We present a systematic analysis of CO adsorption on Pt nanoclusters in the 0.2–1.5 nm size range with the aim of unraveling size-dependent trends and developing predictive models for site-specific adsorption behavior. Using an empirical-potential-based genetic algorithm and density functional theory (DFT) modeling, we show that there exists a size window (40–70 atoms) over which Pt nanoclusters bind CO weakly, the binding energies being comparable to those on (111) or (100) facets. The size-dependent adsorption energy trends are, however, distinctly nonmonotonic and are not readily captured using traditional descriptors such as d-band energies or (generalized) coordination numbers of the Pt binding sites. Instead, by applying machine-learning algorithms, we show that multiple descriptors, broadly categorized as structural and electronic descriptors, are essential for qualitatively capturing the CO adsorption trends. Nevertheless, attaining quantitative accuracy requires further refinement, and we propose the use of an additional descriptorsthe fully frozen adsorption energythat is a computationally inexpensive probe of CO–Pt bond formation. With these three categories of descriptors, we achieve an absolute mean error in CO adsorption energy prediction of 0.12 eV, which is similar to the underlying error of DFT adsorption calculations. Our approach allows for building quantitatively predictive models of site-specific adsorbate binding on realistic, low-symmetry nanostructures, which is an important step in modeling reaction networks as well as for rational catalyst design in general.

Black phosphorus is a fascinating layered material, with extraordinary anisotropic mechanical, optical and electronic properties. However, the sensitivity of black phosphorus to oxygen and moisture poses significant challenges for technological applications of this unique material. Here, we report a viable solution that overcomes degradation of few-layer black phosphorus by passivating the surface with self-assembled monolayers of octadecyltrichlorosilane that provide long-term stability in ambient conditions. Importantly, we show that this treatment does not cause any undesired carrier doping of the bulk channel material, thanks to the emergent hierarchical interface structure. Our approach is compatible with conventional electronic materials processing technologies thus providing an immediate route toward practical applications in black phosphorus devices.

Two-dimensional (2D) materials are believed to hold significant promise in nanoscale optoelectronics. While significant progress has been made in this field over the past decade, the ability to control charge carrier density with high spatial precision remains an outstanding challenge in 2D devices. We present an approach that simultaneously addresses the dual issues of charge-carrier doping and spatial precision based on a functional lithographic resist that employs methacrylate polymers containing zwitterionic sulfobetaine pendent groups for noncovalent surface doping of 2D materials. We demonstrate scalable approaches for patterning these polymer films via electron-beam lithography, achieving precise spatial control over carrier doping for fabrication of high-quality, all-2D, lateral p-n junctions in graphene. Our approach preserves all of the desirable structural and electronic properties of graphene while exclusively modifying its surface potential. The functional polymer resist platform and concept offers a facile route toward lithographic doping of graphene- and other 2D material-based optoelectronic devices.

Defective graphene has been shown experimentally to be an excellent support for transition-metal electrocatalysts in direct methanol fuel cells. Prior computational modeling has revealed that the improved catalytic activity of graphene-supported metal clusters is in part due to increased resistance to catalyst sintering and to CO poisoning, but the increased reaction rate for the methanol decomposition reaction (MDR) is not yet fully explained. Using density functional theory, we investigate the adsorption and reaction thermodynamics of MDR intermediates on defective graphene-supported Pt13 nanoclusters with realistic, low-symmetry morphologies. We find that the support-induced shifts in catalyst electronic structure correlate well with an overall change in adsorption behavior of MDR intermediates and that the reaction thermodynamics are modified in a way that suggests the potential of greater catalytic activity. We also show that adsorption energy predictors established for traditional heterogeneous catalysis studies of MDR on macroscopic crystalline facets are equally valid on catalyst nanoclusters (supported or otherwise) with irregular, low-symmetry surface morphologies. Our studies provide theoretical insights into experimental observations of enhanced catalytic activity of graphene-supported Pt nanoclusters for MDR and suggest promising avenues for further tuning of catalytic activity through engineering of catalyst–support interactions.

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