Multi-eigenmode control for high material contrast in bimodal and higher harmonic atomic force microscopy.

High speed imaging and mapping of nanomechanical properties in atomic force microscopy (AFM) allows the observation and characterization of dynamic sample processes. Recent developments involve several cantilever frequencies in a multifrequency approach. One method actuates the first eigenmode for topography imaging and records the excited higher harmonics to map nanomechanical properties of the sample. To enhance the higher frequencies' response two or more eigenmodes are actuated simultaneously, where the higher eigenmode(s) are used to quantify the nanomechanics. In this paper, we combine each imaging methodology with a novel control approach. It modifies the Q factor and resonance frequency of each eigenmode independently to enhance the force sensitivity and imaging bandwidth. It allows us to satisfy the different requirements for the first and higher eigenmode. The presented compensator is compatible with existing AFMs and can be simply attached with minimal modifications. Different samples are used to demonstrate the improvement in nanomechanical contrast mapping and imaging speed of tapping mode AFM in air. The experiments indicate most enhanced nanomechanical contrast with low Q factors of the first and high Q factors of the higher eigenmode. In this scenario, the cantilever topography imaging rate can also be easily improved by a factor of 10.

Memory-Augmented Generative Adversarial Networks for Anomaly Detection.

We propose a memory-augmented deep learning model for semisupervised anomaly detection (AD). While many traditional AD methods focus on modeling the distribution of normal data, additional constraints in the modeling process are needed to distinguish between normal and abnormal data. The proposed model, named memory augmented generative adversarial networks (MEMGAN), is coupled with external memory units through attentional operations. One property of MEMGAN in the latent space is such that encoded normal data are expected to reside in the convex hull of the memory units, while the abnormal ones are separated outside. This property makes the AD process of MEMGAN more robust and reliable. Experiments on AD datasets adapted from MVTec, MNIST, CIFAR10, and Arrhythmia demonstrate that MEMGAN notably improves over previous AD models. We also find that the decoded memory units in MEMGAN are more diverse and interpretable than those in previous memory-augmented models.

Aggrecan nanoscale solid-fluid interactions are a primary determinant of cartilage dynamic mechanical properties.

Poroelastic interactions between interstitial fluid and the extracellular matrix of connective tissues are critical to biological and pathophysiological functions involving solute transport, energy dissipation, self-stiffening and lubrication. However, the molecular origins of poroelasticity at the nanoscale are largely unknown. Here, the broad-spectrum dynamic nanomechanical behavior of cartilage aggrecan monolayer is revealed for the first time, including the equilibrium and instantaneous moduli and the peak in the phase angle of the complex modulus. By performing a length scale study and comparing the experimental results to theoretical predictions, we confirm that the mechanism underlying the observed dynamic nanomechanics is due to solid-fluid interactions (poroelasticity) at the molecular scale. Utilizing finite element modeling, the molecular-scale hydraulic permeability of the aggrecan assembly was quantified (kaggrecan = (4.8 ± 2.8) × 10(-15) m(4)/N·s) and found to be similar to the nanoscale hydraulic permeability of intact normal cartilage tissue but much lower than that of early diseased tissue. The mechanisms underlying aggrecan poroelasticity were further investigated by altering electrostatic interactions between the molecule's constituent glycosaminoglycan chains: electrostatic interactions dominated steric interactions in governing molecular behavior. While the hydraulic permeability of aggrecan layers does not change across species and age, aggrecan from adult human cartilage is stiffer than the aggrecan from newborn human tissue.