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The quantum anomalous Hall (QAH) effect, which has been realized in magnetic topological insulators (TIs), is the key to applications of dissipationless quantum Hall edge states in electronic devices. However, investigations and utilizations of the QAH effect are limited by the ultralow temperatures needed to reach full quantization-usually below 100 mK in either Cr- or V-doped (Bi,Sb) Te of the two experimentally confirmed QAH materials. Here it is shown that by codoping Cr and V magnetic elements in (Bi,Sb) Te TI, the temperature of the QAH effect can be significantly increased such that full quantization is achieved at 300 mK, and zero-field Hall resistance of 0.97 h/e is observed at 1.5 K. A systematic transport study of the codoped (Bi,Sb) Te films with varied Cr/V ratios reveals that magnetic codoping improves the homogeneity of ferromagnetism and modulates the surface band structure. This work demonstrates magnetic codoping to be an effective strategy for achieving high-temperature QAH effect in TIs.

Grasslands absorb and release carbon dioxide (CO2), emit methane (CH4) from grazing livestock, and emit nitrous oxide (N2O) from soils. Little is known about how the fluxes of these three greenhouse gases, from managed and natural grasslands worldwide, have contributed to past climate change, or the roles of managed pastures versus natural grasslands. Here, global trends and regional patterns of the full greenhouse gas balance of grasslands are estimated for the period 1750 to 2012. A new spatially explicit land surface model is applied, to separate the direct effects of human activities from land management and the indirect effects from climate change, increasing CO2 and regional changes in nitrogen deposition. Direct human management activities are simulated to have caused grasslands to switch from a sink to a source of greenhouse gas, because of increased livestock numbers and accelerated conversion of natural lands to pasture. However, climate change drivers contributed a net carbon sink in soil organic matter, mainly from the increased productivity of grasslands due to increased CO2 and nitrogen deposition. The net radiative forcing of all grasslands is currently close to neutral, but has been increasing since the 1960s. Here, we show that the net global climate warming caused by managed grassland cancels the net climate cooling from carbon sinks in sparsely grazed and natural grasslands. In the face of future climate change and increased demand for livestock products, these findings highlight the need to use sustainable management to preserve and enhance soil carbon storage in grasslands and to reduce greenhouse gas emissions from managed grasslands.

Electrochemical and mechanical properties of lithium‐ion battery materials are heavily dependent on their 3D microstructure characteristics. A quantitative understanding of the role played by stochastic microstructures is critical for the prediction of material properties and for guiding synthesis processes. Furthermore, tailoring microstructure morphology is also a viable way of achieving optimal electrochemical and mechanical performances of lithium‐ion cells. To facilitate the establishment of microstructure‐resolved modeling and design methods, a review covering spatially and temporally resolved imaging of microstructure and electrochemical phenomena, microstructure statistical characterization and stochastic reconstruction, microstructure‐resolved modeling for property prediction, and machine learning for microstructure design is presented here. The perspectives on the unresolved challenges and opportunities in applying experimental data, modeling, and machine learning to improve the understanding of materials and identify paths toward enhanced performance of lithium‐ion cells are presented.

By comparing the spatially resolved unpolarized, polarized reflectance and Mueller matrix elements of skeletal muscle with a scattering medium containing polystyrene microspheres and silk fibers, we demonstrate that the sphere-cylinder scattering model (SCSM) can reproduce the characteristic features of skeletal muscle. Both experiments and polarization sensitive Monte Carlo simulation provide evidences that SCSM may be used to characterize the structural and optical properties of skeletal muscle.

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We demonstrated the use of the powerful machine learning tool to develop the ''safety envelope'' of lithium-ion batteries for electric vehicles that provides the range of mechanical loading conditions ensuring safe operation. The daunting challenge of obtaining a large databank of battery tests was overcome by utilizing a high-accuracy finite element model of a pouch cell to generate over 2,500 numerical simulations. The safety envelope will serve as important guidelines to the design of EV and batteries.