In an effort to improve silicon carbide (SIC) substrates surfaces prior to epitaxial growth, two chemomechanical polishing (CMP) techniques were investigated and the results were compared with a mechanical polishing procedure involving various grades of diamond paste. This work focused on silicon-terminated (0001) SIC surfaces.The two CMP techniques utilized (i) chromium oxide(lll) abrasives and (ii) colloidal silica polishing slurry. The best surfaces were obtained after colloidal silica polishing under conditions that combined elevated temperatures (-55°C) with a high slurry alkalinity (pH > 10) and a high solute content. Cross-sectional transmission electron microscopy showed no observable subsurface damage, and atomic force microscopy showed a significant reduction in roughness compared to commercial diamond-polished wafers. Growth experiments following colloidal silica polishing yielded a much improved film surface morphology.A pressing need in the development of SiC semiconductor technology is to improve the structural and surface quality of epitaxial films used in device fabrication. A flat and defect-free substrate surface is crucial for the epitaxial growth of thin films. Research on the epitaxial growth of 4H-and 6H-SiC has shown that processinduced defects on the substrate surface, such as scratches generated during lapping and polishing, are the primary contributors to unwanted polytype inclusions in the epi layer.14

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Summary Soil organic matter (SOM), total nitrogen (TN), available nitrogen (AN), available phosphorus (AP), available potassium (AK) and pH are key chemical properties for evaluating soil fertility and quality. This study involved the integration of four soil sensors, visible near‐infrared (vis–NIR) spectrometer, mid‐infrared (mid‐IR) spectrometer, portable X‐ray fluorescence (PXRF) analyser and laser‐induced breakdown spectroscopy (LIBS), to achieve rapid measurement of these soil properties. A genetic algorithm and partial least‐squares regression (GA–PLSR) were used to select characteristic bands to reduce data redundancy. We then calibrated models from three aspects: models using partial least‐squares regression (PLSR) based on single sensor data, models using PLSR based on fused sensor data, involving data combined from the four sensors into a new dataset to create a data fusion (DF) model, and models with Bayesian model averaging (BMA) based on prediction results of fused sensor data, involving prediction results combined from the four sensors into a new dataset to form the BMA model. The results showed the following. (i) For the single sensor, the predictive performance decreased as follows: mid‐IR > vis–NIR > LIBS > PXRF. (ii) Compared with the single sensor approach, the DF approach slightly improved or even reduced prediction accuracy and caused a large amount of redundancy. We suggest that this approach is not able to improve predictive ability. (iii) The BMA approach achieved the best prediction for the six soil properties. Our findings suggest that model averaging of vis–NIR, mid‐IR and LIBS could be a reliable and stable approach for the fast measurement of soil properties. Highlights We used four proximal soil sensors to evaluate six key properties for evaluating soil fertility and quality. GA–PLSR was used to select characteristic bands. We compared predictions of six soil properties from single sensor, DF and BMA approaches. BMA predictions were more accurate than predictions from single and fused sensor data.