Strong Light-Matter Coupling Facilitated Charge Carrier Transport in Cavity Organic Solar Cells

Strong light-matter coupling has shown great potential for modifying the electro-optical properties of semiconducting materials in recent years. In the strong coupling regime, excitons and cavity photons form new states named exciton-polaritons, with their properties a hybrid of each constituent. Herein, we report strong coupling observed in solution-processed donor:acceptor bulk-heterojunction organic solar cells (OSCs) evidenced by the observed Rabi splitting of ~300 meV and the effects of strong coupling on OSC operations. Combining the transient photovoltage decay measurement and nanosecond transient absorption spectroscopy, our results reveal that the effective charge carrier lifetimes are longer in cavity devices, attributed to the reduced bimolecular recombination. It is also found that access to CT state(s) of higher energy is enabled in cavity devices. This study demonstrates that strong coupling can effectively modify the device- and photo-physics in OSCs and opens a new pathway for engineering more efficient OSCs

Training and Tuning Generative Neural Radiance Fields for Attribute-Conditional 3D-Aware Face Generation

Generative Neural Radiance Fields (GNeRF) based 3D-aware GANs have demonstrated remarkable capabilities in generating high-quality images while maintaining strong 3D consistency. Notably, significant advancements have been made in the domain of face generation. However, most existing models prioritize view consistency over disentanglement, resulting in limited semantic/attribute control during generation. To address this limitation, we propose a conditional GNeRF model incorporating specific attribute labels as input to enhance the controllability and disentanglement abilities of 3D-aware generative models. Our approach builds upon a pre-trained 3D-aware face model, and we introduce a Training as Init and Optimizing for Tuning (TRIOT) method to train a conditional normalized flow module to enable the facial attribute editing, then optimize the latent vector to improve attribute-editing precision further. Our extensive experiments demonstrate that our model produces high-quality edits with superior view consistency while preserving non-target regions. Code is available at https://github.com/zhangqianhui/TT-GNeRF.Comment: 13 page

One-step Preparation of ZnO Electron Transport Layers Functionalized with Benzoic Acid Derivatives

We present a "one-step" approach to modify ZnO electron transport layers (ETLs) used in organic solar cells. This approach involves adding benzoic acid (BZA) derivatives directly to the ZnO precursor solution, which are then present at the surface of the resulting ZnO film. We demonstrate this approach for three different BZA derivatives, namely benzoic acid, chlorobenzoic acid, and 4-hydrazinobenzoic acid. For all molecules, improved device performance and stability is demonstrated in solar cells using an active layer blend of PTQ10 (donor) and ITIC-Br (non-fullerene acceptor) compared to such cells prepared using untreated ZnO. Furthermore, similar or improved device performance and stability is demonstrated compared to conventional PEIE treatment of ZnO. The presence of the BZA derivatives at the surface after processing is established using X-ray photoelectron spectroscopy and near-edge X-ray absorption fine-structure spectroscopy. From atomic force microscopy analysis and X-ray diffraction studies, the addition of BZA derivatives appears to restrict ZnO grain growth; however, this does not negatively impact device performance. ZnO layers treated with BZA derivatives also exhibit higher water contact angle and lower work function compared to untreated ZnO. This approach enables simplification of device manufacture while still allowing optimization of the surface properties of metal oxide ETLs. Keywords: electron transport layers, zinc oxide, organic solar cells, surface modificationComment: Manuscript: 25 pages, 8 figures, 5 tables. Supplementary Material: 36 pages, 22 figures, 13 tables. Submitted to Solar Energy Materials and Solar Cell

Integrative analysis of transcriptome and miRNAome reveals molecular mechanisms regulating pericarp thickness in sweet corn during kernel development

Pericarp thickness affects the edible quality of sweet corn (Zea mays L. saccharata Sturt.). Therefore, breeding varieties with a thin pericarp is important for the quality breeding of sweet corn. However, the molecular mechanisms underlying the pericarp development remain largely unclear. We performed an integrative analysis of mRNA and miRNA sequencing to elucidate the genetic mechanism regulating pericarp thickness during kernel development (at 15 days, 19 days, and 23 days after pollination) of two sweet corn inbred lines with different pericarp thicknesses (M03, with a thinner pericarp and M08, with a thicker pericarp). A total of 2,443 and 1,409 differentially expressed genes (DEGs) were identified in M03 and M08, respectively. Our results indicate that phytohormone-mediated programmed cell death (PCD) may play a critical role in determining pericarp thickness in sweet corn. Auxin (AUX), gibberellin (GA), and brassinosteroid (BR) signal transduction may indirectly mediate PCD to regulate pericarp thickness in M03 (the thin pericarp variety). In contrast, abscisic acid (ABA), cytokinin (CK), and ethylene (ETH) signaling may be the key regulators of pericarp PCD in M08 (the thick pericarp variety). Furthermore, 110 differentially expressed microRNAs (DEMIs) and 478 differentially expressed target genes were identified. miRNA164-, miRNA167-, and miRNA156-mediated miRNA–mRNA pairs may participate in regulating pericarp thickness. The expression results of DEGs were validated by quantitative real-time PCR. These findings provide insights into the molecular mechanisms regulating pericarp thickness and propose the objective of breeding sweet corn varieties with a thin pericarp

