Search

The settling of microplastics (MPs) in the initial acceleration fall stage, i.e., before reaching the terminal settling velocity, has not been investigated, which is however important for understanding MP transport and fate. MP disks sized 3–5 mm, of three shapes and made of three polymers (1.038–1.343 g/cm3) were examined. Five release ways and three release angles (0°, 45°, 90°) were used. MP disks with the release angle of 0° start to zigzag immediately after the release, while the MP disks with the release angles of 45° and 90° first adjust to a horizontal position and then zigzag. The adjustment distances in the vertical and horizontal directions, as well as the maximum vertical settling velocity, are influenced by MP density, size, release angle and release way. The detailed settling trajectory and velocity were also analyzed. Finally, the time-changing drag coefficient of MP disks was examined and discussed. • MP disks have different adjustments before reaching the terminal settling stage. • MP density, size, release angle and release way are all important factors. • The settling trajectory, velocity and drag coefficient were analyzed. • Equations for the adjustment distances and maximum settling velocity were proposed. [ABSTRACT FROM AUTHOR]

• β F ′ precipitate acts as the nucleate site for β 1 precipitation. • β 1 is transformed from β F ′ by short-range solute diffusion, atomic shuffle and shear mechanism. • The transformation from β ′ to β 1 was not observed in the present work. The formation mechanism of β 1 phase has been a controversy in age-hardenable magnesium-rare earth alloys for over two decades. Its nucleation and growth were not clearly illustrated so far due to the lack of direct experimental evidence. In the present work, we unveil the formation mechanism of β 1 phase based on the in-situ and ex-situ observations using high-angle annular dark-field scanning transmission electron microscopy. The direct evidences reveal that β 1 phase is transformed from β F ′ phase by short-range solute diffusion, atomic shuffle coupled with shear mechanism. The β F ′ precipitate firstly converts to intermediate precipitate via short-range solute diffusion in its zig-zag array on (10 1 ¯ 0) α plane, and then transforms into β 1 precipitate via atomic shuffle in the modified β F ′ lattice and shear of (10 1 ¯ 0) α zig-zag array by ∼5° with respect to [ 11 2 ¯ 0 ] α orientation. These findings shed light on the formation mechanism of β 1 phase, and hopefully address the long-standing controversy. [Display omitted] [ABSTRACT FROM AUTHOR]

Microplastic (MP) disks have not been studied for settling behaviors in aquatic environments, which affects the transport and fate of MPs. Therefore, settling experiments were conducted on MP disks of three shapes and four common-seen materials. Lighter MP disks (with density ρ s = 1.038 g/cm3 and length l ≤ 5 mm) followed rectilinear vertical trajectories, while heavier MP disks (ρ s = 1.161–1.343 g/cm3 and l = 5 mm) followed zigzag trajectories with oscillations and rotations. The mean terminal settling velocities of MP disks were 19.6–48.8 mm/s. Instantaneous settling velocities of heavier MP disks fluctuated. Existing formulas could not accurately predict the settling velocity of MP disks; thus, a new model was proposed with an error of 15.5 %. Finally, the Re d - I* diagram (Re d is the disk Reynolds number and I* is the dimensionless moment of inertia) was extended for MP disks to predict settling trajectories. • MP disks show linear or zigzag settling trajectory, depending mainly on density. • A new model was proposed for predicting the mean settling velocity of MP disks. • Instantaneous settling velocities and trajectoreis of MP disks were examined. • The Re d - I* diagram was extended for MP disks to predict settling trajectories. [ABSTRACT FROM AUTHOR]

