ABSTRACT Carbon capture is the upstream cornerstone of carbon capture, utilization and storage, where dispersed CO 2 is concentrated into controlled streams. As adsorbent performance and process adaptability improve, adsorption‐based capture has attracted increasing attention. Carbon‐based materials, as industrially mature adsorbents, have secured a place in this field owing to their balanced performance in cost‐effectiveness, stability, and efficiency. However, compared with crystalline porous adsorbents, their less ordered pore environments and nonpolar surface characteristics result in relatively weak affinity toward CO 2 . To address these limitations, extensive efforts have been devoted to pore engineering, surface chemistry modification, and composite construction. Nevertheless, the complex nonlinear relationships between synthesis conditions, physicochemical properties, and adsorption performance remain difficult to elucidate through empirical optimization alone. Machine learning has introduced a new paradigm to uncover key descriptors and structure–performance relationships from complex datasets, accelerating the rational design of carbon‐based materials. Despite this progress, existing studies remain fragmented in scope and focus. Therefore, this review begins with the adsorption mechanisms and evaluation criteria of carbon‐based adsorbents, with attention to the limitations of commonly reported performance metrics. Recent progress in material optimization is then discussed according to the main functional roles of different strategies, together with comparisons across representative studies. The review further examines machine learning from descriptor construction and prediction targets to model development, validation, mechanistic interpretation, and synthesis optimization. Particular attention is given to how data‐driven methods can help analyze coupled material variables, while their reliability remains limited by data quality, dataset size, and model generalization. Overall, this review summarizes current understanding of carbon adsorbent design and discusses how experimental and data‐driven approaches can be used together to support further material optimization.
The efficient conversion of CO2 into sustainable fuels remains one of the most demanding challenges in catalysis and energy research. Beyond the discovery of new materials, recent progress reveals that the decisive factor governing activity and selectivity pertains to the microenvironment of the reaction, how space, charge, and molecular motion are confined and regulated. This work integrates mechanistic understanding from electrochemical CO2 reduction and thermocatalytic hydrogenation to establish a unified perspective on confinement engineering. By manipulating spatial confinement, local electric fields, and electronic structures, catalytic interfaces evolve from passive surfaces into dynamic microreactors capable of stabilizing intermediates and directing multi-electron reactions. Experimental and theoretical studies on Cu-, Fe-, and Na-promoted systems demonstrate that hierarchical confinement enhances C–C coupling, regulates proton–electron transfer (ET), and maintains structural integrity over long-term operation. These effects collectively enable direct CO2 hydrogenation to jet-fuel-range hydrocarbons (C8–C16) with balanced olefin–paraffin distributions under mild conditions. The emerging picture reframes catalyst design as an environmental control problem wherein one can achieve programmable selectivity and stability by engineering active sites rather than solely relying on material composition optimization. This confinement-driven strategy offers a blueprint for constructing adaptive catalytic ecosystems that transform CO2 into high-energy fuels, bridging molecular-scale reactivity with global carbon neutrality.
ABSTRACT The electrochemical nitrogen reduction reaction (eNRR) is considered a promising approach to sustainable ammonia production compared with the Haber–Bosch process. However, eNRR remains significantly hindered by the extreme stability of the N≡N bond, slow proton‐coupled electron transfer (PCET), and the kinetically favored hydrogen evolution reaction (HER), which collectively reduce ammonia yield and Faradaic efficiency. MXenes represent a unique class of 2D transition‐metal carbides, nitrides, and carbonitrides, distinguished by chemically versatile transition‐metal active sites, defect‐rich structures, tunable surface terminations, and metallic conductivity. These properties enable unprecedented tuning of N 2 adsorption, interfacial charge redistribution, orbital hybridization, and adsorption energetics, enabling selective N 2 activation while minimizing competing proton reduction. This review provides a critical overview of the design principles of MXene‐based electrocatalysts for selective ammonia synthesis. The distinctive contribution of this review is to systematically link MXene composition, vacancy engineering, heteroatom doping, single‐atom anchoring, and assembling MXene heterostructures to their effects on electronic structure, adsorption free‐energy landscapes, NRR pathways, and HER suppression. Particular emphasis is placed on understanding catalytic selectivity in terms of the underlying mechanism and structure–activity relationships, derived through advanced operando characterization, density functional theory, and emerging machine‐learning‐assisted catalyst discovery.
ABSTRACT Aqueous ammonium‐ion batteries (AAIBs) have emerged as a compelling alternative for large‐scale energy storage due to the abundance, safety, and unique chemistry of NH 4 + charge carriers. However, the commercialization of AAIBs is hindered by significant challenges, including the severe structural strain induced by the bulky NH 4 + ion and the restricted electrochemical stability window of aqueous electrolytes that leads to undesired side reactions. This review provides a systematic overview of the recent progress in AAIBs, focusing on the fundamental charge storage mechanisms and advanced material design strategies. This review categorize and evaluate key electrode materials, including Prussian blue analogs, transition metal oxides, and organic compounds, emphasizing how modification strategy can accommodate the large NH 4 + ions to enhance cycling stability. Furthermore, we discuss the evolution of electrolyte systems from dilute solutions to highly concentrated “Water‐in‐Salt” electrolytes as a means to suppress water splitting and expand operating voltages. This review concludes that the unique tetrahedral geometry and hydrogen‐bonding of NH 4 + are central to designing high‐performance electrodes and electrolytes. These insights provide a clear roadmap for transitioning AAIBs from fundamental research to practical, large‐scale energy storage applications.
ABSTRACT Nickel hydroxyfluoride (Ni 4 OHF 7 ) has attracted increasing attention due to its high theoretical capacitance, yet its rate capability and cycling stability remain to be substantially improved. Here, we achieve dual optimization through elemental doping and support interaction, doping with Fe and Cr to enhance the rate capability, and compositing with reduced graphene oxide (rGO) to improve cycling stability. Experimental results show that the introduction of Cr, with lower electronegativity, further elevates the electronic state of the host element, while rGO facilitates the uniform distribution of active materials. The comprehensively optimized electrode material, denoted NFCHF/G, delivers a specific capacitance of 2852 F g −1 at a current density of 1 A g −1 , and retains 80% of its initial specific capacitance after 10,000 cycles. An asymmetric supercapacitor (ASC) based on NFCHF/G//AC achieves a maximum energy density of 71.9 Wh kg −1 at a power density of 750 W kg −1 , demonstrating promising application prospects. This work provides a reliable strategy for the development of hydroxyfluoride‐based electrode materials and is expected to better meet future energy demands.
