A multi-orientation system for characterizing microstructure changes and mechanical responses of fine-grained gassy sediments associated with gas hydrates.

Fine-grained marine sediments containing veiny and nodular gas hydrates will evolve into fine-grained gassy sediments after hydrate dissociation due to climate-driven ocean warming. The mechanical properties of the fine-grained gassy sediments are basically acquired by ocean engineering design. However, they have not been fully understood, largely due to the lack of microstructure visualization. In this paper, a new system is developed to jointly conduct x-ray computed tomography scans, oedometer tests, and seismic wave testing on a single specimen with temperature being well controlled, allowing varieties of experimental data to be acquired effectively and automatically. The results show that stress history can hardly affect the undrained stiffness of fine-grained gassy sediments, while the drained stiffness of fine-grained sediments without gas bubbles is stress history dependent. After being unloaded, many microstructure changes remain, and examples include the free gas distribution being more concentrated and the connectivity among gas bubbles becoming much better. The multi-orientation system lays the foundation for further studies on the microstructure changes and mechanical responses of fine-grained gassy sediments associated with gas hydrates.

Early Triage of Critically Ill Adult Patients With Mushroom Poisoning: Machine Learning Approach.

BACKGROUND: Early triage of patients with mushroom poisoning is essential for administering precise treatment and reducing mortality. To our knowledge, there has been no established method to triage patients with mushroom poisoning based on clinical data. OBJECTIVE: The purpose of this work was to construct a triage system to identify patients with mushroom poisoning based on clinical indicators using several machine learning approaches and to assess the prediction accuracy of these strategies. METHODS: In all, 567 patients were collected from 5 primary care hospitals and facilities in Enshi, Hubei Province, China, and divided into 2 groups; 322 patients from 2 hospitals were used as the training cohort, and 245 patients from 3 hospitals were used as the test cohort. Four machine learning algorithms were used to construct the triage model for patients with mushroom poisoning. Performance was assessed using the area under the receiver operating characteristic curve (AUC), decision curve, sensitivity, specificity, and other representative statistics. Feature contributions were evaluated using Shapley additive explanations. RESULTS: Among several machine learning algorithms, extreme gradient boosting (XGBoost) showed the best discriminative ability in 5-fold cross-validation (AUC=0.83, 95% CI 0.77-0.90) and the test set (AUC=0.90, 95% CI 0.83-0.96). In the test set, the XGBoost model had a sensitivity of 0.93 (95% CI 0.81-0.99) and a specificity of 0.79 (95% CI 0.73-0.85), whereas the physicians' assessment had a sensitivity of 0.86 (95% CI 0.72-0.95) and a specificity of 0.66 (95% CI 0.59-0.73). CONCLUSIONS: The 14-factor XGBoost model for the early triage of mushroom poisoning can rapidly and accurately identify critically ill patients and will possibly serve as an important basis for the selection of treatment options and referral of patients, potentially reducing patient mortality and improving clinical outcomes.

A one-step synthesis of ultra-long silver nanowires with ultra-high aspect ratio above 2000 and its application in flexible transparent conductive electrodes.

Silver nanowires (AgNWs), appear as an extremely promising candidate for the next generation of flexible transparent conductive electrodes (FTCEs). However, the performance of AgNWs-FTCEs was severely limited by the aspect ratio of AgNWs, while it was still a big challenge to fabricate AgNWs with high aspect ratio nowadays. To improve the aspect ratio of AgNWs, bromide ion (Br-), cupric ion (Cu2+) and polyvinylpyrrolidone (PVP, Mw ≈ 1300 000) which are beneficial for the synthesis of high aspect ratio AgNWs, were introduced in this article. The high quality and uniform AgNWs with the average diameter of 77.6 nm and the aspect ratio above 2000 were fabricated via a one-step solvothermal method. The effects of reaction time, molar ratio of AgNO3 to PVP and the concentration of CuBr2 on the aspect ratio of AgNWs were discussed. The mechanism of the synthesis of high aspect ratio AgNWs was explored. After that, the prepared AgNWs were spin-coated on the surface of PET film, the FTCEs based on the ultra-high aspect ratio AgNWs without any post-treatments exhibits relatively high transmittance, low haze and low sheet resistance, and the AgNWs have little effect on the optical performance of pristine PET film. The outstanding performance of the prepared FTCEs indicated that the ultra-high aspect ratio AgNWs are ideal materials in the application of FTCEs, and the method of fabricating AgNWs could provide a direction to the high aspect ratio AgNWs.

Controlled Hydration, Transition, and Drug Release Realized by Adjusting Layer Thickness in Alginate-Ca2+/poly(N-isopropylacrylamide) Interpenetrating Polymeric Network Hydrogels on Cotton Fabrics.

