N -type β-FeSi2/p-type Si heterojunctions were fabricated from β-FeSi2 films epitaxially grown on Si(111) by facing-target direct-current sputtering. Sharp film-substrate interfaces were confirmed by scanning electron microscopy. The current-voltage and photoresponse characteristics were measured at room temperature. They exhibited good rectifying properties and a change of approximately one order of magnitude in the current at a bias voltage of −1 V under illumination by a 6 mW, 1.31 μm laser. The estimated detectivity was 1.5×109 cm √Hz W at 1.31 μm. The results suggest that the β-FeSi2/Si heterojunctions can be used as near-infrared photodetectors that are compatible with silicon integrated circuits.

$n$ -Type $\beta {\text{-FeSi}}_2$ /intrinsic-$\text{Si}/p$ -type Si heterojunctions, prepared by facing-targets direct-current sputtering, were evaluated as near-infrared photodetectors. The built-in potential was estimated to be approximately 1 V from capacitance-voltage characteristics. Diodes with a junction area of $0.03{\text{mm}}^2$ exhibited a junction capacitance of 4.4 pF at zero bias. At room temperature, the devices exhibited responsivity of 140 mA/W and external quantum efficiency of 13% at a bias voltage of −5 V. The detectivity at zero bias was estimated to be $2.8\times {10}^9\text{cm}\surd \text{Hz}/\text{W}$ at the wavelength of $1.31\mu \text{m}$ . These results indicate their high application potential as near-infrared photodiodes integrated with Si.

N-type β-FeSi₂/p-type Si heterojunction photodiodes were fabricated by facing-targets direct-current sputtering, and their near-infrared photodetection properties were studied in the temperature range of 50–300 K. At 300 K, devices biased at −5 V exhibited a current responsivity of 16.6 mA/W. The measured specific detectivity was remarkably improved from 3.5 × 10⁹ to 1.4 × 10¹¹ cmHz^1/2/W as the devices were cooled from 300 K down to 50 K. This improvement is mainly attributable to distinguished suppression in heterojunction leakage current at low temperatures. The obtained results indicate that β-FeSi₂/Si heterojunctions offer high potential to be employed as near-infrared photodetectors that are compatible with the current Si technology.

Soft soils are commonly located in many regions near seas, oceans, and rivers all over the world. These regions are vital and attractive for population and governments development. Soft soil is classified as problematic soil owing to sustaining low shear strength and high settlement under structures. Constructing structures and/or infrastructures on soft soil is a considerable risk that needs great attention from structural engineers. The bearing capacity of structure foundations on soft soil depends mainly on their undrained shear strength. This soil feature strongly influences the selection of appropriate soil improvement methods. However, determining undrained shear strength is very difficult, costly, and time-consuming, especially for sensitive clay. Consequently, extracting undisturbed samples of sensitive clay faces several difficulties on construction sites. In this research, accurate field-tested data were fed to advanced machine learning models to predict the undrained shear strength of the sensitive clay to save hard effort, time, repeated laboratory testing, and costs. In this context, a dataset of 111 geotechnical testing points were collected based on laboratory and field examinations of the soil’s key features. These features included the water content, liquid limit, dry unit weight, plasticity index, consistency index, void ratio, specific gravity, and pocket penetration shear. Several machine learning algorithms were adopted to provide the soft clay modeling, including the linear, Gaussian process regression, ensemble and regression trees, and the support vector regression. The coefficient of determination was mainly used to assess the performance of each predictive model. The achieved results revealed that the support vector regression model attained the most accurate prediction for soil undrained shear strength. These outcomes lay the groundwork for evaluating soil shear strength characteristics in a practical, fast, and low-cost way.