High-throughput sequential excitation for nanoscale mapping of electrochemical strain in granular ceria.

Dynamic strain based atomic force microscopy (AFM) modes often fail at the interfaces where the most interesting physics occurs because of their incapability of tracking contact resonance accurately under rough topography. To overcome this difficulty, we develop a high-throughput sequential excitation AFM that captures contact dynamics of probe-sample interactions with high fidelity and efficiency, acquiring the spectrum of data on each pixel over a range of frequencies that are excited in a sequential manner. Using electrochemically active granular ceria as an example, we map both linear and quadratic electrochemical strain accurately across grain boundaries with high spatial resolution where the conventional approach fails.

Relationship of Oral Health Literacy with Dental Caries and Oral Health Behavior of Children and Their Parents.

Objectives: This study sought to assess the relationship of oral health literacy (OHL) of parents with the decayed, missing, and filled teeth (DMFT) indices of themselves and their children.

Materials And Methods: This cross-sectional study was performed on 258 children presenting with their parents to the Pediatric Dentistry and Orthodontics Departments of School of Dentistry, Tehran University of Medical Sciences in 2016. The parents were asked to fill out questionnaires related to OHL, oral health behaviors, and background information.

Mapping intrinsic electromechanical responses at the nanoscale via sequential excitation scanning probe microscopy empowered by deep data.

Ever-increasing hardware capabilities and computation powers have enabled acquisition and analysis of big scientific data at the nanoscale routine, though much of the data acquired often turn out to be redundant, noisy and/or irrelevant to the problems of interest, and it remains nontrivial to draw clear mechanistic insights from pure data analytics. In this work, we use scanning probe microscopy (SPM) as an example to demonstrate deep data methodology for nanosciences, transitioning from brute-force analytics such as data mining, correlation analysis and unsupervised classification to informed and/or targeted causative data analytics built on sound physical understanding. Three key ingredients of such deep data analytics are presented.

Multifield Control of Domains in a Room-Temperature Multiferroic 0.85BiTiFeMgO-0.15CaTiO Thin Film.

Single-phase materials that combine electric polarization and magnetization are promising for applications in multifunctional sensors, information storage, spintronic devices, etc. Following the idea of a percolating network of magnetic ions (e.g.

Electromechanical Coupling of Murine Lung Tissues Probed by Piezoresponse Force Microscopy.

Elastin is a major constituent of lung that makes up approximately 30% of lung's dry weight, and the piezoelectricity of elastin is expected to be exhibited in lung tissues. Because hundreds of millions of cycles of inhalation and exhalation occur in one's lifetime, such piezoelectric effect leads to hundreds of millions of cycles of charge generations in lung tissues, suggesting possible physiological significance. Using piezoresponse force microscopy (PFM), we show that the murine lung tissues are indeed piezoelectric, exhibiting predominantly first harmonic piezoresponse in both vertical and lateral modes.