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HighlightsGas-phase functionalization of X-MXene (X = –F, –OH, –O, –Br, –I) films crafted from sub-100 nm thin MXene flakes for highly sensitive NO2 sensors.I-MXene-based senor exhibited significant sensing performances toward trace NO2 at room temperature.The hydrophobicity, larger atomic size, lower electronegativity, and reduced shielding of -I contribute to the excellent sensing enhancement of I-MXene. In this study, precise control over the thickness and termination of Ti3C2TX MXene flakes is achieved to enhance their electrical properties, environmental stability, and gas-sensing performance. Utilizing a hybrid method involving high-pressure processing, stirring, and immiscible solutions, sub-100 nm MXene flake thickness is achieved within the MXene film on the Si-wafer. Functionalization control is achieved by defunctionalizing MXene at 650 °C under vacuum and H2 gas in a CVD furnace, followed by refunctionalization with iodine and bromine vaporization from a bubbler attached to the CVD. Notably, the introduction of iodine, which has a larger atomic size, lower electronegativity, reduce shielding effect, and lower hydrophilicity (contact angle: 99°), profoundly affecting MXene. It improves the surface area (36.2 cm2 g−1), oxidation stability in aqueous/ambient environments (21 days/80 days), and film conductivity (749 S m−1). Additionally, it significantly enhances the gas-sensing performance, including the sensitivity (0.1119 Ω ppm−1), response (0.2% and 23% to 50 ppb and 200 ppm NO2), and response/recovery times (90/100 s). The reduced shielding effect of the –I-terminals and the metallic characteristics of MXene enhance the selectivity of I-MXene toward NO2. This approach paves the way for the development of stable and high-performance gas-sensing two-dimensional materials with promising prospects for future studies.

High-entropy alloys have received considerable attention in the field of catalysis due to their exceptional properties. However, few studies hitherto focus on the origin of their outstanding performance and the accurate identification of active centers. Herein, we report a conceptual and experimental approach to overcome the limitations of single-element catalysts by designing a FeCoNiXRu (X: Cu, Cr, and Mn) High-entropy alloys system with various active sites that have different adsorption capacities for multiple intermediates. The electronegativity differences between mixed elements in HEA induce significant charge redistribution and create highly active Co and Ru sites with optimized energy barriers for simultaneously stabilizing OH * and H * intermediates, which greatly enhances the efficiency of water dissociation in alkaline conditions. This work provides an in-depth understanding of the interactions between specific active sites and intermediates, which opens up a fascinating direction for breaking scaling relation issues for multistep reactions. High-entropy alloy catalysts are an emerging class of materials and identification of catalytically active sites is critical. Here, we provide evidence that metal site electronegativity differences stabilize bound *OH and *H intermediates.

Machine learning potentials have become an important tool for atomistic simulations in many fields, from chemistry via molecular biology to materials science. Most of the established methods, however, rely on local properties and are thus unable to take global changes in the electronic structure into account, which result from long-range charge transfer or different charge states. In this work we overcome this limitation by introducing a fourth-generation high-dimensional neural network potential that combines a charge equilibration scheme employing environment-dependent atomic electronegativities with accurate atomic energies. The method, which is able to correctly describe global charge distributions in arbitrary systems, yields much improved energies and substantially extends the applicability of modern machine learning potentials. This is demonstrated for a series of systems representing typical scenarios in chemistry and materials science that are incorrectly described by current methods, while the fourth-generation neural network potential is in excellent agreement with electronic structure calculations. Machine learning potentials do not account for long-range charge transfer. Here the authors introduce a fourth-generation high-dimensional neural network potential including non-local information of charge populations that is able to provide forces, charges and energies in excellent agreement with DFT data.

