Controllable synthesis of borophene aerogels by utilizing h-BN layers for high-performance next-generation batteries.

Borophene is emerging as a promising electrode material for Li, Na, Mg, and Ca ion batteries due to its anisotropic Dirac properties, high charge capacity, and low energy barrier for ion diffusion. However, practical synthesis of active and stable borophene remains challenging in producing electrochemical devices. Here, we introduce a method for borophene aerogels (BoAs), utilizing hexagonal boron nitride aerogels. Borophene grows between h-BN layers utilizing boron-boron bridges, as a nucleation site, where borophene forms monolayers mixed with sp2-sp3 hybridization. This versatile method produces stable BoAs and is compatible with various battery chemistries. With these BoAs, we accomplish an important milestone to successfully fabricate high-performance next-generation batteries, including Na-ion (478 mAh g-1, at 0.5C, >300 cycles), Mg-ion (297 mAh g-1, at 0.5C, >300 cycles), and Ca-ion (332 mAh g-1, at 0.5C, >400 cycles), and Li-S batteries, with one of the highest capacities to date (1,559 mAh g-1, at 0.3C, >1,000 cycles).

Automated Detection and Classification of Oral Lesions Using Deep Learning to Detect Oral Potentially Malignant Disorders.

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.

Real time chemical and mechanical human motion monitoring with aerogel-based wearable sensors.

Wearable bioelectronic systems are one of the most important tools for human health and motion monitoring. However, there is still a great challenge to fabricate high-performance flexible devices with a conformal integration of the human body and there is no single device that can collect and correlate data simultaneously from chemical and mechanical signals of the human body. We recently developed a new method to build aerogel-based strain and sweat sensors (AB-SSS) that can effectively extract real-time information by combining involuntary human motion and chemical signals due to their gradient functionalities. These sensors provide good mechanical integrity and allow high-density power generation during subtle human motion, allowing sweat monitoring by measuring pH, ion concentration, perspiration rate, etc.

Retraction: Graded bandgap perovskite solar cells.

This corrects the article DOI: 10.1038/nmat4795.

Graded bandgap perovskite solar cells.

Organic-inorganic halide perovskite materials have emerged as attractive alternatives to conventional solar cell building blocks. Their high light absorption coefficients and long diffusion lengths suggest high power conversion efficiencies, and indeed perovskite-based single bandgap and tandem solar cell designs have yielded impressive performances. One approach to further enhance solar spectrum utilization is the graded bandgap, but this has not been previously achieved for perovskites. In this study, we demonstrate graded bandgap perovskite solar cells with steady-state conversion efficiencies averaging 18.4%, with a best of 21.7%, all without reflective coatings. An analysis of the experimental data yields high fill factors of ∼75% and high short-circuit current densities up to 42.1 mA cm-2. The cells are based on an architecture of two perovskite layers (CH3NH3SnI3 and CH3NH3PbI3-xBrx), incorporating GaN, monolayer hexagonal boron nitride, and graphene aerogel.

Screening-engineered field-effect solar cells.

Photovoltaics (PV) are a promising source of clean renewable energy, but current technologies face a cost-to-efficiency trade-off that has slowed widespread implementation. We have developed a PV architecture-screening-engineered field-effect photovoltaics (SFPV)-that in principle enables fabrication of low-cost, high efficiency PV from virtually any semiconductor, including the promising but hard-to-dope metal oxides, sulfides, and phosphides. Prototype SFPV devices have been constructed and are found to operate successfully in accord with model predictions.

Shape-controlled synthesis of single-crystalline nanopillar arrays by template-assisted vapor-liquid-solid process.

Highly regular, single-crystalline nanopillar arrays with tunable shapes and geometry are synthesized by the template-assisted vapor-liquid-solid growth mechanism. In this approach, the grown nanopillars faithfully reproduce the shape of the pores because during the growth the liquid catalyst seeds fill the space available, thereby conforming to the pore geometry. The process is highly generic for various material systems, and as an example, CdS and Ge nanopillar arrays with square, rectangular, and circular cross sections are demonstrated. In the future, this technique can be used to engineer the intrinsic properties of NPLs as a function of three independently controlled dimensional parameters--length, width and height.

Three-dimensional nanopillar-array photovoltaics on low-cost and flexible substrates.

Solar energy represents one of the most abundant and yet least harvested sources of renewable energy. In recent years, tremendous progress has been made in developing photovoltaics that can be potentially mass deployed. Of particular interest to cost-effective solar cells is to use novel device structures and materials processing for enabling acceptable efficiencies. In this regard, here, we report the direct growth of highly regular, single-crystalline nanopillar arrays of optically active semiconductors on aluminium substrates that are then configured as solar-cell modules. As an example, we demonstrate a photovoltaic structure that incorporates three-dimensional, single-crystalline n-CdS nanopillars, embedded in polycrystalline thin films of p-CdTe, to enable high absorption of light and efficient collection of the carriers. Through experiments and modelling, we demonstrate the potency of this approach for enabling highly versatile solar modules on both rigid and flexible substrates with enhanced carrier collection efficiency arising from the geometric configuration of the nanopillars.