning: Retrospective Cohort Study.

BACKGROUND: Machine learning (ML) methods have shown great potential in predicting colorectal cancer (CRC) survival. However, the ML models introduced thus far have mainly focused on binary outcomes and have not considered the time-to-event nature of this type of modeling. OBJECTIVE: This study aims to evaluate the performance of ML approaches for modeling time-to-event survival data and develop transparent models for predicting CRC-specific survival. METHODS: The data set used in this retrospective cohort study contains information on patients who were newly diagnosed with CRC between December 28, 2012, and December 27, 2019, at West China Hospital, Sichuan University. We assessed the performance of 6 representative ML models, including random survival forest (RSF), gradient boosting machine (GBM), DeepSurv, DeepHit, neural net-extended time-dependent Cox (or Cox-Time), and neural multitask logistic regression (N-MTLR) in predicting CRC-specific survival. Multiple imputation by chained equations method was applied to handle missing values in variables. Multivariable analysis and clinical experience were used to select significant features associated with CRC survival. Model performance was evaluated in stratified 5-fold cross-validation repeated 5 times by using the time-dependent concordance index, integrated Brier score, calibration curves, and decision curves. The SHapley Additive exPlanations method was applied to calculate feature importance. RESULTS: A total of 2157 patients with CRC were included in this study. Among the 6 time-to-event ML models, the DeepHit model exhibited the best discriminative ability (time-dependent concordance index 0.789, 95% CI 0.779-0.799) and the RSF model produced better-calibrated survival estimates (integrated Brier score 0.096, 95% CI 0.094-0.099), but these are not statistically significant. Additionally, the RSF, GBM, DeepSurv, Cox-Time, and N-MTLR models have comparable predictive accuracy to the Cox Proportional Hazards model in terms of discrimination and calibration. The calibration curves showed that all the ML models exhibited good 5-year survival calibration. The decision curves for CRC-specific survival at 5 years showed that all the ML models, especially RSF, had higher net benefits than default strategies of treating all or no patients at a range of clinically reasonable risk thresholds. The SHapley Additive exPlanations method revealed that R0 resection, tumor-node-metastasis staging, and the number of positive lymph nodes were important factors for 5-year CRC-specific survival. CONCLUSIONS: This study showed the potential of applying time-to-event ML predictive algorithms to help predict CRC-specific survival. The RSF, GBM, Cox-Time, and N-MTLR algorithms could provide nonparametric alternatives to the Cox Proportional Hazards model in estimating the survival probability of patients with CRC. The transparent time-to-event ML models help clinicians to more accurately predict the survival rate for these patients and improve patient outcomes by enabling personalized treatment plans that are informed by explainable ML models.

Design and assembly of Ag-decorated Bi2O3 @ 3D MXene Schottky heterojunction for the highly permeable and multiple-antifouling of fibrous membrane in the purification of complex emulsified oil pollutants.

Membrane separation technology has potential for purifying emulsified oily wastewater. However, the oils, soluble organic substances, and microorganisms can cause complex membrane fouling problems, thereby reducing the separation efficiency and service life. Herein, a highly permeable and multiple-antifouling composite membrane was prepared using porous PAN fibrous mat as support backbone for the assembly of Ag-decorated Bi2O3 @ 3D MXene Schottky heterojunction and hydrophilic TA as the adhesive. The unique arrangement of 3D MXene heterojunction and hydrophilic functionalization effectively broke through the limitation of separation flux and synergistically enhanced the anti-fouling performance of membrane. Such fibrous composite membrane achieved an exceedingly high permeability (2717-3328 L·m-2·h-1) for various emulsified oils, while ensuring excellent oil/water emulsion retention rate (99.59%) and good cycle stability. Meanwhile, the composite membrane displayed favorable photocatalytic degradation performance toward degrading MeB (96.1%) and antibacterial ability. Furthermore, the MD simulation and free radical trapping experiments were carried out to unravel the molecular interactions during the separation process and the photocatalytic mechanism of composite membrane, respectively. Overall, the combination of photocatalytic self-cleaning, anti-oil adhesion, and antibacterial effect renders the membrane high permeability and multiple-antifouling performance, which provides a new strategy for dealing with complex oily wastewater in petrochemical industry.

