Single-use plastics are inexpensive, durable, light, and chemically resistant compounds. Such properties, while desirable for many applications, make them difficult to process after use and have contributed to a global...

We have screened an array of 23 metals deposited onto the metal−organic framework (MOF) NU-1000 for propyne dimerization to hexadienes. By a first-of-its-kind study utilizing datadriven algorithms and high-throughput experimentation (HTE) in MOF catalysis, yields on Cu-deposited NU-1000 were improved from 0.4 to 24.4%. Characterization of the best-performing catalysts reveal conversion to hexadiene to be due to the formation of large Cu nanoparticles, which is further supported by reaction mechanisms calculated with density functional theory (DFT). Our results demonstrate both the strengths and weaknesses of the HTE approach. As a strength, HTE excels at being able to find interesting and novel catalytic activity; any a priori theoretical approach would be hard-pressed to find success, as high-performing catalysts required highly specific operating conditions difficult to model theoretically, and initial simple single-atom models of the active site did not prove representative of the nanoparticle catalysts responsible for conversion to hexadiene. As a weakness, our results show how the HTE approach must be designed and monitored carefully to find success; in our initial campaign, only minor catalytic performances (up to 4.2% yield) were achieved, which were only improved following a complete overhaul of our HTE approach and questioning our initial assumptions.

Catalytic C–H borylation is an attractive method for the conversion of the most abundant hydrocarbon, methane (CH4), to a mild nucleophilic building block. However, existing CH4 borylation catalysts often suffer from low turnover numbers and conversions, which is hypothesized to result from inactive metal hydride agglomerates. Herein we report that the heterogenization of a bisphosphine molecular precatalyst, [(dmpe)Ir(cod)CH3], onto amorphous silica dramatically enhances its performance, yielding a catalyst that is 12-times more efficient than the current standard for CH4 borylation. The catalyst affords over 2000 turnovers at 150 °C in 16 h with a selectivity of 91.5% for mono- vs diborylation. Higher catalyst loadings improve yield and selectivity for the monoborylated product (H3CBpin) with 82.8% yield and >99% selectivity being achieved with 1255 turnovers. X-ray absorption and dynamic nuclear polarization-enhanced solid-state NMR spectroscopic studies identify the supported precatalyst as an IrI species, and indicate that upon completion of catalysis, multinuclear Ir polyhydrides are not formed. This is consistent with the hypothesis that immobilization of the organometallic Ir species on a surface prevents bimolecular decomposition pathways. Immobilization of the homogeneous IrI fragment onto amorphous silica represents a unique and simple strategy to improve the TON and longevity of a CH4 borylation catalyst.

We have screened an array of 23 metals deposited onto the metal–organic framework (MOF) NU-1000 for propyne dimerization to hexadienes under different reaction conditions for a total of ~1400 experiments. By a first-of-its-kind study utilizing data-driven algorithms and high-throughput experimentation (HTE) in MOF catalysis, yields on Cu-deposited NU-1000 were improved from 4.2% to 24.4%. Characterization of the most-performant catalysts reveal conversion to hexadiene to be due to the formation of large Cu nanoparticles, which is further supported by reaction mechanisms calculated with density functional theory (DFT). Our results demonstrate both the strengths and weaknesses of the HTE approach. As a strength, HTE excels at being able to find interesting and novel catalytic activity; any a priori theoretical approach would be hard-pressed to find success, as high-performing catalysts required highly specific operating conditions difficult to model theoretically, and initial naïve single-atom models of the active site did not prove representative of the nanoparticle catalysts responsible for conversion to hexadiene. As a weakness, our results show how the HTE approach must be designed and monitored carefully to find success; in our initial campaign (~six months and over half of the total experiments conducted) only minor catalytic performances (up to 4.2% yield) were achieved, which was only improved following a complete overhaul of our HTE approach and questioning our initial assumptions. Thus, the HTE approach is much less automated than it may seem, even if driven by machine learning algorithms – one must carefully design their HTE campaign to find success.