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Perovskite quantum dots (PQDs) are a competitive candidate for next-generation display technologies as a result of their superior photoluminescence, narrow emission, high quantum yield, and color tunability. However, due to poor thermal resistance and instability under high energy radiation, most PQD-based white light-emitting diodes (LEDs) show only modest luminous efficiency of ≈50 lm W−1 and a short lifetime of <100 h. In this study, by incorporating cellulose nanocrystals, a new type of QD film is fabricated: CH3NH3PbBr3 PQD paper that features 91% optical absorption, intense green light emission (518 nm), and excellent stability attributed to the complexation effect between the nanocellulose and PQDs. The PQD paper is combined with red K2SiF6:Mn4+ phosphor and blue GaN LED chips to fabricate a high-performance white LED demonstrating ultrahigh luminous efficiency (124 lm W−1), wide color gamut (123% of National Television System Committee), and long operation lifetime (240 h), which paves the way for advanced lighting technology.

© Institute of Mathematical Statistics, 2019. A fundamental assumption used in causal inference with observational data is that treatment assignment is ignorable given measured confounding variables. This assumption of no missing confounders is plausible if a large number of baseline covariates are included in the analysis, as we often have no prior knowledge of which variables can be important confounders. Thus, estimation of treatment effects with a large number of covariates has received considerable attention in recent years. Most existing methods require specifying certain parametric models involving the outcome, treatment and confounding variables, and employ a variable selection procedure to identify confounders. However, selection of a proper set of confounders depends on correct specification of the working models. The bias due to model misspecification and incorrect selection of confounding variables can yield misleading results. We propose a robust and efficient approach for inference about the average treatment effect via a flexible modeling strategy incorporating penalized variable selection. Specifically, we consider an estimator constructed based on an efficient influence function that involves a propensity score and an outcome regression. We then propose a new sparse sufficient dimension reduction method to estimate these two functions without making restrictive parametric modeling assumptions. The proposed estimator of the average treatment effect is asymptotically normal and semiparametrically efficient without the need for variable selection consistency. The proposed methods are illustrated via simulation studies and a biomedical application.

© Institute of Mathematical Statistics, 2019. A fundamental assumption used in causal inference with observational data is that treatment assignment is ignorable given measured confounding variables. This assumption of no missing confounders is plausible if a large number of baseline covariates are included in the analysis, as we often have no prior knowledge of which variables can be important confounders. Thus, estimation of treatment effects with a large number of covariates has received considerable attention in recent years. Most existing methods require specifying certain parametric models involving the outcome, treatment and confounding variables, and employ a variable selection procedure to identify confounders. However, selection of a proper set of confounders depends on correct specification of the working models. The bias due to model misspecification and incorrect selection of confounding variables can yield misleading results. We propose a robust and efficient approach for inference about the average treatment effect via a flexible modeling strategy incorporating penalized variable selection. Specifically, we consider an estimator constructed based on an efficient influence function that involves a propensity score and an outcome regression. We then propose a new sparse sufficient dimension reduction method to estimate these two functions without making restrictive parametric modeling assumptions. The proposed estimator of the average treatment effect is asymptotically normal and semiparametrically efficient without the need for variable selection consistency. The proposed methods are illustrated via simulation studies and a biomedical application.