Creating Atomic Ordering in Electrocatalysis

Catalysis always proceeds in a chaotic fashion. Therefore, identifying the working principles of heterogeneous catalysts is a challenging task. Creating atomic order in heterogeneous catalysts simplifies this task and also offers new opportunities for rationally designing active sites to manipulate catalytic performance. The recent rapid advances in heterogeneous electrocatalysis have led to exciting progress in the construction of atomically ordered materials. Here, the latest progress in electrocatalysts with the periodic atomic arrangement, including intermetallic compounds with long-range order and metal atom-array catalysts with short-range order is summarized. The synthesis principles and the intriguing physical and chemical properties of these electrocatalysts are discussed. Furthermore, the compelling prospects of atomically ordered catalysts in the frontier of catalyst research are outlined.     

3D Printable One-Part Carbon Nanotube-Elastomer Ink for Health Monitoring Applications

3D printing of conductive elastomers is a promising route to personalized health monitoring applications due to its flexibility and biocompatibility. Here, a one-part, highly conductive, flexible, stretchable, 3D printable carbon nanotube (CNT)-silicone composite is developed and thoroughly characterized. The one-part nature of the inks: i) enables printing without prior mixing and cures under ambient conditions; ii) allows direct dispensing at ≈100 µm resolution printability on nonpolar and polar substrates; iii) forms both self-supporting and high-aspect-ratio structures, key aspects in additive biomanufacturing that eliminate the need for sacrificial layers; and iv) lends efficient, reproducible, and highly sensitive responses to various tensile and compressive stimuli. The high electrical and thermal conductivity of the CNT-silicone composite is further extended to facilitate use as a flexible and stretchable heating element, with applications in body temperature regulation, water distillation, and dual temperature sensing and Joule heating. Overall, the facile fabrication of this composite points to excellent synergy with direct ink writing and can be used to prepare patient-specific wearable electronics for motion detection and cardiac and respiratory monitoring devices and toward advanced personal health tracking and bionic skin applications.     

Room-Temperature-Processable Highly Reliable Resistive Switching Memory with Reconfigurability for Neuromorphic Computing and Ultrasonic Tissue Classification

Reversible metal-filamentary mechanism has been widely investigated to design an analog resistive switching memory (RSM) for neuromorphic hardware-implementation. However, uncontrollable filament-formation, inducing its reliability issues, has been a fundamental challenge. Here, an analog RSM with 3D ion transport channels that can provide unprecedentedly high reliability and robustness is demonstrated. This architecture is realized by a laser-assisted photo-thermochemical process, compatible with the back-end-of-line process and even applicable to a flexible format. These superior characteristics also lead to the proposal of a practical adaptive learning rule for hardware neural networks that can significantly simplify the voltage pulse application methodology even with high computing accuracy. A neural network, which can perform the biological tissue classification task using the ultrasound signals, is designed, and the simulation results confirm that this practical adaptive learning rule is efficient enough to classify these weak and complicated signals with high accuracy (97%). Furthermore, the proposed RSM can work as a diffusive-memristor at the opposite voltage polarity, exhibiting extremely stable threshold switching characteristics. In this mode, several crucial operations in biological nervous systems, such as Ca²⁺ dynamics and nonlinear integrate-and-fire functions of neurons, are successfully emulated. This reconfigurability is also exceedingly beneficial for decreasing the complexity of systems—requiring both drift- and diffusive-memristors.     

Comprehensively Strengthened Metal-Oxygen Bonds for Reversible Anionic Redox Reaction

Introducing anionic redox in layered oxides is an effective approach to breaking the capacity limit of conventional cationic redox. However, the anionic redox reaction generally suffers from excessive oxidation of lattice oxygen to O₂ and O₂ release, resulting in local structural deterioration and rapid capacity/voltage decay. Here, a Na_0.71Li_0.22Al_0.05Mn_0.73O₂ (NLAM) cathode material is developed by introducing Al³⁺ into the transition metal (TM) sites. Thanks to the strong Al–O bonding strength and small Al³⁺ radius, the TMO₂ skeleton and the holistic TM–O bonds in NLAM are comprehensively strengthened, which inhibits the excessive lattice oxygen oxidation. The obtained NLAM exhibits a high reversible capacity of 194.4 mAh g^-1 at 20 mA g^-1 and decent cyclability with 98.6% capacity retention over 200 cycles at 200 mA g⁻¹. In situ characterizations reveal that the NLAM experiences phase transitions with an intermediate OP4 phase during the charge–discharge. Theoretical calculations further confirm that the Al substitution strategy is beneficial for improving the overlap between Mn 3d and O 2p orbitals. This finding sheds light on the design of layered oxide cathodes with highly reversible anionic redox for sodium storage.     

