Wei Zhang

Bio: Wei Zhang is an academic researcher from Chinese Academy of Sciences. The author has contributed to research in topics: Amorphous metal & Alloy. The author has an hindex of 96, co-authored 1404 publications receiving 43392 citations. Previous affiliations of Wei Zhang include University of Surrey & Southern Medical University.

Anomalous hysteresis in perovskite solar cells

Abstract: Perovskite solar cells have rapidly risen to the forefront of emerging photovoltaic technologies, exhibiting rapidly rising efficiencies. This is likely to continue to rise, but in the development of these solar cells there are unusual characteristics that have arisen, specifically an anomalous hysteresis in the current–voltage curves. We identify this phenomenon and show some examples of factors that make the hysteresis more or less extreme. We also demonstrate stabilized power output under working conditions and suggest that this is a useful parameter to present, alongside the current-voltage scan derived power conversion efficiency. We hypothesize three possible origins of the effect and discuss its implications on device efficiency and future research directions. Understanding and resolving the hysteresis is essential for further progress and is likely to lead to a further step improvement in performance.

Enhanced photovoltage for inverted planar heterojunction perovskite solar cells

Abstract: The highest power conversion efficiencies (PCEs) reported for perovskite solar cells (PSCs) with inverted planar structures are still inferior to those of PSCs with regular structures, mainly because of lower open-circuit voltages (Voc). Here we report a strategy to reduce nonradiative recombination for the inverted devices, based on a simple solution-processed secondary growth technique. This approach produces a wider bandgap top layer and a more n-type perovskite film, which mitigates nonradiative recombination, leading to an increase in Voc by up to 100 millivolts. We achieved a high Voc of 1.21 volts without sacrificing photocurrent, corresponding to a voltage deficit of 0.41 volts at a bandgap of 1.62 electron volts. This improvement led to a stabilized power output approaching 21% at the maximum power point.

Ultrasmooth organic-inorganic perovskite thin-film formation and crystallization for efficient planar heterojunction solar cells.

Abstract: To date, there have been a plethora of reports on different means to fabricate organic-inorganic metal halide perovskite thin films; however, the inorganic starting materials have been limited to halide-based anions. Here we study the role of the anions in the perovskite solution and their influence upon perovskite crystal growth, film formation and device performance. We find that by using a non-halide lead source (lead acetate) instead of lead chloride or iodide, the perovskite crystal growth is much faster, which allows us to obtain ultrasmooth and almost pinhole-free perovskite films by a simple one-step solution coating with only a few minutes annealing. This synthesis leads to improved device performance in planar heterojunction architectures and answers a critical question as to the role of the anion and excess organic component during crystallization. Our work paves the way to tune the crystal growth kinetics by simple chemistry.

Photo-induced halide redistribution in organic–inorganic perovskite films

Abstract: Organic-inorganic perovskites such as CH3NH3PbI3 are promising materials for a variety of optoelectronic applications, with certified power conversion efficiencies in solar cells already exceeding 21%. Nevertheless, state-of-the-art films still contain performance-limiting non-radiative recombination sites and exhibit a range of complex dynamic phenomena under illumination that remain poorly understood. Here we use a unique combination of confocal photoluminescence (PL) microscopy and chemical imaging to correlate the local changes in photophysics with composition in CH3NH3PbI3 films under illumination. We demonstrate that the photo-induced 'brightening' of the perovskite PL can be attributed to an order-of-magnitude reduction in trap state density. By imaging the same regions with time-of-flight secondary-ion-mass spectrometry, we correlate this photobrightening with a net migration of iodine. Our work provides visual evidence for photo-induced halide migration in triiodide perovskites and reveals the complex interplay between charge carrier populations, electronic traps and mobile halides that collectively impact optoelectronic performance.

Fast parallel algorithms for short-range molecular dynamics

Abstract: Three parallel algorithms for classical molecular dynamics are presented. The first assigns each processor a fixed subset of atoms; the second assigns each a fixed subset of inter-atomic forces to compute; the third assigns each a fixed spatial region. The algorithms are suitable for molecular dynamics models which can be difficult to parallelize efficiently—those with short-range forces where the neighbors of each atom change rapidly. They can be implemented on any distributed-memory parallel machine which allows for message-passing of data between independently executing processors. The algorithms are tested on a standard Lennard-Jones benchmark problem for system sizes ranging from 500 to 100,000,000 atoms on several parallel supercomputers--the nCUBE 2, Intel iPSC/860 and Paragon, and Cray T3D. Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems. For large problems, the spatial algorithm achieves parallel efficiencies of 90% and a 1840-node Intel Paragon performs up to 165 faster than a single Cray C9O processor. Trade-offs between the three algorithms and guidelines for adapting them to more complex molecular dynamics simulations are also discussed.

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Pattern Recognition and Machine Learning

Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Dye-Sensitized Solar Cells

Abstract: Dye-sensitized solar cells (DSCs) offer the possibilities to design solar cells with a large flexibility in shape, color, and transparency. DSC research groups have been established around the worl ...

Electron-hole diffusion lengths exceeding 1 micrometer in an organometal trihalide perovskite absorber.

Abstract: Organic-inorganic perovskites have shown promise as high-performance absorbers in solar cells, first as a coating on a mesoporous metal oxide scaffold and more recently as a solid layer in planar heterojunction architectures. Here, we report transient absorption and photoluminescence-quenching measurements to determine the electron-hole diffusion lengths, diffusion constants, and lifetimes in mixed halide (CH3NH3PbI(3-x)Cl(x)) and triiodide (CH3NH3PbI3) perovskite absorbers. We found that the diffusion lengths are greater than 1 micrometer in the mixed halide perovskite, which is an order of magnitude greater than the absorption depth. In contrast, the triiodide absorber has electron-hole diffusion lengths of ~100 nanometers. These results justify the high efficiency of planar heterojunction perovskite solar cells and identify a critical parameter to optimize for future perovskite absorber development.