Inversion of thermal properties of lunar soil from penetration heat of projectile using a 2D axisymmetric model and optimized PSO algorithm

The thermophysical parameters of lunar soil can be inferred from the temperature field during the invasion process of reconnaissance projectile. This paper adopts a two-dimensional axisymmetric model to reconstruct the projectile invasion process. An optimized particle swarm optimization method is then used to retrieve the thermophysical parameters of lunar soil. When the reconnaissance projectile penetrates the lunar interior, it rubs against the lunar soil and generates heat, which diffuses between the projectile body and the lunar soil. The sensors inside the reconnaissance projectile measure the temperature variation of the projectile body to inverse the thermophysical parameters. This paper carried out physical modeling of the penetration process of reconnaissance projectile. A two-dimensional axisymmetric simulation model is constructed for the physical process, and the adaptive inertial weight particle swarm algorithm is adopted. The inversion experiment of lunar soil thermophysical parameters based on the simulation model shows that the inversion error is less than 10%, which verifies the feasibility of this method

Administration of protopine prevents mitophagy and acute lung injury in sepsis

Introduction: Sepsis is a severe life-threatening infection that induces a series of dysregulated physiologic responses and results in organ dysfunction. Acute lung injury (ALI), the primary cause of respiratory failure brought on by sepsis, does not have a specific therapy. Protopine (PTP) is an alkaloid with antiinflammatory and antioxidant properties. However, the function of PTP in septic ALI has not yet been documented. This work sought to investigate how PTP affected septic ALI and the mechanisms involved in septic lung damage, including inflammation, oxidative stress, apoptosis, and mitophagy.Methods: Here, we established a mouse model induced by cecal ligation and puncture (CLP) and a BEAS-2B cell model exposed to lipopolysaccharide (LPS).Results: PTP treatment significantly reduced mortality in CLP mice. PTP mitigated lung damage and reduced apoptosis. Western blot analysis showed that PTP dramatically reduced the expression of the apoptosis-associated protein (Cleaved Caspase-3, Cyto C) and increased Bcl-2/Bax. In addition, PTP decreased the production of inflammatory cytokines (IL-6, IL-1β, TNF-α), increased glutathione (GSH) levels and superoxide dismutase (SOD) activity, and decreased malondialdehyde (MDA) levels. Meanwhile, PTP significantly reduced the expression of mitophagy-related proteins (PINK1, Parkin, LC-II), and downregulated mitophagy by transmission electron microscopy. Additionally, the cells were consistent with animal experiments.Discussion: PTP intervention reduced inflammatory responses, oxidative stress, and apoptosis, restored mitochondrial membrane potential, and downregulated mitophagy. The research shows that PTP prevents excessivemitophagy and ALI in sepsis, suggesting that PTP has a potential role in the therapy of sepsis

A compendium of genetic regulatory effects across pig tissues

The Farm Animal Genotype-Tissue Expression (FarmGTEx) project has been established to develop a public resource of genetic regulatory variants in livestock, which is essential for linking genetic polymorphisms to variation in phenotypes, helping fundamental biological discovery and exploitation in animal breeding and human biomedicine. Here we show results from the pilot phase of PigGTEx by processing 5,457 RNA-sequencing and 1,602 whole-genome sequencing samples passing quality control from pigs. We build a pig genotype imputation panel and associate millions of genetic variants with five types of transcriptomic phenotypes in 34 tissues. We evaluate tissue specificity of regulatory effects and elucidate molecular mechanisms of their action using multi-omics data. Leveraging this resource, we decipher regulatory mechanisms underlying 207 pig complex phenotypes and demonstrate the similarity of pigs to humans in gene expression and the genetic regulation behind complex phenotypes, supporting the importance of pigs as a human biomedical model.</p

Digital Inclusive Finance and Family Wealth: Evidence from LightGBM Approach

With the rapid development of digital technology in China, Digital Inclusive Finance, which uses digital financial services to promote financial inclusion, is developing rapidly. This paper uses the Peking University Digital Financial Inclusion index of China and China Family Panel Studies (CFPS) data to construct a predictive model using the LightGBM machine learning algorithm to study whether Digital Inclusive Finance can predict household wealth and analyze the characteristics of strong predictive ability for household wealth. They found that: (1) the introduction of the Digital Financial Inclusion index can improve the prediction performance of the household wealth model; (2) financial literacy and age characteristics are the key characteristics of household wealth accumulation; (3) the coverage and depth of Digital Inclusive Finance has a significant effect on family wealth accumulation, but the degree of digitization acts as a disincentive factor. This paper not only uses machine learning methods to do research on Digital Inclusive Finance and family wealth from a more comprehensive perspective, but also provides effective theoretical support for the key factors that enhance family wealth