科研成果

Lead-based organic-inorganic hybrid perovskite materials are widely used in optoelectronic devices due to their excellent photophysical properties. However, the main issues which hinder its commercialization are the toxicity caused by lead and the intrinsic instability of the material. Recently, many lead-free halide materials with good intrinsic stability have been reported, among which bismuth-based halide materials have attracted extensive research due to their structure and promising optoelectronic properties. In this review, bismuth-based materials are divided into binary BiX3 (X = I, Br, Cl), ternary A(a)Bi(b)X(a)(+3)(b) (A = Cs, Rb, MA, Ag, etc.), and quaternary A(2)AgBiX(6) (A = Cs, Rb, MA, etc.) according to its elemental composition. The structure and optoelectronic properties of bismuth-based halide materials, which may be helpful for the development of bismuth-based halide materials and lead-free perovskites in the future, are summarized and highlighted.

Automatic transistor sizing is a challenging problem in circuit design due to the large design space, complex performance tradeoffs, and fast technology advancements. Although there have been plenty of work on transistor sizing targeting on one circuit, limited research has been done on transferring the knowledge from one circuit to another to reduce the re-design overhead. In this paper, we present GCN-RL Circuit Designer, leveraging reinforcement learning (RL) to transfer the knowledge between different technology nodes and topologies. Moreover, inspired by the simple fact that circuit is a graph, we learn on the circuit topology representation with graph convolutional neural networks (GCN). The GCN-RL agent extracts features of the topology graph whose vertices are transistors, edges are wires. Our learning-based optimization consistently achieves the highest Figures of Merit (FoM) on four different circuits compared with conventional black box optimization methods (Bayesian Optimization, Evolutionary Algorithms), random search and human expert designs. Experiments on transfer learning between five technology nodes and two circuit topologies demonstrate that RL with transfer learning can achieve much higher FoMs than methods without knowledge transfer. Our transferable optimization method makes transistor sizing and design porting more effective and efficient.

High-efficiency perovskite solar cells (PSCs) have experienced rapid development and attracted significant attention in recent years. The PSCs based on doctor bladed or slot-die coated perovskite films usually have lower power conversion efficiency (PCE) than that based on spin-coated perovskite films. In this work, we have developed an effective method, called glass rod-sliding and low pressure assisted solution processing composition engineering (GRS-LPASP), to manufacture high quality perovskite film in air. GRS-LPASP composition engineering effectively increases the grain size and thickness of perovskite films and reduces the defect density by increasing the contact area between the perovskite layer and the hole transport layer, thus leading an increased current density (Jsc) of perovskite solar cells. The device with GRS-LPASP composition engineering achieves a maximum PCE of 19.78%. The experimental results demonstrates that GRS-LPASP composition engineering is a feasible method to prepare high-efficiency PSCs. Moreover, GRS-LPASP composition engineering also provides a potential approach for the commercial production of PSCs.

The explosion of time series data in recent years has brought a flourish of new time series analysis methods, for forecasting, clustering, classification and other tasks. The evaluation of these new methods requires either collecting or simulating a diverse set of time series benchmarking data to enable reliable comparisons against alternative approaches. We propose GeneRAting TIme Series with diverse and controllable characteristics, named GRATIS, with the use of mixture autoregressive (MAR) models. We simulate sets of time series using MAR models and investigate the diversity and coverage of the generated time series in a time series feature space. By tuning the parameters of the MAR models, GRATIS is also able to efficiently generate new time series with controllable features. In general, as a costless surrogate to the traditional data collection approach, GRATIS can be used as an evaluation tool for tasks such as time series forecasting and classification. We illustrate the usefulness of our time series generation process through a time series forecasting application.

It is of great significance to evaluate the heavy metal pollution of soil in the water source protection area. Taking Miyun Reservoir water source protection area as the research object,223 soil surface samples were collected from Miyun Reservoir water source protection area,and the contents of 8 heavy metals (Pb,Ni,Cu,Cr,Zn,Cd,Hg and As) were determined. The results showed that the average content of Hg and As in the overall study area were lower than the background value of Beijing soil,and the average content of the other 6 heavy metals were higher than the background value of Beijing soil. Soil heavy metal pollution degree and potential ecological risk were evaluated by the geo-accumulation index and potential ecological risk index,and the possible sources of heavy metals were analyzed. The standard rate of soil heavy metals in the study area was about 83%,and the risk of agricultural soil pollution was low. Cr and Cd in the study area were in the state of no-medium pollution,and the other 6 heavy metal elements were in the state of no pollution. The ecological hazard of Cd was moderate,and the other 7 heavy metal elements were slight,and the overall ecological hazards of the study area was moderate. Based on the analysis of heavy metal pollution and ecological risk in Miyun Reservoir water source protection area,reference for the prevention and control of heavy metal in soil and the protection of water source area in Beijing water source protection area were provided.评价水源保护区土壤重金属污染状况具有重要意义。以密云水库水源保护区为研究对象,采集223个表层土壤样品,测定了Pb、Ni、Cu、Cr、Zn、Cd 、Hg和As 8种重金属的含量。结果表明,整个研究区Hg、As的平均含量低于北京市土壤背景值,其余6种重金属平均含量均高于北京市土壤背景值。采用地累积指数法和 潜在生态风险指数法评估土壤重金属污染程度和潜在生态风险,并分析了重金属的可能来源。研究区内土壤重金属达标率约83%,农用地土壤污染风险低。研究区 Cr和Cd处于无-中污染状态,其余6种重金属元素处于无污染状态;Cd生态危害中等,其余7种重金属生态危害轻微,研究区整体生态危害中等。分析密云水 库水源保护区土壤重金属污染及其生态风险,能为北京市水源保护区土壤重金属污染防治与水源地保护提供参考依据。