Klein Tunneling in ß12 Borophene.

Motivated by the recent observation of Klein tunneling in 8-Pmmn borophene, we delve into the phenomenon in ß12 borophene by employing tight-binding approximation theory to establish a theoretical mode. The tight-binding model is a semi-empirical method for establishing the Hamiltonian based on atomic orbitals. A single cell of ß12 borophene contains five atoms and multiple central bonds, so it creates the complexity of the tight-binding model Hamiltonian of ß12 borophene. We investigate transmission across one potential barrier and two potential barriers by changing the width and height of barriers and the distance between two potential barriers. Regardless of the change in the barrier heights and widths, we find the interface to be perfectly transparent for normal incidence. For other angles of incidence, perfect transmission at certain angles can also be observed. Furthermore, perfect and all-angle transmission across a potential barrier takes place when the incident energy approaches the Dirac point. This is analogous to the "super", all-angle transmission reported for the dice lattice for Klein tunneling across a potential barrier. These findings highlight the significance of our theoretical model in understanding the complex dynamics of Klein tunneling in borophene structures.

Genome-wide DNA methylation dynamics and RNA-seq analysis during grape (cv. 'Cabernet Franc') skin coloration.

This study generated whole genome DNA methylation maps to characterize DNA methylomes of grape (cv. 'Cabernet Franc') skins and examine their functional significance during grape skin coloration. We sampled grape skin tissues at three key stages (the early stage of grape berry swelling, the late stage of grape berry swelling and the veraison) during which the color of grape berries changed from green to red. DNA methylation levels of grape skins at the three stages were higher in transposable element regions than in the genic regions, and the CG and CHG DNA methylation levels of the genic region were higher than the CHH DNA methylation levels. We identified differentially methylated regions (DMRs) in S2_vs_S1 and S3_vs_S1. The results indicated that DMRs predominantly occurred within the CHH context during grape skin coloration. Many gene ontology (GO)-enriched DMR-related genes were involved in "nucleotide binding," "catalytic activity" and "ribonucleotide binding" terms; however, many KEGG-enriched DMR-related genes were involved in the "flavonoid biosynthesis" pathway. Our results could provide an important foundation for future research on the development mechanism of grape berries.

Monolithic three-dimensional integration of RRAM-based hybrid memory architecture for one-shot learning.

In this work, we report the monolithic three-dimensional integration (M3D) of hybrid memory architecture based on resistive random-access memory (RRAM), named M3D-LIME. The chip featured three key functional layers: the first was Si complementary metal-oxide-semiconductor (CMOS) for control logic; the second was computing-in-memory (CIM) layer with HfAlOx-based analog RRAM array to implement neural networks for feature extractions; the third was on-chip buffer and ternary content-addressable memory (TCAM) array for template storing and matching, based on Ta2O5-based binary RRAM and carbon nanotube field-effect transistor (CNTFET). Extensive structural analysis along with array-level electrical measurements and functional demonstrations on the CIM and TCAM arrays was performed. The M3D-LIME chip was further used to implement one-shot learning, where ~96% accuracy was achieved on the Omniglot dataset while exhibiting 18.3× higher energy efficiency than graphics processing unit (GPU). This work demonstrates the tremendous potential of M3D-LIME with RRAM-based hybrid memory architecture for future data-centric applications.

Edge learning using a fully integrated neuro-inspired memristor chip.

Learning is highly important for edge intelligence devices to adapt to different application scenes and owners. Current technologies for training neural networks require moving massive amounts of data between computing and memory units, which hinders the implementation of learning on edge devices. We developed a fully integrated memristor chip with the improvement learning ability and low energy cost. The schemes in the STELLAR architecture, including its learning algorithm, hardware realization, and parallel conductance tuning scheme, are general approaches that facilitate on-chip learning by using a memristor crossbar array, regardless of the type of memristor device. Tasks executed in this study included motion control, image classification, and speech recognition.

Monolithic 3D Integration of Analog RRAM-Based Computing-in-Memory and Sensor for Energy-Efficient Near-Sensor Computing.

