An efficient end-capped engineering of pyrrole-based acceptor molecules for high-performance organic solar cells.

CONTEXT: Various innovative molecules have been designed and explored for use in organic photovoltaics. In this study, we devised novel molecules (KZ1-KZ7) specifically for organic solar cells (OSCs). The newly formulated acceptor compounds possess a lower bandgap (Eg = 1.85-2.02), along with bathochromic shift (λmax = 713-788 nm) compared to the reference (Eg = 2.04 eV and λmax = 774 nm). Moreover, the FMO results identified the distinct charge transfer from HOMO to LUMO, which was strongly corroborated by the TDM maps. Similarly, the new designed molecules show less excitation energy (Ex = 1.31-1.54(gas)) than reference (Ex = 1.72). Likewise, all designed molecules (KZ1-KZ7) have demonstrated an analogous open circuit voltage (Voc) with the donor polymer PTB7-Th. All seven designed molecules (KZ1-KZ7) exhibited more fill factor ranging from 97.08 to 97.29 than reference 95.25 and PCE of between 8 and 20% at short circuit current densities of 9, 12, and 15 mA cm-2. Overall, the findings support that designed molecules can be potential molecules for future practical applications. METHODS: Geometric calculations were conducted with Gaussian 09W software, and the findings were visualized using Gauss View software. DFT and TD-DFT were employed to evaluate various parameters for R and designed molecules (KZ1-KZ7). Firstly, four functionals including B3LYP, CAM-B3LYP, MPW1PW91, and ωB97XD with 6-31G(d,p) DFT level were applied to R to decide the best level for results. After appropriate analysis, the MPW1PW91/6-31G(d,p) was selected for further examination by comparing the experimental and DFT-based absorption graphs of R. External and internal reorganization energy are the two main factors contributing to reorganization energy. External energy refers to changes in external environment, while internal energy deals with information related to internal geometrical symmetry or the internal environment. The effect of outside factors or external reorganizational energy is omitted because it creates too little change.

Charting the Progress of Epilepsy Classification: Navigating a Shifting Landscape.

Epilepsy, a neurological disorder characterized by recurrent seizures, has witnessed a remarkable transformation in its classification paradigm, driven by advances in clinical understanding, neuroimaging, and molecular genetics. This narrative review navigates the dynamic landscape of epilepsy classification, offering insights into recent developments, challenges, and the promising horizon. Historically, epilepsy classification relied heavily on clinical observations, categorizing seizures based on their phenomenology and presumed etiology. However, the field has profoundly shifted from a symptom-based approach to a more refined, multidimensional system. One pivotal aspect of this evolution is the integration of neuroimaging techniques, particularly magnetic resonance imaging (MRI) and functional imaging modalities. These tools have unveiled the intricate neural networks implicated in epilepsy, facilitating the identification of distinct brain abnormalities and the categorization of epilepsy subtypes based on structural and functional findings. Furthermore, the role of genetics has become increasingly prominent in epilepsy classification. Genetic discoveries have not only unraveled the molecular underpinnings of various epileptic syndromes but have also provided valuable diagnostic and prognostic insights. This narrative review delves into the expanding realm of genetic testing and its impact on tailoring treatment strategies to individual patients. As the classification landscape evolves, there are accompanying challenges. The narrative review underscores the transformative potential of artificial intelligence and machine learning in epilepsy classification. These technologies hold promise in automating the analysis of complex neuroimaging and genetic data, offering enhanced accuracy and efficiency in epilepsy diagnosis and classification. In conclusion, navigating the shifting landscape of epilepsy classification is a journey marked by progress, complexity, and the prospect of improved patient care. We are charting a course toward more precise diagnoses and tailored treatments by embracing advanced neuroimaging, genetics, and innovative technologies. As the field continues to evolve, collaborative efforts and a holistic understanding of epilepsy's diverse manifestations will be instrumental in harnessing the full potential of this dynamic landscape.

High electron mobility due to extra π-conjugation in the end-capped units of non-fullerene acceptor molecules: a DFT/TD-DFT-based prediction.

A combination of high open-circuit voltage (Voc) and short-circuit current density (Jsc) typically creates effective organic solar cells (OSCs). To enhance the open-circuit voltage, we have designed three new fullerene-free acceptor molecules with elongated π-conjugation in the end-capped units. Y-series-based newly designed molecules (CPSS-4F, CPSS-4Cl, CPSS-4CN) exhibited a narrow energy bandgap with high electron mobility. Red shift in the absorption spectrum with high intensities is also noted for designed molecules. Low binding and excitation energies of designed molecules favor easy excitation of exciton in the excited state. Further, CPSS-4F, CPSS-4Cl, and CPSS-4CN exhibited better open-circuit voltage with favorable molecular orbitals contributions. Transition density analysis (TDM) was also performed to locate the total transitions in the designed molecules. Outcomes of all analyses suggested that designed molecules are effective contributors to the active layer of organic solar cells.