A Semantic Analysis and Community Detection-Based Artificial Intelligence Model for Core Herb Discovery from the Literature: Taking Chronic Glomerulonephritis Treatment as a Case Study

The Traditional Chinese Medicine (TCM) formula is the main treatment method of TCM. A formula often contains multiple herbs where core herbs play a critical therapeutic effect for treating diseases. It is of great significance to find out the core herbs in formulae for providing evidences and references for the clinical application of Chinese herbs and formulae. In this paper, we propose a core herb discovery model CHDSC based on semantic analysis and community detection to discover the core herbs for treating a certain disease from large-scale literature, which includes three stages: corpus construction, herb network establishment, and core herb discovery. In CHDSC, two artificial intelligence modules are used, where the Chinese word embedding algorithm ESSP2VEC is designed to analyse the semantics of herbs in Chinese literature based on the stroke, structure, and pinyin features of Chinese characters, and the label propagation-based algorithm LILPA is adopted to detect herb communities and core herbs in the herbal semantic network constructed from large-scale literature. To validate the proposed model, we choose chronic glomerulonephritis (CGN) as an example, search 1126 articles about how to treat CGN in TCM from the China National Knowledge Infrastructure (CNKI), and apply CHDSC to analyse the collected literature. Experimental results reveal that CHDSC discovers three major herb communities and eighteen core herbs for treating different CGN syndromes with high accuracy. The community size, degree, and closeness centrality distributions of the herb network are analysed to mine the laws of core herbs. As a result, we can observe that core herbs mainly exist in the communities with more than 25 herbs. The degree and closeness centrality of core herb nodes concentrate on the range of [15, 40] and [0.25, 0.45], respectively. Thus, semantic analysis and community detection are helpful for mining effective core herbs for treating a certain disease from large-scale literature

Boosted efficiency of conductive metal oxide-free pervoskite solar cells using poly(3-(4-methylamincarboxylbutyl)thiophene) buffer layers

Owing to low work functions of transparent anodes and poor contact issues at interfaces, p-i-n conductive metal oxide (CMO)-free perovskite solar cells (PVSCs) commonly suffer from a limited power conversion efficiency. Herein, we report an efficient CMO-free PVSC using poly(3-(4-methylamincarboxylbutyl)thiophene) (P3CT-N) modified poly(3,4-ethylenedioxylenethiophene):poly(styrenesulfonate) (PEDOT:PSS) anodes. The contact angle between PEDOT:PSS anodes and P3CT-N buffer layers tend to be 0 degrees for an intimate contact. Meanwhile, the work function of the PEDOT:PSS anodes coated with P3CT-N is as high as -5.11 eV, which substantially accounted for the raised ability of hole transport. All the parameters (i.e. open-circuit voltage, short-circuit current density and fill factor) were improved simultaneously. As a result, the efficiency of the CMO-free solar cells was significantly improved from 4.63% to 13.13%. Our results indicate that P3CT-N is suitable to the highly conductive but hydrophobic PEDOT:PSS anodes for making high-efficiency CMO-free PVSCs

Vacuum-Free, All-Solution, and All-Air Processed Organic Photovoltaics with over 11% Efficiency and Promoted Stability Using Layer-by-Layer Codoped Polymeric Electrodes

Nonfullerene organic photovoltaics (OPVs) have achieved a breakthrough in pushing the efficiency beyond 15%. Although this sheds light on OPV commercialization, the high cost associated with the scalable device fabrications remains a giant challenge. Herein, a vacuum-free, all-solution and all-air processed OPV is reported that yields 11.12% efficiency with a fill factor of 0.725, due to the usages of high-merit polymeric electrodes and modified active blends. The design principle toward the high-merit electrodes is to induce heavy acid doping into the matrices for a raised carrier concentration and mobility, make a large removal of insulating components in the whole matrices rather than surfaces, and restrain the formation of large-domain aggregates. A unique layer-by-layer doping is developed to enable the polymeric electrodes with record-high trade-offs between optical transmittance and electrical conductivity. Moreover, solvent vapor annealing is proposed to boost device efficiency and it has the advantages of finely adjusting the active blend morphology and raising the electron mobility. The resulting devices are highly efficient and most (approximate to 91%) of the initial efficiency are maintained in 30 day storage. This work indicates bright future for making cost-effective all-solution processed OPVs in air