Artificial synapses based on 2D MoS2 memtransistors have recently attracted considerable attention as a promising device architecture for complex neuromorphic systems. However, previous memtransistor ...devices occasionally cause uncontrollable analog switching and unreliable synaptic plasticity due to random variations in the field‐induced defect migration. Herein, a highly reliable 2D MoS2/Nb2O5 heterostructure memtransistor device is demonstrated, in which the Nb2O5 interlayer thickness is a critical material parameter to induce and tune analog switching characteristics of the 2D MoS2. Ultraviolet photoelectron spectroscopy and photoluminescence analyses reveal that the Schottky barrier height at the 2D channel–electrode junction of the MoS2/Nb2O5 heterostructure films is increased, leading to more effective contact barrier modulation and allowing more reliable resistive switching. The 2D/oxide memtransistors attain dual‐terminal (drain and gate) stimulated heterosynaptic plasticity and highly precise multi‐states. In addition, the memtransistor devices show an extremely low power consumption of ≈6 pJ and reliable potentiation/depression endurance characteristics over 2000 pulses. A high pattern recognition accuracy of ≈94.2% is finally achieved from the synaptic plasticity modulated by the drain pulse configuration using an image pattern recognition simulation. Thus, the novel 2D/oxide memtransistor makes a potential neuromorphic circuitry more flexible and energy‐efficient, promoting the development of more advanced neuromorphic systems.
A highly reliable 2D MoS2/Nb2O5 memtransistor device based on Schottky barrier modulation is demonstrated. The 2D/oxide memtransistor attains dual‐terminal stimulated heterosynaptic plasticity, showing an extremely low power consumption of ≈6 pJ and reliable endurance characteristics over 2000 pulses. Finally, a high pattern recognition accuracy of ≈94.2% is achieved using a pattern recognition simulation, which promotes advanced neuromorphic systems.
The inability to guide the nucleation locations of electrochemically deposited Li has long been considered the main factor limiting the utilization of high‐energy‐density Li‐metal batteries. In this ...study, an electrical conductivity gradient interfacial host comprising 1D high conductivity copper nanowires and nanocellulose insulating layers is used in stable Li‐metal anodes. The conductivity gradient system guides the nucleation sites of Li‐metal to be directed during electrochemical plating. Additionally, the controlled parameter of the intermediate layer affects the highly stable Li‐metal plating. The electrochemical behavior is confirmed through experiments associated with the COMSOL Multiphysics simulation data. The distributed Li‐ion reaction flux resulting from the controlled electrical conductivity enables stable cycling for more than 250 cycles at 1 mA cm−2. The gradient system effectively suppresses dendrite growth even at a high current density of 5 mA cm−2 and ensures Li plating and stripping with ultra‐long‐term stability. To demonstrate the high‐energy‐density full‐cell application of the developed anode, it is paired with the LiNi0.8Co0.1Mn0.1O2 cathode. The cells demonstrate a high capacity retention of 90% with an extremely high Coulombic efficiency of 99.8% over 100 cycles. These results shed light on the formidable challenges involved in exploiting the engineering aspects of high‐energy‐density Li‐metal batteries.
An electrical conductivity gradient interfacial host composed of simply fabricated 1D high conductivity copper nanowires and nanocellulose insulating layers shows stable lithium metal plating/stripping during electrochemical reaction. The conductivity gradient offers to guide the nucleation of lithium metal deposition, resulting in a high capacity retention of 90% with an extremely high Coulombic efficiency of 99.8% over 100 cycles as a full‐cell test.
Ion‐based electrochemical random‐access memory (ECRAM) is proposed for synaptic applications owing to its promising characteristics that have the potential to accelerate data processing through ...neuromorphic systems. However, attaining ideal synaptic functionalities and constructing high‐density vertical synapse arrays are challenging due to issues related to uncontrolled ion migration and constraints in 3D multi‐stacking. Here, a breakthrough using 3D stackable Li ion‐based vertical‐sensing ECRAM (VS‐ECRAM) is presented with an ion‐permeable ultrathin WS2 electrode synthesized through low‐temperature (200 °C) atmospheric‐pressure plasma‐enhanced chemical vapor deposition (AP‐PECVD). The direct AP‐PECVD of the WOx channel layer induces WS2 formation in the surface region, which exhibits sufficient electrical conductivity to function as an electrode. By utilizing the WS2 electrode as an ion‐barrier layer in the VS‐ECRAM synapse, excellent weight update linearity and cycling variability are achieved due to the finely controlled ion migration. Furthermore, a two‐layer stacked 3D VS‐ECRAM is successfully fabricated through the vertical WS2 formation, and independent weight updates without any disturbance are confirmed. Finally, a high pattern recognition accuracy of 95.22% is obtained using a multi‐layer perceptron‐based neural network. Therefore, the proposed 3D stackable WS2‐based VS‐ECRAM exhibits a strong potential for application in high‐density neuromorphic devices with excellent synaptic performances.
This study presents a 3D stackable vertical‐sensing electrochemical random‐access memory (VS‐ECRAM) utilizing the low‐temperature (200 °C) atmospheric‐pressure plasma‐enhanced chemical vapor deposition (AP‐PECVD) process. The AP‐PECVD‐synthesized WS2 drain electrode, which also serves as an ion‐barrier layer, enables near‐ideal linearity in the synaptic weight update behavior, excellent cycling reliability and variability, and 3D multi‐stacking process compatibility.
