脱落酸代谢与信号传递及其调控种子休眠与萌发的分子机制

种子休眠是许多植物在长期系统发育进程中获得的一种适应环境变化的特性,是调控种子萌发和幼苗形成的最适时空分布的一种有效方式,也是物种成功繁衍与传播的一种选择性策略。种子休眠与萌发的激素调控可能是一种高度保守的机制,其中脱落酸(ABA)在种子休眠解除与萌发中起关键作用,赤霉素(GA)在休眠被解除后促进种子萌发。ABA在种子休眠与萌发中的作用主要受ABA代谢(生物合成和分解代谢)和信号传递途径的调控。为此,本文在综述ABA代谢和信号传递研究进展的基础上,阐述了ABA在种子发育、休眠与萌发中的作用,以及种子休眠特异性基因DOG1(萌发延迟1)与ABA信号组分的关系。研究表明,C40环氧类胡萝卜素是ABA生物合成的前体,玉米黄质环氧化酶和9-顺式-环氧类胡萝卜素二加氧酶是ABA生物合成的主要调节酶;ABA的分解代谢包括羟基化作用和与葡萄糖结合,CYP707A家族催化ABA C-8'位置上的羟基化作用,这是ABA分解代谢的重要步骤。在核心ABA信号传递途径中,ABA与PYR/PYL/RCAR受体结合并触发受体发生构象变化,从而允许受体-ABA复合物与2C类蛋白磷酸酶(PP2C)结合并抑制其活性,导致激酶如蔗糖非发酵-1相关的蛋白激酶2(SnRK2)的去抑制和活化。然后,这些激酶磷酸化和活化转录因子(transcription factors,TF),TF与靶启动子结合和诱导下游的ABA反应基因表达。ABA在种子成熟中后期积累,合子组织中合成的ABA诱导初生休眠和促进种子成熟;在发育中积累和在干种子中存留的ABA含量在种子吸胀初期下降。ABA是种子休眠诱导和维持的正调控因子,是萌发的负调控因子。DOG1在种子成熟过程中表达和发挥作用,其表达受可变剪接和可变多腺苷酸化调控。反义DOG1是种子休眠的一种抑制因子,通过干扰转录和转录延伸负调控DOG1的表达和种子休眠。种子的休眠与萌发除了被核心ABA信号途径调控外,也被DOG1-AHG1(ABA过敏感萌发1)/AHG3途径调控。DOG1能与AHG1/AHG3结合,通过结合ABA信号传递的负调控因子和增加对ABA的敏感性而引起种子休眠。最后,提出了该领域需要进一步研究的科学问题,包括ABA代谢中ABA 8'-羟化酶、ABA葡糖基转移酶和β-葡糖苷酶及其基因怎样响应发育和环境的变化以维持正常的ABA水平。ABA的重要调控因子例如Ca2+或者活性氧对核心ABA信号传递途径的影响,核心ABA信号传递途径与DOG1-AHG1/AHG3途径的下游重叠组分PP2C在整合生理条件或者环境信号时优先响应哪一条途径、这两条途径怎样被协调、以及PP2C有哪些新的靶组分。本文将为深入研究ABA调控种子休眠与萌发的分子机理提供参考

Modeling the sensitivity of capacitive pressure sensors with micro-structured wavy surfaces

In recent decades, capacitive pressure sensors (CPSs) with high sensitivity have demonstrated significant potential in applications such as medical monitoring, artificial intelligence, and soft robotics. Efforts to enhance this sensitivity have predominantly focused on material design and structural optimization, with surface microstructures such as wrinkles, pyramids, and micro-pillars proving effective. Although finite element modeling (FEM) has guided enhancements in CPS sensitivity across various surface designs, a theoretical understanding of sensitivity improvements remains underexplored. This paper employs sinusoidal wavy surfaces as a representative model to analytically elucidate the underlying mechanisms of sensitivity enhancement through contact mechanics. These theoretical insights are corroborated by FEM and experimental validations. Our findings underscore that optimizing material properties, such as Young's modulus and relative permittivity, alongside adjustments in surface roughness and substrate thickness, can significantly elevate the sensitivity. The optimal performance is achieved when the amplitude-to-wavelength ratio (H/lambda) is about 0.2. These results offer critical insights for designing ultrasensitive CPS devices, paving the way for advancements in sensor technology

Using spherical indentation to determine creep behavior with considering empirical friction coefficient

