In research on photovoltaic (PV) device degradation, current-voltage (I-V ) datasets carry a large amount of information in addition to the maximum power point. Performance parameters such as short-circuit current, open-circuit voltage, shunt resistance, series resistance, and fill factor are essential for diagnosing the performance and degradation of solar cells and modules. To enable the scaling of I-V studies to millions of I-V curves, we have developed a data-driven method to extract I-V curve parameters and distributed this method as an open-source package in R. In contrast with the traditional practice of fitting the diode equation to I-V curves individually, which requires solving a transcendental equation, this data-driven method can be applied to large volumes of I-V data in a short time. Our data-driven feature extraction technique is tested on I-V curves generated with the single-diode model and applied to I-V curves with different data point densities collected from three different sources. This method has a high repeatability for extracting I-V features, without requiring knowledge of the device or expected parameters to be input by the researcher. We also demonstrate how this method can be applied to large datasets and accommodates nonstandard I-V curves including those showing artifacts of connection problems or shading where bypass diode activation produces multiple "steps." These features together make the data-driven I-V feature extraction method ideal for evaluating time-series I-V data and analyzing power degradation mechanisms in PV modules through cross comparisons of the extracted parameters.
Homoleptic complexes 1-M of groups 13 and 12 elements (M = B−In and M = Zn, respectively) incorporating electron-withdrawing formamidinate ligands {(C 6 F 5 )N=CH−N(C 6 F 5 )} − ({NCN} − ) were synthesized and isolated in high yields. The compounds were characterized by X-ray crystallography, NMR spectroscopy and elemental analysis. While single-component 1-M appeared to be sluggishly active or inactive in reduction of CO 2 with hydrosilanes, a good catalytic performance was achieved with the two-component systems derived from combinations of 1-M and E(C 6 F 5 ) 3 (E = B, Al). In particular, the binary combination 1-Al/B(C 6 F 5 ) 3 showed the best performance within the whole series, thus providing quantitative hydrosilane (Et 3 SiH) conversions under a range of conditions (P CO2 , temperature, benzene or bromobenzene solvents) and affording mainly CH 2 (OSiEt 3 ) 2 and CH 4 as products. Kinetic and mechanistic studies revealed that at the initiation step the binary catalytic systems undergo a complex transformation in the presence of CO 2 /Et 3 SiH affording the products of 1-Al decomposition, namely, (C 6 F 5 )N(H)SiEt 3 , (C 6 F 5 )N(Me)SiEt 3 , {NCN}-SiEt 3 and also some unidentified aluminum species. Thus, the overall process of the reduction of CO 2 with hydrosilanes is presumed to be catalyzed by complex multi-site systems, evolved from the formamidinate precursor 1-Al, implicating different tandem combinations of N-base/B(C 6 F 5 ) 3 with putative Al-containing species.
The dicarbonylation of 1,3‐butadiene to adipic acid derivatives offers the potential for a more cost‐efficient and environmentally benign industrial process. However, the complex reaction network of regioisomeric carbonylation and isomerization pathways, make a selective and direct transformation particularly difficult. Here, we report surprising solvent effects on this palladium‐catalysed process in the presence of 1,2‐bis‐di‐tert‐butylphosphin‐oxylene (dtbpx) ligands, which allow adipate diester formation from 1,3‐butadiene, carbon monoxide, and methanol with 97 % selectivity and 100 % atom‐economy under scalable conditions. Under optimal conditions a variety of di‐ and triesters from 1,2‐ and 1,3‐dienes can be obtained in good to excellent yields.
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