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Electric-field control of spin states offers a promising route to ultralow-power, ultrafast magnetization switching in spintronic devices such as magnetic tunnel junctions (MTJs). However, enhancing voltage-controlled magnetic anisotropy (VCMA) through interfacial engineering often disrupts coherent tunneling and suppresses tunnel magnetoresistance (TMR), limiting practical device performance. Here, we experimentally demonstrate highly energy-efficient, voltage-driven magnetization switching in MTJs enabled by a remote iridium (Ir) doping strategy that tailors the Ir concentration near the MgO/CoFeB interface in the free layer. By inserting an ultrathin Ir layer away from the tunnel barrier and leveraging controlled diffusion during annealing, we achieve sub-nanosecond switching with an energy of only 3.5 femtojoules per bit in nanoscale MTJs while maintaining a TMR ratio up to 160% after 400°C postannealing. These results resolve a long-standing VCMA-TMR trade-off and establish a scalable pathway toward ultralow-power nonvolatile spintronic devices under aggressive energy and scaling constraints. 

Abstract In-sensor computing (ISC) integrates sensing, memory, and processing at the point of data acquisition, enabling real-time, low-power operation. Two-dimensional (2D) materials offer unique advantages for ISC due to their atomic thickness and multifunctional properties. This review highlights 2D material-based ISC devices, covering mechanisms, performance, and architectures, and discusses challenges and solutions toward scalable fabrication and practical deployment in emerging technologies like Internet of Things (IoT), analog computing, and motion detection. 

In the past decades, China has witnessed high air pollution associated with rapid economic development, although regulatory efforts have alleviated the situation since 2013. Haze events characterized by high particulate matter (PM) levels in China are not only of enormous magnitude but also represent a distinct chemical regime. Once driven by direct emissions, these high-PM episodes are now more affected by secondary aerosol, especially secondary organic aerosol (SOA). This Review synthesizes the state of the science of SOA formation in urban China, specifically (i) how the dominance of anthropogenic precursors affects SOA formation, (ii) what are the prevailing SOA formation mechanisms, and (iii) how important are the multipollutant and multiphase processes in SOA formation and evolution. We also highlight essential directions for future studies. 

Abstract The conventional computing paradigm struggles to fulfill the rapidly growing demands from emerging applications, especially those for machine intelligence because much of the power and energy is consumed by constant data transfers between logic and memory modules. A new paradigm, called “computational random-access memory (CRAM),” has emerged to address this fundamental limitation. CRAM performs logic operations directly using the memory cells themselves, without having the data ever leave the memory. The energy and performance benefits of CRAM for both conventional and emerging applications have been well established by prior numerical studies. However, there is a lack of experimental demonstration and study of CRAM to evaluate its computational accuracy, which is a realistic and application-critical metric for its technological feasibility and competitiveness. In this work, a CRAM array based on magnetic tunnel junctions (MTJs) is experimentally demonstrated. First, basic memory operations, as well as 2-, 3-, and 5-input logic operations, are studied. Then, a 1-bit full adder with two different designs is demonstrated. Based on the experimental results, a suite of models has been developed to characterize the accuracy of CRAM computation. Scalar addition, multiplication, and matrix multiplication, which are essential building blocks for many conventional and machine intelligence applications, are evaluated and show promising accuracy performance. With the confirmation of MTJ-based CRAM’s accuracy, there is a strong case that this technology will have a significant impact on power- and energy-demanding applications of machine intelligence.