Indentation testing is a common technique for characterizing the mechanical properties of materials. When it comes to assessing the yield behavior of metals, conical indentation is often favored due to its ability to induce significant plastic deformation at relatively shallow indentation depths. On the other hand, spherical indentation is typically chosen for evaluating metal creep behavior, as it helps minimize the influence of plastic deformation on creep measurements. In spherical indentation tests, the friction at the contact interface is particularly crucial, given the larger contact area and more pronounced slip zones. However, accurately determining the friction coefficient at the contact interface is a complex multi-scale challenge, with the exact coefficient varying based on specific testing conditions and surface treatments. Consequently, empirical friction coefficients are frequently employed, often without thorough justification. In this work, we proposed Bayesian inference method for obtaining the creep constitutive behavior of alloys through spherical indentation tests. Our model considers not only the complexity of creep law, but also the indentation friction, which is unable to consider in most spherical indentation tests with traditional treatment. By using experimental data of spherical indentation tests on 310S stainless steel and pure nickel alloy from literature, present study demonstrates the effectiveness of the Bayesian inference approach, with the inferred constitutive models exhibiting well agreement with uniaxial creep behavior. Furthermore, the method considers the friction coefficient as an extra factor in inferring creep constitutive behavior from indentation tests

超薄不锈精密带钢力学性能的实验表征与数值仿真

新型材料不锈精密带钢的使用逐渐广泛,但现有的关于这种材料的力学性能的实验测试还比较少。本文中通过设计非标准实验测试了不同厚度下304H不锈精密带钢材料的力学性能,利用DIC技术计算得到了不同厚度材料的断裂韧性JIC,并利用有限元模拟验证了实验测试结果的准确性。结果显示,随着厚度的减小,材料的弹性模量逐渐增大,而塑性与断裂韧性先减少后增大。利用晶体塑性模型对此现象进行了定性的解释,即在轧制到更薄的过程中,因晶粒在轧制方向被拉伸而出现了各向异性,进而导致了轧制方向弹性模量逐渐增大;同时在此过程中随着加工硬化,材料的塑性与断裂韧性逐渐降低,而晶粒细化到纳米级后增多的晶界阻碍了裂纹扩展,导致材料的断裂韧性上升

Unifying linear proportionality between real contact area and load in rough surface contact

A long-standing debate and challenge in contact mechanics is to confirm the linearity between the real contact area and load on rough surfaces as well as its proportionality. Here, we first theoretically prove the linearity between the real contact area and load on rough surfaces by considering an infinite number of surface asperities. The mechanism for such linearity is that the applied force on each "small region" on the rough surface is directly proportional to the area of the region, resulting in a statistical proportionality between the total load and area. This explanation is confirmed via Green's function molecular dynamics (GFMD) simulations. On this basis, we develop a novel framework of surface slope-based multi-asperity contact model. The proportionality between the contact load and area is governed by the elastic property, mean absolute slope, and shape coefficient of the contact surface over the pressed depth. The elastic contacts of single-scale and multiscale rough surfaces are investigated using the developed contact model and GFMD. The shape coefficient of rough surfaces predicted by numerical simulations closely resembles that of surfaces with symmetric parabolic asperities. This work not only sheds light on the physical mechanism underlying the linearity between the contact area and load on rough surfaces but also provides a theoretical foundation for designing and evaluating surface contact and friction performance in micro- and nano-engineering systems

自然弯曲体的动态黏附行为

自然界中大量结构在卷曲变形时与外界环境产生动态黏附的相互作用,展现出与静态黏附不同的力学响应与现象.本研究以一种速度可调的自然弯曲体为基础,通过高速摄影实验深入分析了不同自然曲率的自然弯曲体在薄层黏附基底上高速运动的力学特性.结果表明,随着运动速度增加(>1 m/s),黏附表面剥离强度减小.基于上述实验现象,本研究建立了自然弯曲体在黏附基底上定向高速运动的理论模型,采用匹配渐近展开法,并结合打靶法建立了基底动态黏附性能、自然弯曲体运动前沿速度以及自然曲率的联系,成功实现了对自然弯曲体高速运动的调控,揭示了软材料动态黏附性能与界面脱黏速度的关系.受汽车减速带的设计启发,制备了黏附-非黏附模式的基底,发现该模式基底存在黏附基底占比的临界特征尺寸,使得其与全黏附基底表现出相近的减速效果.这一研究为深入理解软材料动态黏附性能与开发新型黏附结构提供了力学基础

