English

Quantum Spin Correlation Amplification Enables Macroscopic Detection of Atomic-Level Fatigue in Ferromagnetic Metals

Materials Science 2025-03-14 v1

Abstract

Structural fatigue failures account for most of catastrophic metal component failures, annually causing thousands of accidents, tens of thousands of casualties, and $100 billion in global economic losses. Current detection methods struggle to identify early-stage fatigue damage characterized by sub-nanometer atomic displacements and localized bond rupture. Here we present a quantum-enhanced monitoring framework leveraging the fundamental symbiosis between metallic bonding forces and magnetic interactions. Through magnetic excitation of quantum spin correlation in metallic structures, we establish a macroscopic quantum spin correlation amplification technology that visualizes fatigue-induced magnetic flux variations corresponding to bond strength degradation. Our multi-scale analysis integrates fatigue life prediction with quantum mechanical parameters (bonding force constants, crystal orbital overlap population) and ferromagnetic element dynamics, achieving unprecedented prediction accuracy (R^2>0.9, p<0.0001). In comprehensive fatigue trials encompassing 193 ferromagnetic metal specimens across 3,700 testing hours, this quantum magnetic signature consistently provided macroscopic fracture warnings prior to failure - a critical advance enabling 100% early detection success. This transformative framework establishes the first operational platform for preemptive fatigue mitigation in critical infrastructure, offering a paradigm shift from post-failure analysis to quantum-enabled predictive maintenance.

Keywords

Cite

@article{arxiv.2503.10030,
  title  = {Quantum Spin Correlation Amplification Enables Macroscopic Detection of Atomic-Level Fatigue in Ferromagnetic Metals},
  author = {Benniu Zhang and Liangshuo Zhang and Xiaodong Wu and Jigang Yu and Xiaochuan Cao and Zhijian Zhang and Xin Li and Fupeng Zhou and Jinglin Pan and Haifei Jiang and Gang Zheng},
  journal= {arXiv preprint arXiv:2503.10030},
  year   = {2025}
}