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There is a need to design a hardware synapse array appropriate for enhancing the efficiency of neuromorphic computing systems while minimizing energy consumption. This study introduces a memristor device with an AlOx overshoot suppression layer (A‐OSL) to achieve a self‐compliance effect. By optimizing each cell within the 16 × 16 crossbar array, synaptic devices are successfully fabricated with reliable characteristics and 3‐bit multilevel capabilities. In addition, the oxygen composition of TiOx and the annealing conditions are optimized to reduce the forming voltage and minimize the variation in the switching voltage. As a result, stable forming‐free characteristics are obtained through A‐OSL insertion, a reduction in forming voltage, and TiOx oxygen composition optimization. Also, target weights are accurately transferred to the A‐OSL memristor crossbar array and conducted the inference process by applying spike signals to the array following the designated time step. The spiking neural network (SNN) is demonstrated by measuring vector‐matrix multiplication (VMM) of the 16 × 16 crossbar array. The VMM results exhibit a classification accuracy of 90.80% for the MNIST dataset, which is close to the accuracy achieved by software‐based approaches, amounting to 91.85%. [ABSTRACT FROM AUTHOR]

This study implements a highly uniform 3D vertically stack resistive random‐access memory (VRRAM) with a four‐layer contact hole structure. The fabrication process of a four‐layer VRRAM is demonstrated, and its physical and electrical properties are thoroughly examined. X‐ray photoelectron spectroscopy and transmission electron microscopy are employed to analyze the chemical distribution and physical structure of the VRRAM device. Multilevel capability, reliable endurance (>104 cycles), and retention (104 s) are successfully obtained. Synaptic memory plasticity, such as spike time‐dependent plasticity, spike rate‐dependent plasticity, excitatory post‐synaptic current, paired‐pulse facilitation, and long‐term potentiation and depression is presented. Finally, the vector‐matrix multiplication (VMM) operation is conducted on a 4 × 12 VRRAM array, according to the low resistance state ratio. It is ascertained that the accuracy drop, which can occur due to VMM error, can be limited to a decrease of less than 0.44% point. Utilizing the high‐density, multilevel, and biological characteristics of VRRAM, it is possible to implement high‐performance neuromorphic systems that require densely integrated synaptic devices. [ABSTRACT FROM AUTHOR]

To efficiently develop an extremely intensive storage memory, the resistive random‐access memory (RRAM), which operates by producing and rupturing conductive filaments, is essential. However, due to the stochastic nature of filament production, this filamentary type resistive switching has an inherent limitation, which entails the unpredictability of the driving voltage and resistance states. Several strategies such as doping, research into multilayer stacks, and interface engineering, are suggested to tackle this challenge. This work fabricates a CMOS‐compatible TiN/HfOx/TiN‐NCs (nanocrystals)/HfOx/TiN RRAM to implement analog resistive switching and advance the development of the synaptic device. Specifically, atomic force microscopy (AFM), scanning electron microscopy (SEM), and transmission electron microscopy (TEM) are utilized to observe the formation of TiN nanocrystals, which play a crucial role in the enhancement of resistive switching. By comparing HfOx–based RRAM devices with and without NCs, the DC I–V curves, retention, endurance, and switching speed are properly examined. Interestingly, it is found that the TiN/HfOx/TiN‐NCs/HfOx/TiN device is more appropriately utilized as an artificial synapse in neuromorphic systems mainly due to its stable and reliable resistive switching properties. Finally, this work demonstrates well‐controlled resistive switching 3D vertical RRAM with TiN‐NCs, which is particularly suitable for high‐density memory. [ABSTRACT FROM AUTHOR]