Semi-analytical model for elastoplastic impact of sphere on plate

During the impact of a sphere on a large plate, the initial kinetic energy can be dissipated through two mechanisms: the plastic work done at the contact point and the flexural wave dissipated throughout the plate. To date, no model has adequately accounted for energy loss from both factors. In this paper, we propose a new model for the elastoplastic impact of a sphere on a large plate by combining the vibration of large plates with the elastoplastic contact model. By solving the governing equation, we demonstrate that such impacts are controlled by two non-dimensional parameters: one related to the plate's flexibility and the other to the material's yielding stress. Furthermore, we derive semi-analytical solutions for the maximum impact depth, maximum contact force, and the coefficient of restitution. The solution is verified by experimental tests from literature, and comparison shows well agreement. Additionally, we discuss the competitive mechanisms of energy dissipation through local plastic deformation and flexural wave propagation

Flexoelectricity at fractal rough surfaces

This study quantifies the role of surface roughness on interfacial flexoelectricity under normal compression and oscillation by examining a series of 3D printed surfaces with diverse roughness features. For incipient contact, the measured flexoelectric charge is found to follow a power law dependence on normal compression load, with the exponent positively correlated with the fractal dimension. The value of this power law exponent is similar to that for contact stiffness. The underlying mechanism for this coincidence is elucidated by contact analyses based on geometric truncations of surface structures. Contact micromechanics show that the interfacial flexoelectric charge will increasingly concentrate on large microcontacts as the compression continues. A rougher surface with higher fractal dimension tends to demonstrate less heterogeneity for flexoelectric polarizations over microcontacts. This study provides systematic experimental measurements and comprehensive explanations for interfacial flexoelectricity and establishes quantitative links to multi scale surface structures, shedding light on novel approaches for contact evaluation and flexoelectricity enhancement. (c) 2023 Elsevier Ltd. All rights reserved

Enabling quantitative analysis of in situ TEM experiments: A high-throughput, deep learning-based approach tailored to the dynamics of dislocations

In situ TEM is by far the most commonly used microscopy method for imaging dislocations, i.e., line-like defects in crystalline materials. However, quantitative image analysis so far was not possible, implying that also statistical analyses were strongly limited. In this work, we created a deep learning-based digital twin of an in situ TEM straining experiment, additionally allowing to perform matching simulations. As application we extract spatio-temporal information of moving dislocations from experiments carried out on a Cantor high entropy alloy and investigate the universality class of plastic strain avalanches. We can directly observe stick- slip motionof single dislocations and compute the corresponding avalanche statistics. The distributions turn out to be scale-free, and the exponent of the power law distribution exhibits independence on the driving stress. The introduced methodology is entirely generic and has the potential to turn meso-scale TEM microscopy into a truly quantitative and reproducible approach

Machine learning informed visco-plastic model for the cyclic relaxation of 316H stainless steel at 550 °C

Among the structural alloys for this fast reactor, 316H stainless steel has emerged as a promising candidate. Because the operating temperature of Sodium-cooled reactor is specifically designed to be 550 degrees C, this operating temperature triggers material inelastic behavior depends more on the coupling of fatigue and creep, which complicates the constitutive model. By introducing static recovery terms, previous studies could capture some experimental features, but failed to describe the interaction by fatigue and creep. In this work, in order to describe the fatigue and creep during cyclic relaxation of 316H stainless steel at 550 degrees C, we propose a modified visco-plastic constitutive model within the framework of unified Chaboche model. In the proposed model, the parameters related to the static recovery items are coupled, and thus cannot be identified from experiments using the traditional trial and error. To address this issue, we employed the Bayesian approach to identify these parameters. The parameter identification involves two steps: (i) con-structing a Gaussian Process surrogate model using data generated from the finite element method, and (ii) obtaining the value of parameters through Markov Chain Monte Carlo sampling under the Bayesian framework. The proposed procedure, is demonstrated by the using experi-mental results of 316H stainless steel at 550 degrees C. Under the coupling of fatigue-creep, the material exhibits a cyclic-dependent accelerated stress relaxation before reaching the saturated stage and a steady state of relaxed stress after a long holding time. These mechanical responses are well predicted by the proposed model. Further, we conducted two kinds of multi-axial cyclic test, tensile test of notched bar and coupled tensile-torsion test, to validate the proposed constitutive model for the cyclic behavior under the multi-axial stress state.</p