Highly effective electrocatalysts promoting CO₂ reduction reaction (CO₂RR) is extremely desirable to produce value-added chemicals/fuels while addressing current environmental challenges. Herein, we develop a layer-stacked, bimetallic two-dimensional conjugated metal-organic framework (2D c-MOF) with copper-phthalocyanine as ligand (CuN₄) and zinc-bis(dihydroxy) complex (ZnO₄) as linkage (PcCu-O₈-Zn). The PcCu-O₈-Zn exhibits high CO selectivity of 88%, turnover frequency of 0.39 s⁻¹ and long-term durability (>10 h), surpassing thus by far reported MOF-based electrocatalysts. The molar H₂/CO ratio (1:7 to 4:1) can be tuned by varying metal centers and applied potential, making 2D c-MOFs highly relevant for syngas industry applications. The contrast experiments combined with operando spectroelectrochemistry and theoretical calculation unveil a synergistic catalytic mechanism; ZnO₄ complexes act as CO₂RR catalytic sites while CuN₄ centers promote the protonation of adsorbed CO₂ during CO₂RR. This work offers a strategy on developing bimetallic MOF electrocatalysts for synergistically catalyzing CO₂RR toward syngas synthesis.

Building upon the success and relevance of the 2014 Magnetism Roadmap, this 2017 Magnetism Roadmap edition follows a similar general layout, even if its focus is naturally shifted, and a different group of experts and, thus, viewpoints are being collected and presented. More importantly, key developments have changed the research landscape in very relevant ways, so that a novel view onto some of the most crucial developments is warranted, and thus, this 2017 Magnetism Roadmap article is a timely endeavour. The change in landscape is hereby not exclusively scientific, but also reflects the magnetism related industrial application portfolio. Specifically, Hard Disk Drive technology, which still dominates digital storage and will continue to do so for many years, if not decades, has now limited its footprint in the scientific Topical Review

Magnetic random access memory schemes employing magnetoelectric coupling to write binary information promise outstanding energy efficiency. We propose and demonstrate a purely antiferromagnetic magnetoelectric random access memory (AF-MERAM) that offers a remarkable 50-fold reduction of the writing threshold compared with ferromagnet-based counterparts, is robust against magnetic disturbances and exhibits no ferromagnetic hysteresis losses. Using the magnetoelectric antiferromagnet Cr₂O₃, we demonstrate reliable isothermal switching via gate voltage pulses and all-electric readout at room temperature. As no ferromagnetic component is present in the system, the writing magnetic field does not need to be pulsed for readout, allowing permanent magnets to be used. Based on our prototypes, we construct a comprehensive model of the magnetoelectric selection mechanisms in thin films of magnetoelectric antiferromagnets, revealing misfit induced ferrimagnetism as an important factor. Beyond memory applications, the AF-MERAM concept introduces a general all-electric interface for antiferromagnets and should find wide applicability in antiferromagnetic spintronics.

The emergence of smart electronics, human friendly robotics and supplemented or virtual reality demands electronic skins with both tactile and touchless perceptions for the manipulation of real and virtual objects. Here, we realize bifunctional electronic skins equipped with a compliant magnetic microelectromechanical system able to transduce both tactile—via mechanical pressure—and touchless—via magnetic fields—stimulations simultaneously. The magnetic microelectromechanical system separates electric signals from tactile and touchless interactions into two different regions, allowing the electronic skins to unambiguously distinguish the two modes in real time. Besides, its inherent magnetic specificity overcomes the interference from non-relevant objects and enables signal-programmable interactions. Ultimately, the magnetic microelectromechanical system enables complex interplay with physical objects enhanced with virtual content data in augmented reality, robotics, and medical applications.