In the era of the Internet of Things, vast amounts of data generated at sensory nodes impose critical challenges on the data-transfer bandwidth and energy efficiency of computing hardware. A near-sensor computing (NSC) architecture places the processing units closer to the sensors such that the generated data can be processed almost in situ with high efficiency. This study demonstrates the monolithic three-dimensional (M3D) integration of a photosensor array, analog computing-in-memory (CIM), and Si complementary metal-oxide-semiconductor (CMOS) logic circuits, named M3D-SAIL. This approach exploits the high-bandwidth on-chip data transfer and massively parallel CIM cores to realize an energy-efficient NSC architecture. The 1st layer of the Si CMOS circuits serves as the control logic and peripheral circuits. The 2nd layer comprises a 1 k-bit one-transistor-one-resistor (1T1R) array with InGaZnOx field-effect transistor (IGZO-FET) and resistive random-access memory (RRAM) for analog CIM. The 3rd layer comprises multiple IGZO-FET-based photosensor arrays for wavelength-dependent optical sensing. The structural integrity and function of each layer are comprehensively verified. Furthermore, NSC is implemented using the M3D-SAIL architecture for a typical video keyframe-extraction task, achieving a high classification accuracy of 96.7% as well as a 31.5× lower energy consumption and 1.91× faster computing speed compared to its 2D counterpart.

Arabidopsis NOTCHLESS plays an important role in root and embryo development.

Ribosome biogenesis is a fundamental process in eukaryotic cells. NOTCHLESS (NLE) is involved in 60S ribosome biogenesis in yeast, but its role in Arabidopsis (A. thaliana) remains exclusive. Here, we found that Arabidopsis NLE (AtNLE) is highly conservative in phylogeny, which encoding a WD40-repeat protein. AtNLE is expressed in actively dividing tissues. AtNLE-GFP is localized in the nucleus. AtNLE physically interacts with the MIDAS domain of AtMDN1, a protein involved in the biogenesis of the 60S ribosomal subunit in Arabidopsis. The underexpressing mutant nle-2 shows short roots and reduced cell number in the root meristem. In addition, the null mutant nle-1 is embryo lethal, and defective embryos are arrested at the early globular stage. This work suggests that AtNLE interacts with AtMDN1, and AtNLE functions in root and embryo development.

Overexpressing Ribosomal Protein L16D Affects Leaf Development but Confers Pathogen Resistance in Arabidopsis.

In plant cells, multiple paralogs from ribosomal protein (RP) families are always synchronously expressed, which is likely contributing to ribosome heterogeneity or functional specialization. However, previous studies have shown that most RP mutants share common phenotypes. Consequently, it is difficult to distinguish whether the phenotypes of the mutants have resulted from the loss of specific genes or a global ribosome deficiency. Here, to investigate the role of a specific RP gene, we employed a gene overexpression strategy. We found that Arabidopsis lines overexpressing RPL16D (L16D-OEs) display short and curled rosette leaves. Microscopic observations reveal that both the cell size and cell arrangement are affected in L16D-OEs. The severity of the defect is positively correlated with RPL16D dosage. By combining transcriptomic and proteomic profiling, we found that overexpressing RPL16D decreases the expression of genes involved in plant growth, but increases the expression of genes involved in immune response. Overall, our results suggest that RPL16D is involved in the balance between plant growth and immune response.

An in-memory computing architecture based on a duplex two-dimensional material structure for in situ machine learning.

The growing computational demand in artificial intelligence calls for hardware solutions that are capable of in situ machine learning, where both training and inference are performed by edge computation. This not only requires extremely energy-efficient architecture (such as in-memory computing) but also memory hardware with tunable properties to simultaneously meet the demand for training and inference. Here we report a duplex device structure based on a ferroelectric field-effect transistor and an atomically thin MoS2 channel, and realize a universal in-memory computing architecture for in situ learning. By exploiting the tunability of the ferroelectric energy landscape, the duplex building block demonstrates an overall excellent performance in endurance (>1013), retention (>10 years), speed (4.8 ns) and energy consumption (22.7 fJ bit-1 µm-2). We implemented a hardware neural network using arrays of two-transistors-one-duplex ferroelectric field-effect transistor cells and achieved 99.86% accuracy in a nonlinear localization task with in situ trained weights. Simulations show that the proposed device architecture could achieve the same level of performance as a graphics processing unit under notably improved energy efficiency. Our device core can be combined with silicon circuitry through three-dimensional heterogeneous integration to give a hardware solution towards general edge intelligence.

