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In this paper, we demonstrate a novel design to accelerate the austenite reversion of a Fe–5Mn-1.5Al-0.2C (wt.%) medium Mn steel (MMS), via a deliberating adding an elevated pre-annealing before conventional intercritical annealing. It is indicated that the kinetics of cementite dissolution and austenite reversion are accelerated simultaneously, and the corresponding heterostructure austenite consisting of a core in low Mn content and a shell in relatively high Mn content is obtained. Meanwhile, the chemical heterogeneity at austenite boundaries significantly improves the thermal stability of reversed austenite during intercritical annealing, as proven by both STEM-EDS measurements and DICTRA simulations. The designed creation of chemical heterogeneity at the austenite boundaries ultimately leads to the appropriate mechanical stability of retained austenite and thus enables sustainable martensite transformation during tensile deformation, enhancing strength and ductility simultaneously.

Abstract In a prospective longitudinal study with 218 Parkinson’s disease (PD) patients in the discovery cohort and 84 in the validation cohort, we aimed to identify novel blood biomarkers predicting disability milestones in PD. Through Least Absolute Shrinkage and Selection Operator-Cox (Lasso-Cox) regression, developed nomogram predictive model and Linear mixed-effects models, we identified low level of plasma fibronectin (pFN) as one of the best-performing risk markers in predicting disability milestones. A low level of pFN was associated with a short milestone-free survival period in PD. Longitudinal analysis showed an annual decline in the rate of pFN was significantly associated with the annual elevation rate in the Hoehn-Yahr stage. Moreover, pFN level was negatively correlated with phosphorylated α-synuclein, and a low level of pFN was associated with BBB disruption in the striatum on neuroimaging, providing evidence for pFN’s role in PD progression. We finally identified pFN as a novel blood biomarker that predicted first-milestone disability in PD.

Abstract The uncontrolled dendrite growth and detrimental parasitic reactions of Zn anodes currently impede the large-scale implementation of aqueous zinc ion batteries. Here, we design a versatile quasi-solid-state polymer electrolyte with highly selective ion transport channels via molecular crosslinking of sodium polyacrylate, lithium magnesium silicate and cellulose nanofiber. The abundant negatively charged ionic channels modulate Zn2+ desolvation process and facilitate ion transport. Moreover, an in-situ formed Zn-Mg-Si medium-entropy alloy on Zn anode allows for an improved Zn nucleation kinetics and homogeneous Zn deposition. These combined advantages of the polymer electrolyte enable Zn anodes to achieve an average Coulombic efficiency of 99.7 % over 2400 cycles and highly reversible cycling up to 600 h with large depth of discharge of 85.6%. The resultant Zn | |V2O5 offers a stable long-term cycling performance and its pouch cell achieves a cycling capacity of 1.13 Ah at industrial-level loading mass of 31.3 mg.

Due to overactive inflammation and hindered angiogenesis, self-healing of diabetic wounds (DW) remains challenging in the clinic. Platelet-derived exosomes (PLT-Exos), a novel exosome capable of anti-inflammation and pro-angiogenesis, show great potential in DW treatment. However, previous administration of exosomes into skin wounds is topical daub or intradermal injection, which cannot intradermally deliver PLT-Exos into the dermis layer, thus impeding its long-term efficacy in anti-inflammation and pro-angiogenesis. Herein, a dissolvable microneedle-based wound dressing (PLT-Exos@ADMMA-MN) was developed for transdermal and long-term delivery of PLT-Exos. Firstly, a photo-crosslinking methacrylated acellular dermal matrix-based hydrogel (ADMMA-GEL), showing physiochemical tailorability, fast-gelling performance, excellent biocompatibility, and pro-angiogenic capacities, was synthesized as a base material of our dressing. For endowing the dressing with anti-inflammation and pro-angiogenesis, PLT-Exos were encapsulated into ADMMA-GEL with a minimum effective concentration determined by our in-vitro experiments. Then, in-vitro results show that this dressing exhibits excellent properties in anti-inflammation and pro-angiogenesis. Lastly, in-vivo experiments showed that this dressing could continuously and transdermally deliver PLT-Exos into skin wounds to switch local macrophage into M2 phenotype while stimulating neovascularization, thus proving a low-inflammatory and pro-angiogenic microenvironment for DW healing. Collectively, this study provides a novel wound dressing capable of suppressing inflammation and stimulating vascularization for DW treatment.

A series of novel Fe-based amorphous alloy coatings (AACs) with outstanding corrosion and wear resistance, composed of Fe57Cr15Mo4P10C7B3M4 (M = Fe, Ni, Al and W, in at. %) with various alloying elements, were fabricated using high-velocity oxygen fuel (HVOF) technique. The effect of Ni, Al, and W on microstructure, thermal behavior, corrosion resistance, and wear resistance of these fabricated Fe-based AACs were investigated systematically compared to 316L stainless steel (316L SS) substrates. Microstructural analysis revealed that the present Fe-based AACs exhibited a dense and uniform microstructure with high thermal stability. Electrochemical test results demonstrated that doping with Ni, Al and W significantly enhanced the corrosion resistance of the base alloy AAC. The W-doped AAC showed the best performance than Ni-doped, Al-doped and base alloy AAC, with a wide passive region (ΔEpass) of 1.7 V, low self-corrosion current density (Icorr) of 1.07 × 10−6 A·cm−2 and the lowest steady-state current densities and highest passivation index. Mott–Schottky analysis and X-ray photoelectron spectroscopy results showed that the W-doped AACs forming the densest and thickest passivation films, ascribed to an increased fraction of protective and stable low-valent metal oxides (Fe2+/Cr3+/Mo4+), enhanced corrosion resistance by promoting film growth and degradation processes. Dry wear tests revealed that among these Fe-based AACs, the W-doped AAC exhibited the superior wear resistance, with the lowest coefficient of friction (0.42) and wear rate (2.8 × 10−5 mm3·N−1·m−1), because of higher amorphous content and higher hardness. Under wear conditions, the primary wear mechanisms identified for the Fe-based AACs were abrasive, fatigue, and oxidative wear.