NO3− is a prominent waste byproduct in ammunition industries, and valorization of NO3− into NH3 is desired. NH3 synthesized from waste using renewable energy could be used as an energy storage medium. For such a process, higher selectivity, stability, and enhanced activity per unit mass of catalyst is required. This work reports CoO nanoclusters functionalized on graphene as an efficient catalyst for electrochemical NO3− reduction to NH3 with >98% NH3 Faradaic efficiency and an NH3 current density (geometric) of ≈400 mA cm−2. A highest NH3 yield of 25.63 mol g−1 h−1 is obtained. The developed catalyst prevents the NN coupling step which is required for promoting the selectivity of NH3 over N2. Density functional theory calculations reveal that CoO NC/graphene has similar selectivity to that of polycrystalline Co and that higher activity can be obtained with lower catalyst loading. [ABSTRACT FROM AUTHOR]

Memristor‐based ternary content‐addressable memory (TCAM) has emerged as an alternative to conventional static random‐access memory (SRAM)‐based TCAM because of its high‐density integration and zero‐static energy consumption. Herein, 0T2R TCAM operation on a 32 × 32 passive memristor crossbar circuit is experimentally verified. The effective margin, which is the difference between the match case and 1‐bit mismatch case, is improved through precise tuning operations. Moreover, the number of mismatch bits and match cases can be accurately detected thanks to the linear relationship between the number of mismatch bits and match‐line current. In addition, the nonideal effects in the passive crossbar array including dynamic voltage drop and sneak current are analyzed through SPICE studies. These results indicate that the proposed TCAM operating conditions can ensure stable TCAM operation in larger arrays. [ABSTRACT FROM AUTHOR]

In this work, a memristor using Si nano-tip bottom electrode has been fabricated and evaluated. Compared with the control device fabricated in a planar structure, the invented Si nano-tip device statistically demonstrates 400 times of area shrinkage and 3 and 8 times of current reductions in the low- and high-resistance states, respectively, owing to the effects of highly confined switching area on the operations of multiple-weak-filament (MWF)-type memristor. The results reveal the validity of structural scaling on the MWF-type memristors as effective as a material approach. Moreover, the fabricated Si memristor has been evaluated as a synaptic device via pattern recognition toward neuromorphic applications in consideration of interconnect resistance. The proposed Si memristor shows substantially reduced inference power consumption by an increase in resistances and improved accuracy by 5% compared with the control device. [ABSTRACT FROM AUTHOR]

Leveraging the phononic sensitivity and scalability of nano‐biointerfaces has accelerated the growth of unique and versatile biosensors. Graphene has the properties of a near‐ideal signal transducer, due to the strong coupling between its interfacial and phononic properties. This enables sensitive yet quick detection of surface interactions on graphene via Raman spectral analysis. The Raman‐active vibrational bands of graphene are demonstrated to be sensitive to structural, electrical, and interfacial modifications. This sensitivity is attributed to graphene's electron–phonon coupling and high quantum capacitance. The fundamental understanding of graphene phonons is crucial for developing reliable platforms for disease and infectious agent detection. This review provides a mechanistic explanation of these phenomena at the interface between graphene and various biosystems (including cancerous, bacterial, viral, and biophysical specimens) to set the foundation for next‐generation chemeo‐phononic medical devices. [ABSTRACT FROM AUTHOR]

To apply resistive random‐access memory (RRAM) to the neuromorphic system and improve performance, each cell in the array should be able to operate independently by reducing device variation. In addition, it is necessary to lower the operating current of the RRAM cell and enable gradual switching characteristics to mimic the low‐energy operations of biological. In most filamentary RRAMs, however, overshoot current occurs in the forming stage, and the RRAM shows large device variation, high operating current, and abrupt set and reset switching characteristics. Herein, the shortcomings occurring in the forming stage are overcome by introducing and optimizing an overshoot suppression layer. Consequently, the RRAM exhibits gradual switching characteristics both in the set and reset regions, thereby enabling implementation of 4‐bit multilevel operation. In addition, the forming step can be easily performed in a 16 × 16 crossbar array owing to its self‐compliance characteristics without disturbing neighboring cells in the array. The tuning and vector–matrix multiplication (VMM) operations are also experimentally verified in the array. Finally, classification performance with off‐chip training is compared in terms of accuracy and robustness to tuning tolerance depending on the number of bits of the implemented multiconductance levels. [ABSTRACT FROM AUTHOR]

