Carbon Neutralization最新文献

Lithium-metal anodes offer exceptional theoretical capacity and the lowest electrochemical potential, but their practical use is limited by dendrite growth, unstable SEI formation, and large volume fluctuations. Carbon nanofibers (CNFs), with their low weight, high conductivity, and tunable structures, serve as effective hosts for regulating lithium deposition. Heteroatom doping further enhances lithiophilicity and interfacial stability: nitrogen creates abundant nucleation sites, oxygen and sulfur increase surface polarity and strengthen the SEI, and fluorine facilitates LiF-rich interphases for dendrite-free growth. Multi-element doping can also provide synergistic improvements in Coulombic efficiency and cycling stability. Despite these advances, challenges remain, including electrolyte consumption in high-surface-area structures, nonuniform dopant distribution, and potential degradation of CNF properties at high doping levels. This article summarizes recent progress in heteroatom-doped CNFs for lithium-metal anodes and outlines key limitations and future directions toward scalable, high-performance lithium-metal batteries.

The electrocatalytic CO₂ reduction reaction (CO₂RR) to formate provides a sustainable pathway for CO₂ conversion. Indium oxide (In₂O₃)-based catalysts have exceptional selectivity toward formate; however, the regulation and effects of crystalline structure on their performance need to be thoroughly investigated. Herein, we present the methodically controlled synthesis of In₂O₃ catalysts with unique crystallographic structures: rhombohedral In₂O₃ (h-In₂O₃), cubic In₂O₃ (c-In₂O₃), and mixed-phase In₂O₃ (h/c-In₂O₃), and conduct a comprehensive evaluation of their performance in CO₂RR to formate. Remarkably, the h-In₂O₃ catalyst demonstrates a Faradaic efficiency of formate (~95%) and current density surpassing both c-In₂O₃ and h/c-In₂O₃. In addition, the h-In₂O₃ catalyst exhibits excellent comprehensive performance in terms of operating potential range (−0.87 ~ −1.27 V vs. RHE), catalyst stability (70 h), pH range of electrolyte (3.00 ~ 14.00), and CO₂ concentration (20% ~ 100%). Density functional theory studies reveal that among various phases and facets of In₂O₃, the (104) facet of the h-In₂O₃ most effectively stabilizes the critical reaction intermediate, a contribution that is key to its enhanced activity for formate generation from CO₂RR. This investigation elucidates key insights into the engineering crystalline structure of In₂O₃ catalysts pertinent to CO₂RR, thereby presenting a methodical approach for developing highly efficient electrocatalysts.

Hydrogel electrolytes have emerged as promising candidates for flexible zinc-ion batteries (ZIBs) owing to their intrinsic mechanical robustness and biocompatibility. However, realizing high electrochemical performance and long-term operational stability remains a significant challenge, primarily due to the low ionic conductivity of hydrogel matrices and the uncontrolled growth of zinc dendrites, along with parasitic side reactions at the zinc anode interface. In this work, we propose a vertically aligned, zincophilic porous polyacrylamide-based hydrogel electrolyte (o-PAM) featuring strong interfacial adhesion. The unique structure, characterized by a locally alternating gel–liquid phase distribution, effectively overcomes the limitations of conventional hydrogel electrolytes by facilitating rapid Zn²⁺ transport and ensuring uniform ion deposition. This design bridges the ionic conductivity gap between gels and liquid electrolytes while mitigating Zn²⁺ concentration gradients. Moreover, the incorporation of multifunctional lithium bis(trifluoromethanesulfonyl)imide (LiTFSI) into the hydrogel not only enhances the electrolyte–anode interfacial adhesion, thereby lowering interfacial resistance, but also contributes to electrochemical stability. The abundant hydrogen bond acceptors in LiTFSI interact with water molecules to form hydrogen bonds, reducing the activity of free water and effectively suppressing side reactions such as hydrogen evolution (HER). As a result, the o-PAM hydrogel electrolyte delivers a high Zn²⁺ transference number of 0.65 and an impressive ionic conductivity of 20.14 mS cm⁻¹. In Zn||o-PAM||Zn symmetric cells, the electrolyte demonstrates outstanding cycling stability, with a lifespan of 3000 h at 1 mA cm⁻². Furthermore, a full Zn||o-PAM||I₂ cell exhibits remarkable capacity retention of 95.4% after 500 cycles at 1 mA cm⁻². These results highlight a promising strategy for the rational design of high-performance hydrogel electrolytes for next-generation zinc-ion batteries.

