Recent advances in emerging Janus two-dimensional materials including fundamental physics, unique properties and potential device applications are reviewed.

BackgroundEstrogen is an established enhancer of breast cancer development, but less is known on its effect on local progression or metastasis. We studied the effect of estrogen receptor recruitment on actin cytoskeleton remodeling and breast cancer cell movement and invasion. Moreover, we characterized the signaling steps through which these actions are enacted.Methodology/Principal FindingsIn estrogen receptor (ER) positive T47-D breast cancer cells ER activation with 17β-estradiol induces rapid and dynamic actin cytoskeleton remodeling with the formation of specialized cell membrane structures like ruffles and pseudopodia. These effects depend on the rapid recruitment of the actin-binding protein moesin. Moesin activation by estradiol depends on the interaction of ERα with the G protein Gα13, which results in the recruitment of the small GTPase RhoA and in the subsequent activation of its downstream effector Rho-associated kinase-2 (ROCK-2). ROCK-2 is responsible for moesin phosphorylation. The Gα13/RhoA/ROCK/moesin cascade is necessary for the cytoskeletal remodeling and for the enhancement of breast cancer cell horizontal migration and invasion of three-dimensional matrices induced by estrogen. In addition, human samples of normal breast tissue, fibroadenomas and invasive ductal carcinomas show that the expression of wild-type moesin as well as of its active form is deranged in cancers, with increased protein amounts and a loss of association with the cell membrane.Conclusions/SignificanceThese results provide an original mechanism through which estrogen can facilitate breast cancer local and distant progression, identifying the extra-nuclear Gα13/RhoA/ROCK/moesin signaling cascade as a target of ERα in breast cancer cells. This information helps to understand the effects of estrogen on breast cancer metastasis and may provide new targets for therapeutic interventions.

Machine vision systems (MVSs) are an important component of intelligent systems, such as autonomous vehicles and robots. However, with the continuous increase in data and new application scenarios, new requirements are put forward for the next generation of MVS. There is an urgent need to find new material systems to complement the existing semiconductor technology based on thin‐film materials, and new architectures must be explored to improve efficiency. Because of their unique physical properties, two‐dimensional (2D) materials have received extensive attention for use in MVSs, especially in biomimetic ones: the human visual system, which can process complex visual information with low power consumption, provides a model for next‐generation MVSs. This review paper summarizes the progress and challenges of applying 2D material photodetectors in sense‐memory‐computational integration and biomimetic image sensors for machine vision.

A MoS2 nanosphere memristor with lateral gold electrodes was found to show photoresistive switching. The new device can be controlled by the polarization of nanospheres, which causes resistance switching in an electric field in the dark or under white light illumination. The polarization charge allows to change the switching voltage of the photomemristor, providing its multi-level operation. The device, polarized at a voltage 6 V, switches abruptly from a high resistance state (HRSL6) to a low resistance state (LRSL6) with the On/Off resistance ratio of about 10 under white light and smooth in the dark. Analysis of device conductivity in different resistive states indicates that its resistive state could be changed by the modulation of the charge in an electric field in the dark or under light, resulting in the formation/disruption of filaments with high conductivity. A MoS2 photomemristor has great potential as a multifunctional device designed by using cost-effective fabrication techniques.

4H-SnS layered crystals synthesized by a hydrothermal method were used to obtain via liquid phase exfoliation quantum dots (QDs), consisting of a single layer (SLQDs) or multiple layers (MLQDs). Systematic downshift of the peaks in the Raman spectra of crystals with a decrease in size was observed. The bandgap of layered QDs, estimated by UV-visible absorption spectroscopy and the tunneling current measurements using graphene probes, increases from 2.25 eV to 3.50 eV with decreasing size. 2-4 nm SLQDs, which are transparent in the visible region, show selective absorption and photosensitivity at wavelengths in the ultraviolet region of the spectrum while larger MLQDs (5-90 nm) exhibit a broad band absorption in the visible spectral region and the photoresponse under white light. The results show that the layered quantum dots obtained by liquid phase exfoliation exhibit well-controlled and regulated bandgap absorption in a wide tunable wavelength range. These novel layered quantum dots prepared using an inexpensive method of exfoliation and deposition from solution onto various substrates at room temperature can be used to create highly efficient visible-blind ultraviolet photodetectors and multiple bandgap solar cells.

No abstract

The emerging monoelemental 2D materials named as Xenes including borophene, silicene, germanene, stanene, phosphorene, arsenene, antimonene, bisthumene, selenene, and tellurene, have attracted rising attention experimentally and theoretically. Because of their excellent and versatile physical, chemical, electrical, and optical advantages, Xenes have been shown or have been predicted to have excellent performance in nanotechnology applications, addressing challenges and advances in electronics, energy, healthcare, and environment. In this review, the basic fundamentals in the classification of the periodic table group and the synthesis methods for the emerging materials are summarized. Then, the hybridization, doping and functionalization of 2D Xenes, and their corresponding applications are presented. Furthermore, a summary of research progress on 2D Xenes and the challenges and perspectives for their further development are discussed.

Conventional artificial intelligence (AI) machine vision technology, based on the von Neumann architecture, uses separate sensing, computing, and storage units to process huge amounts of vision data generated in sensory terminals. The frequent movement of redundant data between sensors, processors and memory, however, results in high-power consumption and latency. A more efficient approach is to offload some of the memory and computational tasks to sensor elements that can perceive and process the optical signal simultaneously. Here, we proposed a non-volatile photomemristor, in which the reconfigurable responsivity can be modulated by the charge and/or photon flux through it and further stored in the device. The non-volatile photomemristor has a simple two-terminal architecture, in which photoexcited carriers and oxygen-related ions are coupled, leading to a displaced and pinched hysteresis in the current-voltage characteristics. For the first time, non-volatile photomemristors implement computationally complete logic with photoresponse-stateful operations, for which the same photomemristor serves as both a logic gate and memory, using photoresponse as a physical state variable instead of light, voltage and memresistance. The polarity reversal of photomemristors shows great potential for in-memory sensing and computing with feature extraction and image recognition for neuromorphic vision.