Electro-mechano responsive elastomers with self-tunable conductivity and stiffness.

Materials with programmable conductivity and stiffness offer new design opportunities for next-generation engineered systems in soft robotics and electronic devices. However, existing approaches fail to harness variable electrical and mechanical properties synergistically and lack the ability to self-respond to environmental changes. We report an electro-mechano responsive Field's metal hybrid elastomer exhibiting variable and tunable conductivity, strain sensitivity, and stiffness. By synergistically harnessing these properties, we demonstrate two applications with over an order of magnitude performance improvement compared to state-of-the-art, including a self-triggered multiaxis compliance compensator for robotic manipulators, and a resettable, highly compact, and fast current-limiting fuse with an adjustable fusing current. We envisage that the extraordinary electromechanical properties of our hybrid elastomer will bring substantial advancements in resilient robotic systems, intelligent instruments, and flexible electronics.

Silver Nanoflakes-Enhanced Anisotropic Hybrid Composites for Integratable Pressure Sensors.

Flexible pressure sensors based on polymer elastomers filled with conductive fillers show great advantages in their applications in flexible electronic devices. However, integratable high-sensitivity pressure sensors remain understudied. This work improves the conductivity and sensitivity of PDMS-Fe/Ni piezoresistive composites by introducing silver flakes and magnetic-assisted alignment techniques. As secondary fillers, silver flakes with high aspect ratios enhance the conductive percolation network in composites. Meanwhile, a magnetic field aligns ferromagnetic particles to further improve the conductivity and sensitivity of composites. The resistivity of the composite decreases sharply by 1000 times within a tiny compression strain of 1%, indicating excellent sensing performance. On the basis of this, we demonstrate an integratable miniature pressure sensor with a small size (2 × 2 × 1 mm), high sensitivity (0.966 kPa-1), and wide sensing range (200 kPa). Finally, we develop a flexible E-skin system with 5 × 5 integratable sensor units to detect pressure distribution, which shows rapid real-time response, high resolution, and high sensitivity.

Design, analysis and optimization of a waveguide-type near-eye display using a pin-mirror array and a concaved reflector.

Waveguides have become one of the most promising optical combiners for see-through near-eye displays due to the thickness, weight, and transmittance. In this study, we propose a waveguide-type near-eye display using a pin-mirror array and a concaved reflector with a compact outlook, optimized image uniformity and stray light. Issues have been discussed in detail, which include field of view (FOV), eye-box, resolution, depth of field (DOF), display uniformity and stray light artifacts. It can be shown that the DOF can be extended (when compared with traditional waveguide-type near-eye displays) to alleviate the vergence-accommodation conflict (VAC) problem, and the uniformity & stray light can be improved with an optimal structure. Moreover, reflective surfaces have been introduced as the input and output coupling with a compact outlook, an easy-processing structure and the achromatic performance. A prototype based on the proposed method have been successfully developed, and virtual images with an extended DOF can be shown along with the real-world.

Large-Scale Integrated Flexible Tactile Sensor Array for Sensitive Smart Robotic Touch.

