发表机构
Iowa State University（爱荷华州立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出生成-物理框架，将稀疏AFM观测扩展为微观结构集合，经有限元均匀化获得有效弹性能分布，并传播至宏观拉伸模拟，建立跨尺度概率力学联系。
AI 中文摘要
发育中植物细胞壁的纳米级成像成本高昂，且有限数量的原子力显微镜（AFM）扫描无法捕捉细胞壁的全部结构变异性。我们提出了一种生成-物理工作流程，将棉花（Gossypium hirsutum）纤维细胞壁在开花后8天（DPA）的稀疏AFM观测与有效弹性能的分布及更大尺度的力学响应联系起来。我们采用带有低秩适配（LoRA）的Stable Diffusion模型，将实验扫描扩展为一系列AFM类微观结构，并使用微纤丝交叉计数和交叉角评估合成结构。每个微观结构被映射为空间变化的杨氏模量和泊松比场，并通过应变控制的有限元均匀化进行分析。在图像集合及规定的基体-纤丝刚度比扫描范围内重复此过程，可产生有效杨氏模量和泊松比的分布。单一的构象对无法捕捉这种变异性。所得分布强烈依赖于基体-纤丝刚度比，并在若干情况下表现出明显的多峰性。我们进一步将配对的有效性能传播到20个随机实现的更大试样拉伸模拟中，得到宏观应力响应的分布。该框架提供了稀疏纳米级观测与连续介质尺度力学之间的概率联系，同时保留了跨尺度的变异性。目前的结果确立了计算工作流程，而针对纳米力学测量校准强度-性能映射对于纤维尺度的定量预测仍是必要的。
英文摘要
Nanoscale imaging of developing plant cell walls is expensive, and a limited number of atomic force microscopy (AFM) scans cannot capture the full structural variability of the wall. We present a generative-physics workflow that links sparse AFM observations of cotton (Gossypium hirsutum) fiber cell walls at 8 days post-anthesis (DPA) to distributions of effective elastic properties and a larger-scale mechanical response. We adapt a Stable Diffusion model with Low-Rank Adaptation (LoRA) to expand the experimental scans into an ensemble of AFM-like microstructures and assess the synthetic structures using microfibril crossover count and crossover angle. Each microstructure is mapped to spatially varying Young's modulus and Poisson's ratio fields and analyzed using strain-controlled finite element homogenization. Repeating this process across the image ensemble and a prescribed sweep of matrix-to-fibril stiffness ratios produces distributions of effective Young's modulus and Poisson's ratio. A single constitutive pair cannot capture this variation. The resulting distributions depend strongly on the matrix-to-fibril stiffness ratio and, in several cases, exhibit apparent multimodality. We further propagate the paired effective properties into 20 stochastic realizations of a tensile simulation of a larger specimen, yielding a distribution of macroscale stress response. The framework provides a probabilistic link between sparse nanoscale observations and continuum-scale mechanics while retaining variability across scales. The present results establish the computational workflow, while calibration of the intensity-to-property mapping against nanomechanical measurements remains necessary for quantitative prediction at the fiber scale.
Comments28 pages (16 main + 12 supplementary), 10 figures. Supplementary material (5 figures, 6 tables) included at the end