• Li addition overcomes the strength-ductility trade-off of precipitation-strengthened Mg-Gd alloy. • Li addition into Mg-Gd alloy reduces its density and increases its specific yield strength. • Li addition change the dominant phase from β ′ to β H − I I ′ in the peak-aged Mg-Gd-Li alloy. • β , β 1 R , β H − I I ′ phases and Li clusters offer better combined strengthening effect than β ′ phase. • Li addition enhances the activity of non-basl dislocations to accommodate the strain. The ambient strength-ductility trade-off has been a long-standing dilemma in metallic alloys, in particular Mg alloys. Here we report a new strategy to solve such a strength-ductility dilemma in precipitation-strengthened Mg-Gd alloys via Li addition. Different from the strengthening of traditional β ′ phase in Mg-7Gd (wt%) alloy, 1 wt% Li addition to this alloy not only boosts the precipitation of different sized β , β 1 R , β H − I I ′ phases and Li clusters to offer better combined strengthening effect, but also enhances the activity of dislocations to accommodate the strains during plastic deformation. Consequently, both the ambient tensile yield strength and ductility are simultaneously improved as compared to Mg-7Gd (wt%) alloy. Moreover, Li addition brings a reduction in density, in turn increasing the specific yield strength. The present strategy with Li addition offers a new insight into the development of Mg alloys with high strength-ductility synergy and with high specific yield strength. [Display omitted] [ABSTRACT FROM AUTHOR]

This research addresses fundamentals regarding the temperature-dependent composition of η phase in Al-Zn-Mg-Cu alloys under high pressure torsion (HPT) to uncover kinetics and thermodynamics governing the η phase composition evolution. Two-stage HPT experiments are deployed to study the stability of the high-temperature η under low-temperature processing. The first-stage HPT on a solid-solution-treated Al-Zn-Mg-Cu alloy (AA7075) at 200 ℃ for 10-revolutions is to get the high-temperature η, and the second-stage HPT is performed at either 100 ℃ or RT. Atom probe tomography reveals that the high-temperature η phase becomes chemically unstable during the low-temperature processing and its chemistry evolves towards low-temperature values. Surprisingly, a high-Mg Al-Mg phase is, for the first time, observed to form in the alloy under the 2nd-stage HPT processing, in correlation with the decomposition of the high-temperature η phase under the low-temperature processing. Unlike the 1st-stage HPT processed alloy, the 2nd-stage HPT processed alloy develops the segregation of Zn with other elements at grain boundary, accompanied with the chemical modification of the high-temperature η phase. The thermodynamics driving η phase composition variation with temperature provides the Al alloys with temperature variable for multi-stage treatments to engineer precipitation for optimised properties. [Display omitted] [ABSTRACT FROM AUTHOR]

Microplastic (MP) settling process is important for the transport of microplastic particles (MPs, <5 mm) in water bodies. However, for the control parameter of the drag coefficient (C d), no generalized formula has been proposed for MPs of different shapes and materials. In this study, a total of 1343 MP settling data were collected from the literature. It was found that the drag law for perfect spheres cannot reasonably predict C d for MPs with particle Reynolds number of 1–103. A new formula for C d was developed by introducing the dimensionless particle diameter (d ⁎) and two shape descriptors. The absolute error of the new formula is 15.2%, smaller than those (42.5–72.8%) of other existing formulas. Moreover, an explicit model was developed for MP settling velocity by correlating C d , d ⁎ , and shape descriptors, with lower absolute error (8.8%) than those (15.4–77.2%) of existing models. [Display omitted] • Spherical drag laws greatly underestimate C d for MPs in the transitional regime. • MP shapes have a great impact on C d ; CSF and Ф can fully describe the shape effects. • The new model can more accurately predict C d and terminal settling velocity of MPs. [ABSTRACT FROM AUTHOR]

Detecting natural gas (NG) micro-leaks is critical for NG transport and storage. In the event of underground NG leaks in vegetation covered areas, a priori knowledge derived from analyzing vegetation growth mechanisms can effectively describe the degree of vegetation response to NG stress, which is of theoretical importance for the quantitative detection of NG leaks. This study proposed a knowledge-guided deep learning framework and use it for underground NG leaks detection, in which an image and spectrum combined multi-branching deep learning (DL) network (Img-Spec-PGE model) was used to estimate the leaf area index (LAI) of NG-stressed plants, and an improved physical model for describing plant NG stress mechanisms was proposed, then assimilating the SAFY-NG model with LAI as an intermediate variable to obtain an accurate NG stress factor (Ka). The results showed that the Img-Spec-PGE model with the NIR-RE-R spectral bands' combination had the highest LAI estimation accuracy (R2 = 0.867, RMSE = 0.312) among the three designed DL models. The assimilated SAFY-NG model can accurately simulate the wheat LAI growth curve under NG leaks, and the calibration results of environmental parameters for NG leaks (leak start time and leak point depth) were consistent with the realistic experimental settings. Ka is sensitive to NG leaks in all experimental periods (p < 0.05). Enabling distinction between leak and non-leak scenarios as early as 1 day after NG leaks. 30 days after NG leaks, Ka can distinguish between 1 L min−1 and 1.5 L min−1 gas flow. The proposed approach provides a reference for detecting micro-leaks in NG transit and storage. • The Img-Spec-PGE model with a multi-branch image and spectrum combined structure was proposed. • SAFY-NG model was proposed for describing plant stress mechanisms under NG leaks. • The SAFY-NG model was assimilated with the estimated LAI from the Img-Spec-PGE model. • The factor Ka output from the SAFY-NG model is sensitive to NG leaks in all experimental periods, allowing for early detection. [ABSTRACT FROM AUTHOR]

