Investigating the Impact of Shading Effect on the Characteristics of a Large-Scale Grid-Connected PV Power Plant in Northwest China

Northwest China is an ideal region for large-scale grid-connected PV system installation due to its abundant solar radiation and vast areas. For grid-connected PV systems in this region, one of the key issues is how to reduce the shading effect as much as possible to maximize their power generation. In this paper, a shading simulation model for PV modules is established and its reliability is verified under the standard testing condition (STC) in laboratory. Based on the investigation result of a 20 MWp grid-connected PV plant in northwest China, the typical shading phenomena are classified and analyzed individually, such as power distribution buildings shading and wire poles shading, plants and birds droppings shading, and front-row PV arrays shading. A series of experiments is also conducted on-site to evaluate and compare the impacts of different typical shading forms. Finally, some feasible solutions are proposed to avoid or reduce the shading effect of PV system during operation in such region

Poly(delta-gluconolactone) and Poly(delta-gluconolactone- ε

Poly(delta-gluconolactone) (PGL) and poly(delta-gluconolactone-ε-caprolactone) (P(GL-CL)) were synthesized through ring-opening polymerization (ROP) and characterized by FT-IR, NMR, XRD, intrinsic viscosity, GPC, DSC, and TGA. The crystallinity of P(GL-CL) with various d-GL/CL ratios (d-GL/CL = 5 : 5, 4 : 6, 3 : 7, 2 : 8, and 1 : 9) was 12.09 to 59.78% while PGL was amorphous. Melting temperature (Tm) of these polymers was 49.8 to 62.0°C and decomposition temperature was 282 to 489°C depending on the d-GL/CL ratios. In addition, all these polymers were degradable and the degradation rates could be controlled by adjusting d-GL/CL ratios. These results indicated that PGL and P(GL-CL) might be promising novel absorbable materials

CMTCN: a web tool for investigating cancer-specific microRNA and transcription factor co-regulatory networks

Transcription factors (TFs) and microRNAs (miRNAs) are well-characterized trans-acting essential players in gene expression regulation. Growing evidence indicates that TFs and miRNAs can work cooperatively, and their dysregulation has been associated with many diseases including cancer. A unified picture of regulatory interactions of these regulators and their joint target genes would shed light on cancer studies. Although online resources developed to support probing of TF-gene and miRNA-gene interactions are available, online applications for miRNA-TF co-regulatory analysis, especially with a focus on cancers, are lacking. In light of this, we developed a web tool, namely CMTCN (freely available at http://www.cbportal.org/CMTCN), which constructs miRNA-TF co-regulatory networks and conducts comprehensive analyses within the context of particular cancer types. With its user-friendly provision of topological and functional analyses, CMTCN promises to be a reliable and indispensable web tool for biomedical studies

Predicting Benefit From Immune Checkpoint Inhibitors in Patients With Non-Small-Cell Lung Cancer by CT-Based Ensemble Deep Learning: A Retrospective Study

BACKGROUND: Only around 20-30% of patients with non-small-cell lung cancer (NCSLC) have durable benefit from immune-checkpoint inhibitors. Although tissue-based biomarkers (eg, PD-L1) are limited by suboptimal performance, tissue availability, and tumour heterogeneity, radiographic images might holistically capture the underlying cancer biology. We aimed to investigate the application of deep learning on chest CT scans to derive an imaging signature of response to immune checkpoint inhibitors and evaluate its added value in the clinical context. METHODS: In this retrospective modelling study, 976 patients with metastatic, EGFR/ALK negative NSCLC treated with immune checkpoint inhibitors at MD Anderson and Stanford were enrolled from Jan 1, 2014, to Feb 29, 2020. We built and tested an ensemble deep learning model on pretreatment CTs (Deep-CT) to predict overall survival and progression-free survival after treatment with immune checkpoint inhibitors. We also evaluated the added predictive value of the Deep-CT model in the context of existing clinicopathological and radiological metrics. FINDINGS: Our Deep-CT model demonstrated robust stratification of patient survival of the MD Anderson testing set, which was validated in the external Stanford set. The performance of the Deep-CT model remained significant on subgroup analyses stratified by PD-L1, histology, age, sex, and race. In univariate analysis, Deep-CT outperformed the conventional risk factors, including histology, smoking status, and PD-L1 expression, and remained an independent predictor after multivariate adjustment. Integrating the Deep-CT model with conventional risk factors demonstrated significantly improved prediction performance, with overall survival C-index increases from 0·70 (clinical model) to 0·75 (composite model) during testing. On the other hand, the deep learning risk scores correlated with some radiomics features, but radiomics alone could not reach the performance level of deep learning, indicating that the deep learning model effectively captured additional imaging patterns beyond known radiomics features. INTERPRETATION: This proof-of-concept study shows that automated profiling of radiographic scans through deep learning can provide orthogonal information independent of existing clinicopathological biomarkers, bringing the goal of precision immunotherapy for patients with NSCLC closer

