Replacing insulating aliphatic ligands with conjugated oligoelectrolytes (COEs) enables strong binding, effective passivation, and Förster resonance energy transfer (FRET) to amplify the fluorescence of CsPbBr₃ perovskite nanocrystals (PNCs). Herein, we report multifunctional COEs with systematically varied ionic densities (2, 4, and 6 quaternary ammonium bromides) as surface ligands for CsPbBr₃ PNCs. Increasing ionic density enhances binding intimacy with the PNC surface, leading to photoluminescence quantum yield (PLQY) over 94% with more efficient FRET efficiency. Time-resolved photoluminescence reveals FRET efficiencies of 40%, 63%, and 84% for QTF2Br, QTF4Br, and QTF6Br-capped PNCs, respectively. While higher ionic densities achieve superior FRET performance, they compromise colloidal dispersibility due to increased surface polarity. The study provides molecular-level insights into how ionic density modulates interfacial coupling and affects the luminescent property of PNCs, offering design principles of semiconducting ligands and unleash their potential for their optoelectronic applications.

Perovskite quantum dots (PQDs) have attracted significant attention as promising photovoltaic materials due to their unique optoelectronic properties, defect tolerance, and facile solution processing. Since the first report of PQD solar cells in 2016 with 10.77% efficiency, rapid development has been achieved, with recent certified values exceeding 19%. A key factor in this development is the control of initial long-chain ligands that must be replaced or removed to enable efficient charge transport. Because ligand binding strength and exchange behavior differ depending on the A-site cation, tailored strategies have been developed for inorganic Cs-based, organic FA/MA-based, and mixed cation PQDs. These efforts have led to steady improvements in device efficiency and stability. This review highlights recent advances in ligand engineering with respect to A-site composition and discusses remaining challenges for high-performance, scalable PQD solar cells.

Traditionally, expensive metals like gold or silver have been used as the rear electrode for perovskite solar cells (PSCs), which have highly efficient performance. Within this framework, carbon nanotube (CNT) electrodes have been regarded as promising low cost electrodes due to their mechanical strength and excellent electrical conductivity. In spite of these advantages, concerns have arisen about the power conversion efficiency (PCE) and stability of PSCs based on CNTs due to the porosity of CNT electrodes and interfacial issue. In this work, we introduced PM6 hole transport material infiltration method to address issues related to the porosity of carbon nanotube (CNT) electrodes, aiming to improve the performances. As a result, the infiltration of PM6 into the CNT electrode improved the interfacial contact between the CNT electrode and the perovskite layer, thereby the best PCE of 15.49% was achieved in PSCs based on CNT rear electrodes.

The impact of bottom-layer selection on the structual, optical, and photovoltaic properties of vacuum-deposited perovskite films was systematically investigated. Atomic force microscopy (AFM) revealed that films grown on CsI exhibited the lowest root-mean-square(RMS) roughness (16.2 nm) and densely packed, large grains, indicating reduced defect density and suppressed non-radiative recombination, X-ray diffraction (XRD) and UV-Vis spectroscopy showed that CsI effectively showed the alpha-phase while suppressing the delta-phase, resulting in improved crystallinity and enhanced light absorption. Photoluminescence(PL) measurements demonstrated minimal peak shift(25.7 nm) over 15 minutes for CsI-based films, confirming superior phase stability compared to other bottom-layers. Consequently, perovskite solar cells incorporating CsI exhibited enhanced open-circuit voltage(V_OC), short-circuit current density(J_SC), and fill factor(FF), maintaining 101.1% of the initial efficiency and 99.2% of the maximum efficiency over 12 hours of maximum power point(MPP) tracking under 25℃ and 40% relative humidity without encapsulation. These findings highlight that bottom-layer engineering critically influences perovskite crystal growth, phase stability, and device performance. CsI, in particular, provides a combination of alpha-phase stabilization, high optical absorption, and superior film quality, making it an optimal bottom-layer for high-efficiency and long term stable perovkite solar cells.

Effective surface passivation is essential for high-efficiency crystalline silicon (c-Si) solar cells. Aluminum oxide (Al₂O₃) films deposited via atomic layer deposition (ALD) are a leading passivation material due to their high negative fixed charge density (Q_f) and low interface trap density (D_it). However, the optimal passivation performance is highly dependent on the post-deposition annealing (PDA) temperature, a relationship that is often reported inconsistently in the literature. This study systematically investigates the integrated passivation mechanism of an ~10 nm amorphous AlO_x film on c-Si. By precisely controlling the PDA temperature, we employ combined electrical, chemical, and optoelectronic analyses to link process condition to passivation performance. Our findings reveal that both the magnitude of negative Q_f and the overall passivation quality are maximized at a PDA temperature of approximately 450℃. Fourier-transform infrared (FT-IR) spectroscopy confirms that this optimal performance correlates with densification and rearrangement of the Al-O network, which stabilizes negative charge centers near the interface. At higher temperatures (≥ 500℃), a clear decline in passivation is observed, which we attribute to the growth of an interfacial SiO₂ layer and hydrogen loss. This study establishes a direct link between PDA temperature, structural changes, and passivation performance, providing quantitative, physically grounded guidelines for optimizing Al₂O₃-based passivation in high-efficiency c-Si solar cells.

With the expansion of solar energy as a leading renewable energy source due to its sustainability, ease of installation, and long operational lifetime, autonomous photovoltaic operation and maintenance (O&M) systems are increasingly important. Here, we present a YOLO_v11n-based deep learning model specializing in precise detection of hot spots on solar panels. We analyze the impact of augmentation hyperparameters (mixup, mosaic, copy_paste and close_mosaic) on small object detection. Our observations exhibit that a mixup value of 0.2 improves mAP@50, which represents the average accuracy, by over 3.7%, while excessive mixup increases image transparency, relaxing boundaries between small objects and reducing accuracy. Copy_paste has minimal effect when sufficient training data is available. Properly balancing the use of original and mosaic images, which combine four images into one, is crucial for high accuracy, and setting close_mosaic at 10% of entire training progress optimizes performance. Consequently, our model achieves over 95% of outstanding mAP@50, demonstrating robust and high-precision hot spot detection.