The structure of experimentally designed solar cells was optimized in terms of the photoactive layer thickness for both organic bulk heterojunction and hybrid perovskite solar cells. The photoactive layer thickness had a totally different behavior on the performance of the organic and hybrid solar cells. Analysis of the optical parameters using transfer matrix modeling within the Maxwell–Garnett effective refractive index model shows that light absorbance and exciton generation rate in the photoactive layer can be used to optimize the thickness range of the photoactive layer. Complete agreement between experimental and simulated data for solar cells with photoactive materials that have very different natures proves the validity of the proposed modeling method. The proposed simple method which is not time-consuming to implement permits to obtain a preliminary assessment of the reasonable range of layer thickness that will be needed for designing experimental samples.
Both human and animal studies have demonstrated remarkable findings of experience-induced plasticity in the cortex. Here, we investigated whether the widely used monetary incentive delay (MID) task changes the neural processing of incentive cues that code expected monetary outcomes. We used a novel auditory version of the MID task, where participants responded to acoustic cues that coded expected monetary losses. To investigate task-induced brain plasticity, we presented incentive cues as deviants during passive oddball tasks before and after two sessions of the MID task. During the oddball task, we recorded the mismatch-related negativity (MMN) as an index of cortical plasticity. We found that two sessions of the MID task evoked a significant enhancement of MMN for incentive cues that predicted large monetary losses, specifically when monetary cue discrimination was essential for maximising monetary outcomes. The task-induced plasticity correlated with the learning-related neural activity recorded during the MID task. Thus, our results confirm that the auditory processing of (loss) incentive cues is dynamically modulated by previous monetary outcomes
Computational methods to predict Z-DNA regions are in high demand to understand the functional role of Z-DNA. The previous state-of-the-art method Z-Hunt is based on statistical mechanical and energy considerations about B- to Z-DNA transition using sequence information. Z-DNA CHiP-seq experiment results showed little overlap with Z-Hunt predictions implying that sequence information only is not sufficient to explain emergence of Z-DNA at different genomic locations. Adding epigenetic and other functional genomic mark-ups to DNA sequence level can help revealing the functional Z-DNA sites. Here we take advantage of the deep learning approach that can analyze and extract information from large volumes of molecular biology data. We developed a machine learning approach DeepZ that aggregates information from genome-wide maps of epigenetic markers, transcription factor and RNA polymerase binding sites, and chromosome accessibility maps. With the developed model we not only verify the experimental Z-DNA predictions, but also generate the whole-genome annotation, introducing new possible Z-DNA regions, which have not yet been found in experiments and can be of interest to the researchers from various fields.