研究目的
To develop empirical models to predict the contribution of topographic variations in a sample to near-field scanning probe microwave microscopy (NSMM) images, specifically for |S11| images of perovskite photovoltaic materials and GaN nanowires, and to estimate material property variations by subtracting this prediction from measured images.
研究成果
The empirical models successfully predict topographic contributions to NSMM images for both perovskite samples and GaN nanowires, with good agreement between predictions from reference samples and self-calibration methods. For the GaN nanowire, the empirical model outperforms a simple physical model in interior regions. The approach is effective for samples with small material property variations and can be extended to other imaging techniques, though future work should explore machine learning for parameter selection and uncertainty analysis.
研究不足
The method may fail if the fraction of the sample affected by material property variations is large (greater than 0.5), as robust regression could break down. It relies on empirical models that may not capture all complexities, and adjustable parameters are selected based on scientific judgment rather than automated methods like cross-validation. Instrumental drift and noise can affect results, and the approach is complementary but not a replacement for physical models.
1：Experimental Design and Method Selection:
The study uses empirical statistical models, including robust linear regression and non-parametric local regression (LOCFIT), to predict topographic contributions to NSMM images. Methods involve analyzing |S11| and topography measurements from samples.
2：11| and topography measurements from samples.
Sample Selection and Data Sources:
2. Sample Selection and Data Sources: Samples include a pristine perovskite photovoltaic reference sample (fabricated under dry humidity) and a deteriorated perovskite sample (aged under ambient conditions), as well as a GaN nanowire sample. Data are acquired using a near-field scanning microwave microscope.
3：List of Experimental Equipment and Materials:
Equipment includes a commercial NSMM system (Keysight 5400), atomic force microscope (AFM) in contact mode, vector network analyzer (VNA), and cantilevers from Rocky Mountain Nanotechnology. Materials involve perovskite CH3NH3PbI3 thin films and GaN nanowires.
4：Experimental Procedures and Operational Workflow:
Measurements are performed at around 18.7–18.8 GHz with an IFBW of 100 Hz. Topography and |S11| images are acquired simultaneously. Data processing includes smoothing topography with Gaussian kernels, calculating Laplacians, and applying robust regression to determine model parameters.
5：7–8 GHz with an IFBW of 100 Hz. Topography and |S11| images are acquired simultaneously. Data processing includes smoothing topography with Gaussian kernels, calculating Laplacians, and applying robust regression to determine model parameters.
Data Analysis Methods:
5. Data Analysis Methods: Analysis uses R software for robust regression (rlm function), filtering (applyFilter), and non-parametric smoothing (LOCFIT). Methods include determining bandwidth parameters, adjusting for instrument drift, and denoising images with Adaptive Weight Smoothing (AWS).
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