Electronegativity is a key property of the elements. Being useful in rationalizing stability, structure and properties of molecules and solids, it has shaped much of the thinking in the fields of structural chemistry and solid state chemistry and physics. There are many definitions of electronegativity, which can be roughly classified as either spectroscopic (these are defined for isolated atoms) or thermochemical (characterizing bond energies and heats of formation of compounds). The most widely used is the thermochemical Pauling’s scale, where electronegativities have units of eV 1/2 . Here we identify drawbacks in the definition of Pauling’s electronegativity scale—and, correcting them, arrive at our thermochemical scale, where electronegativities are dimensionless numbers. Our scale displays intuitively correct trends for the 118 elements and leads to an improved description of chemical bonding (e.g., bond polarity) and thermochemistry. Pauling’s electronegativity scale has a fundamental value and uses accessible thermochemical data, but fails at predicting the bonding behavior for several elements. The authors propose their thermochemical scale based on experimental dissociation energies that provides dimensionless values for the electronegativity and recovers the correct trends throughout the periodic table.

Low temperatures severely impair the performance of lithium-ion batteries, which demand powerful electrolytes with wide liquidity ranges, facilitated ion diffusion, and lower desolvation energy. The keys lie in establishing mild interactions between Li + and solvent molecules internally, which are hard to achieve in commercial ethylene-carbonate based electrolytes. Herein, we tailor the solvation structure with low-ε solvent-dominated coordination, and unlock ethylene-carbonate via electronegativity regulation of carbonyl oxygen. The modified electrolyte exhibits high ion conductivity (1.46 mS·cm −1 ) at −90 °C, and remains liquid at −110 °C. Consequently, 4.5 V graphite-based pouch cells achieve ~98% capacity over 200 cycles at −10 °C without lithium dendrite. These cells also retain ~60% of their room-temperature discharge capacity at −70 °C, and miraculously retain discharge functionality even at ~−100 °C after being fully charged at 25 °C. This strategy of disrupting solvation dominance of ethylene-carbonate through molecular charge engineering, opens new avenues for advanced electrolyte design. Low-temperature operation remains challenging for batteries. Here, the authors report an electrolyte solvation structure design strategy to break solvation dominance of ethylene carbonate to facilitate the desolvation process that improves the low-temperature performance of lithium-ion batteries even below −100 °C.

High entropy oxides (HEOs) with chemically disordered multi-cation structure attract intensive interest as negative electrode materials for battery applications. The outstanding electrochemical performance has been attributed to the high-entropy stabilization and the so-called ‘cocktail effect’. However, the configurational entropy of the HEO, which is thermodynamically only metastable at room-temperature, is insufficient to drive the structural reversibility during conversion-type battery reaction, and the ‘cocktail effect’ has not been explained thus far. This work unveils the multi-cations synergy of the HEO Mg 0.2 Co 0.2 Ni 0.2 Cu 0.2 Zn 0.2 O at atomic and nanoscale during electrochemical reaction and explains the ‘cocktail effect’. The more electronegative elements form an electrochemically inert 3-dimensional metallic nano-network enabling electron transport. The electrochemical inactive cation stabilizes an oxide nanophase, which is semi-coherent with the metallic phase and accommodates Li + ions. This self-assembled nanostructure enables stable cycling of micron-sized particles, which bypasses the need for nanoscale pre-modification required for conventional metal oxides in battery applications. This demonstrates elemental diversity is the key for optimizing multi-cation electrode materials. Though high entropy oxides have been explored as possible conversion-type negative electrodes for Li-ion batteries, the roles of the different elements remain unclear. Here the authors determine the behavior of each element during electrochemical cycling and connect it to the nanoscale structure.

