We demonstrate optimization of thermal conductance across nanostructures by developing a method combining atomistic Green’s function and Bayesian optimization. With an aim to minimize and maximize ...the interfacial thermal conductance (ITC) across Si-Si and Si-Ge interfaces by means of the Si/Ge composite interfacial structure, the method identifies the optimal structures from calculations of only a few percent of the entire candidates (over 60 000 structures). The obtained optimal interfacial structures are nonintuitive and impacting: the minimum ITC structure is an aperiodic superlattice that realizes 50% reduction from the best periodic superlattice. The physical mechanism of the minimum ITC can be understood in terms of the crossover of the two effects on phonon transport: as the layer thickness in the superlattice increases, the impact of Fabry-Pérot interference increases, and the rate of reflection at the layer interfaces decreases. An aperiodic superlattice with spatial variation in the layer thickness has a degree of freedom to realize optimal balance between the above two competing mechanisms. Furthermore, the spatial variation enables weakening the impact of constructive phonon interference relative to that of destructive interference. The present work shows the effectiveness and advantage of material informatics in designing nanostructures to control heat conduction, which can be extended to other nanostructures and properties.
Chemical composition alteration is a general strategy to optimize the thermoelectric properties of a thermoelectric material to achieve high-efficiency conversion of waste heat into electricity. ...Recent studies show that the Al2Fe3Si3 intermetallic compound with a relatively high power factor of ∼700 μW m–1 K–2 at 400 K is promising for applications in low-cost and nontoxic thermoelectric devices. To accelerate the exploration of the thermoelectric properties of this material in a mid-temperature range and to enhance its power factor, a machine-learning method was employed herein to assist the synthesis of off-stoichiometric samples (namely, Al23.5+x Fe36.5Si40–x ) of the Al2Fe3Si3 compound by tuning the Al/Si ratio. The optimal Al/Si ratio for a high power factor in the mid-temperature range was found rapidly and efficiently, and the optimal ratio of the sample at x = 0.9 was found to increase the power factor at ∼510 K by about 40% with respect to that of the initial sample at x = 0.0. The possible mechanism for the enhanced power factor is discussed in terms of the precipitations of the metallic secondary phases in the Al23.5+x Fe36.5Si40–x samples. Furthermore, the maximum achievable thermal conductivity of Al2Fe3Si3 estimated by the Slack model is ∼10 W m–1 K–1 at the Debye temperature. An avoided-crossing behavior of the acoustic and the low-lying optical modes along several crystallographic directions is found in the phonon dispersion of Al2Fe3Si3 calculated by ab initio density functional theory method. These preliminary results suggest that Al2Fe3Si3 can have a low thermal conductivity. The calculated formation energies of point defects suggest that the antisite defects between Al and Si are likely to cause the Al and Si off-stoichiometries in Al2Fe3Si3. The theoretically obtained insight provides additional information for the further understanding of Al2Fe3Si3.
Magnetic refrigeration exploits the magnetocaloric effect, which is the entropy change upon the application and removal of magnetic fields in materials, providing an alternate path for refrigeration ...other than conventional gas cycles. While intensive research has uncovered a vast number of magnetic materials that exhibit a large magnetocaloric effect, these properties remain unknown for a substantial number of compounds. To explore new functional materials in this unknown space, machine learning is used as a guide for selecting materials that could exhibit a large magnetocaloric effect. By this approach, HoB2 is singled out and synthesized, and its magnetocaloric properties are evaluated, leading to the experimental discovery of a gigantic magnetic entropy change of 40.1 J kg−1 K−1 (0.35 J cm−3 K−1) for a field change of 5 T in the vicinity of a ferromagnetic second-order phase transition with a Curie temperature of 15 K. This is the highest value reported so far, to the best of our knowledge, near the hydrogen liquefaction temperature; thus, HoB2 is a highly suitable material for hydrogen liquefaction and low-temperature magnetic cooling applications.Machine learning: The search for cooler materialsA material for magnetically cooling hydrogen to its liquid form has been identified by a data-driven approach. Some materials get colder when they are exposed to an alternating magnetic field. This so-called magnetocaloric effect enables refrigeration to within one thousandth of a degree of absolute zero. Trial and error have uncovered many magnetocaloric materials, but Pedro Baptista de Castro, from the National Institute for Materials Science in Tsukuba, Japan, and co-workers have instead approached material discovery in a more systematic way using machine learning. They trained their algorithm to screen prospective compounds using data from the scientific literature. In this way they identified, and then experimentally confirmed, that holmium boride, HoB2, has a giant magnetocaloric effect at temperatures around 15 Kelvin (–258 °C), near the liquefaction point of hydrogen.
