RESUMEN
Planar heterojunction perovskite solar cells (PHJ-PSCs) constructed with one-step precursor solution spin-coating deposition (OPSSD) usually give an extremely low performance mainly due to the poor morphology and low crystallinity of the perovskite films. In this work, by incorporating a suitable HONH3Cl additive in the perovskite precursor solution, a high quality perovskite film with improved morphology and crystallinity was obtained. The UV-vis measurement of the CH3NH3I solutions without and with HONH3Cl demonstrates that the improved quality of the perovskite film can be easily attributed to a combined effect of N2, I2, H2O and CH3NH3Cl originating from the oxidation of CH3NH3I triggered by the HONH3Cl additive, which can manipulate the crystallization process of the perovskite. Accordingly, the improved performance for the HONH3Cl-induced PHJ-PSCs can also be demonstrated. At the optimized molar ratio of 1 : 1 : 0.1 for PbI2 : CH3NH3I : HONH3Cl, the PHJ-PSCs exhibit an average power conversion efficiency (PCE) of 10.61 ± 0.51%, which is much higher than that of pristine 1 : 1 : 0 based cells without additive (7.21 ± 0.61%), and the best performing HONH3Cl-induced device can yield a PCE as high as 11.12% with a Jsc of 18.42 mA cm-2, Voc of 0.95 V and FF of 0.63. Introducing suitable HONH3Cl as an additive into the perovskite precursor solution is really an effective route to enhance the performance of the PHJ-PSCs via OPSSD.
RESUMEN
The use of computational modeling algorithms to guide the design of novel enzyme catalysts is a rapidly growing field. Force-field based methods have now been used to engineer both enzyme specificity and activity. However, the proportion of designed mutants with the intended function is often less than ten percent. One potential reason for this is that current force-field based approaches are trained on indirect measures of function rather than direct correlation to experimentally-determined functional effects of mutations. We hypothesize that this is partially due to the lack of data sets for which a large panel of enzyme variants has been produced, purified, and kinetically characterized. Here we report the kcat and KM values of 100 purified mutants of a glycoside hydrolase enzyme. We demonstrate the utility of this data set by using machine learning to train a new algorithm that enables prediction of each kinetic parameter based on readily-modeled structural features. The generated dataset and analyses carried out in this study not only provide insight into how this enzyme functions, they also provide a clear path forward for the improvement of computational enzyme redesign algorithms.