To analyze the effect of the intrinsic variations of the memristor device on the neuromorphic system, we fabricated 32 × 32 Al2O3/TiO x -based memristor crossbar array and implemented 3 bit multilevel conductance as weight quantization by utilizing the switching characteristics to minimize the performance degradation of the neural network. The tuning operation for 8 weight levels was confirmed with a tolerance of ±4 μ A (±40 μ S). The endurance and retention characteristics were also verified, and the random telegraph noise (RTN) characteristics were measured according to the weight range to evaluate the internal stochastic variation effect. Subsequently, a memristive neural network was constructed by off-chip training with differential memristor pairs for the Modified National Institute of Standards and Technology (MNIST) handwritten dataset. The pre-trained weights were quantized, and the classification accuracy was evaluated by applying the intrinsic variations to each quantized weight. The intrinsic variations were applied using the measured weight inaccuracy given by the tuning tolerance, RTN characteristics, and the fault device yield. We believe these results should be considered when the pre-trained weights are transferred to a memristive neural network by off-chip training. [ABSTRACT FROM AUTHOR]

Herein, stable resistive switching characteristics are demonstrated in an atomic-layer-deposited SiOX-based resistive memory device. The thickness and chemical properties of the Pt/SiOX/TaN stack are verified by transmission electron microscopy (TEM) and X-ray photoemission spectroscopy (XPS). It is demonstrated that much better resistive switching is obtained using a negative set and positive reset compared to the opposite polarity. In addition, multi-level switching is demonstrated by changing the compliance current (CC) and the reset stop voltage, and potentiation and depression are emulated by applying pulses to achieve a synaptic device. Finally, a pulse endurance of 10,000 cycles and a retention time of 5000 s are confirmed by modulating the pulse input and reading voltage, respectively. [ABSTRACT FROM AUTHOR]

Recently, various memory devices have been actively studied as suitable candidates for synaptic devices, which are important memory and computing units in neuromorphic systems. One of the ways to manage these devices is off-chip training, where it is essential to transfer the pretrained weights accurately. Previous studies, however, have a few limitations, such as a lack of consideration of program errors that occur during the transfer process. Although the smaller the program error, the higher the accuracy, the corresponding increase in the program time must be considered. To evaluate the practical applicability, we fabricated Al2O3/TiOx-based resistive random access memory (RRAM) and investigated the effect of program errors on program time and system degradation. It was confirmed that for smaller program errors, the program time was exponentially longer. Furthermore, we examined the effect of variation with respect to the number of quantized weight states (${N}_{state}$) through system-level simulation. We observed that the optimized ${N}_{state}$ varies depending on whether the program error is small or large. This result is meaningful as it experimentally shows the tradeoff between the program error, program time, and system performance. We expect it to be useful in the development of neuromorphic systems. [ABSTRACT FROM AUTHOR]

As interest in artificial intelligence (AI) and relevant hardware technologies has been developed rapidly, algorithms and network structures have become significantly complicated, causing serious power consumption issues because an enormous amount of computation is required. Neuromorphic computing, a hardware AI technology with memory devices, has emerged to solve this problem. For this application, multilevel operations of synaptic devices are important to imitate floating point weight values in software AI technologies. Furthermore, weight transfer methods to desired weight targets must be arranged for off-chip training. From this point of view, we fabricate 32 × 32 memristor crossbar array and verify the 3-bit multilevel operations. The programming accuracy is verified for 3-bit quantized levels by applying a reset-voltage-control programming scheme to the fabricated TiOx/Al2O3-based memristor array. After that, a synapse composed of two differential memristors and a fully-connected neural network for modified national institute of standards and technology (MNIST) pattern recognition are constructed. The trained weights are post-training quantized in consideration of the 3-bit characteristics of the memristor. Finally, the effect of programming error on classification accuracy is verified based on the measured data, and we obtained 98.12% classification accuracy for MNIST data with the programming accuracy of 1.79% root-mean-square-error. These results imply that the proposed reset-voltage-control programming scheme can be utilized for a precise tuning, and expected to contribute for the development of a neuromorphic system capable of highly precise weight transfer. [ABSTRACT FROM AUTHOR]

