Effect of molecular weight in diketopyrrolopyrrole‐based polymers in transistor and photovoltaic applications

Uncovering the precise effect of the conjugated polymer chain length on the semiconducting properties in thin‐film devices is confounded by the step‐growth polymerization techniques typically used. Here, we use preparatory size exclusion chromatography to isolate fractions of two diketopyrrolopyrrole‐thiophene based co‐polymers with low molar‐mass dispersity, ?_M, and number average molecular weights up to 180 kDa. We find that the charge carrier mobility can vary over three orders of magnitude in the range from 9 to 70 kDa, while a factor of 3–4 increase in photovoltaic performance was noted over the same range. The effect of ?_M was found to be most drastic when the largest chains were mixed with the shortest. The study of the thin‐film morphology and crystallinity by GIWAXS give further insights into the origin of these effects. © 2016 Wiley Periodicals, Inc. J. Polym. Sci., Part B: Polym. Phys. 2016 , 54, 2245–2253     

Cellulose nanocrystals from native and mercerized cotton

Cellulose - Nanocelluloses occur under various crystalline forms that are currently being selectively used for a wide variety of high performance materials. In the present study, two cellulose...     

Targeted smoothing parameter selection for estimating average causal effects

The non-parametric estimation of average causal effects in observational studies often relies on controlling for confounding covariates through smoothing regression methods such as kernel, splines or local polynomial regression. Such regression methods are tuned via smoothing parameters which regulates the amount of degrees of freedom used in the fit. In this paper we propose data-driven methods for selecting smoothing parameters when the targeted parameter is an average causal effect. For this purpose, we propose to estimate the exact expression of the mean squared error of the estimators. Asymptotic approximations indicate that the smoothing parameters minimizing this mean squared error converges to zero faster than the optimal smoothing parameter for the estimation of the regression functions. In a simulation study we show that the proposed data-driven methods for selecting the smoothing parameters yield lower empirical mean squared error than other methods available such as, e.g., cross-validation.