Rational design and exploration of robust and low‐cost bifunctional oxygen reduction/evolution electrocatalysts are greatly desired for metal–air batteries. Herein, a novel high‐performance oxygen ...electrode catalyst is developed based on bimetal FeCo nanoparticles encapsulated in in situ grown nitrogen‐doped graphitic carbon nanotubes with bamboo‐like structure. The obtained catalyst exhibits a positive half‐wave potential of 0.92 V (vs the reversible hydrogen electrode, RHE) for oxygen reduction reaction, and a low operating potential of 1.73 V to achieve a 10 mA cm−2 current density for oxygen evolution reaction. The reversible oxygen electrode index is 0.81 V, surpassing that of most highly active bifunctional catalysts reported to date. By combining experimental and simulation studies, a strong synergetic coupling between FeCo alloy and N‐doped carbon nanotubes is proposed in producing a favorable local coordination environment and electronic structure, which affords the pyridinic N‐rich catalyst surface promoting the reversible oxygen reactions. Impressively, the assembled zinc–air batteries using liquid electrolytes and the all‐solid‐state batteries with the synthesized bifunctional catalyst as the air electrode demonstrate superior charging–discharging performance, long lifetime, and high flexibility, holding great potential in practical implementation of new‐generation powerful rechargeable batteries with portable or even wearable characteristic.
Bamboo‐like FeCo alloy encapsulated in nitrogen‐doped carbon nanotubes exhibits superior catalytic oxygen reduction and oxygen evolution performance than that of noble metal benchmarks, which benefits from the nitrogen‐rich and defect‐rich catalyst surface. The all‐solid‐state zinc–air batteries equipped by the synthesized materials show low charging/discharging overpotentials, long lifetime, and high flexibility, suitable for practical application.
Carbon alloy catalysts (CACs) are promising oxygen reduction reaction (ORR) catalysts to substitute platinum. However, despite extensive studies on CACs, the reaction sites and mechanisms for ORR are ...still in controversy. Herein, we present rather general consideration on possible ORR mechanisms for various structures in nitrogen doped CACs based on the first-principles calculations. Our study indicates that only a particular structure of a nitrogen pair doped Stone–Wales defect provides a good active site. The ORR activity of this structure can be tuned by the curvature around the active site, which makes its limiting potential approaching the maximum limiting potential (0.80 V) in the volcano plot for the ORR activity of CACs. The calculated results can be compared with the recent experimental ones of the half-wave potential for CAC systems that range from 0.60 to 0.80 V in the reversible-hydrogen-electrode (RHE) scale.
Bismuth (Bi) is a topological crystalline insulator (TCI), which has gapless topological surface states (TSSs) protected by a specific crystalline symmetry that strongly depends on the facet. Bi is ...also a promising electrochemical CO2 reduction reaction (ECO2RR) electrocatalyst for formate production. In this study, single‐crystalline Bi rhombic dodecahedrons (RDs) exposed with (104) and (110) facets are developed. The Bi RDs demonstrate a very low overpotential and high selectivity for formate production (Faradic efficiency >92.2%) in a wide partial current density range from 9.8 to 290.1 mA cm−2, leading to a remarkably high full‐cell energy efficiency (69.5%) for ECO2RR. The significantly reduced overpotential is caused by the enhanced *OCHO adsorption on the Bi RDs. The high selectivity of formate can be ascribed to the TSSs and the trivial surface states opening small gaps in the bulk gap on Bi RDs, which strengthens and stabilizes the preferentially adsorbed *OCHO and mitigates the competing adsorption of *H during ECO2RR. This study describes a promising application of Bi RDs for high‐rate formate production and high‐efficiency energy storage of intermittent renewable electricity. Optimizing the geometry of TCIs is also proposed as an effective strategy to tune the TSSs of topological catalysts.
The topological surface states and the trivial surface states opening small gaps in the bulk gap of Bi rhombic dodecahedrons facilitate highly selective formate production in a wide current density range, leading to a high full‐cell energy efficiency (maximum value of 69.5%) for electrochemical CO2 reduction reaction. This electrocatalyst can potentially be used for intermittent energy storage.
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In many subfields of chemistry and physics, numerous attempts have been made to accelerate scientific discovery using data-driven experimental design algorithms. Among them, Bayesian ...optimization has been proven to be an effective tool. A standard implementation (e.g., scikit-learn), however, can accommodate only small training data. We designed an efficient protocol for Bayesian optimization that employs Thompson sampling, random feature maps, one-rank Cholesky update and automatic hyperparameter tuning, and implemented it as an open-source python library called COMBO (COMmon Bayesian Optimization library). Promising results using COMBO to determine the atomic structure of a crystalline interface are presented. COMBO is available at https://github.com/tsudalab/combo.
Nitrogen-doped graphene (N-graphene) has important implications in graphene-based devices and catalysts. Nitrogen incorporation into graphene via postsynthetic treatment is likely to produce a ...non-negligible amount of defects and bond disorders, and the resulting nitrogen content is usually dominated by graphitic N and pyridinic N. To understand the kinetic stability of doped N and the effect of doped N on the self-healing of monovacancy in graphene, we have performed density functional theory calculations to study the adsorption and migration of an adsorbed C atom on undoped and N-doped graphene with and without a monovacancy (MV). The effects of N doping and hydrogenation on the migration of a MV in graphene are also studied. Our results suggest that the graphitic N doped in the vicinity of MV is kinetically unstable, and it could be transformed into a pyridinic N due to the migration of MV when N-graphene is through high-temperature annealing. The presence of a C adatom would easily repair the vacancy of defective graphene with MV and either restore perfect graphene or form a Stone–Wales defect. Similar repairing processes were also found in the case of a C adatom near MV with a pyridinic N.
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