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Large area reduced graphene oxide (RGO) thin films have been grown using pulsed laser deposition (PLD) technique. A very large carrier mobility of 372 cm 2 V -1 s -1 has been observed in a PLD grown RGO thin film with a large sp 2 carbon fraction of 87% along with narrow Raman 2D peak profile. The fraction of sp 2 carbon and carbon/oxygen ratios are tuned through PLD growth parameters, and these are estimated from X-ray photoelectron spectroscopy (XPS) data. The electrical properties of the RGO thin films are comprehended by the intensity ratios between different optical phonon vibrational modes of Raman Spectra. The photoluminescence spectra also indicate a less intense and broader blue fluorescence spectrum detecting the presence of miniature sized sp 2 domains in the near vicinity of π* electronic states which favor the variable range hopping transport phenomena. This study on large area RGO thin films with very large carrier mobility fabricated by PLD process will be very useful for high mobility electronic device applications and could open a roadmap for further extensive research in functionalized 2D materials.

Recent studies suggest that electron transport layers (ETLs) comprising [6,6]-phenyl-C61-butyric acid methyl ester (PCBM), employed in planar perovskite solar cells, reduce hysteresis by passivating the deep trap states, thereby underscoring the importance of interfacial structures. To gain physical insights into the PCBM–perovskite interfaces during solution processing, we performed molecular dynamics simulations of PCBMs solvated in chlorobenzene near (110) and (100) perovskite surfaces. Our results indicate strong orientational preferences of deposited PCBMs with the strongest associations between the carbonyl oxygen atom of PCBM and the terminating Pb and H atoms of (110) and (100) faces of perovskite, respectively. The phenyl moiety shows weak associations with the (100) perovskite surface enabling two-pronged anchoring that might facilitate charge transfer. In-plane ordering of PCBMs on perovskite surfaces indicates that a more densely packed monolayer is formed on the (110) surface compared to that on the (100) surface and might lead to more efficient electron transport.

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We identify compositionally complex alloys (CCAs) that offer exceptional mechanical properties for elevated temperature applications by employing machine learning (ML) in conjunction with rapid synthesis and testing of alloys for validation to accelerate alloy design. The advantages of this approach are scalability, rapidity, and reasonably accurate predictions. ML tools were implemented to predict Young’s modulus of refractory-based CCAs by employing different ML models. Our results, in conjunction with experimental validation, suggest that average valence electron concentration, the difference in atomic radius, a geometrical parameter λ and melting temperature of the alloys are the key features that determine the Young’s modulus of CCAs and refractory-based CCAs. The Gradient Boosting model provided the best predictive capabilities (mean absolute error of 6.15 GPa) among the models studied. Our approach integrates high-quality validation data from experiments, literature data for training machine-learning models, and feature selection based on physical insights. It opens a new avenue to optimize the desired materials property for different engineering applications.