RF Magnetron Sputtering Aluminum Oxide Film for Surface Passivation on Crystalline Silicon Wafers

Aluminum oxide films were deposited on crystalline silicon substrates by reactive RF magnetron sputtering. The influences of the deposition parameters on the surface passivation, surface damage, optical properties, and composition of the films have been investigated. It is found that proper sputtering power and uniform magnetic field reduced the surface damage from the high-energy ion bombardment to the silicon wafers during the process and consequently decreased the interface trap density, resulting in the good surface passivation; relatively high refractive index of aluminum oxide film is benefic to improve the surface passivation. The negative-charged aluminum oxide film was then successfully prepared. The surface passivation performance was further improved after postannealing by formation of an SiOx interfacial layer. It is demonstrated that the reactive sputtering is an effective technique of fabricating aluminum oxide surface passivation film for low-cost high-efficiency crystalline silicon solar cells

Quantifying Efficiency Limitations in All‐Inorganic Halide Perovskite Solar Cells

While halide perovskites have excellent optoelectronic properties, their poor stability is a major obstacle toward commercialization. There is a strong interest to move away from organic A-site cations such as methylammonium and formamidinium toward Cs with the aim of improving thermal stability of the perovskite layers. While the optoelectronic properties and the device performance of Cs-based all-inorganic lead-halide perovskites are very good, they are still trailing behind those of perovskites that use organic cations. Here, the state-of-the-art of all-inorganic perovskites for photovoltaic applications is reviewed by performing detailed meta-analyses of key performance parameters on the cell and material level. Key material properties such as carrier mobilities, external photoluminescence quantum efficiency, and photoluminescence lifetime are discussed and what is known about defect tolerance in all-inorganic is compared relative to hybrid (organic–inorganic) perovskites. Subsequently, a unified approach is adopted for analyzing performance losses in perovskite solar cells based on breaking down the losses into several figures of merit representing recombination losses, resistive losses, and optical losses. Based on this detailed loss analysis, guidelines are eventually developed for future performance improvement of all-inorganic perovskite solar cells

Parameters extraction from commercial solar cells I-V characteristics and shunt analysis

In this paper, an optimized method on the basis of polynomial fitting and Lambert W function is presented to extract parameters from the current-voltage (I-V) characteristics of commercial silicon solar cells. Since the experimental outcomes have significant impact on the precision of extracted parameters, polynomial fitting serves to overcome the obstacles of measurement noise in this method. The Lambert W function is employed to translate the transcendental equation into explicit analytical solution. Comparing with the as-reported parameters of a silicon cell and a plastic cell in the previous literature, the interesting outcomes demonstrate that the proposed approach is helpful for obtaining precise extracted data. This is further showed by the good agreements between the fitted I-V curve and the experimental results of a commercial monocrystalline silicon solar cell. Moreover, full extracted parameters of a badly shunted multicrystalline silicon solar cells before and after laser isolation process are conducted and investigated, the good fitting results finally show the validity of this attempt again.I-V characteristics Lambert W function Polynomial curve fitting Shunt

Ultra-Scratch-Resistant, Hydrophobic and Transparent Organosilicon-Epoxy-Resin Coating with a Double Cross-Link Structure

In this paper, an ultra-scratch-resistant, hydrophobic and transparent coating was fabricated by the sol–gel method using (3-Glycidyloxypropyl) triethoxysilane (GPTES) and curing agents. When the silanol was condensated, the ring-opening reaction of the epoxy groups also took place, which formed a double-cross-linked network (Si–O–Si and R3N). This network structure restricted the molecule chains from being twisted or dislocated, resulting in a great improvement of the abrasion resistance of the coating. A pencil hardness grade up to 8H was obtained. The coating also showed excellent stability after being soaked in pH = 2 and pH = 12 solutions, seawater and acetone, respectively. In addition, a water contact angle of 121° was obtained by post-treatment with hexamethyldisilazane (HMDS). The average transmittance of the coating reached to 90% in the wavelength range of 400~800 nm, nearly identical to the glass substrate. With multiple desirable properties and a simple fabrication process, this low-cost coating shows great potential in many practical applications