Although many studies have been continuously conducted to reduce the power consumption of a resistive random access memory (RRAM) cross-point array with the current-compliance effect, it has been difficult yet to realize intrinsic self-compliance effects in an RRAM device itself. In this study, a simple oxygen-rich TiOy layer is inserted into the Al2O3/TiOx-based RRAM stack as a current suppression layer, and XPS analysis is provided to compare the stoichiometry of the TiOx and TiOy layers. A self-compliance region is formed between the different breakdown voltages of Al2O3 and TiOy layers, and a relatively thinner current path is formed in the Al2O3 layer than a device without the TiOy layer and the overall current level is significantly decreased since the TiOy layer limits the overshoot current. [ABSTRACT FROM AUTHOR]

This study describes a novel chemical route to grow ZnS thin films via atomic layer deposition ( ALD). By using diethylzinc and 1,5-pentanedithiol as precursors of Zn and S, respectively, ZnS films are grown on substrates with an atomic precision by repeating self-limiting chemisorption of each precursor. The growth-per-cycle of the ALD process is around 0.1 Å per cycle at 150°C, and the as-grown films are characterized to be amorphous ZnS by X-ray diffraction and X-ray photoelectron spectroscopy. [ABSTRACT FROM AUTHOR]

Mobile phones emit radiofrequency (RF) electromagnetic waves (EMWs), a low-level RF that can be absorbed by the human body and exert potential adverse effects on the brain, heart, endocrine system, and reproductive function. Owing to the novel findings of numerous studies published since 2012 regarding the effect of mobile phone use on sperm quality, we conducted a systematic review and updated meta-analysis to determine whether the exposure to RF-EMWs affects human sperm quality. This study was conducted in accordance with the PRISMA guidelines. The outcome measures depicting sperm quality were motility, viability, and concentration, which are the most frequently used parameters in clinical settings to assess fertility. We evaluated 18 studies that included 4280 samples. Exposure to mobile phones is associated with reduced sperm motility, viability, and concentration. The decrease in sperm quality after RF-EMW exposure was not significant, even when the mobile phone usage increased. This finding was consistent across experimental in vitro and observational in vivo studies. Accumulated data from in vivo studies show that mobile phone usage is harmful to sperm quality. Additional studies are needed to determine the effect of the exposure to EMWs from new mobile phone models used in the present digital environment. [ABSTRACT FROM AUTHOR]

In this study, oxygen‐rich TiOy and TiOx layers are intentionally designed to have different oxygen compositions, functioning as an overshoot suppression layer (OSL) and oxygen reservoirs. Furthermore, by natural oxidation reactions occurring between the TiOy/TiOx/Al2O3 switching layer and the Pt/Al top electrode, an additional AlOy layer can be induced to act as an additional OSL. The proposed annealing process accelerates the oxidation reaction of AlOy/TiOy OSLs, thereby enhancing the self‐compliance feature of devices. Moreover, the ultrathin AlN serves as an oxygen barrier layer (OBL) that inhibits the movement of oxygen ions at the interface between the Al2O3 layer and the Pt/Ti bottom electrode. The optimized devices are tested by DC sweep and pulses for neuromorphic computing systems. To realize biological synapse characteristics, several key synaptic memory plasticities are proposed. Finally, a 24 × 24 crossbar array based on the 0T‐1R structure, incorporating optimized AlOy/TiOy OSLs and OBL via the annealing process, is characterized. During the electroforming step, all specified target cells (marked with the letters “ESDL”) achieved self‐compliance at low current levels without experiencing hard‐breakdown failures or interference among neighboring cells. The successful array performance is demonstrated by the accurate tuning of target weights. [ABSTRACT FROM AUTHOR]

