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Register a new userBimodal Magnetic Force Microscopy with Capacitive Tip-Sample Distance Control
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A single-passage, bimodal magnetic force microscopy technique optimized for scanning samples with arbitrary topography is discussed. A double phase-locked loop (PLL) system is used to mechanically excite a high quality factor cantilever under vacuum conditions on its first mode and via an oscillatory tip-sample potential on its second mode. The obtained second mode oscillation amplitude is then used as a proxy for the tip-sample distance, and for the control thereof. With appropriate $z$-feedback parameters two data sets reflecting the magnetic tip-sample interaction and the sample topography are simultaneously obtained.
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We propose a theoretical framework for reconstructing tip-surface interactions using the intermodulation technique when more than one eigenmode is required to describe the cantilever motion. Two particular cases of bimodal motion are studied numerically: one bending and one torsional mode, and two bending modes. We demonstrate the possibility of accurate reconstruction of a two-dimensional conservative force field for the former case, while dissipative forces are studied for the latter.
We report the quantum calibration of a magnetic force microscope (MFM) by measuring the two-dimensional magnetic stray field distribution of the tip MFM using a single nitrogen vacancy (NV) center in diamond. From the measured stray field distribution and the mechanical properties of the cantilever a calibration function is derived allowing to convert MFM images in quantum calibrated stray field maps. This novel approach overcomes limitations of prior MFM calibration schemes and allows quantum calibrated nanoscale stray field measurements in a field range inaccessible by scanning NV magnetometry. Quantum calibrated measurements of a stray field reference sample allow its use as a transfer standard opening the road towards fast and easily accessible quantum traceable calibration of virtually any MFM.
Light emission spectrum from a scanning tunnelling microscope (LESTM) is investigated as a function of relative humidity and shown to be a novel and sensitive means for probing the growth and properties of a water meniscus in the nm-scale. An empirical model of the light emission process is formulated and applied successfully to replicate the decay in light intensity and spectral changes observed with increasing relative humidity. The modelling indicates a progressive water filling of the tip-sample junction with increasing humidity or, more pertinently, of the volume of the localized surface plasmons responsible for light emission; it also accounts for the effect of asymmetry in structuring of the water molecules with respect to polarity of the applied bias. This is juxtaposed with the case of a non-polar liquid in the tip-sample nano cavity where no polarity dependence of the light emission is observed. In contrast to the discrete detection of the presence/absence of water bridge in other scanning probe experiments by measurement of the feedback parameter for instrument control LESTM offers a means of continuously monitoring the development of the water bridge with sub-nm sensitivity. The results are relevant to applications such as dip-pen nanolithography and electrochemical scanning probe microscopy.
We demonstrate the quantitative measurement of the magnetization of individual magnetic nanoparticles (MNP) using a magnetic force microscope (MFM). The quantitative measurement is realized by calibration of the MFM signal using an MNP reference sample with traceably determined magnetization. A resolution of the magnetic moment of the order of 10^(-18) Am^2 under ambient conditions is demonstrated which is presently limited by the tips magnetic moment and the noise level of the instrument. The calibration scheme can be applied to practically any MFM and tip thus allowing a wide range of future applications e.g. in nanomagnetism and biotechnology.
While offering unprecedented resolution of atomic and electronic structure, Scanning Probe Microscopy techniques have found greater challenges in providing reliable electrostatic characterization at the same scale. In this work, we introduce Electrostatic Discovery Atomic Force Microscopy, a machine learning based method which provides immediate quantitative maps of the electrostatic potential directly from Atomic Force Microscopy images with functionalized tips. We apply this to characterize the electrostatic properties of a variety of molecular systems and compare directly to reference simulations, demonstrating good agreement. This approach opens the door to reliable atomic scale electrostatic maps on any system with minimal computational overhead.
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