The accumulation of persistent organic pollutants (POPs) in aquatic systems poses severe environmental and health risks, underscoring the need for sustainable, efficient remediation technologies. Biomass-derived carbon materials have emerged as cost-effective photocatalysts owing to their high surface area, tunable electronic structure, and excellent charge transport properties. This review summarizes recent progress in their synthesis, structural design, and surface modification for photocatalytic degradation of organic pollutants. Emphasis is placed on key mechanisms such as reactive oxygen species (ROS) generation, band gap tuning, and interfacial charge separation, as well as performance-enhancing strategies including heteroatom doping, heterojunction formation, and hybrid integration for improved visible-light activity. The dual functionality of these materials in adsorption and photocatalysis is also highlighted, revealing synergistic pollutant removal pathways. Finally, critical challenges related to scalability, stability, and reproducibility are discussed, along with future perspectives for translating biomass-derived carbon photocatalysts from laboratory research to practical environmental applications.

Front cover image: Transition metal phosphides are considered highly promising cathode catalysts for zinc-air batteries. However, issues such as phase separation, particle agglomeration, and insufficient active sites during synthesis severely compromise the battery's cycling stability and power density. Interface engineering strategies can effectively mitigate these problems. In article number e70065, a “nanoconfinement phosphorization” strategy was proposed, successfully synthesizing nitrogen-doped carboncoated FeP nanoparticles (FeP–NPC) catalysts. Systematic characterization and theoretical calculations revealed their outstanding bifunctional oxygen electrocatalytic performance. Furthermore, the innovative FeP–N3–C interfacial structure design significantly enhances the long-term cycling stability and power density of zinc-air batteries by regulating the Fe d-band center and optimizing the adsorption energy of reaction intermediates, offering a novel approach for achieving efficient, low-cost metal-air batteries.

Heterostructures show great potential on highly efficient electrocatalysts for oxygen evolution reaction (OER) owing to optimization of electronic structure, synergies, exposure to multiple active sites. In present work, we establish a spherical nanoflower-structured nickel-iron carbonate hydroxides/silicate hydroxides (denoted as NiFeCH/SH) with crystalline/amorphous heterostructure by a facile hydrothermal synthesis strategy. Characterization analysis confirms the controlled partial phase conversion without structural collapse, which is based on the Ni-Fe bi-metallic effect. The heterostructures under bimetallic effect provides the optimized catalyst with good electrical conductivity and abundant active sites, which makes it achieve exceptional OER performance with an ultralow overpotential of 251 mV at 10 mA cm⁻² and a small Tafel slope of 31.8 mV dec⁻¹, alongside outstanding long-term stability. The enhanced stability is originated from the protection of silicate. Density functional theory (DFT) methods reveal that the enhanced activity stems from moderate electronic structure caused by suppressing electron transition to e_g orbitals of metal active sites. This work establishes a dual-regulation strategy integrating tetrahedral silicate engineering and bimetallic cooperation to simultaneously enhance OER activity and durability, offering new perspectives for designing robust alkaline water electrolysis catalysts through electronic and defect structure manipulation.

Iron-chromium redox flow batteries (ICRFBs) are promising for large-scale energy storage but suffer from sluggish Cr³⁺/Cr²⁺ redox kinetics and severe hydrogen evolution reaction (HER) at the anode. To address these issues, a bougainvillea-like indium-doped BiOCl nanosheet architecture on carbon cloth (C-In/BiOCl-CC) was developed as a high-performance electrode. The unique hierarchical structure was found to significantly increase the specific surface area and active sites, thereby facilitating efficient Cr ion conversion. Simultaneously, indium doping effectively suppresses HER by elevating the hydrogen evolution overpotential, while the synergistic effect between In and BiOCl enhances electronic conductivity and reduces charge transfer resistance. As a result, the electrode demonstrates a low Cr³⁺ reduction overpotential of 0.35 V at 140 mA cm⁻² and a charge transfer resistance of 0.492 Ω. The assembled ICRFB achieves an energy efficiency of 84.7% and a voltage efficiency of 86.5% at 140 mA cm⁻², while maintaining stable performance over 800 cycles with coulombic efficiency exceeding 97%. This work offers an effective electrode design strategy for high-performance and long-life ICRFBs.

