Graphene (G) films were grown on copper foils by chemical vapor deposition and transferred onto n-type silicon (Si) to form G/Si Schottky heterojunction solar cells. The power conversion efficiencies (PCEs) of the G/Si solar cells were in the range of 1.94-2.66%. Four volatile oxidants HNO 3 , HCl, H 2 O 2 and SOCl 2 were employed to treat the graphene films in the G/Si solar cells, and the PCEs could be greatly enhanced after being treated by all the volatile oxidants and SOCl 2 doping showed the best improvement. A solar cell with an initial PCE of 2.45% could be increased to 5.95% upon SOCl 2 doping treatment. The PCE stability of the volatile oxidant-treated cells was also investigated. The PCEs decreased with time, while SOCl 2 and HCl showed much better PCE stability than HNO 3 and H 2 O 2 .

The effect of reaction temperature on the synthesis of graphitic thin film on nickel substrate was investigated in the range of 400°C to 1,000°C. Amorphous carbon (a-C) film was obtained at 400°C on nickel foils by chemical vapor deposition; hybrid films of multilayer graphene (MLG) and a-C were synthesized at a temperature of 600°C, while MLG was obtained at temperatures in excess of 800°C. Schottky-junction solar cell devices prepared using films produced at 400°C, 600°C, 800°C, and 1,000°C coupled with n-type Si demonstrate power conversion efficiencies of 0.003%, 0.256%, 0.391%, and 0.586%, respectively. A HNO3 treatment has further improved the efficiencies of the corresponding devices to 0.004%, 1.080%, 0.800%, and 0.820%, respectively. These films are promising materials for application in low-cost and simple carbon-based solar cells.

Ordinary least square (OLS) estimation of a linear regression model is well-known to be highly sensitive to outliers. It is common practice to (1) identify and remove outliers by looking at the data and (2) to fit OLS and form confidence intervals and p-values on the remaining data as if this were the original data collected. This standard "detect-and-forget" approach has been shown to be problematic, and in this paper we highlight the fact that it can lead to invalid inference and show how recently developed tools in selective inference can be used to properly account for outlier detection and removal. Our inferential procedures apply to a general class of outlier removal procedures that includes several of the most commonly used approaches. We conduct simulations to corroborate the theoretical results, and we apply our method to three real data sets to illustrate how our inferential results can differ from the traditional detect-and-forget strategy. A companion R package, outference, implements these new procedures with an interface that matches the functions commonly used for inference with lm in R.

Interfacial instability between solid electrolytes (SEs) and lithium metal remains a daunting challenge for solid-sate batteries. Here, a conformal C 60 interlayer is efficiently constructed on Li 1.5 Al 0.5 Ge 1.5 (PO 4) 3 (LAGP) SEs by physical vapor deposition, and an ideal interfacial contact is achieved via forming an ionically conducting matrix of Li x C 60 with lithium metal. The obtained Li x C 60 is beneficial to hinder the growth of lithium dendrites at interface and release the local stress during the lithiation and delithiation. As a result, the Li/LAGP-C 60 /Li symmetric cells demonstrate ultra-stable cycling performance for more than 4,500 h at a current density of 0.034 mA cm À2. The Li/LAGP-C 60 /LiFePO 4 full cells deliver a reversible capacity of 152.4 mAh g À1 at room temperature, and the capacity retention rate is 85% after more than 100 cycles. This work provides a feasible and scalable strategy to improve the SEs/Li interface for high-performance solid-state batteries.

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Prostate cancer (PCa), known as a heterogenous disease, has a high incidence and mortality rate around the world and seriously threatens public health. As an inevitable by-product of cellular metabolism, reactive oxygen species (ROS) exhibit beneficial effects by regulating signaling cascades and homeostasis. More and more evidence highlights that PCa is closely associated with age, and high levels of ROS are driven through activation of several signaling pathways with age, which facilitate the initiation, development, and progression of PCa. Nevertheless, excessive amounts of ROS result in harmful effects, such as genotoxicity and cell death. On the other hand, PCa cells adaptively upregulate antioxidant genes to detoxify from ROS, suggesting that a subtle balance of intracellular ROS levels is required for cancer cell functions. The current review discusses the generation and biological roles of ROS in PCa and provides new strategies based on the regulation of ROS for the treatment of PCa.

The ability to align individual cellular information from multiple experimental sources, techniques and systems is fundamental for a true systems-level understanding of biological processes. While single-cell transcriptomic studies have transformed our appreciation for the complexities and contributions of diverse cell types to disease, they can be limited in their ability to assess protein-level phenotypic information and beyond. Therefore, matching and integrating single-cell datasets which utilize robust protein measurements across multiple modalities is critical for a deeper understanding of cell states, and signaling pathways particularly within their native tissue context. Current available tools are mainly designed for single-cell transcriptomics matching and integration, and generally rely upon a large number of shared features across datasets for mutual Nearest Neighbor (mNN) matching. This approach is unsuitable when applied to single-cell proteomic datasets, due to the limited number of parameters simultaneously accessed, and lack of shared markers across these experiments. Here, we introduce a novel cell matching algorithm, Matching with pARtIal Overlap (MARIO), that takes into account both shared and distinct features, while consisting of vital filtering steps to avoid sub-optimal matching. MARIO accurately matches and integrates data from different single-cell proteomic and multi-modal methods, including spatial techniques, and has cross-species capabilities. MARIO robustly matched tissue macrophages identified from COVID-19 lung autopsies via CODEX imaging to macrophages recovered from COVID-19 bronchoalveolar lavage fluid via CITE-seq. This cross-platform integrative analysis enabled the identification of unique orchestrated immune responses within the lung of complement-expressing macrophages and their impact on the local tissue microenvironment. MARIO thus provides an analytical framework for unified analysis of single-cell data for a comprehensive understanding of the underlying biological system.