Metal Single-Site Molecular Complex–MXene Heteroelectrocatalysts Interspersed Graphene Nanonetwork for Efficient Dual-Task of Water Splitting and Metal–Air Batteries

Development of multifunctional electrocatalysts with high efficiency and stability is of great interest in recent energy conversion technologies. Herein, a novel heteroelectrocatalyst of molecular iron complex (Fe_MC)-carbide MXene (Mo₂TiC₂T_x) uniformly embedded in a 3D graphene-based hierarchical network (GrH) is rationally designed. The coexistence of Fe_MC and MXene with their unique interactions triggers optimum electronic properties, rich multiple active sites, and favorite free adsorption energy for excellent trifunctional catalytic activities. Meanwhile, the highly porous GrH effectively promotes a multichannel architecture for charge transfer and gas/ion diffusion to improve stability. Therefore, the Fe_MC–MXene/GrH results in superb performances towards oxygen reduction reaction (ORR), oxygen evolution reaction (OER), and hydrogen evolution reaction (HER) in alkaline medium. The practical tests indicate that Zn/Al–air batteries derived from Fe_MC–MXene/GrH cathodic electrodes produce high power densities of 165.6 and 172.7 mW cm⁻², respectively. Impressively, the liquid-state Zn–air battery delivers excellent cycling stability of over 1100 h. In addition, the alkaline water electrolyzer induces a low cell voltage of 1.55 V at 10 mA cm⁻² and 1.86 V at 0.4 A cm⁻² in 30 wt.% KOH at 80 °C, surpassing recent reports. The achievements suggest an exciting multifunctional electrocatalyst for electrochemical energy applications.     

Towards Sustainable Agricultural Systems: A Lightweight Deep Learning Model for Plant Disease Detection

A country’s economy heavily depends on agricultural development. However, due to several plant diseases, crop growth rate and quality are highly suffered. Accurate identification of these diseases via a manual procedure is very challenging and time-consuming because of the deficiency of domain experts and low-contrast information. Therefore, the agricultural management system is searching for an automatic early disease detection technique. To this end, an efficient and lightweight Deep Learning (DL)-based framework (E-GreenNet) is proposed to overcome these problems and precisely classify the various diseases. In the end-to-end architecture, a MobileNetV3Small model is utilized as a backbone that generates refined, discriminative, and prominent features. Moreover, the proposed model is trained over the PlantVillage (PV), Data Repository of Leaf Images (DRLI), and a new Plant Composite (PC) dataset individually, and later on test samples, its actual performance is evaluated. After extensive experimental analysis, the proposed model obtained 1.00%, 0.96% and 0.99% accuracies on all three included datasets. Moreover, the proposed method achieves better inference speed when compared with other State-Of-The-Art (SOTA) approaches. In addition, a comparative analysis is conducted where the proposed strategy shows tremendous discriminative scores as compared to the various pre-trained models and other Machine Learning (ML) and DL methods.     

Analysis of issues in gate recess etching in the InAlAs/InGaAs HEMT manufacturing process

We have developed an InAlAs/InGaAs metamorphic high electron mobility transistor device fabrication process where the gate length can be tuned within the range of 0.13 μm–0.16 μm to suit the intended application. The core processes are a two-step electron-beam lithography process using a three-layer resist and gate recess etching process using citric acid. An electron-beam lithography process was developed to fabricate a T-shaped gate electrode with a fine gate foot and a relatively large gate head. This was realized through the use of three-layered resist and two-step electron beam exposure and development. Citric acid-based gate recess etching is a wet etching, so it is very important to secure etching uniformity and process reproducibility. The device layout was designed by considering the electrochemical reaction involved in recess etching, and a reproducible gate recess etching process was developed by finding optimized etching conditions. Using the developed gate electrode process technology, we were able to successfully manufacture various monolithic microwave integrated circuits, including low noise amplifiers that can be used in the 28 GHz to 94 GHz frequency range.     

Implementation and characterization of flash-based hardware security primitives for cryptographic key generation

Hardware security primitives, also known as physical unclonable functions (PUFs), perform innovative roles to extract the randomness unique to specific hardware. This paper proposes a novel hardware security primitive using a commercial off-the-shelf flash memory chip that is an intrinsic part of most commercial Internet of Things (IoT) devices. First, we define a hardware security source model to describe a hardware-based fixed random bit generator for use in security applications, such as cryptographic key generation. Then, we propose a hardware security primitive with flash memory by exploiting the variability of tunneling electrons in the floating gate. In accordance with the requirements for robustness against the environment, timing variations, and random errors, we developed an adaptive extraction algorithm for the flash PUF. Experimental results show that the proposed flash PUF successfully generates a fixed random response, where the uniqueness is 49.1%, steadiness is 3.8%, uniformity is 50.2%, and min-entropy per bit is 0.87. Thus, our approach can be applied to security applications with reliability and satisfy high-entropy requirements, such as cryptographic key generation for IoT devices.     

Implementation of mmWave long-range backhaul for UAV-BS

Uncrewed aerial vehicles (UAVs) have become a vital element in nonterrestrial networks, especially with respect to 5G communication systems and beyond. The use of UAVs in support of 4G/5G base station (uncrewed aerial vehicle base station [UAV-BS]) has proven to be a practical solution for extending cellular network services to areas where conventional infrastructures are unavailable. In this study, we introduce a UAV-BS system that utilizes a high-capacity wireless backhaul operating in millimeter-wave frequency bands. This system can achieve a maximum throughput of 1.3 Gbps while delivering data at a rate of 300 Mbps, even at distances of 10 km. We also present the details of our testbed implementation alongside the performance results obtained from field tests.     

Joint frame rate adaptation and object recognition model selection for stabilized unmanned aerial vehicle surveillance

We propose an adaptive unmanned aerial vehicle (UAV)-assisted object recognition algorithm for urban surveillance scenarios. For UAV-assisted surveillance, UAVs are equipped with learning-based object recognition models and can collect surveillance image data. However, owing to the limitations of UAVs regarding power and computational resources, adaptive control must be performed accordingly. Therefore, we introduce a self-adaptive control strategy to maximize the time-averaged recognition performance subject to stability through a formulation based on Lyapunov optimization. Results from performance evaluations on real-world data demonstrate that the proposed algorithm achieves the desired performance improvements.