Against the backdrop of dual carbon targets and the rapid development of new energy systems, large-scale grid integration of distributed photovoltaic (PV) generation and battery energy storage system (BESS) has emerged as a crucial pathway for the energy transition. This paper systematically investigates the advantages and risks associated with grid-connected PV-BESS integration. This paper reviews the structural configurations and key technologies of distributed PV-BESS systems, and analyzes representative engineering projects and control/optimization methods, including AC-coupled and DC-coupled demonstration cases, grid-forming control based on virtual synchronous generator technology, and two-stage coordinated optimization using second-order cone programming (SOCP) relaxation. Results indicate that properly coordinated PV-BESS integration can effectively improve voltage quality, mitigate power fluctuations, enhance PV hosting capacity, and provide ancillary services. Nevertheless, it may also introduce voltage violations, power quality degradation, relay protection coordination challenges, and elevated investment costs. Furthermore, this paper presents constructive recommendations from technical, planning, economic, and policy perspectives, providing a reference for the safe and stable planning and operation of distributed PV-storage integration.

Colloidal quantum dot light-emitting diodes (QD-LEDs) stand out as a premier technology for next-generation displays, particularly in smartphones and near-eye AR/VR systems. This enthusiasm is largely driven by their remarkable optical traits, such as near-perfect color purity, a massive color gamut, and high photoluminescence quantum yield. Yet, moving ultra-high-resolution QD-LEDs from the lab to mass production hits a severe roadblock. In this paper, six leading strategies for QD patterning—photolithography, inkjet printing, laser processing, transfer printing, self-assembly, and optical microcavities are review. Then, it points out that each patterning technology has its own advantages and limitations. The future development direction may focus on the collaborative integration of multiple patterning technologies, such as combining the high-precision structure definition ability of lithography with the material deposition advantages of printing and transfer technologies, to achieve the large-scale manufacturing of ultra-high PPI quantum dot micro-display devices. By mapping out these critical bottlenecks, this paper offers a strategic roadmap for developing the high-density patterning solutions necessary to finally realize the commercial promise of advanced QD-LEDs.

Three-phase inverters are widely used in renewable energy conversion, industrial drives, and energy storage systems. Their IGBTs and other power switching devices often work for long periods under high-frequency and high-current conditions, which makes fault diagnosis an important issue for reliable operation. Traditional diagnosis methods usually depend on manually designed features obtained from Fourier transform, wavelet analysis, or related signal-processing tools. Although these features are interpretable, their performance is closely tied to expert experience. Purely data-driven deep learning models can learn features from raw waveforms, but they often show limited physical interpretability and may overfit when fault samples are insufficient. This paper proposes a lightweight Multi-view Fault Feature Fusion Network (MFF-Net) for fault waveform classification in three-phase inverter circuits. The model contains a lightweight one-dimensional convolutional neural network branch for temporal waveform representation and a physics-guided branch for extracting single-phase statistics, three-phase imbalance indices, zero-sequence components, and multi-band harmonic energy ratios. A gated fusion module is then used to combine the two feature groups according to sample-specific fault characteristics. A simulated dataset with 3,200 samples is built under normal, overcurrent, phase-loss, and IGBT bridge-arm open-circuit conditions. Experimental results show that MFF-Net achieves stable training, high classification accuracy, and clear feature separation, offering a feasible solution for lightweight online fault diagnosis in power electronic systems.

This paper investigates how multi-energy storage microgrids provide intelligent backup power functions under diverse operating conditions for intelligent buildings. With increasing renewable energy penetration and growing concerns about grid reliability, building-integrated microgrids provide a promising solution for enhancing resilience, flexibility and sustainability. This research first reviews the framework of the building integrated microgrid, the key storage technologies including battery, thermal and hydrogen storage and intelligent control strategies. Four representative scenarios are then analyzed: short-duration outages, long-duration outages, extreme climate conditions, and normal grid-connected operation. The results of recent studies reveal that battery systems are effective for rapid response while hybrid multi-energy storage significantly improves long-duration autonomy and climate adaptability. In grid-connected mode, intelligent optimization and requirement response can drastically reduce operation costs. Finally, future trends such as digital twins, peer-to-peer energy trading and AI predictive control are playing important roles. The results from this review show that coupling multi-energy storage technologies with advanced control strategies is essential for achieving resilient, low-carbon and autonomous building energy systems.

Amino acids are high-value chemicals widely used in the food, pharmaceutical, and agricultural industries, but their conventional synthesis routes often carry a substantial carbon footprint. Electrocatalytic conversion of nitrogen oxides (NOx) into amino acids represents a promising sustainable alternative. Among the possible products, glycine is an important model system for understanding the fundamentals of NOx-derived amino acid electrosynthesis. However, achieving high selectivity remains difficult because NOxreduction involves multiple reactive intermediates and competing reaction pathways, leading to complex product distributions. These challenges make mechanistic understanding and advanced characterization central to progress in the field. This review examines glycine electrosynthesis as a representative case to uncover key mechanistic aspects of electrocatalytic NOxreduction. It discusses how characterization techniques, including vibrational spectroscopy, mass spectrometry, electron paramagnetic resonance, and X-ray-based methods, enable the detection of intermediates and the elucidation of reaction pathways. The paper also analyzes how catalyst structure and reaction conditions, especially pH, regulate intermediate evolution, C–N coupling, and glycine formation. Finally, current challenges and future opportunities are summarized, aiming to provide guidance for the rational design of selective electrocatalysts for NOx-to-amino acid conversion.

Metal additive manufacturing uses metallic powders or wires as feedstock and produces near-net-shape components through layer-wise melting, deposition, binding or sintering. It has attracted sustained attention in aerospace, biomedical implants, rail transit and energy equipment because it can fabricate complex internal channels, integrated lightweight structures, porous functional architectures and customized components. Process characteristics, application fit, engineering constraints and development trends are examined for laser powder bed fusion, electron beam melting, directed energy deposition, wire arc additive manufacturing and metal binder jetting. Powder bed fusion is generally suitable for small-to-medium precision components and porous structures, whereas directed energy deposition and wire arc additive manufacturing are more suitable for large components, near-net-shape deposition and local repair. Metal binder jetting has potential for batch production of small and medium-sized complex parts, although dimensional control after sintering remains challenging. Engineering use is still limited by pores, lack-of-fusion defects, cracking, residual stress, microstructural anisotropy, fatigue reliability, post-processing consistency and qualification. Further development requires coordinated progress in material-process-structure design, in-situ monitoring, closed-loop control, part-level performance evaluation and traceable quality data.