Rabbit Anti-Human LPP Polyclonal antibody

Light absorption plays an important role in improving the power conversion efficiency (PCE) of quantum dot-sensitized solar cells (QDSSCs). In this study, a multifunctional long-persistence phosphor (LPP) layer was introduced into the CdS/CdSe QDSSCs via a simple doctor blade method. The LPP layer can simultaneously improve the light harvesting and photo charge transfer in CdS/CdSe QDSSCs. As a result, their short-circuit current and corresponding PCE are effectively enhanced. The PCE can reach up to 5.07%, which is about 24% larger than that of the conventional CdS/CdSe QDSSCs without LPP layer. The solar cells can work in dark for a while due to the long-lasting fluorescence of the LPP layer. This research provides an effective way to improve the PCE of QDSSCs, and finds the possibility for all-weather QDSSCs.

Isotonic separation (IS) is a non-parametric classification technique which constructs an isotonic function from ordered data. The rationale is to convert partially isotonic data into isotonic using a linear programming problem (LPP) and partition the input space into isotonic and non-isotonic regions to make predictions easier. Despite the widespread applications of IS in diverse domains where monotonicity exists, it has certain limitations: Firstly, computing time and the constraints of the LPP in isotonic separation increase polynomially as size of the data increases and it is highly complex to solve the LPP and obtain the model on large data sets. In order to support dynamic stream data and address the computational overhead and size of the LPP issues, this paper proposes an online isotonic separation algorithm called Cascade-IS (CIS) for binary classification. The rationale behind CIS is that it splits the data set into a sequence of partitions and models are obtained and combined in cascade. Statistical and experimental analysis are done on datasets with isotonic properties and the results prove that CIS is superior to its variants in terms of training time, performance measures and number of constraints in the LPP. (C) 2020 Elsevier Ltd. All rights reserved.