Porphyrins Containing a Triphenylamine Donor and up to Eight Alkoxy Chains for Dye-Sensitized Solar Cells: A High Efficiency of 10.9%

Porphyrins are promising DSSC sensitizers due to their structural similarity to chlorophylls as well as their tunable strong absorption. Herein, a novel D−π–A porphyrin dye <b>XW14</b> containing a strongly electron-donating triphenylamine moiety as the electron donor was designed and synthesized. To avoid undesirably decreased <i>V</i>_oc caused by dye aggregation effect, two methoxy or hexyloxy chains were introduced to the <i>para</i> positions of the triphenylamine moiety to afford <b>XW15</b> and <b>XW16</b>, respectively. To further extend the absorption to a longer wavelength, a benzothiadiazole unit was introduced as an auxiliary acceptor to furnish <b>XW17</b>. Compared with <b>XW14</b>, the introduction of additional methoxy or hexyloxy groups in <b>XW15</b> and <b>XW16</b> red-shift the onset wavelengths from 760 to 780 and 790 nm, respectively. More impressively, <b>XW17</b> has a more extended π-conjugation framework, and thus, it exhibits a much broader IPCE spectrum with an extremely red-shifted onset wavelength of 830 nm, resulting in the highest <i>J</i>_sc (18.79 mA cm^–2). On the other hand, the hexyloxy chains are favorable for suppressing the dye aggregation effect, and thus <b>XW16</b> shows the highest <i>V</i>_oc of 734 mV. As a result, <b>XW16</b> and <b>XW17</b> demonstrate photovoltaic efficiencies of 9.1 and 9.5%, respectively, higher than those of <b>XW14</b> (8.6%) and <b>XW15</b> (8.7%), and obviously higher than that of 7.94% for our previously reported dye, <b>XW4</b>. On the basis of optimized porphyrin dye <b>XW17</b>, we used a nonporphyrin dye with a high <i>V</i>_oc and strong absorption around 500 nm (<b>WS-5</b>) as the cosensitizer to improve the <i>V</i>_oc from 700 to 748 mV, with synergistical <i>J</i>_sc enhancement from 18.79 to 20.30 mA cm^–2. Thus, the efficiency was dramatically enhanced to 10.9%, which is among the highest efficiencies obtained for the DSSCs based on traditional iodine electrolyte. In addition, the DSSCs based on <b>XW17</b> + <b>WS-5</b> exhibit good photostability, which is beneficial for practical applications

Effects of Medium- and Long-Chain Triacylglycerols on Lipid Metabolism and Gut Microbiota Composition in C57BL/6J Mice

Obesity is related to an increasing risk of chronic diseases. Medium- and long-chain triacylglycerols (MLCT) have been recognized as a promising choice to reduce body weight. In this study, three MLCT with different contents of medium-chain fatty acids (MCFA) (10–30%, w/w) were prepared, and their effects on lipid metabolism and fecal gut microbiota composition of C57BL/6J mice were systematically investigated. MLCT with 30% (w/w) MCFA showed the best performance in decreasing body weight gain as well as optimizing serum lipid parameters and liver triacylglycerol content. The expression levels of genes encoding enzymes for fatty acid degradation increased markedly and expression levels of genes encoding enzymes for <i>de novo</i> fatty acid biosynthesis decreased significantly in the liver of mice treated with MLCT containing 30% (w/w) MCFA. Interestingly, the dietary intake of a high fat diet containing MLCT did significantly decrease the ratio of <i>Firmicutes</i> to <i>Bacteroidetes</i> and down-regulate the relative abundance of <i>Proteobacteria</i> that may attribute to weight loss. Furthermore, we found a notable increase in the total short-chain fatty acid (SCFA) content in feces of mice on a MLCT containing diet. All these results may be concomitantly responsible for the antiobesity effect of MLCT with relatively high contents of MCFA

Porphyrin Cosensitization for a Photovoltaic Efficiency of 11.5%: A Record for Non-Ruthenium Solar Cells Based on Iodine Electrolyte

Dye-sensitized solar cells (DSSCs) are promising for utilizing solar energy. To achieve high efficiencies, it is vital to synergistically improve the photocurrent (<i>J</i>_sc) and the photovoltage (<i>V</i>_oc). In this respect, conjugation framework extension and cosensitization are effective for improving the absorption and the <i>J</i>_sc, which, however, is usually accompanied by undesirably decreased <i>V</i>_oc. Herein, based on a rationally optimized porphyrin dye, we develop a targeted coadsorption/cosensitization approach for systematically improving the <i>V</i>_oc from 645 to 727, 746, and 760 mV, with synergistical <i>J</i>_sc enhancement from 18.83 to 20.33 mA cm^–2. Thus, the efficiency has been dramatically enhanced to 11.5%, which keeps the record for nonruthenium DSSCs using the I₂/I₃^– electrolyte. These results compose an alternative approach for developing highly efficient DSSCs with relatively high <i>V</i>_oc using traditional iodine electrolyte

