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Solar-thermal energy storage using latent heat of phase change materials (PCMs) offers renewable penetration in wide range of smart applications. The limiting solar energy harvesting efficiency of existing photo-thermal dopant materials and their negative impact on thermal storage capacity have remained fundamental impediment to further advancement. Herein, we explore a phosphorene based bandgap engineered broadband photonic nanoheater for efficient thermal charging of solid-solid PCMs. In our devised composite system, we benefitted from the synergistic effect of highly efficient photonic energy harvesting characteristic of exfoliated phosphorene nanoflakes (PNF) and latent heat storage capability as well as encapsulating feature of PCM matrix. The solar-thermal energy storage efficiency of our developed materials exceeds 95 % even at lower phosphorene doping level (1 wt. %) and under full solar spectrum with improved latent heat storage capacity (150 J g−1). The achieved efficiency is highest among all photo-thermal storage materials and attributed to the intense and broadband solar absorbance of PNF featured by wide thickness distribution. Further, coating of developed solar heat storage material on fabric substrate has shown promising results toward real world applications.

Background The coronavirus disease 2019 (COVID-19) has become a pandemic. Few studies have been conducted to investigate the spatio-temporal distribution of COVID-19 on nationwide city-level in China. Objective To analyze and visualize the spatiotemporal distribution characteristics and clustering pattern of COVID-19 cases from 362 cities of 31 provinces, municipalities and autonomous regions in mainland China. Methods A spatiotemporal statistical analysis of COVID-19 cases was carried out by collecting the confirmed COVID-19 cases in mainland China from January 10, 2020 to October 5, 2020. Methods including statistical charts, hotspot analysis, spatial autocorrelation, and Poisson space–time scan statistic were conducted. Results The high incidence stage of China’s COVID-19 epidemic was from January 17 to February 9, 2020 with daily increase rate greater than 7.5%. The hot spot analysis suggested that the cities including Wuhan, Huangshi, Ezhou, Xiaogan, Jingzhou, Huanggang, Xianning, and Xiantao, were the hot spots with statistical significance. Spatial autocorrelation analysis indicated a moderately correlated pattern of spatial clustering of COVID-19 cases across China in the early phase, with Moran’s I statistic reaching maximum value on January 31, at 0.235 (Z = 12.344, P = 0.001), but the spatial correlation gradually decreased later and showed a discrete trend to a random distribution. Considering both space and time, 19 statistically significant clusters were identified. 63.16% of the clusters occurred from January to February. Larger clusters were located in central and southern China. The most likely cluster (RR = 845.01, P Conclusions Spatio-temporal cluster detection plays a vital role in the exploration of epidemic evolution and early warning of disease outbreaks and recurrences. This study can provide scientific reference for the allocation of medical resources and monitoring potential rebound of the COVID-19 epidemic in China.

Additional file 2: Fig. S1. Gi* Cluster Map of COVID-19 incidence in mainland China. Fig. S2. Spatio-temporal distribution of the incidence of COVID-19 in mainland China.

Additional file 1: The list of research cities.

Low-cost nickels can be used as cocatalyst to improve the performance of photocatalysts, which may be promising materials applied in the field of photocatalytic water splitting. In this study, different nickel species Ni, Ni(OH)2, NiO, NiOx, and NiS are used to modified titanium dioxide (P25) to investigate their roles on the photocatalytic hydrogen evolution activities. UV-visible, X-ray diffraction (XRD), Brunner-Emmet-Teller (BET) measurements, transmission electron microscopy (TEM), X-ray photoelectron spectroscopy (XPS) analysis etc. are employed to characterize the physical and chemical properties of catalysts. The results indicate that all the nickel species can improve the photocatalytic hydrogen production activity of P25. The P25 modified with NiOx and NiS has more superior photocatalytic hydrogen evolution activities than those modified with other nickel species. The reason for this is that NiOx and NiS can form p-n junctions with P25 respectively. In addition, NiOx can be selectively deposited on the active sites of P25 via in situ the photodeposition method and NiS is beneficial for H+ reacting with photo-excited electrons.

BackgroundClinical named entity recognition (CNER) is important for medical information mining and establishment of high-quality knowledge map. Due to the different text features from natural language and a large number of professional and uncommon clinical terms in Chinese electronic medical records (EMRs), there are still many difficulties in clinical named entity recognition of Chinese EMRs. It is of great importance to eliminate semantic interference and improve the ability of autonomous learning of internal features of the model under the small training corpus.MethodsFrom the perspective of deep learning, we integrated the attention mechanism into neural network, and proposed an improved clinical named entity recognition method for Chinese electronic medical records called BiLSTM-Att-CRF, which could capture more useful information of the context and avoid the problem of missing information caused by long-distance factors. In addition, medical dictionaries and part-of-speech (POS) features were also introduced to improve the performance of the model.ResultsBased on China Conference on Knowledge Graph and Semantic Computing (CCKS) 2017 and 2018 Chinese EMRs corpus, our BiLSTM-Att-CRF model finally achieved better performance than other widely-used models without additional features(F1-measure of 85.4% in CCKS 2018, F1-measure of 90.29% in CCKS 2017), and achieved the best performance with POS and dictionary features (F1-measure of 86.11% in CCKS 2018, F1-measure of 90.48% in CCKS 2017). In particular, the BiLSTM-Att-CRF model had significant effect on the improvement of Recall.ConclusionsOur work preliminarily confirmed the validity of attention mechanism in discovering key information and mining text features, which might provide useful ideas for future research in clinical named entity recognition of Chinese electronic medical records. In the future, we will explore the deeper application of attention mechanism in neural network.