Efficient catalysis of unsaturated hydrocarbon hydrogenation/isomerization reactions is important for realizing sustainable chemical processes and enhancing the whole energy efficiency. However, the development of “one-pot” catalysts with high activity, excellent selectivity and outstanding stability remains a major challenge. This study presents a novel catalyst design that utilizes NU-1000 with open metal sites to enhance metal-molecule interactions and promote selective adsorption. By using a strategic multimetal doping technique Ti/Zr/Hf, homomeric high-density frustrated Lewis pairs (FLPs) architecture with different coordination metals namely M-NU-1000-X (M=Zr, Hf, Ti, X = 1~6 represented various metal combinations) were obtained. The strategic multimetal doping finely tune FLPs’ acidity/basicity and electron structure favorable for improve acid-base synergism effect and steric hindrance effect. DFT calculations reveal a mechanism that generated active hydrogen through cleaving H₂ at the FLPs site then attack cycloolefin double bond selectively. The hydrogenation/isomerization mechanism was promoted greatly by catalysis effect induced by metals-based π anti-donation effect. Furthermore, we constructed a robust connection model between the calculated Gibbs free energy values of the transition state and some parameter and obtained activation energy barriers based on the descriptor model, thus significantly decreasing huge computational cost. Dynamic Time Warping (DTW) analysis reveals that the dynamic response of polarizability and LUMO energy levels is a key factor determining catalytic activity. The introduction of Ti significantly enhances these dynamic differences, while dynamic site regulation of the local coordination environment further amplifies the differentiation in catalytic performance. A novel approach has been established that integrates electronic structure properties, reaction path evolution, and energy descriptors. This opens a new gateway for developing highly efficient hydrogenation catalysts and provides innovative strategies for catalyst design.

Metal-organic frameworks (MOFs) exhibit significant potential for the adsorption of volatile organic compounds (VOCs) due to their tunable pore structures and high specific surface areas. However, identifying high-performing MOFs within the vast structural space remains challenging, primarily due to unclear structure–performance relationships. Moreover, existing studies often overlook realistic adsorption scenarios that involve coexisting atmospheric components such as O₂, N₂, and water vapor, and rarely address capacity–selectivity trade-offs or conducted systematic comparisons of model performance. Herein, we developed a data-driven machine learning framework integrating multi-model comparisons, stacking ensemble modeling, and interpretability analyses for predicting the adsorption performance of MOFs for airborne toluene with high accuracy. The stacking model, comprising eight complementary base models and a multilayer perceptron (MLP) as the meta-learner, demonstrated an enhanced capability to capture complex nonlinear relationships between descriptors and performance, achieving superior predictive accuracy (R² = 0.922, RMSE = 0.186) compared to the best-performing individual model, CatBoost (R² = 0.890, RMSE = 0.326). Furthermore, by incorporating SHAP, PDP, and feature interaction analyses, this study elucidated the synergistic regulatory mechanisms associated with key structural descriptors. Statistical analysis further revealed that the structural parameters of high-performing MOFs exhibited significant convergence, with metal centers such as Cu and their open metal sites (OMS) quantitatively identified as critical performance-enhancing factors. Finally, the stacking model was successfully deployed as an interactive web platform that enables real-time prediction and visual interpretability of MOF performance, serving as a practical tool for the efficient screening of MOF candidates for airborne toluene adsorption.

Lithium-ion batteries are widely used in various fields, including electric vehicles and energy storage systems. Accurate battery life prediction is essential for effective safety management. However, acquiring sufficient aging information from limited cycle data for accurate life prediction often results in increased feature dimensionality and model complexity. To solve this problem, this paper proposes a method to achieve lossless information dimensionality reduction through the deep variational autoencoder. Based on the lithium iron phosphate battery dataset, only a limited number of cycles are utilized. A comprehensive feature set with 1519 features is constructed to capture more detailed aging characteristics from limited data. After correlation analysis, 76 high-quality features are preliminarily screened. To balance the preservation of aging information with the complexity of the subsequent network, we propose a dimensionality reduction approach that minimizes feature redundancy while retaining essential information. This method reduces the feature set to 10 key features while preserving the original aging information with minimal loss. The maximum mean square error before and after dimension reduction is 0.02139. The proposed method enables life prediction only with the support of simple machine learning method, with only a few parameters required. The adopted dimensionality reduction method offers useful guidance for high-dimensional feature processing in similar scenarios.