Metal-organic frameworks (MOFs) and their derivatives have emerged as promising photocatalysts for CO2 reduction owing to their large surface area, tunable porosity, and adjustable coordination environment. However, a comprehensive understanding of how organic ligands govern the structure-function relationship in MOF-based photocatalysis remains limited. This review provides a ligand-centered perspective on the photocatalytic CO2 reduction (PCR) process, focusing on how ligand functionalization, electronic modulation, and coordination design influence catalytic activity, stability, and selectivity. We summarize recent progress in ligand-regulated MOF systems from both experimental and theoretical viewpoints and discuss the associated structure-activity mechanisms. Finally, current challenges and future opportunities—such as cost-effective synthesis, scalability, and sustainable catalyst design—are discussed to guide the rational development of high-performance MOF photocatalysts for efficient CO2 conversion.
PurposeWhereas conventional views emphasize symmetry in dyadic buyer-supplier relationships, emerging research suggests that advantage asymmetry may strengthen value co-creation (VCC) in multi-actor project networks. Our study examines whether and how organizational asymmetries derived from knowledge complementarity facilitate VCC in large interorganizational projects (LIPs), where multiple organizations need to integrate heterogeneous knowledge and coordinate across interorganizational boundaries. To address this question, we develop multidimensional indicators to evaluate organizational asymmetry and identify the scenarios through which asymmetry influences VCC.Design/methodology/approachOur study adopts a value network analysis approach to identify and measure organizational asymmetry in LIPs. Empirical data were collected from a single LIP, the Shanghai West Bund Media Port project. Multi-source data were collected, including semi-structured interviews with project experts and two-wave questionnaire surveys, to construct a knowledge value flow (KVF) network comprising 95 KVFs across seven organizational kinds. The design structure matrix method, combined with Danielson's algorithm, was used to identify and analyze interorganizational KVFs and structural relationships.FindingsWe developed three indicators, value advantage, network advantage, and integrated advantage, to measure organizational advantages and used them to classify organizations in LIPs as powerful, wealthy, central, or disadvantaged. An advantage-asymmetry indicator was further constructed to assess asymmetry across the KVF network, revealing that asymmetric relationships among disadvantaged, underpowered, and powerful organizations dominate the network structure (75.68%). Building on these results, we identify three recurring advantage-asymmetry scenarios that explain how heterogeneous organizational advantages enhance VCC in LIPs: complementarity between value advantage and network advantage within organizations, complementarity across different levels of organizational advantage, and broker selection that translates asymmetry into collaborative synergy.Originality/valueOur study advances research on LIPs by developing and empirically applying a structured measurement framework for organizational asymmetry derived from knowledge complementarity. By operationalizing asymmetry through value advantage, network advantage, and integrated advantage within a KVF network, we make organizational asymmetry analytically observable and comparable in LIP settings. The case-based analysis illustrates how this framework can reveal complementary configurations and broker roles that shape VCC. Methodologically, the value network analysis approach provides a structured procedure for examining knowledge-based asymmetry in LIPs.
A series of porphyrin-based ionic polymeric materials, IP1, IP2, and IP1 & hybull;M (M=Zn, Mg, Ni), were syn- thesized via olefin polymerization and thoroughly characterized by Fourier transform infrared spectroscopy (FTIR), thermogravimetric analysis (TGA), scanning electron microscopy (SEM), energy dispersive X - ray spectroscopy (EDS), specific surface area and porosity analyses, and X-ray photoelectron spectroscopy (XPS). These materials all exhibited excellent catalytic performance in the cycloaddition reaction of CO2 with epoxides under mild conditions of low temperature and atmospheric pressure. Among them, IP1 & hybull;Zn, with Zn2+ coordinated to the porphyrin ring, demonstrated the highest catalytic activity. Under solvent-free conditions at 80 degrees C for 5 h under 101 kPa CO2, it cata- lyzed the conversion of epichlorohydrin to the corresponding cyclic carbonate with a yield of 94.1%, along with a certain degree of substrate generality. Furthermore, IP1 & hybull;Zn showed remarkable stability and reusability, maintain- ing a catalytic yield above 90% even after eight consecutive cycles, indicating promising potential for industrial applications.
Mechanical metamaterials have demonstrated broad application prospects in impact isolation and energy absorption due to their exceptional mechanical properties. However, existing metamaterials still face bottlenecks, such as limited stiffness adjustment range, insufficient impact resistance, and poor reusability. To address these challenges, this paper proposes a bio-inspired variable-stiffness metamaterial based on carbon fiber-reinforced polymer (CFRP). By integrating the design advantages of the Bouligand structure and S-shaped spring units, this structure innovatively achieves wide-range stiffness modulation and outstanding cushioning properties. In this study, a theoretical model of the S-shaped spring units was established and validated through finite element simulations and experimental tests. The variable-stiffness characteristics under compressive loading were systematically analyzed via simulations and experiments, confirming the structure’s variable stiffness behavior. Subsequently, drop hammer experiments were conducted on the metamaterial. In the experiments, this structure demonstrated excellent cushioning property. Finally, after 40 cycles of compression testing, the metamaterial retained excellent mechanical properties with a loss rate below 4.2 %, highlighting its durability and reliability.
The rational design of polyurethanes (PUs) with tailored thermal properties is often impeded by the complex interplay between molecular architecture and microphase-separated morphology. While machine learning (ML) offers a promising method to accelerate materials discovery, existing models often lack mechanistic interpretability. Herein, we develop a robust predictive model for the glass transition temperature (T-g) by benchmarking a suite of ML algorithms against a carefully curated and expanded PU data set. Among the evaluated architectures, ensemble-based methods demonstrate better performance, with the optimized eXtreme Gradient Boosting (XGBoost) model achieving R-2 > 0.91 and RMSE approximate to 9.6 K, outperforming Lasso regression, Random Forest and Support-Vector Regression. Beyond accurate prediction, Shapley Additive Explanations (SHAP) methodology is employed to elucidate the mechanistic underpinnings of feature contributions in determining T-g. The SHAP analysis has been shown to quantitatively identify the density of quaternary carbon units within the hard segment (c HS), the hard segment weight fraction (HS wt %) and the molecular volume of the hard segment (HS MV) as the three dominant physicochemical descriptors governing T-g. Furthermore, all of the significant features that were examined can be categorized into three distinct groups, i.e., positive, negative, and dual effect groups, reflecting the competition between intramolecular chain stiffening and the disruption of intermolecular packing and the hydrogen-bond network. Overall, this study establishes concise data-driven design rules for modulating the thermal behavior of PUs, bridging the gap between statistical learning and fundamental polymer physics and enabling more informed accelerated PU formulation.
