RESUMEN
Despite recent revolutionary advancements in photovoltaic (PV) technology, further improving cell efficiencies toward their Shockley-Queisser (SQ) limits remains challenging due to inherent optical, electrical, and thermal losses. Currently, most research focuses on improving optical and electrical performance through maximizing spectral utilization and suppressing carrier recombination losses, while there is a serious lack of effective opto-electro-thermal coupled management, which, however, is crucial for further improving PV performance and the practical application of PV devices. In this article, the energy conversion and loss processes of a PV device (with a specific focus on perovskite solar cells) are detailed under both steady-state and transient processes through rigorous opto-electro-thermal coupling simulation. By innovatively coupling multi-physical behaviors of photon management, carrier/ion transport, and thermodynamics, it meticulously quantifies and analyzes energy losses across optical, electrical, and thermal domains, identifies heat components amenable to regulation, and proposes specific regulatory means, evaluates their impact on device efficiency and operating temperature, offering valuable insights to advance PV technology for practical applications.
RESUMEN
Background: Alzheimer's disease (AD) and age-related macular degeneration (AMD) share similar pathological features, suggesting common genetic aetiologies between the two. Investigating gene associations between AD and AMD may provide useful insights into the underlying pathogenesis and inform integrated prevention and treatment for both diseases. Methods: A stratified quantile-quantile (QQ) plot was constructed to detect the pleiotropy among AD and AMD based on genome-wide association studies data from 17 008 patients with AD and 30 178 patients with AMD. A Bayesian conditional false discovery rate-based (cFDR) method was used to identify pleiotropic genes. UK Biobank was used to verify the pleiotropy analysis. Biological network and enrichment analysis were conducted to explain the biological reason for pleiotropy phenomena. A diagnostic test based on gene expression data was used to predict biomarkers for AD and AMD based on pleiotropic genes and their regulators. Results: Significant pleiotropy was found between AD and AMD (significant leftward shift on QQ plots). APOC1 and APOE were identified as pleiotropic genes for AD-AMD (cFDR <0.01). Network analysis revealed that APOC1 and APOE occupied borderline positions on the gene co-expression networks. Both APOC1 and APOE genes were enriched on the herpes simplex virus 1 infection pathway. Further, machine learning-based diagnostic tests identified that APOC1, APOE (areas under the curve (AUCs) >0.65) and their upstream regulators, especially ZNF131, ADNP2 and HINFP, could be potential biomarkers for both AD and AMD (AUCs >0.8). Conclusion: In this study, we confirmed the genetic pleiotropy between AD and AMD and identified APOC1 and APOE as pleiotropic genes. Further, the integration of multiomics data identified ZNF131, ADNP2 and HINFP as novel diagnostic biomarkers for AD and AMD.