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Low donor content solar cells are an intriguing class of photovoltaic device about which there is still considerable discussion with respect to their mode of operation. We have synthesized a series of triphenylamine-based materials for use in low donor content devices with the electron accepting [6,6]-phenyl-C71-butyric acid methyl ester (PC(7)0BM). The triphenylamine-based materials absorb light in the near UV enabling the PC(7)0BM to be be the main light absorbing organic semiconducting material in the solar cell. It was found that the devices did not operate as classical Schottky junctions but rather photocurrent was generated by hole transfer from the photo-excited PC(7)0BM to the triphenylamine-based donors. We found that replacing the methoxy surface groups with methyl groups on the donor material led to a decrease in hole mobility for the neat films, which was due to the methyl substituted materials having the propensity to aggregate. The thermodynamic drive to aggregate was advantageous for the performance of the low donor content (6 wt%) films. It was found that the 6 wt% donor devices generally gave higher performance than devices containing 50 wt% of the donor.
Research synthesis on simple yet general hypotheses and ideas is challenging in scientific disciplines studying highly context-dependent systems such as medical, social, and biological sciences. This study shows that machine learning, equation-free statistical modeling of artificial intelligence, is a promising synthesis tool for discovering novel patterns and the source of controversy in a general hypothesis. We apply a decision tree algorithm, assuming that evidence from various contexts can be adequately integrated in a hierarchically nested structure. As a case study, we analyzed 163 articles that studied a prominent hypothesis in invasion biology, the enemy release hypothesis. We explored if any of the nine attributes that classify each study can differentiate conclusions as classification problem. Results corroborated that machine learning can be useful for research synthesis, as the algorithm could detect patterns that had been already focused in previous narrative reviews. Compared with the previous synthesis study that assessed the same evidence collection based on experts' judgement, the algorithm has newly proposed that the studies focusing on Asian regions mostly supported the hypothesis, suggesting that more detailed investigations in these regions can enhance our understanding of the hypothesis. We suggest that machine learning algorithms can be a promising synthesis tool especially where studies (a) reformulate a general hypothesis from different perspectives, (b) use different methods or variables, or (c) report insufficient information for conducting meta-analyses.
Radical reactions have found many applications in carbohydrate chemistry, especially in the construction of carbon–carbon bonds. The formation of carbon–heteroatom bonds has been less intensively studied. This mini-review will summarize the efforts to add heteroatom radicals to unsaturated carbohydrates like endo-glycals. Starting from early examples, developed more than 50 years ago, the importance of such reactions for carbohydrate chemistry and recent applications will be discussed. After a short introduction, the mini-review is divided in sub-chapters according to the heteroatoms halogen, nitrogen, phosphorus, and sulfur. The mechanisms of radical generation by chemical or photochemical processes and the subsequent reactions of the radicals at the 1-position will be discussed. This mini-review cannot cover all aspects of heteroatom-centered radicals in carbohydrate chemistry, but should provide an overview of the various strategies and future perspectives