Twist engineering, or the alignment of two-dimensional (2D) crystalline layers with desired orientations, has led to tremendous success in modulating the charge degree of freedom in heteroand homo-structures, in particular, in achieving novel correlated and topological electronic phases in moiré electronic crystals 1,2 . However, although pioneering theoretical efforts have predicted nontrivial magnetism 3,4 and magnons 5 out of twisting 2D magnets, experimental realization of twist engineering spin degree of freedom remains elusive. Here, we leverage the archetypal 2D Ising magnet chromium triiodide (CrI3) to fabricate twisted double bilayer homostructures with tunable twist angles and demonstrate the successful twist engineering of 2D magnetism in them. Using linear and circular polarization-resolved Raman spectroscopy, we identify magneto-Raman signatures of a new magnetic ground state that is sharply distinct from those in natural bilayer (2L) and four-layer (4L) CrI3. With careful magnetic field and twist angle dependence, we reveal that, for a very small twist angle (~ 0.5 o ), this emergent magnetism can be well-approximated by a weighted linear superposition of those of 2L and 4L CI3 whereas, for a relatively large twist angle (~ 5 o ), it mostly resembles that of isolated 2L CrI3. Remarkably, at an intermediate twist angle (~ 1.1 o ), its magnetism cannot be simply inferred from the 2L and 4L cases, because it lacks sharp spin-flip transitions that are present in 2L and 4L CrI3 and features a dramatic Raman circular dichroism that is absent in natural 2L and 4L ones. Our results demonstrate the possibility of designing and controlling the spin degree of freedom in 2D magnets using twist engineering.
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Acceleration of the chemistry solver for engine combustion is of much interest due to the fact that in practical engine simulations extensive computational time is spent solving the fuel oxidation and emission formation chemistry. A dynamic adaptive chemistry (DAC) scheme based on a directed relation graph error propagation (DRGEP) method has been applied to study homogeneous charge compression ignition (HCCI) engine combustion with detailed chemistry (over 500 species) previously using an R-valuebased breadth-first search (RBFS) algorithm, which significantly reduced computational times (by as much as 30-fold). The present paper extends the use of this on-the-fly kinetic mechanism reduction scheme to model combustion in direct-injection (DI) engines. It was found that the DAC scheme becomes less efficient when applied to DI engine simulations using a kinetic mechanism of relatively small size and the accuracy of the original DAC scheme decreases for conventional non-premixed combustion engine. The present study also focuses on determination of search-initiating species, involvement of the NO x chemistry, selection of a proper error tolerance, as well as treatment of the interaction of chemical heat release and the fuel spray. Both the DAC schemes were integrated into the ERC KIVA-3v2 code, and simulations were conducted to compare the two schemes. In general, the present DAC scheme has better efficiency and similar accuracy compared to the previous DAC scheme. The efficiency depends on the size of the chemical kinetics mechanism used and the engine operating conditions. For cases using a small n-heptane kinetic mechanism of 34 species, 30% of the computational time is saved, and 50% for a larger n-heptane kinetic mechanism of 61 species. The paper also demonstrates that by combining the present DAC scheme with an adaptive multi-grid chemistry (AMC) solver, it is feasible to simulate a direct-injection engine using a detailed n-heptane mechanism with 543 species with practical computer time.
The chemical kinetics of hydrocarbon fuels determines the combustion characteristics and pollutant emissions of homogeneous charge compression ignition (HCCI) engines. Including comprehensive chemical mechanisms in HCCI engine models provides accurate predictive results that can be used to improve engine designs. However, a large number of simulations are usually required to optimize an HCCI engine, and the use of comprehensive chemical mechanisms is prohibitive. Furthermore, an increased demand for surrogate fuels that better represent real fuels has resulted in further increases in the size of chemical mechanisms as the carbon number of surrogate fuel species and the number of fuel components considered increases. Consequently, reduced mechanisms of smaller sizes, which are able to represent their corresponding comprehensive mechanisms over a wide range of conditions are necessary. This paper presents an approach that fully automates the process of reducing comprehensive chemical mechanisms of fuels for HCCI engines. The approach is based on the directed relation graph with error propagation (DRGEP) and principal component analysis (PCA) methods. In the first stage, the DRGEP method is used to efficiently remove redundant species. This is followed by the use of the PCA method to further remove insignificant reactions and species. During the entire process, the performance of the reduced mechanism is monitored to ensure that the generated mechanism satisfies user-specified error tolerances. In the present study three comprehensive mechanisms that include n-heptane, iso-octane, and methyl decanoate (MD) were investigated. The proposed approach successfully reduced the comprehensive mechanisms of n-heptane (561 species and 2539 reactions), iso-octane (857 species and 3606 reactions), and MD (2878 species and 8555 reactions) to reduced mechanisms with sizes of 140 species and 491 reactions, 195 species and 647 reactions, and 435 species and 1098 reactions, respectively, while maintaining small errors compared to the full mechanisms.
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