Allelic Variations at Four Major Maturity <i>E</i> Genes and Transcriptional Abundance of the <i>E1</i> Gene Are Associated with Flowering Time and Maturity of Soybean Cultivars

<div><p>The time to flowering and maturity are ecologically and agronomically important traits for soybean landrace and cultivar adaptation. As a typical short-day crop, long day conditions in the high-latitude regions require soybean cultivars with photoperiod insensitivity that can mature before frost. Although the molecular basis of four major <i>E</i> loci (<i>E1</i> to <i>E4</i>) have been deciphered, it is not quite clear whether, or to what degree, genetic variation and the expression level of the four <i>E</i> genes are associated with the time to flowering and maturity of soybean cultivars. In this study, we genotyped 180 cultivars at <i>E1</i> to <i>E4</i> genes, meanwhile, the time to flowering and maturity of those cultivars were investigated at six geographic locations in China from 2011 to 2012 and further confirmed in 2013. The percentages of recessive alleles at <i>E1</i>, <i>E2</i>, <i>E3</i> and <i>E4</i> loci were 38.34%, 84.45%, 36.33%, and 7.20%, respectively. Statistical analysis showed that allelic variations at each of four loci had a significant effect on flowering time as well as maturity. We classified the 180 cultivars into eight genotypic groups based on allelic variations of the four major <i>E</i> loci. The genetic group of e1-nf representing dysfunctional alleles at the <i>E1</i> locus flowered earliest in all the geographic locations. In contrast, cultivars in the E1E2E3E4 group originated from the southern areas flowered very late or did not flower before frost at high latitude locations. The transcriptional abundance of functional <i>E1</i> gene was significantly associated with flowering time. However, the ranges of time to flowering and maturity were quite large within some genotypic groups, implying the presence of some other unknown genetic factors that are involved in control of flowering time or maturity. Known genes (e.g. <i>E3</i> and <i>E4</i>) and other unknown factors may function, at least partially, through regulation of the expression of the <i>E1</i> gene.</p></div

Correlation coefficients between the transcript abundance of the <i>E1</i> (A) or <i>e1-as</i> (B) genes and flowering time of cultivars grown at Harbin, in 2012.

<p>Correlation coefficients between the transcript abundance of the <i>E1</i> (A) or <i>e1-as</i> (B) genes and flowering time of cultivars grown at Harbin, in 2012.</p

Geographic locations, daylength, and temperature of six experimental sites.

<p>A: The geographic locations of the six experimental sites. B: the average day length (hr) between 2011 and 2012. C: The changes in temperature recorded in 2011. Since there was no temperature data available in Gongzhuling (43°53′ N, 124°84′E), we used the data from the neighboring city Changchun (43°88′ N, 125°35′ E) (60 Km apart) instead.</p

Genotypic groups were classified based on the allelic variations in <i>E1</i>, <i>E2</i>, <i>E3</i> and <i>E4</i> genes.

<p>*No more detailed geographic information available.</p

The correlation analyses of the time to flowering (R1) between 2011 and 2012.

<p>A: Huaian; B: Mudanjiang.</p

The correlation between R1 and R3, R7 or R8 at Nanjing, Huaian, Gongzhuling and Mudanjiang locations, average of the two years of 2011 and 2012.

<p>A: Correlation between R1 and R3 in Mudanjiang; B: Correlation between R1 and R7 in Mudanjiang. C: correlation between R1 and R3 in Gongzhuling; D: Correlation between R1 and R8 in Gongzhuling. E: Correlation between R1 and R3 in Huaian; F: Correlation between R1 and R8 in Huaian. G: Correlation between R1 and R3 in Nanjing; H: Correlation between R1 and R8 in Nanjing.</p

The phenotypic variations in R7 or R8 among different genotypic groups.

<p>The phenotypic segregation is shown in box-plot format. The interquartile region, median, and range are indicated by the box, the bold horizontal line, and the vertical line, respectively. A: R8 at Mudanjiang in 2011; B: R8 at Mudanjiang in 2012; C: R7 at Gongzhuling in 2011; D: R7 at Gongzhuling in 2012; E: R7 at Huaian in 2011; F: R7 at Huaian in 2012; G: R7 at Nanjing in 2011; H: R7 at Nanjing in 2012.</p