A new physical analysis of the filament formation in a Ag conducting-bridge random-access memory (CBRAM) device in consideration of the existence of inter-atomic attractions caused by metal bonding is suggested. The movement of Ag atoms inside the switching layer is characterized hydrodynamically using the Young-Laplace equation during set and reset operations. Both meridional and azimuthal curvatures of the Ag filament protruding from the Ag electrode are accurately calculated to track down the exact shape of the Ag filament with change in the applied voltage. The second-order partial differential equation for the Ag filament geometry is derived from the equation of equilibrium between the electrostatic pressure and the Laplace one. The solution to the equation is numerically obtained, and furthermore, the abrupt set operation in the forming process, bipolar resistive-switching, and the threshold switching operation in the reset operations are successfully simulated in accordance with the numerical solutions. Also, it is demonstrated that the currents extracted from the suggested model show good agreement with the I - V characteristics measured from the fabricated Ag CBRAM device.

With an incubation time of about 5 days, early diagnosis of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is critical to control the spread of the coronavirus disease 2019 (COVID-19) that killed more than 3 million people in its first 1.5 years. Here, we report on the modification of the dopant density and the phononic energy of antibody-coupled graphene when it interfaces with SARS-CoV-2 spike protein. This graphene chemeo-phononic system was able to detect SARS-CoV-2 spike protein at the limit of detection of ∼3.75 and ∼1 fg/mL in artificial saliva and phosphate-buffered saline, respectively. It also exhibited selectivity over proteins in saliva and MERS-CoV spike protein. Since the change in graphene phononics is monitored instead of the phononic signature of the analyte, this optical platform can be replicated for other COVID variants and specific-binding-based biodetection applications.

Purpose: To develop a deep learning model to estimate the visual field (VF) from spectral-domain optical coherence tomography (SD-OCT) and swept-source OCT (SS-OCT) and to compare the performance between them., Methods: Two deep learning models based on Inception-ResNet-v2 were trained to estimate 24-2 VF from SS-OCT and SD-OCT images. The estimation performance of the two models was evaluated by using the root mean square error between the actual and estimated VF. The performance was also compared among different glaucoma severities, Garway-Heath sectorizations, and central/peripheral regions., Results: The training dataset comprised images of 4391 eyes from 2350 subjects, and the test dataset was obtained from another 243 subjects (243 eyes). In all subjects, the global estimation errors were 5.29 ± 2.68 dB (SD-OCT) and 4.51 ± 2.54 dB (SS-OCT), and the estimation error of SS-OCT was significantly lower than that of SD-OCT (P < 0.001). In the analysis of sectors, SS-OCT showed better performance in all sectors except for the inferonasal sector in normal vision and early glaucoma. In advanced glaucoma, the estimation error of the central region was worsened in both OCTs, but SS-OCT was still significantly better in the peripheral region., Conclusions: Our deep learning model estimated the VF 24-2 better with a wide field image of SS-OCT than did with retinal nerve fiber layer and ganglion cell-inner plexiform layer images of SD-OCT., Translational Relevance: This deep learning method can help clinicians to determine the VF from OCT images. OCT manufacturers can equip this system to provide additional VF data.

As interest in artificial intelligence (AI) and relevant hardware technologies has been developed rapidly, algorithms and network structures have become significantly complicated, causing serious power consumption issues because an enormous amount of computation is required. Neuromorphic computing, a hardware AI technology with memory devices, has emerged to solve this problem. For this application, multilevel operations of synaptic devices are important to imitate floating point weight values in software AI technologies. Furthermore, weight transfer methods to desired weight targets must be arranged for off-chip training. From this point of view, we fabricate 32 × 32 memristor crossbar array and verify the 3-bit multilevel operations. The programming accuracy is verified for 3-bit quantized levels by applying a reset-voltage-control programming scheme to the fabricated TiO x /Al 2 O 3 -based memristor array. After that, a synapse composed of two differential memristors and a fully-connected neural network for modified national institute of standards and technology (MNIST) pattern recognition are constructed. The trained weights are post-training quantized in consideration of the 3-bit characteristics of the memristor. Finally, the effect of programming error on classification accuracy is verified based on the measured data, and we obtained 98.12% classification accuracy for MNIST data with the programming accuracy of 1.79% root-mean-square-error. These results imply that the proposed reset-voltage-control programming scheme can be utilized for a precise tuning, and expected to contribute for the development of a neuromorphic system capable of highly precise weight transfer., (© 2021 IOP Publishing Ltd.)