The addition of chemical additives is considered as a promising approach for obtaining high‐quality perovskite films under mild conditions, which is essential for both the efficiency and the ...stability of organic–inorganic hybrid perovskite solar cells (PeSCs). Although such additive engineering yields high‐quality films, the inherent insulating property of the chemical additives prevents the efficient transport and extraction of charge carriers, thereby limiting the applicability of this approach. Here, it is shown that organic conjugated molecules having rhodanine moieties (i.e., SA‐1 and SA‐2) can be used as semiconducting chemical additives that simultaneously yield large‐sized perovskite grains and improve the charge extraction. Using this strategy, a high power conversion efficiency of 20.3% as well as significantly improved long‐term stability under humid air conditions is achieved. It is believed that this approach can provide a new pathway to designing chemical additives for further improving the efficiency and stability of PeSCs.
A simple method for obtaining highly efficient and stable inverted perovskite solar cells (PeSCs) is suggested. A defect‐free perovskite film with large‐sized grains is achieved by adding an organic conjugated molecule, which improves the charge extraction and reduces defect sites in perovskite crystals, resulting in highly efficient and stable PeSCs.
We report the production of a two-dimensional (2D) heterostructured gas sensor. The gas-sensing characteristics of exfoliated molybdenum disulfide (MoS2) connected to interdigitated metal electrodes ...were investigated. The MoS2 flake-based sensor detected a NO2 concentration as low as 1.2 ppm and exhibited excellent gas-sensing stability. Instead of metal electrodes, patterned graphene was used for charge collection in the MoS2-based sensing devices. An equation based on variable resistance terms was used to describe the sensing mechanism of the graphene/MoS2 device. Furthermore, the gas response characteristics of the heterostructured device on a flexible substrate were retained without serious performance degradation, even under mechanical deformation. This novel sensing structure based on a 2D heterostructure promises to provide a simple route to an essential sensing platform for wearable electronics.
The ventromedial nucleus of the hypothalamus (VMH) plays a critical role in regulating systemic glucose homeostasis. How neurons in this brain area adapt to the changing metabolic environment to ...regulate circulating glucose levels is ill defined. Here, we show that glucose load results in mitochondrial fission and reduced reactive oxygen species in VMH neurons mediated by dynamin-related peptide 1 (DRP1) under the control of uncoupling protein 2 (UCP2). Probed by genetic manipulations and chemical-genetic control of VMH neuronal circuitry, we unmasked that this mitochondrial adaptation determines the size of the pool of glucose-excited neurons in the VMH and that this process regulates systemic glucose homeostasis. Thus, our data unmasked a critical cellular biological process controlled by mitochondrial dynamics in VMH regulation of systemic glucose homeostasis.
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•UCP2 in the VMH is required for glucose-induced DRP1-mediated mitochondrial fission•UCP2 in the VMH is required for glucose-induced neuronal activation•UCP2-mediated activation of the VMH neurons regulates peripheral glucose metabolism
To adapt to changing metabolic environments, glucose metabolism in the body is controlled by UCP2-mediated mitochondrial dynamics in a subset of hypothalamic neurons.
•Precise management of greenhouse conditions is needed for optimized crop production.•Temperature, humidity, and CO2 were predicted using the deep neural networks.•The best model results were ...obtained using RNN-LSTM.•Our results demonstrate the potential for deep-learning-based greenhouse management.
Greenhouses provide controlled environmental conditions for crop cultivation but require careful management to ensure ideal growing conditions. In this study, we tested three deep-learning-based neural network models (Artificial neural network, ANN; Nonlinear autoregressive exogenous model, NARX; and Recurrent neural networks – Long short-term memory, RNN-LSTM) to determine the best approach to predicting environmental changes in temperature, humidity, and CO2 within a greenhouse to improve management strategies. This study determined the prediction performance for time steps from 5 to 30 min and showed that the accuracy of the time-based algorithm gradually decreased as prediction time increased. The best model for all datasets was RNN-LSTM, even after 30 min, with an R2 of 0.96 for temperature, 0.80 for humidity, and 0.81 for CO2 concentration. The results of this study show that it is possible to apply deep-learning-based prediction models for more precisely managing greenhouse.
Monoclinic S8, an uncommon allotrope of sulfur at room temperature, can be formed when common orthorhombic S8 is heat‐treated under enclosed environments in nanometer dimensions. Monoclinic S8 ...prevents the formation of soluble polysulfides during battery operation, resulting in unprecedented cycling performance over 1000 cycles under the highest sulfur content to date.
Two-dimensional (2D) molybdenum disulphide (MoS2) atomic layers have a strong potential to be used as 2D electronic sensor components. However, intrinsic synthesis challenges have made this task ...difficult. In addition, the detection mechanisms for gas molecules are not fully understood. Here, we report a high-performance gas sensor constructed using atomic-layered MoS2 synthesised by chemical vapour deposition (CVD). A highly sensitive and selective gas sensor based on the CVD-synthesised MoS2 was developed. In situ photoluminescence characterisation revealed the charge transfer mechanism between the gas molecules and MoS2, which was validated by theoretical calculations. First-principles density functional theory calculations indicated that NO2 and NH3 molecules have negative adsorption energies (i.e., the adsorption processes are exothermic). Thus, NO2 and NH3 molecules are likely to adsorb onto the surface of the MoS2. The in situ PL characterisation of the changes in the peaks corresponding to charged trions and neutral excitons via gas adsorption processes was used to elucidate the mechanisms of charge transfer between the MoS2 and the gas molecules.
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