Electrifying amine carbon capture with robust redox-tunable acids
Not provided.
Science & Technology - Other Topics↗
Engineering topics
Musgrave, Charles
Publications and source records attributed to Musgrave, Charles.
BEAST DB: Grand-Canonical Database of Electrocatalyst Properties
We present BEAST DB, an open-source database comprised of ab initio electrochemical data computed using grand-canonical density functional theory in implicit solvent at consistent calculation parameters. The database contains over 20,000 surface calculations and covers a broad set of heterogeneous catalyst materials and electrochemical reactions. Calculations were performed at self-consistent fixed potential as well as constant charge to facilitate comparisons to the computational hydrogen electrode. This article presents common use cases of the database to rationalize trends in catalyst activity, screen catalyst material spaces, understand elementary mechanistic steps, analyze the electronic structure, and train machine learning models to predict higher fidelity properties. Users can interact graphically with the database by querying for individual calculations to gain a granular understanding of reaction steps or by querying for an entire reaction pathway on a given material using an interactive reaction pathway tool. BEAST DB will be periodically updated, with planned future updates to include advanced electronic structure data, surface speciation studies, and greater reaction coverage.
database↗
Dual atom catalysts for rapid electrochemical reduction of CO to ethylene
Strong CO adsorption and facile CO dimerization are the key challenges in electrochemical CO2 reduction towards multi-carbon (C2+) products. We recently showed that CoPc immobilized on a single-walled carbon nanotube can selectively reduce CO2 to methanol. This is enabled through molecular strain, which dramatically improves the CO adsorption energy to CoPc, which in turn facilitates methanol formation. We now examine the extended Phthalocyanine (PcEx) dual atom catalyst (DAC), which is intrinsically strained and contains two catalyst centers, making it a candidate for reducing CO to C2+ products. Using Quantum Mechanics (QM), we screened 20 elements embedded in the PcEx, seeking catalysts with weak hydrogen binding, strong CO binding, and facile CO dimerization. We identi>ied Fe, Ru, Co, and Ir as the best performers and subsequently evaluated the entire CO to C2H4 mechanism (9 steps) using each of these elements as catalysts. In terms of limiting potential and overall exergonicity, we identi>ied CoPcEx as the best catalyst, followed by IrPcEx. We then examined the full CO to C2H4 mechanism on the bimetallic IrCoPcEx catalyst using grand canonical QM to obtain the reaction energetics as a function of applied potential. We conclude that the bimetallic IrCoPcEx is most promising for ef>iciently converting CO to ethylene.
Musgrave, Charles B.↗
Accurate Prediction of HSE06 Band Structures for a Diverse Set of Materials Using Δ-Learning
Here we used machine learning (ML) to accurately predict eigenvalues of the hybrid HSE06 functional using eigenvalues computed by the less computationally expensive PBE functional and associated electronic features based on the k-point resolved atomic band character. The ML model was trained by using eigenvalues from only one k-point for each of the 168 compounds in the training set. The HSE06 eigenvalues across all k-points were then predicted for a separate set of 169 compounds with a mean absolute error (MAE) of 0.13 eV, representing a significant improvement over the error of PBE-computed eigenvalues relative to that of HSE06 (MAE = 0.96 eV). These accurately predicted eigenvalues result in remarkably accurate predictions for the band structures, projected density of states, and band gaps, even though the model was not explicitly trained on these other properties. Finally, we demonstrate that our ML model has a similar accuracy for both ternary and quaternary compounds well outside the initial training set and on systems with 112 and 160 atoms, demonstrating its potential to rapidly predict HSE06-quality electronic structures of complex materials that are practically unfeasible for HSE06.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Compositions and methods of promoting organic photocatalysis
The invention provides novel compounds and methods that are useful in promoting reactions that proceed through an oxidative quenching pathway. In certain embodiments, the reactions comprise atom transfer radical polymerization.
Miyake, Garret↗
Compositions and methods of promoting organic photocatalysis
The invention provides novel compounds and methods that are useful in promoting reactions that proceed through an oxidative quenching pathway. In certain embodiments, the reactions comprise atom transfer radical polymerization.
Miyake, Garret M.↗
Wurtzite materials in alloys of rock salt compounds
Materials with crystal structures containing tetrahedral motifs are preferable for optoelectronic applications because they often have direct band gaps and low electron effective masses. However, crystal structures of manganese chalcogenides typically contain octahedral motifs, such as in rocksalt (RS) MnS and MnSe materials. Here we experimentally show that MnS 1-x Se x alloys with tetrahedrally-bonded wurtzite (WZ) structure can form between MnSe and MnS parent compounds with octahedral RS structures, at S-rich compositions (x<0.4) and low synthesis temperatures (~300°C). The calculated mixing enthalpies of MnS1-xSex alloys in RS and WZ structures cannot explain this experimental observation, so we hypothesize that WZ stabilization may be related to smaller structure density and lower surface energy compared to RS. The resulting WZ MnS 1-x Se x alloys have 3.0-3.2 eV optical absorption onset and lower electrical conductivity (<0.0001 S/cm) compared to the parent RS compounds. These experimental measurement result are consistent with computationally predicted band gaps and effective masses.
36 MATERIALS SCIENCE↗