Elastic graphene aerogels have promising applications in flexible pressure sensors. However, it is still difficult to prepare graphene aerogels with high sensitivity and reliable mechanical stability due to the challenge in structural regulation. This study proposes an innovative method for preparing graphene aerogel by combining wet-electrospinning and freeze-drying. The resultant is a cotton-like 3D graphene fiber aerogel (GFA) with soft exterior but firm essence. The aerogel assembled of randomly stacked 1D graphene fiber, exhibiting softness and low compressive strength, thus ensuring the highly sensitive. Furthermore, this graphene fiber possessing strong mechanical properties after high-temperature thermal annealing, guarantees the fatigue resistance and elasticity of the aerogel. Therefore, the assembled GFA-based pressure sensor is ultrasensitive with a high sensitivity of 18.55 kPa−1 and a low detection limit of 2 Pa. Additionally, it has a rapid response time of 2 ms and maintains good stability even after 3000 cycles. The exceptional properties of GFA suggest its immense potential for application in human health monitoring and motion detection. In addition, the wet-electrospinning preparation of GFA provides a new strategy for the fabrication of graphene aerogels. [Display omitted] • A novel method for preparing graphene aerogel by combining wet-electrospinning and freeze-drying. • The resultant is a cotton-like 3D graphene fiber aerogel (GFA) with soft exterior but firm essence. • The GFA is fibrous aerogel assembled of randomly stacked 1D graphene fiber. • The GFA-based sensor has ultrahigh sensitivity of 18.55 kPa−1 and a low detection limit of 2 Pa. [ABSTRACT FROM AUTHOR]

• Machine learning well predicts settling velocity of microplastics with various shapes. • Microplastic shape categories and thresholds were identified based on dimensionality. • Optimal shape parameters were determined, varying for different microplastic shapes. • Importance and effect of feature parameters were revealed by interpretable analysis. Microplastics can easily enter the aquatic environment and be transported between water bodies. The terminal settling velocity of microplastics, which affects their transport and distribution in the aquatic environment, is mainly influenced by their size, density, and shape. Due to the difficulty in accurately predicting the terminal settling velocity of microplastics with various shapes, this study focuses on establishing high-performance prediction models and understanding the importance and effect of each feature parameter using machine learning. Based on the number of principal dimensions, the shapes of microplastics are classified into fiber, film, and fragment, and their thresholds are identified. The microplastics of different shape categories have different optimal shape parameters for predicting the terminal settling velocity: Corey shape factor, flatness, elongation, and sphericity for the fragment, film, fiber, and mixed-shape MPs, respectively. By including the dimensionless diameter, relative density and optimal shape parameter in the input parameter combination, the machine learning models can well predict the terminal settling velocity for the microplastics of different shape categories and mixed-shape with R2 > 0.867, achieving significantly higher performance than the existing theoretical and regression models. The interpretable analysis of machine learning reveals the highest importance of the microplastic size and its marginal effect when the dimensionless diameter D* = d n (g/v 2)1/3 > 80, where d n is the equivalent diameter, g is the gravitational acceleration, and ν is the fluid kinematic viscosity. The effect of shape is weak for small microplastics and becomes significant when D* exceeds 65. [Display omitted] [ABSTRACT FROM AUTHOR]