Material Analysis by Ultrasonic Atomic Force Microscopy

2000 ◽  
Vol 627 ◽  
Author(s):  
Chiaki Miyasaka ◽  
Lily Jia ◽  
Bernhard R. Tittmann
Keyword(s):  
Atomic Force Microscopy ◽  
Surface Layer ◽  
Diagnostic Tool ◽  
Ceramic Powders ◽  
Detailed Structure ◽  
Force Microscopy ◽  
Nano Indentation ◽  
Atomic Force ◽  
Good Contrast ◽  
Spray Dried

ABSTRACTSpray-dried ceramic powders (e.g., Al2O3) are composed of a plurality of granules, each of which, includes ceramic particles and organic binders. It is assumed that the binders become concentrated in the surface layer of the granule in accordance with its type or its volume mixed into a ceramic portion of the granule. However, evidence to prove the assumption was limited because conventional microscopes were not able to clearly visualize the segregation. This paper presents a technique for imaging detailed structure of the spray-dried ceramic powders with the ultrasonic-atomic force microscope (U-AFM). The distribution of binder vis-a-vis Al2O3 particles is highly resolved with good contrast. The distribution was confirmed by nano -indentation. Thus, the U-AFM is shown to be a useful diagnostic tool for the development of approaches to spray-dried process evaluation.

scholarly journals An investigation of surface properties, local elastic modulus and interaction with simulated pulmonary surfactant of surface modified inhalable voriconazole dry powders using atomic force microscopy

RSC Advances ◽  
2016 ◽  
Vol 6 (31) ◽  
pp. 25789-25798 ◽  
Author(s):  
Sumit Arora ◽  
Michael Kappl ◽  
Mehra Haghi ◽  
Paul M. Young ◽  
Daniela Traini ◽  
...  
Keyword(s):  
Atomic Force Microscopy ◽  
Elastic Modulus ◽  
Surface Properties ◽  
Pulmonary Surfactant ◽  
Dry Powders ◽  
Force Microscopy ◽  
Atomic Force ◽  
Surface Modified ◽  
Spray Dried

l-Leucine modified voriconazole spray dried micropartcles.

Application of Atomic-Force Microscopy for Nanoindentation of the Surface Layer of Filled Polymer Films

2019 ◽  
Vol 62 (8) ◽  
pp. 681-685 ◽  
Author(s):  
T. R. Aslamazova ◽  
V. I. Zolotarevskii ◽  
V. A. Kotenev ◽  
A. Yu. Tsivadze
Keyword(s):  
Atomic Force Microscopy ◽  
Surface Layer ◽  
Polymer Films ◽  
Filled Polymer ◽  
Force Microscopy ◽  
Atomic Force

scholarly journals nanite: using machine learning to assess the quality of atomic force microscopy-enabled nano-indentation data

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Paul Müller ◽  
Shada Abuhattum ◽  
Stephanie Möllmert ◽  
Elke Ulbricht ◽  
Anna V. Taubenberger ◽  
...  
Keyword(s):  
Atomic Force Microscopy ◽  
Data Analysis ◽  
Supervised Learning ◽  
Mean Squared Error ◽  
Single Cells ◽  
Contact Point ◽  
Base Line ◽  
Force Microscopy ◽  
Nano Indentation ◽  
Atomic Force

Abstract Background Atomic force microscopy (AFM) allows the mechanical characterization of single cells and live tissue by quantifying force-distance (FD) data in nano-indentation experiments. One of the main problems when dealing with biological tissue is the fact that the measured FD curves can be disturbed. These disturbances are caused, for instance, by passive cell movement, adhesive forces between the AFM probe and the cell, or insufficient attachment of the tissue to the supporting cover slide. In practice, the resulting artifacts are easily spotted by an experimenter who then manually sorts out curves before proceeding with data evaluation. However, this manual sorting step becomes increasingly cumbersome for studies that involve numerous measurements or for quantitative imaging based on FD maps. Results We introduce the Python package nanite, which automates all basic aspects of FD data analysis, including data import, tip-sample separation, base line correction, contact point retrieval, and model fitting. In addition, nanite enables the automation of the sorting step using supervised learning. This learning approach relates subjective ratings to predefined features extracted from FD curves. For ratings ranging from 0 to 10, our approach achieves a mean squared error below 1.0 rating points and a classification accuracy between good and poor curves that is above 87%. We showcase our approach by quantifying Young’s moduli of the zebrafish spinal cord at different classification thresholds and by introducing data quality as a new dimension for quantitative AFM image analysis. Conclusion The addition of quality-based sorting using supervised learning enables a fully automated and reproducible FD data analysis pipeline for biological samples in AFM.

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