Carbon Neutralization最新文献

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.

The environmental issues caused by carbon dioxide (CO₂), a major greenhouse gas, have garnered increasing attention, driving the widespread application of electrocatalytic CO₂ reduction reactions (eCO₂RR) in pollutant treatment. Metal-CO₂ batteries (MCBs) have emerged as a promising alternative to conventional fuel cells, garnering significant interest due to their capacity to integrate energy storage with eCO₂RR. The electrolyte is of pivotal significance in MCBs, given its considerable impact on battery performance, service life, and safety. However, due to the inherent limitations of conventional electrolytes, such as flammability, thermal instability, poor low-temperature performance, side reactions, achieving simultaneous optimization of all required performance parameters remains a formidable scientific challenge. Electrolytes should simultaneously possess high ionic conductivity, substantial CO₂ solubility, broad electrochemical stability window, and thermodynamically robust interfaces with the electrode materials to ensure overall system performance and stability. It is fortunate that a range of methodologies have been established for the purpose of modifying electrolytes. In this review, we provide a concise overview of the structural characteristics of conventional MCBs, systematically classify MCBs electrolytes into liquid, solid-state, and semi-solid-state categories, and highlight the unique advantages and challenges. We further explore key optimization strategies like bulk composition tuning and additive engineering to enhance performance and put forward several suggestions for the future development of MCBs electrolytes according to persistent challenges. The findings of this study can provide valuable insights for the development of MCBs.

Chain diamines have gained attention in carbon capture recently for their high CO₂ absorption capacity and rate. However, how diamine structure regulates the activation barrier of CO₂ absorption remains unclear, and the large number of amine candidates hinders efficient screening of low-energy absorbents. To resolve these issues, this study first used DFT to investigate the regulation mechanism of diamines on CO₂ absorption and clarify key reaction pathways and structure-activity relationships. It was confirmed that diamines react with CO₂ via a zwitterion mechanism, while diamine/tertiary amine mixtures react with CO₂ through single-step proton transfer. Diamines with more primary amine sites have lower barriers; methyl/ethyl substitution, carbon chain extension (on either amine), or hydroxyl substitution (on diamines) increases the proton transfer barrier. To address low screening efficiency from excessive candidates, an efficient framework integrating DFT and active learning was constructed. Using DFT-calculated reaction barriers, a feature mapping with RDKit descriptors was built, and an active learning model was developed via 10 iterative rounds. The model achieved high prediction accuracy (R² = 0.821) for the rate-determining step's activation barrier. SHAP analysis identified the steric-related first-order molecular connectivity index (T_Chi1v) as the dominant feature. Finally, the optimal amine pair (AEEA + EDMA, activation barrier: 0.8 kcal·mol⁻¹) was identified. This work clarifies the core mechanism via DFT, enables efficient candidate screening via active learning, and explains the optimal combination's performance through mechanistic tracing—providing an interpretable route for developing low-energy, high-efficiency mixed amine absorbents and advancing carbon capture technology.

The porous carbon-coated Ni_0.5Zn_0.5Fe₂O₄ ferrite embedded within Ti₃C₂T_x MXene interlayers was successfully synthesized via solvothermal and electrostatic self-assembly, followed by carbonization. The resulting Ni_0.5Zn_0.5Fe₂O₄@C/Ti₃C₂T_x composites exhibit superior electromagnetic wave absorption properties, achieving a minimum reflection loss of −63.25 dB at 17.32 GHz with a coating thickness of only 1.53 mm. Notably, heat treatment at 800°C induces the formation of an open interlayer porous microstructure and abundant heterogeneous interfaces, which effectively suppress nanoparticle agglomeration, enhance interfacial polarization, and optimize impedance matching. This study demonstrates a novel strategy to integrate MOF-derived ferrite with MXene for constructing hierarchical porous structures, offering new insights into the rational design of lightweight, high-performance microwave absorbing materials.