In the long pursuit of smart robotics, it has been envisioned to empower robots with human-like senses, especially vision and touch. While tremendous progress has been made in image sensors and computer vision over the past decades, tactile sense abilities are lagging behind due to the lack of large-scale flexible tactile sensor array with high sensitivity, high spatial resolution, and fast response. In this work, we have demonstrated a 64 × 64 flexible tactile sensor array with a record-high spatial resolution of 0.9 mm (equivalently 28.2 pixels per inch) by integrating a high-performance piezoresistive film (PRF) with a large-area active matrix of carbon nanotube thin-film transistors. PRF with self-formed microstructures exhibited high pressure-sensitivity of ∼385 kPa-1 for multi-walled carbon nanotubes concentration of 6%, while the 14% one exhibited fast response time of ∼3 ms, good linearity, broad detection range beyond 1400 kPa, and excellent cyclability over 3000 cycles. Using this fully integrated tactile sensor array, the footprint maps of an artificial honeybee were clearly identified. Furthermore, we hardware-implemented a smart tactile system by integrating the PRF-based sensor array with a memristor-based computing-in-memory chip to record and recognize handwritten digits and Chinese calligraphy, achieving high classification accuracies of 98.8% and 97.3% in hardware, respectively. The integration of sensor networks with deep learning hardware may enable edge or near-sensor computing with significantly reduced power consumption and latency. Our work could empower the building of large-scale intelligent sensor networks for next-generation smart robotics.

Enhanced Spin Thermopower in Phosphorene Nanoribbons via Edge-State Modifications.

We investigated spin-dependent thermoelectric transport in zigzag phosphorene nanoribbons with a ferromagnetic stripe. We explored the possibility to enhance the spin thermopower via modifications of the edge states in zigzag ribbons. Two methods are proposed to modulate the edge transport: one is applying gate voltages on the edges; the other is including notches on the ribbon edges. The transport gap is enlarged by the edge-state modifications, which enhance the charge and spin Seebeck coefficients almost twofold. Our results suggest phosphorene to be a promising material for thermoelectric applications and open a possibility to design a tunable spin-thermoelectric device.

Novel transport properties of the α-T3 lattice with uniform electric and magnetic fields.

We report a theoretical study of electronic transport properties of α-T3 lattice nanoribbons in the presence of uniform electric and magnetic fields. Landau levels with an unexcepted fashion are obtained in the system, and unique flat bands are observed due to the crossed electric and magnetic fields. We found that the nondispersive flat band of α-T3 lattice is distorted and split to many dispersive energy levels when electric and magnetic fields are applied. A double constriction structure of α-T3 lattice is considered to investigate the quantum transport in the flat band, and novel quantum transport properties are obtained, which shows great differences from conventional Dirac electrons. Our results show that the flat bands of α-T3 lattice can also contribute to the quantum transport properties and play an important role in the development of novel Dirac electron device.

Memristor-based analogue computing for brain-inspired sound localization with in situ training.

The human nervous system senses the physical world in an analogue but efficient way. As a crucial ability of the human brain, sound localization is a representative analogue computing task and often employed in virtual auditory systems. Different from well-demonstrated classification applications, all output neurons in localization tasks contribute to the predicted direction, introducing much higher challenges for hardware demonstration with memristor arrays. In this work, with the proposed multi-threshold-update scheme, we experimentally demonstrate the in-situ learning ability of the sound localization function in a 1K analogue memristor array. The experimental and evaluation results reveal that the scheme improves the training accuracy by ∼45.7% compared to the existing method and reduces the energy consumption by ∼184× relative to the previous work. This work represents a significant advance towards memristor-based auditory localization system with low energy consumption and high performance.

Impact of heavy metals (cadmium and copper) stress on the bioturbation potential of polychaete Perinereis aibuhitensis (family Nereididae).

The change of macrofaunal bioturbation potential (BP) under environmental stress has application value in ecological restoration and ecological models. Single and combined toxic effects of metals cadmium and copper on the BP of polychaete Perinereis aibuhitensis were studied. The distribution of tracer sand showed a negative correlation between the transportation capacity of P. aibuhitensis and the Cd concentration; Cu stress indicated a stronger ability of promoting downward transportation, and the linear relationship with concentration was not so obvious as Cd. The toxicity stress of Cd and Cu also showed a significant effect on the oxidation-reduction potential (ORP) in the sediment matrix. There was a certain synergistic effect between cadmium and copper, and toxicity effects were associated with metal concentration and stress duration. In the later stage of the combined experiment, P. aibuhitensis avoided living in depth near the bottom, especially when the concentrations of Cd and Cu were high. For the bioturbation model, it was suggested that the two parameters of mobility and reworking would be reduced by half or one category scale depending on the cadmium and copper concentration and stress duration. The results can be used for ecological restoration prediction and ecological risk assessment; it is necessary to carry out more studies with a variety of environmental factors and indicators, since a variety of coexisted pollutants would show complex influence on the BP of macrobenthos.