Abstract An analytical and optimisation method is presented to improve the air‐gap magnetic field of the surface‐mounted permanent magnet machine with multi‐level array magnets, which can reduce air‐gap magnetic field harmonics and torque ripple. Different from the conventional analytical method (AM) around the entire single‐magnet pole, the modelling method of multi‐level equal‐thickness ferrite magnets with different remanences for improving the air‐gap flux density is investigated. Considering the influence of air‐gap relative permeability by stator slots and multi‐level magnet remanences on the air‐gap flux density, the proposed AM for improving air‐gap flux density is derived by analysing the slotless air‐gap flux density and slotted air‐gap relative permeability. The cogging torque is predicted with energy method by analysing the tangential magnetic field distribution at stator slots. Then, the back electromotive force is predicted by analysing the winding distribution and radial air‐gap flux density. Based on the proposed AM, the simulated annealing particle swarm optimisation algorithm is used by optimising the widths and thicknesses of the multi‐level array magnets to further reduce torque ripple and increase output torque. Finally, the accuracy of the proposed AM is verified by comparing with the finite element method.

Traditional aqueous electrolyte systems in zinc-ion batteries (ZIBs) often face challenges such as sluggish ion transfer kinetics, dendrite formation, and sudden battery failures in harsh temperature environments. Herein, we introduce a pioneering approach by integrating a bifunctional additive composed of ethylene glycol (EG) and sodium gluconate (Ga) into ZnSO4 (ZSO) electrolyte to overcome these obstacles. The polyhydroxy structures of EG and Ga can reconstruct the hydrogen bond network of H2O to improve its liquid stability, and also adjust the coordination environment around hydrated Zn2+. Additionally, Ga in the H2O–EG mixture leads to the formation of a robust protective layer that promotes uniform deposition of Zn2+ ions and minimizes unwanted side reactions. Therefore, Zn anodes with 40% ZSO–Ga electrolyte can cycle for more than 3,000 h at 25 °C and 800 h at 50 °C. Furthermore, Zn||NH4V4O10 (NVO) full batteries demonstrate remarkable cycle stability, lasting up to 10,000 cycles at 1 A g−1 with a capacity retention of 79.1%. The multifunctional electrolyte additive employed in this study emerges as a promising candidate for enabling highly stable zinc anodes under diverse temperature conditions.

Groundwater seepage can easily cause large deformation and fracture instability of the surrounding rock in deep roadways, and the coupling support of “yield-bolt-grouting” can effectively control the occurrence of such accidents. This paper takes the specific engineering geological conditions of deep water-enriched roadway as the research background, revealing the coupling control mechanism of yield-bolt-grouting. The mechanical characteristics of the yielding tube were determined using lab analysis, and an investigation was conducted for the support control mechanism of high-strength yielding bolts. The control mechanism of grouting reinforcement is summarized, and a comprehensive coupling control technology system of “yield-bolt-grouting” is proposed based on the rheological large deformation characteristics of the surrounding rock of deep water-enriched roadway, with high-strength yielding grouting anchor rods and high-strength yielding grouting anchor cables as the core. The on-site monitoring results indicate that this technology effectively controls the deformation of the surrounding rock. The research results provide new ideas and technical approaches for controlling the surrounding rock of deep water-enriched roadways.

PurposeBody Mass Index (BMI) is an important indicator for assessing obesity and related health risks. With the rapid socio-economic development and changes in lifestyle, abnormal BMI (such as underweight, overweight, and obesity) has become an increasingly serious public health issue. This study aims to explore the impact of exercise frequency on BMI among Chinese adults aged 19 to 59, and to analyze the role of dietary behaviors in regulating BMI, providing a basis for BMI intervention strategies.MethodThe study employs a multi-stage sampling method across 23 provinces, provincial capitals, and four municipalities in China, randomly selecting 120 cities from each region. Online surveys were conducted using Wenjuanxing by trained surveyors.ResultA total of 8,611 individuals participated in the survey. Among them, 1,066 (12.38%) had a BMI

Cadmium (Cd) is a heavy metal recognized for its notable biotoxicity. Excessive Cd levels can have detrimental effects on crop growth, development, and yield. Real-time, rapid, and nondestructive monitoring of Cd content in leaves (LCd) is essential for food security. Previous research has primarily utilized traditional statistical methods and heavy metal–related vegetation indices (VIs) to develop models for estimating LCd, often resulting in a lack of generalizability. Herein, 252 sets of leaf samples with varying Cd contents were collected under six Cd concentration gradients in hydroponic and soil cultivation conditions. An LCd estimation model was developed by integrating VIs, color indices (CIs), and machine learning (ML) algorithms. Results indicate that VIs and CIs were strongly correlated with LCd, exhibiting correlation coefficients (r) of 0.73 and 0.57, respectively. The ML estimation model, which integrated both indices, was more effective than the single-parameter model developed using traditional statistical methods. Notably, the LCd estimation model developed using the random forest method exhibited the highest accuracy, with a coefficient of determination (R2) of 0.81 and a root-mean-square error of 0.120. These results indicate that multisource index data based on ML algorithms can effectively estimate LCd. This study presents an accurate, reliable, and generalized method to estimate LCd, providing valuable insights for assessing the large-scale heavy metal pollution status of rice using unmanned aerial vehicle remote sensing technology.