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A stable solid‐electrolyte interphase (SEI) is crucial for cycling reversibility of Na‐ion batteries by mitigating continuous side reactions. So far, the severe SEI dissolution leads to low Coulombic efficiency (CE) and short cycle life. Meanwhile, the quantified relationship between SEI components and their solubility remains unclear. In this work, we establish the direct correlation between SEI components and SEI solubility, and quantify that the solubility of organic‐rich SEI is 3.26 times of inorganic‐rich SEI. We further propose a feasible strategy to preform inorganic‐rich insoluble SEI and demonstrate a practical hard carbon (HC)||NaMn0.33Fe0.33Ni0.33O2 full cell in a commercial electrolyte of 1 M NaPF6 in propylene carbonate (PC) with 80.0 % capacity retention for 900 cycles, and achieve a record‐high average CE of 99.95 % for a practical Na‐ion full cell. This study provides an effective strategy of preforming insoluble SEI to suppress its dissolution towards highly reversible Na‐ion batteries. [ABSTRACT FROM AUTHOR]

In organic solar cells (OSCs), the electron transport layer (ETL) is vital for high performance devices. As a widely used ETL, N-oxide-functionalized perylene-diimide (PDINO) is often limited by its low conductivity. Herein, we develop an improved ETL by doping indole carbonized polymer dots (CPDs) into PDINO. The graphitic N and oxoindole groups of CPDs could increase the self-doping of PDINO. Compared with the pristine PDINO ETL, the PDINO + CPDs ETL shows a more suitable work function and higher conductivity due to the graphitic nature of CPDs and the enhanced self-doping of PDINO. PM6:Y6 OSCs with a 10 nm PDINO + CPDs ETL demonstrate a superior average power conversion efficiency (PCE) (17.0%) compared with OSCs with only a PDINO ETL (15.6%). This improvement in PCE is attributed to the enhanced efficiency of exciton dissociation and reduced charge recombination within the devices. Furthermore, OSCs utilizing PDINO + CPDs ETL display a notable resilience to variations in ETL thickness, maintaining a high average PCE of 15.0% even when the ETL thickness is increased to 70 nm. This work provides a simple interfacial modification strategy to optimize ETLs for OSCs. [ABSTRACT FROM AUTHOR]

Purpose: Lung adenocarcinoma (LUAD) significantly contributes to cancer-related mortality worldwide. The heterogeneity of the tumor immune microenvironment in LUAD results in varied prognoses and responses to immunotherapy among patients. Consequently, a clinical stratification algorithm is necessary and inevitable to effectively differentiate molecular features and tumor microenvironments, facilitating personalized treatment approaches. Methods: We constructed a comprehensive single-cell transcriptional atlas using single-cell RNA sequencing data to reveal the cellular diversity of malignant epithelial cells of LUAD and identified a novel signature through a computational framework coupled with 10 machine learning algorithms. Our study further investigates the immunological characteristics and therapeutic responses associated with this prognostic signature and validates the predictive efficacy of the model across multiple independent cohorts. Results: We developed a six-gene prognostic model (MYO1E, FEN1, NMI, ZNF506, ALDOA, and MLLT6) using the TCGA-LUAD dataset, categorizing patients into high- and low-risk groups. This model demonstrates robust performance in predicting survival across various LUAD cohorts. We observed distinct molecular patterns and biological processes in different risk groups. Additionally, analysis of two immunotherapy cohorts (N = 317) showed that patients with a high-risk signature responded more favorably to immunotherapy compared to those in the low-risk group. Experimental validation further confirmed that MYO1E enhances the proliferation and migration of LUAD cells. Conclusion: We have identified malignant cell-associated ligand–receptor subtypes in LUAD cells and developed a robust prognostic signature by thoroughly analyzing genomic, transcriptomic, and immunologic data. This study presents a novel method to assess the prognosis of patients with LUAD and provides insights into developing more effective immunotherapies. [ABSTRACT FROM AUTHOR]

Defects present on the top surface of perovskite films have a pronounced detrimental impact on the photovoltaic performance and stability of perovskite solar cells (PSCs). Consequently, the development of effective defect passivation strategies has become key in enhancing both the power conversion efficiency (PCE) and stability of PSCs. In this study, a small molecule material, 4‐Aminophthalonitrile (4‐APN), was introduced as a means to mitigate surface defects within perovskite films. Obviously, 4‐APN effectively passivates the defects at grain boundaries by combining cyano groups (−C≡N) with Pb2+, significantly reducing the density of defect states, inhibiting non‐radiative recombination at the interface, and promoting the charge transfer efficiency from the perovskite layer to the hole transport layer. The 4‐APN modification led to a significant upswing in the PCE, while concurrently bolstering the overall device stability. Importantly, the devices on 4‐APN as passivation additive exhibited negligible performance degradation aging for 1200 h. [ABSTRACT FROM AUTHOR]

We present an inversion algorithm with a deep-learning-based model compression scheme. Models are described with latent parameters of a trained variational autoencoder (VAE) neural network. Given observed data, latent parameters are inverted by minimizing the data misfit cost function using the Gauss–Newton method. This inversion algorithm is tested using both synthetic and experimental datasets. We achieve a 0.87% compression rate while maintaining high-quality reconstruction. The deep neural network renders nonlinear model compression, which largely reduces the number of unknowns; hence, it has higher computational efficiency. Furthermore, various prior knowledge that is difficult to describe with rigorous forms can be incorporated into inversion through training the neural network, which mitigates the ill-posedness of the inverse problem. [ABSTRACT FROM AUTHOR]