Poly(arylene ether)s-Based Polymeric Membranes Applied for Water Purification in Harsh Environment Conditions: A Mini-Review.

Confronting the pressing challenge of freshwater scarcity, polymeric membrane-based water treatment technology has emerged as an essential and effective approach. Poly(arylene ether)s (PAEs) polymers, a class of high-performance engineering thermoplastics, have garnered attention in recent decades as promising membrane materials for advanced water treatment approaches. The PAE-Based membranes are employed to resist the shortages of most common polymeric membranes, such as chemical instability, structural damage, membrane fouling, and shortened lifespan when deployed in harsh environments, owing to their excellent comprehensive performance. This article presents the advancements in the research of several typical PAEs, including poly(ether ether ketone) (PEEK), polyethersulfone (PES), and poly(arylene ether nitrile) (PEN). Techniques for membrane formation, modification strategies, and applications in water treatment have been reviewed. The applications encompass processes for oil/water separation, desalination, and wastewater treatment, which involve the removal of heavy metal ions, dyes, oils, and other organic pollutants. The commendable performance of these membranes has been summarized in terms of corrosion resistance, high-temperature resistance, anti-fouling properties, and durability in challenging environments. In addition, several recommendations for further research aimed at developing efficient and robust PAE-based membranes are proposed.

Fabrication of High-Performance Colorimetric Membrane by Incorporation of Polydiacetylene into Polyarylene Ether Nitriles Electrospinning Nanofibrous Membranes.

Polyarylene ether nitrile (PEN) is a novel high-performance engineering plastic with various applications, particularly in thermoresistance-required fields. In this study, a well-known stimuli-response polydiacetylene monomer, 10, 12-pentacosadiynoic acid (PCDA), was encapsulated within electrospun PEN nanofibers to fabricate a colorimetric membrane with satisfactory thermal and corrosion resistance. To optimize the compatibility with PCDA, two PENswith distinct molecular chains were utilized: PEN−PPL and PEN−BPA. The chemical structure and elemental mapping analysis revealed that the PCDA component was successfully incorporated into the PEN fibrous. The PCDA bound significantly better to the PEN−PPL than to the PEN−BPA; due to the carboxyl groups present on the side chains of PEN−PPL, the surface was smooth and the color changed uniformly as the temperature rose. However, owing to its poor compatibility with PEN−BPA, the PCDA formed agglomerations on the fibers. The thermal analysis demonstrated that the membranes obtained after PCDA compounding maintained their excellent heat resistance. The 5% weight loss temperatures of composite nanofibrous membranes manufactured by PEN−PPL and PEN−BPA were 402 °C and 506 °C, respectively, and their glass transition temperatures were 219 °C and 169 °C, respectively, indicating that the blended membranes can withstand high temperatures. The evaluation of application performance revealed that the composite membranes exhibited good dimensional stability upon high thermal and corrosive situations. Specifically, the PEN−P−PCDA did not shrink at 170 °C. Both composite membranes were dimensionally stable when exposed to the alkali aqueous solution. However, PEN−P−PCDA is more sensitive to OH−, exhibiting color transition at pH > 8, whereas PEN−B−PCDA exhibited color transition at high OH− concentrations (pH ≥ 13), with enhanced alkali resistance stability owing to its nanofibrous architecture. This exploratory study reveals the feasibility of PEN nanofibers functionalized using PCDA as a desirable stimulus-response sensor even in high-temperature and corrosive harsh environments.

Large-Scale Preparation of Uniform Millet Bread-like Durable Benzoxazine-Phthalonitrile Foam with Outstanding Mechanical and Thermal Properties.