Magnetic sensors have great potential for biomedical applications, particularly, detection of magnetically-labeled biomolecules and cells. On the basis of the advantage of the planar Hall effect sensor, which consists of improved thermal stability as compared with other magnetic sensors, we have designed a portable biosensor platform that can detect magnetic labels without applying any external magnetic field. The trilayer sensor, with a composition of Ta (5 nm)/NiFe (10 nm)/Cu ( x = 0 nm~1.2 nm)/IrMn (10 nm)/Ta (5 nm), was deposited on a silicon wafer using photolithography and a sputtering system, where the optimized sensor sensitivity was 6 μV/(Oe∙mA). The detection of the magnetic label was done by comparing the signals obtained in first harmonic AC mode (1f mode) using an external magnetic field and in the second harmonic AC mode (2f mode) with a self-field generated by current passing through the sensor. In addition, a technique for the β-amyloid biomarker-based antibody-antigen sandwich model was demonstrated for the detection of a series of concentrations of magnetic labels using the self-field mode method, where the signal-to-noise ratio (SNR) was high. The generated self-field was enough to detect an immobilized magnetic tag without an additional external magnetic field. Hence, it could be possible to reduce the device size to use the point-of-care testing using a portable circuit system.

Zinc oxide (ZnO) is a commonly used material for the front contact layer of thin film solar cells based on chalcopyrite CuInGaSe₂ (CIGS), since it satisfies the optical and electrical properties with low cost and is abundantly available. For high-performance, the front contact electrode in the CIGS solar cell should have low resistivity and high transmittance. Hence, efforts to improve ZnO's electrical and optical properties have been widely carried out. The corrosion resistance of the front contact film, however, has not been studied well. So, this paper compared the electrochemical stability of ZnO based transparent conducting oxide (TCO) films such as aluminum zinc oxide (AZO), gallium zinc oxide (GZO) and aluminum gallium zinc oxide (AGZO) grown in H₂. All films predominately grew in the (002) c -axis direction and their crystallites improved with increasing H₂ ratio, reflecting the enhanced electrical properties. Hydrogen acts as a shallow donor in the n -type ZnO semiconductor and increases carrier densities by forming oxygen vacancies by combining with oxygen molecules to form OH - functionalities. The electrochemical corrosion resistance of prepared films showed a linear improvement with increasing hydrogen. This change was attributed to the grain morphology and size and resulting grain boundaries. Since corrosion occurs mainly at grain boundaries, corrosion resistance seems to be better with a lower density of grain boundaries, due to larger grains caused by hydrogen. Electric conductivity was better in the order of GZO > AGZO > AZO, while corrosion resistance was in the reverse order. By adding hydrogen, the electrical resistivity of AGZO improved to near GZO, and the corrosion resistance was also enhanced to near AZO.

Mechanoreceptors in a fingertip convert external tactile stimulations into electrical signals, which are transmitted by the nervous system through synaptic transmitters and then perceived by the brain with high accuracy and reliability. Inspired by the human synapse system, this paper reports a robust tactile sensing system consisting of a remote touch tip and a magnetic synapse. External pressure on the remote touch tip is transferred in the form of air pressure to the magnetic synapse, where its variation is converted into electrical signals. The developed system has high sensitivity and a wide dynamic range. The remote sensing system demonstrated tactile capabilities over wide pressure range with a minimum detectable pressure of 6 Pa. In addition, it could measure tactile stimulation up to 1,000 Hz without distortion and hysteresis, owing to the separation of the touching and sensing parts. The excellent performance of the system in terms of surface texture discrimination, heartbeat measurement from the human wrist, and satisfactory detection quality in water indicates that it has considerable potential for various mechanosensory applications in different environments.

