Jiafan Yu

Plasmonic core-shell gold nanoparticle enhanced optical absorption in photovoltaic devices

The enhancement effect of optical absorption with core-shell goldnanoparticles on the surface of wafer-based siliconphotovoltaic devices has been studied. The obvious enhanced photocurrent is observed, especially when the wavelength is above 800 nm, and the highest enhancement value is about 14% around the wavelength of 1100 nm. The simulation results indicate that the presence of dielectric coating shell could improve the surface plasmon based scattering effect at longer wavelength range, which provides a good understanding of the experiment results.

Rapid thermal processing chamber for in-situ x-ray diffraction

Rapid thermal processing (RTP) is widely used for processing a variety of materials, including electronics and photovoltaics. Presently, optimization of RTP is done primarily based on ex-situ studies. As a consequence, the precise reaction pathways and phase progression during the RTP remain unclear. More awareness of the reaction pathways would better enable process optimization and foster increased adoption of RTP, which offers numerous advantages for synthesis of a broad range of materials systems. To achieve this, we have designed and developed a RTP instrument that enables real-time collection of X-ray diffraction data with intervals as short as 100 ms, while heating with ramp rates up to 100 °Cs(-1), and with a maximum operating temperature of 1200 °C. The system is portable and can be installed on a synchrotron beamline. The unique capabilities of this instrument are demonstrated with in-situ characterization of a Bi2O3-SiO2 glass frit obtained during heating with ramp rates 5 °C s(-1) and 100 °C s(-1), revealing numerous phase changes.

Plasmonic core-shell nanoparticle enhanced optical absorption in thin film organic solar cells

Utilizing plasmonic metal nanoparticles is considered as one of the promising methods for increasing the conversion efficiency in thin film organic solar cells. However, the bare metal nanoparticles may suffer from the energy loss introduced by themselves due to the recombination of electro-hole pairs. In this paper, the optical absorption enhancement of thin film organic solar cells with plasmonic metal-dielectric core-shell nano-particles in the active layer has been proposed and studied. It is expected that the metal core could increase the optical absorption, and consequently the conversion efficiency of thin film organic solar cells due to the localized surface plasmon based field enhancement effect, and meanwhile the dielectric shell could prevent the metal core becoming a new bulk recombination center of the light-induced excitons. Simulations are carried out by means of the finite element method in a three-dimensional model. The results show that the absorption enhancement up to 110% could be obtained when the active layer thickness is 30nm. And there is a largest thickness for the active layer, below which plasmonic metal-dielectric core-shell nanoparticles are available for increasing the light absorption of thin film organic solar cells. Then, some initial experiments have been done. The Au-citrate core-shell nanoparticles synthesized by the sodium citrate reduction method are deposited on the wafer-based silicon solar cells. And the obvious photocurrent enhancement has been observed.

Probabilistic estimation of the potentials of intervention-based demand side energy management

Successful enrollment of customers in intervention-based demand side energy management (DSM) programs, such as energy efficiency and installation of PV panels, depends on having accurate estimates of the benefits of these programs available and communicated to the customers. The program benefits may include long-term financial savings, and their contribution to managing supply and demand in transition to a sustainable grid. Among them, the most needed measure of benefit is the estimated energy saving for each individual after an intervention. Accurate estimates of energy savings for each individual customer are thus crucial for ensuring high program enrollment. In this paper, we formulate the problem of estimating energy savings to understand the potential benefits of enrolling customer in an intervention-based DSM program. Due to highly uncertain customer load and estimation of long-term (infinite time horizon) savings, traditional deterministic analysis approaches, such as load forecasting, will yield poor results. We propose a Gaussian Process (GP)-based approach capable of capturing uncertainty and adapting arbitrary data length for infinite time horizon estimation. This allows utilization of probability estimation to show customers their potential energy savings and resultant revenues for participating in an intervention program for a certain period of time. Such property is verified by highly accurate estimation results running on a set of customer AMI data obtained from Pacific Gas and Electric Company. The simulation results not only highlight the feasibility of the intervention concept, but also provide benefit potentials that could be used to persuade customers to enroll in energy efficiency programs.

Robust mapping rule estimation for power flow analysis in distribution grids

The increasing integration of distributed energy resources (DERs) calls for new monitoring and operational planning tools to ensure stability and sustainability in distribution grids. One idea is to use existing monitoring tools in transmission grids and some primary distribution grids. However, they usually depend on the knowledge of the system model, e.g., the topology and line parameters, which may be unavailable in primary and secondary distribution grids. Furthermore, a utility usually has limited modeling ability of active controllers as they may belong to a third party like residential customers. To solve the modeling problem in traditional power flow analysis, we propose a support vector regression (SVR) approach to reveal the mapping rules between different variables and recover useful variables based on historical data. We illustrate the advantages of using the SVR model over traditional regression method that finds line parameters in distribution grids. Specifically, the SVR model is robust enough to recover the mapping rules when the regression method fails. This happens when 1) there are measurement outliers, 2) there are active controllers, or 3) measurements are only available at some part of a distribution grid. We demonstrate the superior performance of our method through extensive numerical validation on different scales of distribution grids and IEEE test buses. Robustness of our method is observed.

Front contact metallization of Si solar cells: Insights from in-situ X-ray diffraction

The front contact metallization of Si solar cells begins with printing a mixture of an Ag powder, glass frit (mixture of metal oxides such as PbO, SiO2, B2O3, and Bi2O3) and an organic binder over the antireflection coating that is subsequently fired up to about 825 °C. It is known that the frit allows the paste to react with and burn through the anti-reflective coating such that the metal can react with the underlying c-Si during firing. However, the precise phase transformations between Ag, Si, SiNx, and the frit constituents, which happen within a few seconds during rapid thermal processing (RTP), giving rise to an Ag-Si contact, are not well understood in the absence of in-situ characterization under the actual processing conditions. We have carried out in-situ x-ray diffraction studies on sample mixtures of different component powders (Ag, sinks, PbO-fruit and Si) under realistic processing conditions using an in-situ rapid thermal processing setup. We track the phase progression and reaction pathways at a time resolution of 100 milliseconds. We show the direct evidence of SiNx oxidation by PbO between 600-750 °C. On subsequent heating to higher temperature, up to 825 °C, Ag dissolves into the frit etches the c-Si surface and is deposited on etch pits forming intimate electrical contacts.