从伦敦到莫尔斯：经由宾尼希、奎特和格伯
From London to Morse via Binnig, Quate, and Gerber
浏览论文内容
中文总结 AI 辅助
本文回顾诺丁汉大学团队二十年原子力显微镜研究，涵盖从范德华力到共价键的力谱，强调探针作为主动参与者，并探讨机器学习在原子操纵中的作用。
中文摘要 AI 辅助
在介绍原子力显微镜的开创性论文中[Phys. Rev. Lett. \ extbf{56}, 930 (1986)]，宾尼希、奎特和格伯富有远见地预测，该技术最终将能够探测从弱范德华相互作用到强共价键合的各类相互作用。他们还强调，原子力显微镜中至关重要的针尖-样品力在扫描隧道显微镜中也存在，且往往具有高度影响；事实上，这一认识直接启发了力显微镜的发明。在《原子力显微镜四十年》特刊的这篇展望文章中，我们回顾了诺丁汉大学团队二十年来工作的若干方面，这些工作涵盖了BQG所强调的力范围，并以一个共同的核心主题统一：探针作为主动参与者而非被动观察者。我们的结果选择还覆盖了从微观尺度直至单化学键极限的长度和关联尺度，追踪了从范德华/哈梅克力、经氢键、到共价键，以及最终通过垂直针尖-样品转移实现金属团簇逐原子组装的相互作用谱。呼应BQG关于STM中探针-样品力普遍存在的观察，我们还讨论了近期证据表明针尖诱导的异质性支撑着分子扩散中的首达动力学，并强调了获取易受探针扰动的吸附分子扩散势垒非侵入性测量的挑战。最后，我们对机器学习在自动化针尖驱动的原子和分子操纵中日益增长的作用提出了展望。
英文摘要
In their landmark paper introducing the atomic force microscope [Phys. Rev. Lett. \textbf{56}, 930 (1986)], Binnig, Quate, and Gerber presciently anticipated that the technique would ultimately be capable of probing interactions running the gamut from weak van der Waals interactions to strong covalent bonding. They also highlighted that the tip-sample forces central to AFM are present, and often highly influential, in scanning tunnelling microscopy; indeed, this realisation directly inspired the invention of the force microscope. In this perspective for the \textit{Forty Years of AFM} special issue, we review selected aspects of two decades of work from our group at the University of Nottingham that span the force range highlighted by BQG and are united by a common, central theme: the probe as active participant rather than passive observer. Our selection of results also covers length- and correlation-scales from the microscopic right down to the single chemical bond limit, tracking a spectrum of interactions from van der Waals/Hamaker forces, through hydrogen bonding, to covalent bonds and, finally, atom-by-atom assembly of metal clusters via vertical tip-sample transfer. Echoing BQG's own observations on the prevalence of probe-sample forces in STM, we also discuss recent evidence that tip-induced heterogeneity underpins first-passage dynamics in molecular diffusion and highlight the challenges in acquiring non-invasive measurements of diffusion barriers for adsorbed molecules that are readily perturbed by the probe. We close with a perspective on machine learning's growing role in automating tip-driven atomic and molecular manipulation.
发表机构
- University of Nottingham（诺丁汉大学）
- King’s College London（伦敦国王学院）
- La Trobe University（拉筹伯大学）
- Cornell University（康奈尔大学）
- Wuhan University（武汉大学）
机构由 AI 辅助整理，请以论文原文为准。
