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.

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We wish to estimate the amount of carbon (C) stored in the soil at high altitudes, for which there is little information. Collecting and transporting large numbers of soil samples from such terrain are difficult, and we have therefore evaluated the feasibility of scanning with visible near-infrared (vis-NIR) spectroscopy in situ for the rapid measurement of the soil in the field. We took 28 cores (≈1 m depth and 5 cm diameter) of soil at altitudes from 2900 to 4500 m in the Sygera Mountains on the Qinghai-Tibet Plateau, China. Spectra were acquired from fresh, vertical faces 5 × 5 cm in area from the centers of the cores to give 413 spectra in all. The raw spectra were pretreated by several methods to remove noise, and statistical models were built to predict of the organic C in the samples from the spectra by partial least-squares regression (PLSR) and least-squares support vector machine (LS-SVM). The bootstrap was used to assess the uncertainty of the predictions by the several combinations of pretreatment and models. The predictions by LS-SVM from the field spectra, for which R(2) = 0.81, the root-mean-square error RMSE = 8.40, and the ratio of the interquartile distance RPIQ = 2.66, were comparable to the PLSR predictions from the laboratory spectra (R(2) = 0.85, RMSE = 7.28, RPIQ = 3.09). We conclude that vis-NIR scanning in situ in the field is a sufficiently accurate rapid means of estimating the concentration of organic C in soil profiles in this high region and perhaps elsewhere.

In this paper, highly mesoporous hierarchical nickel and cobalt double hydroxide composites (NCDHs) have been synthesized via a simple reflux method. The synthesized NCDH composites with a mass ratio of 100 : 20 for Ni(NO 3 ) 2 $6H 2 O/Co(NO 3 ) 2 $6H 2 O (NCDH-20) contained a lot of oxygen defect sites, and chemisorbed water connects the adjacent Ni(OH) 2 layers. The porous hierarchical nanostructures provide natural channels for the effective and fast transportation of carriers. Furthermore, the NCDH-20 sensor exhibits excellent gas-sensing properties, such as a low detection limit of 0.97 ppm and short response time of 0.6 s to 97 ppm NO x , due to the layered single crystal structure and unique gallery pathway. The NCDH-20 sensor with ultrafast NO x sensing has significant applications for gas sensing.

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