BODIPY Chemisorbed on SnO2 and TiO2 Surfaces for Photoelectrochemical Applications.

Advancement toward dye-sensitized photoelectrochemical cells to produce solar fuels by solar-driven water splitting requires a photosensitizer that is firmly attached to the semiconducting photoelectrodes. Covalent binding enhances the efficiency of electron injection from the photoexcited dye into the metal oxide. Optimization of charge transfer, efficient electron injection, and minimal electron-hole recombination are mandatory for achieving high efficiencies. Here, a BODIPY-based dye exploiting a novel surface-anchoring mode via boron is compared to a similar dye bound by a traditional carboxylic acid anchoring group. Through terahertz and transient absorption spectroscopic studies, along with interfacial electron transfer simulations, we find that, when compared to the traditional carboxylic acid anchoring group, electron injection of boron-bound BODIPY is faster into both TiO2 and SnO2. Although the surface coverage is low compared with carboxylic acids, the binding stability is improved over a wide range of pH. Subsequent photoelectrochemical studies using a sacrificial electron donor showed that this combined dye and anchoring group maintained photocurrent with good stability over long-time irradiation. This recently discovered binding mode of BODIPY shows excellent electron injection and good stability over time, making it promising for future investigations.

A conductive metal-organic framework photoanode.

We report the development of photosensitizing arrays based on conductive metal-organic frameworks (MOFs) that enable light harvesting and efficient charge separation. Zn2TTFTB (TTFTB = tetrathiafulvalene tetrabenzoate) MOFs are deposited directly onto TiO2 photoanodes and structurally characterized by pXRD and EXAFS measurements. Photoinduced interfacial charge transfer dynamics are investigated by combining time-resolved THz spectroscopy and quantum dynamics simulations. Sub-600 fs electron injection into TiO2 is observed for Zn2TTFTB-TiO2 and is compared to the corresponding dynamics for TTFTB-TiO2 analogues that lack the extended MOF architecture. Rapid electron injection from the MOF into TiO2 is enhanced by facile migration of the hole away from the interfacial region. Holes migrate through strongly coupled HOMO orbitals localized on the tetrathiafulvalene cores of the columnar stacks of the MOF, whereas electrons are less easily transferred through the spiral staircase arrangement of phenyl substituents of the MOF. The reported findings suggest that conductive MOFs could be exploited as novel photosensitizing arrays in applications to slow, and thereby make difficult, photocatalytic reactions such as those required for water-splitting in artificial photosynthesis.

Hammett neural networks: prediction of frontier orbital energies of tungsten-benzylidyne photoredox complexes.

The successful application of Hammett parameters as input features for regressive machine learning models is demonstrated and applied to predict energies of frontier orbitals of highly reducing tungsten-benzylidyne complexes of the form W([triple bond, length as m-dash]CArR)L4X. Using a reference molecular framework and the meta- and para-substituent Hammett parameters of the ligands, the models predict energies of frontier orbitals that correlate with redox potentials. The regressive models capture the multivariate character of electron-donating trends as influenced by multiple substituents even for non-aryl ligands, harnessing the breadth of Hammett parameters in a generalized model. We find a tungsten catalyst with tetramethylethylenediamine (tmeda) equatorial ligands and axial methoxyl substituents that should attract significant experimental interest since it is predicted to be highly reducing when photoactivated with visible light. The utilization of Hammett parameters in this study presents a generalizable and compact representation for exploring the effects of ligand substitutions.

Search for Catalysts by Inverse Design: Artificial Intelligence, Mountain Climbers, and Alchemists.

In silico catalyst design is a grand challenge of chemistry. Traditional computational approaches have been limited by the need to compute properties for an intractably large number of possible catalysts. Recently, inverse design methods have emerged, starting from a desired property and optimizing a corresponding chemical structure. Techniques used for exploring chemical space include gradient-based optimization, alchemical transformations, and machine learning. Though the application of these methods to catalysis is in its early stages, further development will allow for robust computational catalyst design. This review provides an overview of the evolution of inverse design approaches and their relevance to catalysis. The strengths and limitations of existing techniques are highlighted, and suggestions for future research are provided.