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The fundamental interplay between F4TCNQ and PTAA is systematically studied, which reveals that the π–π stacking formed between F4TCNQ and PTAA is favorable for improving the spatial configuration of PTAA and thus the charge transfer.

The derivatives of cinnamic acids are applied for the surface functionalization of inorganic perovskites for efficient and stable perovskite solar cells.

The interfacial properties of the perovskite photovoltaic layer and electron transport layer (ETL) are critical to minimize energy losses of perovskite solar cells (PSCs) induced by interfacial recombination. Herein, the interface engineering of the SnO2/FAPbI3 perovskite using PbX2 (X = Cl, Br, or I) as an interlayer is extensively studied using first-principles calculations. The results reveal that the thickness of the PbI2 interlayer needs to be finely controlled, which may limit charge transport if there is a large amount of PbI2 precipitation at the interface. The high lattice mismatch of the PbBr2 with the SnO2/FAPbI3 interface makes PbBr2 an unfavorable passivation material. Due to the strong coupling of the PbCl2 with both SnO2 and FAPbI3, an efficient electron transport pathway could be built after applying PbCl2 as an interlayer. Meanwhile, the PbCl2 interlayer could also effectively passivate interface defects, therefore lowering the energy losses of PSCs.

The interfacial recombination at the perovskite/hole conductor interface generally results in significant energy losses in inverted perovskite solar cells (PSCs) with a p–i–n device architecture. H...

et al., Large datasets are now ubiquitous as technology enables higher-throughput experiments, but rarely can a research field truly benefit from the research data generated due to inconsistent formatting, undocumented storage or improper dissemination. Here we extract all the meaningful device data from peer-reviewed papers on metal-halide perovskite solar cells published so far and make them available in a database. We collect data from over 42,400 photovoltaic devices with up to 100 parameters per device. We then develop open-source and accessible procedures to analyse the data, providing examples of insights that can be gleaned from the analysis of a large dataset. The database, graphics and analysis tools are made available to the community and will continue to evolve as an open-source initiative. This approach of extensively capturing the progress of an entire field, including sorting, interactive exploration and graphical representation of the data, will be applicable to many fields in materials science, engineering and biosciences., Open access funding provided by Helmholtz-Zentrum Berlin für Materialien und Energie GmbH., The core funding of the project has been received from the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 787289. We acknowledge the following sources for individual funding. Cambridge India Ramanujan Scholarship, China Scholarship Council, Deutscher Akademischer Austauschdienst (DAAD), EPSRC (grant no. EP/S009213/1), European Union’s Horizon 2020 research and innovation programme (grant no. 764787, EU Project ‘MAESTRO’), (grant no. 756962, ERC Project ‘HYPERION’), (grant no. 764047, EU Project ‘ESPResSo’ and grant no. 850937), GCRF/EPSRC SUNRISE (EP/P032591/1), German Federal Ministry for Education and Research (BMBF), HyPerFORME, NanoMatFutur (grant no. 03XP0091). PEROSEED (ZT-0024), Helmholtz Energy Materials Foundry, The Helmholtz Innovation Laboratory HySPRINT. BMBF (grant nos. 03SF0540, 03SF0557A), HyPerCells graduate school, Helmholtz Association, Helmholtz International Research School (HI-SCORE), the Erasmus programme (CDT-PV, grant no. EP/L01551X/1), the European Union’s Horizon 2020 research and innovation programme (Marie Skłodowska-Curie grant agreement nos. 841386, 795079 and 840751), Royal Society University Research Fellowship (grant no. UF150033). SNaPSHoTs (BMBF), SPARC II, German Research Foundation (DFG, grant no. SPP2196), The National Natural Science Foundation of China (grant no. 51872014), the Recruitment Programme of Global Experts, Fundamental Research Funds for the Central Universities and the ‘111’ project (grant no. B17002), the US Department of Energy’s Office of Energy Efficiency and Renewable Energy under Solar Energy Technologies Office (SETO) agreement no. DE-EE0008551, the Colombia Scientific Programme in the framework of the call Ecosistema Cientifíco (Contract no. FP44842-218-2018), the committee for the development of research (CODI) of the Universidad de Antioquia (grant no. 2017-16000), Spanish MINECO (Severo Ochoa programme, grant no. SEV‐2015‐0522), the Swedish research council (VR, grant no. 2019-05591) and the Swedish Energy Agency (grant no. 2020-005194).

Surface properties of colloidal quantum dots (CQDs) are critical for the transportation and recombination of the photoinduced charge carrier in CQD solar cells, therefore dominating the photovoltai ...

