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Group V doping in CdSeTe device can improve power conversion efficiency (PCE) and device stability. Arsenic (As) incorporation into CdSeTe has been demonstrated via both in situ and ex situ techniques; however, optimizing the back contact for group V‐doped CdSeTe devices remains a critical challenge. Here, solution‐processed arsenic chalcogenides (i.e., As₂Te₃and As₂Se₃) as dual‐role materials, serving as both dopants and back‐contact materials for high‐efficiency CdSeTe devices, are investigated. During the formation of the back contact, a portion of the arsenic chalcogenides diffuses into the CdSeTe absorber, facilitating p‐type doping. The remaining materials forms a stable back‐contact layer that facilitate carrier collection and reducing recombination losses at the CdSeTe back surface. Particularly, CdSeTe device employing Te rich As₂Te₃layer as the dopant and back‐contact materials achieves a PCE of 18.34%, demonstrating the dual functionality of solution‐processed arsenic chalcogenides in simultaneously doping the absorber and optimizing charge extraction. This solution based cost‐effective As doping approach offers a promising pathway for advancing CdSeTe photovoltaic technology. 

This study compares Spiro-OMeTAD, CuSCN, and PTAA as hole transport layers in carbon-based perovskite solar cells. Spiro-OMeTAD showed best efficiency, CuSCN better stability, while PTAA underperformed, highlighting a performance-stability trade-off. 

Kidney transplantation remains the preferred treatment for patients with end-stage kidney disease. However, the ongoing shortage of donor organs continues to limit the availability of transplant treatments. Existing evaluation methods, such as the kidney donor profile index (KDPI) and pre-transplant donor biopsy (PTDB), have various limitations, including low discriminative power, invasiveness, and sampling errors, which reduce their effectiveness in organ quality assessment and contribute to the risk of unnecessary organ discard. In this study, we explored the dynamic optical coherence tomography (DOCT) as a label-free, non-invasive approach to monitor the viability ofex vivomouse kidneys during static cold storage over 48 hours. The dynamic metrics logarithmic intensity variance (LIV), early OCT correlation decay speed (OCDS_e), and late OCT correlation decay speed (OCDS_l) were extracted from OCT signal fluctuations to quantify temporal and spatial tissue activity and deterioration. Our results demonstrate that DOCT provides complementary information relevant to tissue viability, in addition to the morphological assessment offered by conventional OCT imaging, showing potential to improve pre-transplant organ evaluation and clinic decision-making. 

To scale out the massive metadata access, the Ceph distributed file system (CephFS) adopts adynamic subtree partitioningmethod, splitting the hierarchical namespace and distributingsubtreesacross multiple metadata servers. However, this method suffers from a severe imbalance problem that may result in poor performance due to its inaccurate imbalance prediction, ignorance of workload characteristics, and unnecessary/invalid migration activities. To eliminate these inefficiencies, we propose Lunule, a novel CephFS metadata load balancer, which employs animbalance factor modelfor accurately determiningwhento trigger re-balance and tolerate unharmful imbalanced situations. Lunule further adopts aworkload-aware migration plannerto appropriately select subtree migration candidates. Finally, we extend Lunule to Lunule⁺, which models metadata accesses into matrices, and employs matrix-based formulas for more accurate load prediction and re-balance decision. Compared to baselines, Lunule achieves better load balance, increases the metadata throughput by up to 315.8%, and shortens the tail job completion time by up to 64.6% for five real-world workloads and their mixture, respectively. Besides, Lunule is capable of handling the metadata cluster expansion and the workload growth, and scales linearly on a 16-node cluster. Compared to Lunule, Lunule⁺achieves up to 64.96% better metadata load balance, and 13.53-86.09% higher throughput.