Department of Physics, University of Michigan, Ann Arbor, MI, 48109, USA.
Nat Commun. 2020 Oct 14;11(1):5170. doi: 10.1038/s41467-020-18995-4.
All materials respond heterogeneously at small scales, which limits what a sensor can learn. Although previous studies have characterized measurement noise arising from thermal fluctuations, the limits imposed by structural heterogeneity have remained unclear. In this paper, we find that the least fractional uncertainty with which a sensor can determine a material constant λ of an elastic medium is approximately [Formula: see text] for a ≫ d ≫ ξ, [Formula: see text], and D > 1, where a is the size of the sensor, d is its spatial resolution, ξ is the correlation length of fluctuations in λ, Δ is the local variability of λ, and D is the dimension of the medium. Our results reveal how one can construct devices capable of sensing near these limits, e.g. for medical diagnostics. We use our theoretical framework to estimate the limits of mechanosensing in a biopolymer network, a sensory process involved in cellular behavior, medical diagnostics, and material fabrication.
所有材料在小尺度上都会呈现出不均匀的响应,这限制了传感器的学习能力。尽管先前的研究已经描述了由热波动引起的测量噪声,但结构异质性带来的限制仍不清楚。在本文中,我们发现,对于一个弹性介质的材料常数 λ,传感器能够以大约[公式:见正文]的最小分数不确定性来确定,其中 [公式:见正文],并且 [公式:见正文],D>1,其中 a 是传感器的大小,d 是其空间分辨率,ξ是 λ 的波动的相关长度,Δ是 λ 的局部变化性,D 是介质的维度。我们的结果揭示了如何构建能够接近这些极限的设备,例如用于医学诊断。我们使用我们的理论框架来估计生物聚合物网络中机械传感的极限,这是一个涉及细胞行为、医学诊断和材料制造的传感过程。