We present a method by which multi-harmonic signals acquired during a normal tapping mode
(amplitude modulated) AFM scan of a sample in air or vacuum with standard microcantilevers
can be used to map quantitatively the local mechanical properties of the sample such as elastic
modulus, adhesion, and indentation. The approach is based on the observation that during the
tapping mode operation in air or vacuum, the 0th and 2nd harmonic signals of microcantilever
vibration are encountered under most operating conditions and can be mapped with sufficient
signal to noise ratio. By measuring the amplitude and phase of the driven harmonic as well as
the 0th and 2nd harmonic observables, we find analytical/semi-analytical formulas that relate
these multi-harmonic observables to local mechanical properties for several classical tip-sample
interaction models. Least squares estimation of the local mechanical properties is performed
pixel by pixel. The method is validated through computations as well as experimental data
acquired on a polymer blend made of Polystyrene and Polyolefin elastomer.
more »
« less
Discrimination of adhesion and viscoelasticity from nanoscale maps of polymer surfaces using bimodal atomic force microscopy
The simultaneous excitation and measurement of two eigenmodes in bimodal atomic force microscopy (AFM) during sub-micron scale surface imaging augments the number of observables at each pixel of the image compared to the normal tapping mode. However, a comprehensive connection between the bimodal AFM observables and the surface adhesive and viscoelastic properties of polymer samples remains elusive. To address this gap, we first propose an algorithm that systematically accommodates surface forces and linearly viscoelastic three-dimensional deformation computed via Attard's model into the bimodal AFM framework. The proposed algorithm simultaneously satisfies the amplitude reduction formulas for both resonant eigenmodes and enables the rigorous prediction and interpretation of bimodal AFM observables with a first-principles approach. We used the proposed algorithm to predict the dependence of bimodal AFM observables on local adhesion and standard linear solid (SLS) constitutive parameters as well as operating conditions. Secondly, we present an inverse method to quantitatively predict the local adhesion and SLS viscoelastic parameters from bimodal AFM data acquired on a heterogeneous sample. We demonstrate the method experimentally using bimodal AFM on polystyrene-low density polyethylene (PS-LDPE) polymer blend. This inverse method enables the quantitative discrimination of adhesion and viscoelastic properties from bimodal AFM maps of such samples and opens the door for advanced computational interaction models to be used to quantify local nanomechanical properties of adhesive, viscoelastic materials using bimodal AFM.
more »
« less
- Award ID(s):
- 1726274
- PAR ID:
- 10308563
- Date Published:
- Journal Name:
- Nanoscale
- Volume:
- 13
- Issue:
- 41
- ISSN:
- 2040-3364
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Machine learning (ML) accelerates the exploration of material properties and their links to the structure of the underlying molecules. In previous work [Shi et al. ACS Applied Materials & Interfaces 2022, 14, 37161−37169.], ML models were applied to predict the adhesive free energy of polymer–surface interactions with high accuracy from the knowledge of the sequence data, demonstrating successes in inverse-design of polymer sequence for known surface compositions. While the method was shown to be successful in designing polymers for a known surface, extensive data sets were needed for each specific surface in order to train the surrogate models. Ideally, one should be able to infer information about similar surfaces without having to regenerate a full complement of adhesion data for each new case. In the current work, we demonstrate a transfer learning (TL) technique using a deep neural network to improve the accuracy of ML models trained on small data sets by pretraining on a larger database from a related system and fine-tuning the weights of all layers with a small amount of additional data. The shared knowledge from the pretrained model facilitates the prediction accuracy significantly on small data sets. We also explore the limits of database size on accuracy and the optimal tuning of network architecture and parameters for our learning tasks. While applied to a relatively simple coarse-grained (CG) polymer model, the general lessons of this study apply to detailed modeling studies and the broader problems of inverse materials design.more » « less
-
more » « less
Abstract Here, a polymer blend active layer that exhibits both electronic and adhesive properties is introduced. Various conjugated polymers are blended with a catechol‐based polymer that shows high adhesion, such that blends serve as the active layer of multifunctional sticky organic thin‐film transistors (OTFTs). Blend films maintain relatively constant field‐effect charge carrier mobility in OTFTs regardless of composition. Lap shear adhesion strength tests show that all blend films exhibit adhesive properties with adhesion values ranging from 0.05 to 4.30 MPa. With relatively consistent mobility and the presence of adhesive properties at different compositions, blends of conjugated and adhesive polymers can lead to next‐generation organic transistors for stable 3D stacking and waterproof adhesive sensors.
-
Determination of the surface hydrophobicity or wettability of nanomaterials and nanoparticles (NPs) is often challenged by the heterogeneous properties of NPs that vary with particle size, shape, surface charge, aggregation states, and surface sorption or coating. This study first summarized inherent limitations of the water contact angle, octanol–water partition coefficient ( K ow ) and surface adsorption of probe molecules in probing nanomaterial hydrophobicity. Then, we demonstrated the principle of a scanning probe method based on atomic force microscopy (AFM) for the local surface hydrophobicity measurement. Specifically, we measured the adhesion forces between functionalized AFM tips and self-assembled monolayers (SAMs) to establish a linear relationship between the adhesion forces and water contact angles based on the continuum thermodynamic approach (CTA). This relationship was used to determine the local surface hydrophobicity of seven different NPs ( i.e. , TiO 2 , ZnO, SiO 2 , CuO, CeO 2 , α-Fe 2 O 3 , and Ag), which agreed well with bulk contact angles of these NPs. Some discrepancies were observed for Fe 2 O 3 , CeO 2 and SiO 2 NPs, probably because of surface hydration and roughness effects. Moreover, the solution pH and ionic strength had negligible effects on the adhesion forces between the AFM tip and MWCNTs or C 60 , indicating that the hydrophobicity of carbonaceous nanomaterials is not influenced by pH or ionic strength (IS). By contrast, natural organic matter (NOM) appreciably decreased the hydrophobicity of MWCNTs and C 60 due to surface coating of hydrophilic NOM. This scanning probe method has been proved to be reliable and robust toward the accurate measurement of the nanoscale hydrophobicity of individual NPs or nanomaterials in liquid environments.more » « less
-
Mechanical properties play important roles at different scales in biology. At the level of a single cell, the mechanical properties mediate mechanosensing and mechanotransduction, while at the tissue and organ levels, changes in mechanical properties are closely connected to disease and physiological processes. Over the past three decades, atomic force microscopy (AFM) has become one of the most widely used tools in the mechanical characterization of soft samples, ranging from molecules, cell organoids and cells to whole tissue. AFM methods can be used to quantify both elastic and viscoelastic properties, and significant recent developments in the latter have been enabled by the introduction of new techniques and models for data analysis. Here, we review AFM techniques developed in recent years for examining the viscoelastic properties of cells and soft gels, describe the main steps in typical data acquisition and analysis protocols, and discuss relevant viscoelastic models and how these have been used to characterize the specific features of cellular and other biological samples. We also discuss recent trends and potential directions for this field.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1039/d1nr03437e
