The utilization of solar energy to drive energy conversion and simultaneously realize pollutant degradation via photocatalysis is one of most promising strategies to resolve the global energy and environment issues. During the past decade, graphite carbon nitride (g-C₃N₄) has attracted dramatically growing attention for solar energy conversion due to its excellent physicochemical properties as a photocatalyst. However, its practical application is still impeded by several limitations and shortcomings, such as high recombination rate of charge carriers, low visible-light absorption, etc. As an effective solution, the electronic structure tuning of g-C₃N₄ has been widely adopted. In this context, firstly, the paper critically focuses on the different strategies of electronic structure tuning of g-C₃N₄ like vacancy modification, doping, crystallinity modulation and synthesis of a new molecular structure. And the recent progress is reviewed. Finally, the challenges and future trends are summarized.

With the rapid technological innovation in materials engineering and device integration, a wide variety of textile-based wearable biosensors have emerged as promising platforms for personalized healthcare, exercise monitoring, and pre-diagnostics. This paper reviews the recent progress in sweat biosensors and sensing systems integrated into textiles for wearable body status monitoring. The mechanisms of biosensors that are commonly adopted for biomarkers analysis are first introduced. The classification, fabrication methods, and applications of textile conductors in different configurations and dimensions are then summarized. Afterward, innovative strategies to achieve efficient sweat collection with textile-based sensing patches are presented, followed by an in-depth discussion on nanoengineering and system integration approaches for the enhancement of sensing performance. Finally, the challenges of textile-based sweat sensing devices associated with the device reusability, washability, stability, and fabrication reproducibility are discussed from the perspective of their practical applications in wearable healthcare.

This paper introduces a high-precision bandgap reference (BGR) designed for battery management systems (BMS), featuring an ultra-low temperature coefficient (TC) and line sensitivity (LS). The BGR employs a current-mode scheme with chopped op-amps and internal clock generators to eliminate op-amp offset. A low dropout regulator (LDO) and a pre-regulator enhance output driving and LS, respectively. Curvature compensation enhances the TC by addressing higher-order nonlinearity. These approaches, effective near room temperature, employs trimming at both 20 and 60 °C. When combined with fixed curvature correction currents, it achieves an ultra-low TC for each chip. Implemented in a CMOS 180 nm process, the BGR occupies 0.548 mm² and operates at 2.5 V with 84 μA current draw from a 5 V supply. An average TC of 2.69 ppm/°C with two-point trimming and 0.81 ppm/°C with multi-point trimming are achieved over the temperature range of −40 to 125 °C. It accommodates a load current of 1 mA and an LS of 42 ppm/V, making it suitable for precise BMS applications.

In DSP-based SerDes application, it is essential for AFE to implement a pre-ADC equalization to provide a better signal for ADC and DSP. To meet the various equalization requirements of different channel and transmitter configurations, this paper presents a 112 Gbps DSP-Based PAM4 SerDes receiver with a wide band equalization tuning AFE. The AFE is realized by implementing source degeneration transconductance, feedforward high-pass branch and inductive feedback peaking TIA. The AFE offers a flexible equalization gain tuning of up to 17.5 dB at Nyquist frequency without affecting the DC gain. With the proposed AFE, the receiver demonstrates eye opening after digital FIR equalization and achieves 6 × 10⁻⁹ BER with a 29.6 dB insertion loss channel.

Diamond, as an ultra-wide bandgap semiconductor, has become a promising candidate for next-generation microelectronics and optoelectronics due to its numerous advantages over conventional semiconductors, including ultrahigh carrier mobility and thermal conductivity, low thermal expansion coefficient, and ultra-high breakdown voltage, etc. Despite these extraordinary properties, diamond also faces various challenges before being practically used in the semiconductor industry. This review begins with a brief summary of previous efforts to model and construct diamond-based high-voltage switching diodes, high-power/high-frequency field-effect transistors, MEMS/NEMS, and devices operating at high temperatures. Following that, we will discuss recent developments to address scalable diamond device applications, emphasizing the synthesis of large-area, high-quality CVD diamond films and difficulties in diamond doping. Lastly, we show potential solutions to modulate diamond’s electronic properties by the “elastic strain engineering” strategy, which sheds light on the future development of diamond-based electronics, photonics and quantum systems.

Organic zinc-ion batteries (OZIBs) are emerging rechargeable energy storage devices and have attracted increasing attention as one of the promising alternatives of lithium-ion batteries, benefiting from the Zn metal (low cost, safety and small ionic size) and organic electrodes (flexibility, green and designable molecular structure). Organic electrodes have exhibited fine electrochemical performance in ZIBs, but the research is still in infancy and hampered by some issues. Hence, to provide insight into OZIBs, this review summarizes the progress of organic cathode materials for ZIBs and points out the existing challenges and then addresses potential solutions. It is hoped that this review can stimulate the researchers to further develop high-performance OZIBs.

Robotic computing systems play an important role in enabling intelligent robotic tasks through intelligent algorithms and supporting hardware. In recent years, the evolution of robotic algorithms indicates a roadmap from traditional robotics to hierarchical and end-to-end models. This algorithmic advancement poses a critical challenge in achieving balanced system-wide performance. Therefore, algorithm-hardware co-design has emerged as the primary methodology, which analyzes algorithm behaviors on hardware to identify common computational properties. These properties can motivate algorithm optimization to reduce computational complexity and hardware innovation from architecture to circuit for high performance and high energy efficiency. We then reviewed recent works on robotic and embodied AI algorithms and computing hardware to demonstrate this algorithm-hardware co-design methodology. In the end, we discuss future research opportunities by answering two questions: (1) how to adapt the computing platforms to the rapid evolution of embodied AI algorithms, and (2) how to transform the potential of emerging hardware innovations into end-to-end inference improvements.