The interfacial properties between a perovskite layer and carbon electrode are critical for the photovoltaic performance of carbon electrode-based perovskite solar cells (PSCs). Herein, a methylammonium lead mixed halide (MAPbIxBr3−x) perovskite layer is in situ grown on the top of a methylammonium lead iodide (MAPbI3) perovskite layer forming a MAPbI3/MAPbIxBr3−x perovskite stacking structure (PSS) to improve the interfacial properties at the perovskite/carbon electrode interface. The charge carrier dynamics in both the perovskite and the PSC device induced by the MAPbIxBr3−x perovskite stacking layer are studied using extensive characterization. The charge interfacial recombination at the perovskite/carbon electrode interface is significantly diminished using the PSS within the PSC, resulting in largely improved charge extraction and therefore high photovoltaic performance. The PSS-based PSC shows a power conversion efficiency of up to 16.2% (increased by 43% compared with that of a conventional MAPbI3-based PSC), which is among the highest efficiencies of carbon electrode-based hole conductor free PSCs. Meanwhile, the PSS-based PSC also exhibits good stability under both continuous illumination and storage under dark conditions. This work may provide a new avenue to fine tune the interfacial properties of carbon electrode-based PSCs for further improving their photovoltaic performance.

Aiming at the low efficiency of the Big data query of the internet of Things, the research based on the smart urban management platform, and on the premise of retaining the original platform data integration framework. The query model and process are redesigned and a new data improved query model is proposed by introducing hash coding, distributed index and data dictionary. Compared with the one by one comparison query method, the speed of the improved query model is increased by 106 times when processing millions of data, which significantly improves the query efficiency of big data. The research results can help improve the data processing and analysis ability of Chongqing Liangjiang Smart Urban Management Platform in China.

In order to scientific manage the mine risk and safety events which under the supervision of the government regulatory departments and coal mining enterprises. The factors affecting coal mine safety risk are identified synthetically by AHP analysis method, and the index system of coal mine safety risk assessment is constructed from the aspects of personnel, equipment, environment, management, etc. It uses cloud computing, GIS, big data analysis and visual technology, which takes a basis of SOA technology architecture and data warehouse, that help realizes the analysis of the coal mine safety risk system, the analysis of the coal mine safety status, prevention of the coal mine safety accident and provides the reliable judgment basis for the auxiliary decision of the coal mine safety production. The system has been applied in many projects such as the National Mine Safety Production Supervision IoT application demonstration project and the coal mine accident risk analysis platform of Chongqing Coal Mine Safety Supervision Bureau, which improves the information regulate standard of coal mine using by national and regional authorities.

This paper proposes an interference avoidance scheme based on reinforcement learning for the communication stations exposed to interference. Firstly, we design the signal to interference noise ratio-mean opinion score (SINR-MOS) as the reward function to obtain optimal channel handoff strategy through reinforcement learning. Then, two specific methods of interference avoidance are proposed by applying Q-learning algorithm, which enables an automatic avoidance by switching to a new idle channel when the station is interfered. Finally, it is proved by computer simulation experiments that the methods have a low miss detection rate and false alarm rate, showing good interference avoidance performance.

This paper designs a spectrum handoff method based on reinforcement and transfer learning in a cognitive radio environment. In the context of secondary users adopting reinforcement learning to form a spectrum handoff strategy, transfer learning is used to increase the convergence speed of reinforcement learning for new users. First, the original secondary user completes reinforcement learning in a radio environment. Then, the original secondary users are considered as an expert user, and the Q table obtained through reinforcement learning is transferred to the newly arrived secondary users. Finally, the new users complete their own reinforcement learning based on the Q table. Through simulation experiments, comparing the reinforcement learning convergence process of new secondary users with and without transfer learning, it is found that transfer learning can significantly improve the convergence rate of new users.

The spectrum handoff is an important technique for modern communication especially in the field of cognitive radio networks (CRNs). Reinforcement learning maximizes the rewards in interaction with the environment by learning strategies and can be used to realize the automatic spectrum handoff. A Q-learning based spectrum handoff scheme with signal to interference plus noise ratio-mean opinion score (SINR-MOS) is proposed in this paper. Firstly, two different MOSs based on SINR of each channel are designed. Secondly, the spectrum handoff scheme is realized by Q-learning, a tipical reinforcement learning method, with the SINR-MOSs as the reward functions. Finally, some simulation experiments are designed and the experimental results demonstrate that the proposed scheme has a good convergence performance.