It is essentially important to develop durable polymer foams for services in high-temperature conditions. The current study reported the preparations and properties of a high-performance benzoxazine-phthalonitrile (BZPN) foam by utilizing azodicarbonamide and tween-80 as the blowing agent and stabilizer, respectively. Rheological and curing studies indicated that the appropriate foaming temperature for BZPN foam is below 180 °C, and its foaming viscosity window is below 20 Pa·s. Guided by these results, uniform millet bread-like BZPN foams with decimeter leveling size were successfully realized, suggesting the high prospect of large-scale production. The structural, mechanical, and thermal properties of BZPN foams were then investigated in detail. BZPN foam involves a hierarchical fracture mechanism during the compressive test, and it shows a high compression strength of over 6 MPa. During a burning test over 380 °C, no visible smoke, softening, or droplet phenomena appeared and the macroscopic structure of BZPN foam was well maintained. Mechanically robust, flame-retardant, and uniform large-size BZPN foam are promising light durable materials with high service temperatures, i.e., as filling materials even in a very narrow pipette.

Experimental validation for the quasi-shear wave behavior of LiF single crystal along a low-symmetry orientation under uniaxial shock loading.

To investigate the quasi-shear wave behavior and the underlying microscopic mechanism of an anisotropic solid under dynamic deformation beyond its Hugoniot elastic limit, LiF single crystals are shock-compressed along the [310] low-symmetry crystallographic orientation via normal plate-impact method. Interfacial velocity profiles are measured with a Doppler pin system. Peak normal stresses in samples vary between 1.91 GPa and 3.23 GPa. Under the lowest stress in this study, the resultant wave profile shows typical elastoplastic two-wave structures. In the second lowest stress experiment, an irregularity of the plastic wave or the inelastic deformation wave appears in the wave profile. At two higher stresses, a third wave is found following the elastoplastic two waves propagating along the normal direction. Our observations of three-wave structures in the [310] LiF are in excellent agreement with the simulation result of literature. This fact confirms that the immobilization of dislocations and rotation of slip planes are responsible for the microscopic mechanism of the three-wave propagations in the [310] LiF under uniaxial shock loading. The mechanism of the elastoplastic two-wave to anomalous three-wave structures evolution of material under different peak normal stresses will also be discussed.

Controllable synthesis, magnetism and solubility enhancement of graphene nanosheets/magnetite hybrid material by covalent bonding.

Hybrids of Fe(3)O(4) nanoparticles and surface-modified graphene nanosheets (GNs) were synthesized by a two-step process. First, graphene nanosheets were modified by SOCl(2) and 4-aminophenoxyphthalonitrile to introduce nitrile groups on their surface. Second, the nitrile groups of surface-modified graphene nanosheets were reacted with ferric ions on the surface of Fe(3)O(4) with the help of relatively high boiling point solvent ethylene glycol to form a GNs/Fe(3)O(4) hybrid. The covalent attachment of Fe(3)O(4) nanoparticles on the graphene nanosheet surface was confirmed by Fourier-transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), energy-dispersive X-ray spectrometer (EDS) and scanning electron microscopy (SEM). TEM and HRTEM observations indicated that the sizes of the nanoparticles and their coverage density on GNs could be easily controlled by changing the concentration of the precursor and the weight ratio to GNs. Magnetic measurements showed that magnetization of the hybrid materials is strongly influenced by the reaction conditions. Chemically bonded by phthalocyanine, the solubility of as-synthesized GNs/Fe(3)O(4) hybrid materials was greatly enhanced, which was believed to have potential for applications in the fields of composites, wastewater treatment and biomaterials.

Persistent toxic substances in remote lake and coastal sediments from Svalbard, Norwegian Arctic: levels, sources and fluxes.

Surface sediments from remote lakes and coastal areas from Ny-Alesund, Svalbard, Norwegian Arctic were analyzed for polycyclic aromatic hydrocarbons (PAHs), polybrominated diphenyl ethers (PBDEs), polychlorinated biphenyls (PCBs) and organochlorine pesticides (OCPs). Relatively high levels of PAHs were encountered from several lakes from Ny-Alesund, which were within the range of levels reported for European high mountain lakes and some urban/industrialized areas in the world, pointing to the role of remote Arctic lakes as potential reservoir of semi-volatile organic compounds. Specific patterns of PBDEs were observed, showing higher concentrations of lower brominated compounds such as BDE-7, 17 and 28. Estimated surface sediment fluxes of PAHs in Ny-Alesund remote lakes were similar to those observed for some European high mountain lakes. The current PAH levels in sediments from three lakes exceeded Canadian sediment quality guidelines, suggesting the presence of possible risks for aquatic organisms and the need for further studies.