Linguistic q-rung orthopair fuzzy numbers (Lq-ROFNs) are an effective tool for representing fuzzy linguistic information, and they can obtain a wider expression scope than linguistic intuitionistic fuzzy numbers and linguistic Pythagorean fuzzy numbers by increasing the value of parameter q. In this paper, we propose a new similarity measure called the grey similarity degree between any two Lq-ROFNs based on the concept of the grey correlation degree. Considering the significance of determining unknown weights, we also propose a grey correlation method to determine each expert's weight under different alternatives and attributes, and we construct an optimization model to determine incompletely known attribute weights. Furthermore, an approach to linguistic q-rung orthopair fuzzy multiple-attribute group decision making is proposed that combines the grey similarity degree with the PROMETHEE II method. Finally, a numerical example is given to illustrate the effectiveness of the proposed method, and a sensitivity analysis and comparison analysis are also performed. [ABSTRACT FROM AUTHOR]

SnO2 is a candidate material for electron transport layers (ETLs) in perovskite solar cells (PSCs). However, a large number of defects at the SnO2/perovskite interface lead to notable non-radiative interfacial recombination. Moreover, the energy level arrangement between SnO2/perovskite does not match well. In this study, a SnO2/CsF-SnO2 double-layer ETL was prepared by doping CsF into SnO2, effectively passivating the defects of the SnO2 ETL and SnO2/perovskite interface. The formation of a good energy level arrangement with the perovskite layer reduces the interface non-radiative recombination and improves the performance of the interface charge extraction. The photoelectric conversion efficiency of the optimal CsF-modified PSC reached 22.18%, owing to the significant increase in the open-circuit voltage to 1.180 V. [ABSTRACT FROM AUTHOR]

In this work, we design an iterative deep neural network to solve full-wave inverse scattering problems (ISPs) in the 2-D case. Forward modeling neural networks that predict the scattered field are embedded in an inversion neural network. In an iterative manner, the inversion network predicts the model update from the residual between the simulated data and the observed data. The proposed inversion network can achieve super-resolution reconstruction meanwhile keeping the simulated data of reconstructed models well consistent with the observed data. We validate this method with both synthetic and experimental data inversion. Results show that the inversion network can predict models with high accuracy, efficiency, and good generalization ability. By combining deep learning and physical simulation together, the proposed method provides a way for real-time imaging with high reliability and accuracy. [ABSTRACT FROM AUTHOR]

Background: Based on bioinformatics analysis and experimental validation, we investigated the expression, clinical significance, immune infiltration, and potential signaling pathways of miR-3940-5p in lung adenocarcinoma (LUAD). Methods: 521 LUAD tissue samples and 46 normal lung tissue samples from The Cancer Genome Atlas (TCGA) database. We evaluated the relationship between clinical features and miR-3940-5p expression using Kruskal–Wallis, Wilcoxon sign-rank, and logistic regression, explored the relationship between miR-3940-5p expression and the prognosis of LUAD patients using Kaplan–Meier survival curve analysis. Several databases were used to identify miRNA targets. MiR-3940-5p target genes were analyzed based on Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. The significant role of miR-3940-5p in function was evaluated using immune infiltration analysis. LUAD cell lines were tested for miR-3940-5p expression using QRT-PCR. Results: There was a significant association between high miR-3940-5p expression in LUAD and T stage (P=0.005), pathologic stage (P=0.047), race (White vs Asian & Black or African American) (P=0.041), residual tumor (P=0.043), and anatomic neoplasm subdivision2 (P=0.030). MiR-3940-5p expression predicted poor overall survival (HR: 1.35; 95% CI: 1.01– 1.81; P=0.045), disease-specific survival (HR: 1.53; 95% CI: 1.05– 2.23; P=0.026), and progression-free survival (HR: 1.35; 95% CI: 1.03– 1.77; P=0.032). BAP1, BBS1, CCR2, KCNE3, PEBP1, and RABL2A were all associated with poor OS in LUAD patients with low miR-3940-5p expression levels. According to GO and KEGG analyses, miR-3940-5p may play a role in LUAD development by regulating pathways such as measles, PI3K-Akt signaling pathway, and p53 signaling pathway. There was a correlation between the expression level of miR-3940-5p and immune infiltration. LUAD cell lines showed significantly higher levels of miR-3940-5p than Beas-2B cells. Conclusion: A high expression of miR-3940-5p is significantly associated with a poor prognosis in patients with LUAD, suggesting that it could be used as a prognostic biomarker. [ABSTRACT FROM AUTHOR]

For high‐performance perovskite solar cells (PSCs), transition metal complexes as modifiers are used to optimize the energy‐level alignment of the SnO2 electron transport layer (ETL), reduce the defects at the ETL/perovskite interface, and improve the crystallinity of the perovskite layer. Herein, it is shown that application of [Ni(NH3)6]F2 or [Co(NH3)6]F2 complexes yields a substantially improved open‐circuit voltage and fill factor, and thereby photoelectric conversion efficiency (PCE). The optimum PCEs of the [Ni(NH3)6]F2‐ or [Co(NH3)6]F2‐treated PSCs reach 22.58% and 22.47%, respectively, which are much higher than that of the unmodified PSC (20.21%). This study provides a direction for improving the performance of a planar PSC using a complex‐modified ETL. [ABSTRACT FROM AUTHOR]