The transient receptor potential melastatin 2 (TRPM2) channel, a Ca 2+ permeable channel activated by cAMP, is expressed on pancreatic β-cells and is responsible for the regulation of insulin secretion. It is known that glucose-stimulated insulin secretion (GSIS) can be potentiated by glucagon like peptide-1 (GLP-1), and that the changes in the extracellular glucose concentration alter the levels of intracellular adenosine ATP and cAMP. The present study hypothesized that TRPM2 mediates the modulatory effect of GLP-1 on insulin secretion. The results demonstrated that silencing of TRPM2 eliminated GLP-1-enhanced insulin secretion, indicating the involvement of TRPM2 in this process. In addition, the results of current recordings of TRPM2 and measurement of the resulting insulin secretion in β-cells in the presence of GLP-1 and various concentrations of glucose suggest that GLP-1 regulates GSIS via the TRPM2 channel. Furthermore, inhibiting the activity or expression of TRPM2 attenuated GLP-1-induced GSIS. By using specific activators or inhibitors, the present study demonstrated that the two primary downstream effectors of the GLP-1 receptor, exchange protein directly activated by cAMP and protein kinase A, differentially influence GSIS and GLP-1-potentiated GSIS. In conclusion, the present study revealed the role of TRPM2 in GLP-1-regulated insulin secretion. The results of the present study provide a novel avenue for the prevention and treatment of diabetes and its complications.

MoS 2 is a promising material to replace the Pt catalyst in the electrochemical hydrogen evolution reaction (HER). It is well known that the activity of the MoS 2 catalyst in the HER is significantly promoted by doping cobalt atoms. Recently, the Co-Mo-S phase, in which cobalt atoms decorate the edge positions of the MoS 2 slabs, has been identified as a co-catalytic phase in the Co-doped MoS 2 (Co-MoS x ) with low Co content. Here, we report the effect of the incorporation of cobalt atoms in the chemical state of the Co-MoS x catalyst, which gives rise to the co-catalytic effect. Co-MoS x catalysts with various Co contents were prepared on carbon fiber paper by a simple hydrothermal process. On the Co-MoS x catalyst with high Co content (Co/Mo ≈ 2.3), a dramatically higher catalytic activity was observed compared to that for the catalyst with low Co content (Co/Mo ≈ 0.36). Furthermore, the co-catalytic phase in the Co-MoS x catalyst with the high Co content was found not to be the Co-Mo-S phase but was identified as CoS 2 by Raman spectroscopy, X-ray photoelectron spectroscopy, X-ray diffraction, and transmission electron microscopy. It is believed that CoS 2 is an alternative choice to co-catalyze HER on MoS 2 -based catalysts.

The light-emitting diode (LED) with an improved hole injection and straightforward process integration is proposed. p-type GaN direct hole injection plugs (DHIPs) are formed on locally etched multiple-quantum wells (MQWs) by epitaxial lateral overgrowth (ELO) method. We confirm that the optical output power is increased up to 23.2% at an operating current density of 100 A/cm 2 . Furthermore, in order to identify the origin of improvement in optical performance, the transient light decay time and light intensity distribution characteristics were analyzed on the DHIP LED devices. Through the calculation of the electroluminescence (EL) decay time, internal quantum efficiency (IQE) is extracted along with the recombination parameters, which reveals that the DHIPs have a significant effect on enhancement of radiative recombination and reduction of efficiency droop. Furthermore, the mapping PL reveals that the DHIP LED also has a potential to improve the light extraction efficiency by hexagonal pyramid shaped DHIPs.