Photocatalytic overall water splitting into hydrogen and oxygen is desirable for long-term renewable, sustainable and clean fuel production on earth. Metal sulfides are considered as ideal hydrogen-evolved photocatalysts, but their component homogeneity and typical sulfur instability cause an inert oxygen production, which remains a huge obstacle to overall water-splitting. Here, a distortion-evoked cation-site oxygen doping of ZnIn 2 S 4 (D-O-ZIS) creates significant electronegativity differences between adjacent atomic sites, with S 1 sites being electron-rich and S 2 sites being electron-deficient in the local structure of S 1 –S 2 –O sites. The strong charge redistribution character activates stable oxygen reactions at S 2 sites and avoids the common issue of sulfur instability in metal sulfide photocatalysis, while S 1 sites favor the adsorption/desorption of hydrogen. Consequently, an overall water-splitting reaction has been realized in D-O-ZIS with a remarkable solar-to-hydrogen conversion efficiency of 0.57%, accompanying a ~ 91% retention rate after 120 h photocatalytic test. In this work, we inspire an universal design from electronegativity differences perspective to activate and stabilize metal sulfide photocatalysts for efficient overall water-splitting. Solving component homogeneity and sulfur instability of metal sulfides is crucial for photocatalytic overall water splitting. Here, the authors develop a distortion evoked cation site oxygen doping to create electronegativity differences between adjacent atom and enhance photocatalytic activity

Adsorption is essential for many processes on surfaces; therefore, an accurate prediction of adsorption properties is demanded from both fundamental and technological points of view. Particularly, identifying the intrinsic determinants of adsorption energy has been a long-term goal in surface science. Herein, we propose a predictive model for quantitative determination of the adsorption energies of small molecules on metallic materials and oxides, by using a linear combination of the valence and electronegativity of surface atoms and the coordination of active sites, with the corresponding prefactors determined by the valence of adsorbates. This model quantifies the effect of the intrinsic properties of adsorbates and substrates on adsorbate–substrate bonding, derives naturally the well-known adsorption-energy scaling relations, and accounts for the efficiency and limitation of engineering the adsorption energy and reaction energy. All involved parameters are predictable and thus allow the rapid rational design of materials with optimal adsorption properties. Adsorption of molecules at surfaces is at the basis of many processes in chemistry. Here the authors propose an approach to determine the adsorption energies of different chemical species on a variety of solid surfaces based on fundamental and accessible properties of adsorbate and surface atoms.

High-entropy alloys are a class of materials that contain five or more elements in near-equiatomic proportions 1 , 2 . Their unconventional compositions and chemical structures hold promise for achieving unprecedented combinations of mechanical properties 3 – 8 . Rational design of such alloys hinges on an understanding of the composition–structure–property relationships in a near-infinite compositional space 9 , 10 . Here we use atomic-resolution chemical mapping to reveal the element distribution of the widely studied face-centred cubic CrMnFeCoNi Cantor alloy 2 and of a new face-centred cubic alloy, CrFeCoNiPd. In the Cantor alloy, the distribution of the five constituent elements is relatively random and uniform. By contrast, in the CrFeCoNiPd alloy, in which the palladium atoms have a markedly different atomic size and electronegativity from the other elements, the homogeneity decreases considerably; all five elements tend to show greater aggregation, with a wavelength of incipient concentration waves 11 , 12 as small as 1 to 3 nanometres. The resulting nanoscale alternating tensile and compressive strain fields lead to considerable resistance to dislocation glide. In situ transmission electron microscopy during straining experiments reveals massive dislocation cross-slip from the early stage of plastic deformation, resulting in strong dislocation interactions between multiple slip systems. These deformation mechanisms in the CrFeCoNiPd alloy, which differ markedly from those in the Cantor alloy and other face-centred cubic high-entropy alloys, are promoted by pronounced fluctuations in composition and an increase in stacking-fault energy, leading to higher yield strength without compromising strain hardening and tensile ductility. Mapping atomic-scale element distributions opens opportunities for understanding chemical structures and thus providing a basis for tuning composition and atomic configurations to obtain outstanding mechanical properties. In high-entropy alloys, atomic-resolution chemical mapping shows that swapping some of the atoms for larger, more electronegative elements results in atomic-scale modulations that produce higher yield strength, excellent strain hardening and ductility.