The controlled hydration, transition, and drug release are realized by adjusting layer thickness in thermoresponsive interpenetrating polymeric network (IPN) hydrogels on cotton fabrics. IPN hydrogels are synthesized by sodium alginate (SA) and poly(N-isopropylacrylamide) (PNIPAM) with a ratio of 1:5/% (w/v). The cotton-fabric-supported IPN hydrogels with a thickness of 1000 µm exhibit a transition temperature (TT) at 35.2 °C. When the hydrogel thicknesses are thinned to 500 and 250 µm, the TTs are reduced to 34.8 and 34.1 °C, respectively. Interestingly, the morphology of IPN hydrogels switches from a well-defined honeycomb-like network structure (1000 µm) to a densely packed layer structure (250 µm). The thinner layers not only present a smaller extent of hydration and collapse but also require longer time to reach an equilibrium state, which can be attributed to the more pronounced hindrance of the chain rearrangement by the cotton fabrics. To address the influence of layer thickness on the drug release, we compare the release rate and cumulative release percentage of the test drugs tetracycline hydrochloride (TCH) and levofloxacin hydrochloride (LH) between pure IPN hydrogels and cotton-fabric-supported IPN hydrogels (250, 500, and 1000 µm) at 25 °C (below the TT) and 37 °C (above the TT). Because of the compressive stress from the collapsed hydrogels, a higher release is observed in both hydrogels when the temperature is above TT. The cotton fabric induces a slower and less prominent drug release in IPN hydrogels. Thus, combining the obtained correlation between the transition and hydrogels layer thickness, the drug release in cotton-fabric-supported IPN hydrogels can be regulated by the layer thickness, which appears especially suitable for a controlled release in wound dressing applications.

Enhanced Stain Removal and Comfort Control Achieved by Cross-Linking Light and Thermo Dual-Responsive Copolymer onto Cotton Fabrics.

Enhanced capabilities of stain removal and comfort control are simultaneously achieved by the light and thermo dual-responsive copolymer poly(triethylene glycol methyl ether methacrylate- co-ethylene glycol methacrylate- co-acrylamide azobenzene) (P(MEO3MA- co-EGMA- co-AAAB)) cross-linked on cotton fabrics. P(MEO3MA- co-EGMA- co-AAAB) is synthesized by sequential atom transfer radical polymerization with a molar ratio of 8 (MEO3MA):1 (EGMA):1 (AAAB). The MEO3MA units induce a thermoresponsive behavior to the copolymer. The hydrophilicity of the copolymer films can be further improved by the light-induced trans- cis isomerization of the AAAB units with UV radiation. The copolymer is facilely immobilized onto cotton fabrics with 1,2,3,4-butane tetracarboxylic acid as cross-linker. Due to the immobilization of P(MEO3MA- co-EGMA- co-AAAB), the hydrophilicity of the fabric surface is increased under UV radiation. Therefore, by simply installing a UV light source in the washing machine, better capability of stain removal is realized for the cross-linked cotton fabrics. It can prominently reduce the consumption of energy, water, and surfactants in laundry. In addition, the trans-AAAB units of the copolymer cause the cross-linked P(MEO3MA- co-EGMA- co-AAAB) layer to be more hydrophobic under ambient conditions. Hence, the copolymer can more easily collapse and form a porous structure on the fabrics. Thus, the air permeability of cotton fabrics cross-linked with P(MEO3MA- co-EGMA- co-AAAB) is enhanced by 13% at human body temperature as compared to P(MEO3MA- co-EGMA), giving improved comfort control during daily wear.

Lattice Boltzmann heat transfer model for permeable voxels.

We develop a gray-scale lattice Boltzmann (LB) model to study fluid flow combined with heat transfer for flow through porous media where voxels may be partially solid (or void). Heat transfer in rocks may lead to deformation, which in turn can modulate the fluid flow and so has significant contribution to rock permeability. The LB temperature field is compared to a finite difference solution of the continuum partial differential equations for fluid flow in a channel. Excellent quantitative agreement is found for both Poiseuille channel flow and Brinkman flow. The LB model is then applied to sample porous media such as packed beds and also more realistic sandstone rock sample, and both the convective and diffusive regimes are recovered when varying the thermal diffusivity. It is found that while the rock permeability can be comparatively small (order milli-Darcy), the temperature field can show significant variation depending on the thermal convection of the fluid. This LB method has significant advantages over other numerical methods such as finite and boundary element methods in dealing with coupled fluid flow and heat transfer in rocks which have irregular and nonsmooth pore spaces.

Nanoparticle melting as a stefan moving boundary problem.

The melting of spherical nanoparticles is considered from the perspective of heat flow in a pure material and as a moving boundary (Stefan) problem. The dependence of the melting temperature on both the size of the particle and the interfacial tension is described by the Gibbs-Thomson effect, and the resulting two-phase model is solved numerically using a front-fixing method. Results show that interfacial tension increases the speed of the melting process, and furthermore, the temperature distribution within the solid core of the particle exhibits behaviour that is qualitatively different to that predicted by the classical models without interfacial tension.