Oral cancer is the most common type of head and neck cancer worldwide, leading to approximately 177,757 deaths every year. When identified at early stages, oral cancers can achieve survival rates of up to 75–90%. However, the majority of the cases are diagnosed at an advanced stage mainly due to the lack of public awareness about oral cancer signs and the delays in referrals to oral cancer specialists. As early detection and treatment remain to be the most effective measures in improving oral cancer outcomes, the development of vision-based adjunctive technologies that can detect oral potentially malignant disorders (OPMDs), which carry a risk of cancer development, present significant opportunities for the oral cancer screening process. In this study, we explored the potential applications of computer vision techniques in the oral cancer domain within the scope of photographic images and investigated the prospects of an automated system for detecting OPMD. Exploiting the advancements in deep learning, a two-stage model was proposed to detect oral lesions with a detector network and classify the detected region into three categories (benign, OPMD, carcinoma) with a second-stage classifier network. Our preliminary results demonstrate the feasibility of deep learning-based approaches for the automated detection and classification of oral lesions in real time. The proposed model offers great potential as a low-cost and non-invasive tool that can support screening processes and improve detection of OPMD.

Materials and device architecture innovations are essential for further enhancing the performance of solar cells while potentially enabling their large-scale integration as a viable source of alternative energy. In this regard, tremendous research has been devoted in recent years with continuous progress in the field. In this article, we review the recent advancements in nanopillar-based photovoltaics while discussing the future challenges and prospects. Nanopillar arrays provide unique advantages over thin films in the areas of optical properties and carrier collection, arising from their three-dimensional geometry. The choice of the material system, however, is essential in order to gain the advantage of the large surface/interface area associated with nanopillars with the constraints different from those of the thin film devices.

One of the challenges for the nanoscale device fabrication of III-V semiconductors is controllable postdeposition doping techniques to create ultrashallow junctions. Here, we demonstrate nanoscale, sulfur doping of InAs planar substrates with high dopant areal dose and uniformity by using a self-limiting monolayer doping approach. From transmission electron microscopy and secondary ion mass spectrometry, a dopant profile abruptness of $\sim 3.5\text{nm}$ /decade is observed without significant defect density. The $n^{+}/p^{+}$ junctions fabricated by using this doping scheme exhibit negative differential resistance characteristics, further demonstrating the utility of this approach for device fabrication with high electrically active sulfur concentrations of $\sim 8\times {10}^{18}{\text{cm}}^{-3}$ .

InAs nanowires have been actively explored as the channel material for high performance transistors owing to their high electron mobility and ease of ohmic metal contact formation. The catalytic growth of nonepitaxial InAs nanowires, however, has often relied on the use of Au colloids which is non-CMOS compatible. Here, we demonstrate the successful synthesis of crystalline InAs nanowires with high yield and tunable diameters by using Ni nanoparticles as the catalyst material on amorphous SiO2 substrates. The nanowires show superb electrical properties with field-effect electron mobility ~2700 cm2/Vs and ION/IOFF >103. The uniformity and purity of the grown InAs nanowires are further demonstrated by large-scale assembly of parallel arrays of nanowires on substrates via the contact printing process that enables high performance, “printable” transistors, capable of delivering 5 10 mA ON currents (~400 nanowires).