Rotating neurons for all-analog implementation of cyclic reservoir computing.

Hardware implementation in resource-efficient reservoir computing is of great interest for neuromorphic engineering. Recently, various devices have been explored to implement hardware-based reservoirs. However, most studies were mainly focused on the reservoir layer, whereas an end-to-end reservoir architecture has yet to be developed. Here, we propose a versatile method for implementing cyclic reservoirs using rotating elements integrated with signal-driven dynamic neurons, whose equivalence to standard cyclic reservoir algorithm is mathematically proven. Simulations show that the rotating neuron reservoir achieves record-low errors in a nonlinear system approximation benchmark. Furthermore, a hardware prototype was developed for near-sensor computing, chaotic time-series prediction and handwriting classification. By integrating a memristor array as a fully-connected output layer, the all-analog reservoir computing system achieves 94.0% accuracy, while simulation shows >1000× lower system-level power than prior works. Therefore, our work demonstrates an elegant rotation-based architecture that explores hardware physics as computational resources for high-performance reservoir computing.

Ultra-Robust and Extensible Fibrous Mechanical Sensors for Wearable Smart Healthcare.

Fibrous material with high strength and large stretchability is an essential component of high-performance wearable electronic devices. Wearable electronic systems require a material that is strong to ensure durability and stability, and a wide range of strain to expand their applications. However, it is still challenging to manufacture fibrous materials with simultaneously high mechanical strength and the tensile property. Herein, the ultra-robust (≈17.6 MPa) and extensible (≈700%) conducting microfibers are developed and demonstrated their applications in fabricating fibrous mechanical sensors. The mechanical sensor shows high sensitivity in detecting strains that have high strain resolution and a large detection range (from 0.0075% to 400%) simultaneously. Moreover, low frequency vibrations between 0 and 40 Hz are also detected, which covers most tremors that occur in the human body. As a further step, a wearable and smart health-monitoring system has been developed using the fibrous mechanical sensor, which is capable of monitoring health-related physiological signals, including muscle movement, body tremor, wrist pulse, respiration, gesture, and six body postures to predict and diagnose diseases, which will promote the wearable telemedicine technology.

Analog memristive synapse based on topotactic phase transition for high-performance neuromorphic computing and neural network pruning.

Inspired by the human brain, nonvolatile memories (NVMs)-based neuromorphic computing emerges as a promising paradigm to build power-efficient computing hardware for artificial intelligence. However, existing NVMs still suffer from physically imperfect device characteristics. In this work, a topotactic phase transition random-access memory (TPT-RAM) with a unique diffusive nonvolatile dual mode based on SrCoO x is demonstrated. The reversible phase transition of SrCoO x is well controlled by oxygen ion migrations along the highly ordered oxygen vacancy channels, enabling reproducible analog switching characteristics with reduced variability. Combining density functional theory and kinetic Monte Carlo simulations, the orientation-dependent switching mechanism of TPT-RAM is investigated synergistically. Furthermore, the dual-mode TPT-RAM is used to mimic the selective stabilization of developing synapses and implement neural network pruning, reducing ~84.2% of redundant synapses while improving the image classification accuracy to 99%. Our work points out a new direction to design bioplausible memristive synapses for neuromorphic computing.

Design of a near-eye display measurement system using an anthropomorphic vision imaging method.

We developed a new near-eye display measurement system using anthropomorphic vision imaging to measure the key parameters of near-eye displays, including field-of-view (FOV), angular resolution, eye box, and virtual image depth. The characteristics of the human eye, such as pupil position, pupil size variation, accommodation function, and the high resolution of the fovea, are imitated by the proposed measurement system. A FOV scanning structure, together with a non-vignetting image-telecentric lens system, captures the virtual image from the near-eye display by imitating human eye function. As a proof-of-concept, a prototype device was used to obtain large-range, high-resolution measurements for key parameters of near-eye displays.