Tunable Electron and Hole Injection Enabled by Atomically Thin Tunneling Layer for Improved Contact Resistance and Dual Channel Transport in MoS₂/WSe₂ van der Waals Heterostructure

Two-dimensional (2D) material-based heterostructures provide a unique platform where interactions between stacked 2D layers can enhance the electrical and opto-electrical properties as well as give rise to interesting new phenomena. Here, the operation of a van der Waals heterostructure device comprising of vertically stacked bilayer MoS₂ and few layered WSe₂ has been demonstrated in which an atomically thin MoS₂ layer has been employed as a tunneling layer to the underlying WSe₂ layer. In this way, simultaneous contacts to both MoS₂ and WSe₂ 2D layers have been established by forming a direct metal–semiconductor to MoS₂ and a tunneling-based metal–insulator–semiconductor contacts to WSe₂, respectively. The use of MoS₂ as a dielectric tunneling layer results in an improved contact resistance (80 kΩ μm) for WSe₂ contact, which is attributed to reduction in the effective Schottky barrier height and is also confirmed from the temperature-dependent measurement. Furthermore, this unique contact engineering and type-II band alignment between MoS₂ and WSe₂ enables a selective and independent carrier transport across the respective layers. This contact engineered dual channel heterostructure exhibits an excellent gate control and both channel current and carrier types can be modulated by the vertical electric field of the gate electrode, which is also reflected in the on/off ratio of 10⁴ for both electron (MoS₂) and hole (WSe₂) channels. Moreover, the charge transfer at the heterointerface is studied quantitatively from the shift in the threshold voltage of the pristine MoS₂ and the heterostructure device, which agrees with the carrier recombination-induced optical quenching as observed in the Raman spectra of the pristine and heterostructure layers. This observation of dual channel ambipolar transport enabled by the hybrid tunneling contacts and strong interlayer coupling can be utilized for high-performance opto-electrical devices and applications

Prospective, multicenter validation of the deep learning-based cardiac arrest risk management system for predicting in-hospital cardiac arrest or unplanned intensive care unit transfer in patients admitted to general wards

Abstract Background Retrospective studies have demonstrated that the deep learning-based cardiac arrest risk management system (DeepCARS™) is superior to the conventional methods in predicting in-hospital cardiac arrest (IHCA). This prospective study aimed to investigate the predictive accuracy of the DeepCARS™ for IHCA or unplanned intensive care unit transfer (UIT) among general ward patients, compared with that of conventional methods in real-world practice. Methods This prospective, multicenter cohort study was conducted at four teaching hospitals in South Korea. All adult patients admitted to general wards during the 3-month study period were included. The primary outcome was predictive accuracy for the occurrence of IHCA or UIT within 24 h of the alarm being triggered. Area under the receiver operating characteristic curve (AUROC) values were used to compare the DeepCARS™ with the modified early warning score (MEWS), national early warning Score (NEWS), and single-parameter track-and-trigger systems. Results Among 55,083 patients, the incidence rates of IHCA and UIT were 0.90 and 6.44 per 1,000 admissions, respectively. In terms of the composite outcome, the AUROC for the DeepCARS™ was superior to those for the MEWS and NEWS (0.869 vs. 0.756/0.767). At the same sensitivity level of the cutoff values, the mean alarm counts per day per 1,000 beds were significantly reduced for the DeepCARS™, and the rate of appropriate alarms was higher when using the DeepCARS™ than when using conventional systems. Conclusion The DeepCARS™ predicts IHCA and UIT more accurately and efficiently than conventional methods. Thus, the DeepCARS™ may be an effective screening tool for detecting clinical deterioration in real-world clinical practice. Trial registration This study was registered at ClinicalTrials.gov ( NCT04951973 ) on June 30, 2021