Mimicking Natural Antioxidant Systems for Improved Photostability in Wide-Band-Gap Perovskite Solar Cells.

Fostered by the top power conversion efficiencies (PCEs) of lab-scale devices, industrialization of perovskite solar cells is underway. Nevertheless, the intrinsically poor stability of these materials still represents a major concern. Herein, inspired by Nature, the use of ß-carotene in perovskite solar cells is proposed to mimic its role as a protective pigment, as occurs in natural photosynthesis. Laser-mediated photostability (LMPS) assessment, Fourier-transform infrared spectra analysis acquired in attenuate total reflectance (ATR-FTIR), spectroscopy ellipsometry (SE), and time-resolved photoluminescence (TRPL) measurements under stress conditions prove that the inclusion of a thin ß-carotene interlayer promotes a high improvement in the photostability of the perovskite films against photooxidation. Importantly, this is accompanied by an improvement of the solar cell PCE that approaches 20% efficiency with no hysteresis, which is among the highest values reported for a mixed halide (I-Br) perovskite with a band gap of 1.74 eV, relevant for coupling with silicon in tandem cells.

Synthesis and Investigation of Electro-Optical Properties of H-Shape Dibenzofulvene Derivatives.

We have synthetized two classes of dibenzofulvene-arylamino derivatives with an H-shape design, for a total of six different molecules. The molecular structures consist of two D-A-D units connected by a thiophene or bitiophene bridge, using diarylamino substituents as donor groups anchored to the 2,7- (Group A) and 3,6- (Group B) positions of the dibenzofulvene backbone. The donor units and the thiophene or bithiophene bridges were used as chemico-structural tools to modulate electro-optical and morphological-electrical properties. A combination of experiments, such as absorption measurements (UV-Vis spectroscopy), cyclic voltammetry, ellipsometry, Raman, atomic force microscopy, TD-DFT calculation and hole-mobility measurements, were carried out on the synthesized small organic molecules to investigate the differences between the two classes and therefore understand the relevance of the molecular design of the various properties. We found that the anchoring position on dibenzofulvene plays a crucial key for fine-tuning the optical, structural, and morphological properties of molecules. In particular, molecules with substituents in 2,7 positions (Group A) showed a lower structural disorder, a larger molecular planarity, and a lower roughness.

Control of Electron Transfer Processes in Multidimensional Arylamine-Based Mixed-Valence Compounds by Molecular Backbone Design.

Four trigonal topology compounds with three diarylamines redox centers and dibenzofulvene as core bridge have been synthesized. Their radical cations exhibit appealing intramolecular electron transfer pathways between three redox centers, depending on their position on the core bridge. By changing such positions (on either 2,7- or 3,6-), and the length of the bridge, the control of the intramolecular electron transfer pathways was achieved through the electron self-exchange route. These processes were investigated by absorption spectroscopy, electron paramagnetic resonance spectroscopy, and (time-dependent) density functional theory calculations. Hole mobility measurements were carried out as well, to correlate the intramolecular electron transfer with the hole-transporting ability for possible applications in optoelectronic devices.

CMStalker: A Combinatorial Tool for Composite Motif Discovery.

Controlling the differential expression of many thousands different genes at any given time is a fundamental task of metazoan organisms and this complex orchestration is controlled by the so-called regulatory genome encoding complex regulatory networks: several Transcription Factors bind to precise DNA regions, so to perform in a cooperative manner a specific regulation task for nearby genes. The in silico prediction of these binding sites is still an open problem, notwithstanding continuous progress and activity in the last two decades. In this paper, we describe a new efficient combinatorial approach to the problem of detecting sets of cooperating binding sites in promoter sequences, given in input a database of Transcription Factor Binding Sites encoded as Position Weight Matrices. We present CMStalker, a software tool for composite motif discovery which embodies a new approach that combines a constraint satisfaction formulation with a parameter relaxation technique to explore efficiently the space of possible solutions. Extensive experiments with 12 data sets and 11 state-of-the-art tools are reported, showing an average value of the correlation coefficient of 0.54 (against a value 0.41 of the closest competitor). This improvements in output quality due to CMStalker is statistically significant.

Direct vs 2-stage approaches to structured motif finding.

BACKGROUND: The notion of DNA motif is a mathematical abstraction used to model regions of the DNA (known as Transcription Factor Binding Sites, or TFBSs) that are bound by a given Transcription Factor to regulate gene expression or repression. In turn, DNA structured motifs are a mathematical counterpart that models sets of TFBSs that work in concert in the gene regulations processes of higher eukaryotic organisms. Typically, a structured motif is composed of an ordered set of isolated (or simple) motifs, separated by a variable, but somewhat constrained number of "irrelevant" base-pairs. Discovering structured motifs in a set of DNA sequences is a computationally hard problem that has been addressed by a number of authors using either a direct approach, or via the preliminary identification and successive combination of simple motifs. RESULTS: We describe a computational tool, named SISMA, for the de-novo discovery of structured motifs in a set of DNA sequences. SISMA is an exact, enumerative algorithm, meaning that it finds all the motifs conforming to the specifications. It does so in two stages: first it discovers all the possible component simple motifs, then combines them in a way that respects the given constraints. We developed SISMA mainly with the aim of understanding the potential benefits of such a 2-stage approach w.r.t. direct methods. In fact, no 2-stage software was available for the general problem of structured motif discovery, but only a few tools that solved restricted versions of the problem. We evaluated SISMA against other published tools on a comprehensive benchmark made of both synthetic and real biological datasets. In a significant number of cases, SISMA outperformed the competitors, exhibiting a good performance also in most of the cases in which it was inferior. CONCLUSIONS: A reflection on the results obtained lead us to conclude that a 2-stage approach can be implemented with many advantages over direct approaches. Some of these have to do with greater modularity, ease of parallelization, and the possibility to perform adaptive searches of structured motifs. As another consideration, we noted that most hard instances for SISMA were easy to detect in advance. In these cases one may initially opt for a direct method; or, as a viable alternative in most laboratories, one could run both direct and 2-stage tools in parallel, halting the computations when the first halts.

K-Boost: a scalable algorithm for high-quality clustering of microarray gene expression data.

Microarray technology for profiling gene expression levels is a popular tool in modern biological research. Applications range from tissue classification to the detection of metabolic networks, from drug discovery to time-critical personalized medicine. Given the increase in size and complexity of the data sets produced, their analysis is becoming problematic in terms of time/quality trade-offs. Clustering genes with similar expression profiles is a key initial step for subsequent manipulations and the increasing volumes of data to be analyzed requires methods that are at the same time efficient (completing an analysis in minutes rather than hours) and effective (identifying significant clusters with high biological correlations). In this paper, we propose K-Boost, a clustering algorithm based on a combination of the furthest-point-first (FPF) heuristic for solving the metric k-center problem, a stability-based method for determining the number of clusters, and a k-means-like cluster refinement. K-Boost runs in O (|N| x k) time, where N is the input matrix and k is the number of proposed clusters. Experiments show that this low complexity is usually coupled with a very good quality of the computed clusterings, which we measure using both internal and external criteria. Supporting data can be found as online Supplementary Material at www.liebertonline.com.