High-dimensional generalized median adaptive lasso with application to omics data.

Recently, there has been a growing interest in variable selection for causal inference within the context of high-dimensional data. However, when the outcome exhibits a skewed distribution, ensuring the accuracy of variable selection and causal effect estimation might be challenging. Here, we introduce the generalized median adaptive lasso (GMAL) for covariate selection to achieve an accurate estimation of causal effect even when the outcome follows skewed distributions. A distinctive feature of our proposed method is that we utilize a linear median regression model for constructing penalty weights, thereby maintaining the accuracy of variable selection and causal effect estimation even when the outcome presents extremely skewed distributions. Simulation results showed that our proposed method performs comparably to existing methods in variable selection when the outcome follows a symmetric distribution. Besides, the proposed method exhibited obvious superiority over the existing methods when the outcome follows a skewed distribution. Meanwhile, our proposed method consistently outperformed the existing methods in causal estimation, as indicated by smaller root-mean-square error. We also utilized the GMAL method on a deoxyribonucleic acid methylation dataset from the Alzheimer's disease (AD) neuroimaging initiative database to investigate the association between cerebrospinal fluid tau protein levels and the severity of AD.

Magnetic Nanoparticles: Synthesis, Anisotropy, and Applications.

Anisotropy is an important and widely present characteristic of materials that provides desired direction-dependent properties. In particular, the introduction of anisotropy into magnetic nanoparticles (MNPs) has become an effective method to obtain new characteristics and functions that are critical for many applications. In this review, we first discuss anisotropy-dependent ferromagnetic properties, ranging from intrinsic magnetocrystalline anisotropy to extrinsic shape and surface anisotropy, and their effects on the magnetic properties. We further summarize the syntheses of monodisperse MNPs with the desired control over the NP dimensions, shapes, compositions, and structures. These controlled syntheses of MNPs allow their magnetism to be finely tuned for many applications. We discuss the potential applications of these MNPs in biomedicine, magnetic recording, magnetotransport, permanent magnets, and catalysis.

Au/Pt Bimetallic Nanowires with Stepped Pt Sites for Enhanced C-C Cleavage in C2+ Alcohol Electro-oxidation Reactions.

Efficient C-C bond cleavage and oxidation of alcohols to CO2 is the key to developing highly efficient alcohol fuel cells for renewable energy applications. In this work, we report the synthesis of core/shell Au/Pt nanowires (NWs) with stepped Pt clusters deposited along the ultrathin (2.3 nm) stepped Au NWs as an active catalyst to effectively oxidize alcohols to CO2. The catalytic oxidation reaction is dependent on the Au/Pt ratios, and the Au1.0/Pt0.2 NWs have the largest percentage (∼75%) of stepped Au/Pt sites and show the highest activity for ethanol electro-oxidation, reaching an unprecedented 196.9 A/mgPt (32.5 A/mgPt+Au). This NW catalyst is also active in catalyzing the oxidation of other primary alcohols, such as methanol, n-propanol, and ethylene glycol. In situ X-ray absorption spectroscopy and infrared spectroscopy are used to characterize the catalyst structure and to identify key reaction intermediates, providing concrete evidence that the synergy between the low-coordinated Pt sites and the stepped Au NWs is essential to catalyze the alcohol oxidation reaction, which is further supported by DFT calculations that the C-C bond cleavage is indeed enhanced on the undercoordinated Pt-Au surface. Our study provides important evidence that a core/shell structure with stepped core/shell sites is essential to enhance electrochemical oxidation of alcohols and will also be central to understanding electro-oxidation reactions and to the future development of highly efficient direct alcohol fuel cells for renewable energy applications.

Subgroup analysis for longitudinal data based on a partial linear varying coefficient model with a change plane.

Subgroup analysis has become an important tool to characterize the treatment effect heterogeneity, and finally towards precision medicine. On the other hand, longitudinal study is widespread in many fields, but subgroup analysis for this data type is still limited. In this article, we study a partial linear varying coefficient model with a change plane, in which the subgroups are defined based on linear combination of grouping variables, and the time-varying effects in different subgroups are estimated to capture the dynamic association between predictors and response. The varying coefficients are approximated by basis functions and the group indicator function is smoothed by kernel function, which are included in the generalized estimating equation for estimation. Asymptotic properties of the estimators for the varying coefficients, the constant coefficients and the change plane coefficients are established. Simulations are conducted to demonstrate the flexibility, efficiency and robustness of the proposed method. Based on the Standard and New Antiepileptic Drugs study, we successfully identify a subgroup in which patients are sensitive to the newer drug in a specific period of time.

Multiply robust estimator for the difference in survival functions using pseudo-observations.

BACKGROUND: When estimating the causal effect on survival outcomes in observational studies, it is necessary to adjust confounding factors due to unbalanced covariates between treatment and control groups. There is no study on multiple robust method for estimating the difference in survival functions. In this study, we propose a multiply robust (MR) estimator, allowing multiple propensity score models and outcome regression models, to provide multiple protection. METHOD: Based on the previous MR estimator (Han 2014) and pseudo-observation approach, we proposed a new MR estimator for estimating the difference in survival functions. The proposed MR estimator based on the pseudo-observation approach has several advantages. First, the proposed estimator has a small bias when any PS and OR models were correctly specified. Second, the proposed estimator considers the advantage pf the pseudo-observation approach, which avoids proportional hazards assumption. A Monte Carlo simulation study was performed to evaluate the performance of the proposed estimator. And the proposed estimator was used to estimate the effect of chemotherapy on triple-negative breast cancer (TNBC) in real data. RESULTS: The simulation studies showed that the bias of the proposed estimator was small, and the coverage rate was close to 95% when any model for propensity score or outcome regression is correctly specified regardless of whether the proportional hazard assumption holds, finite sample size and censoring rate. And the simulation results also showed that even though the propensity score models are misspecified, the bias of the proposed estimator was still small when there is a correct model in candidate outcome regression models. And we applied the proposed estimator in real data, finding that chemotherapy could improve the prognosis of TNBC. CONCLUSIONS: The proposed estimator, allowing multiple propensity score and outcome regression models, provides multiple protection for estimating the difference in survival functions. The proposed estimator provided a new choice when researchers have a "difficult time" choosing only one model for their studies.

An improved multiply robust estimator for the average treatment effect.

BACKGROUND: In observational studies, double robust or multiply robust (MR) approaches provide more protection from model misspecification than the inverse probability weighting and g-computation for estimating the average treatment effect (ATE). However, the approaches are based on parametric models, leading to biased estimates when all models are incorrectly specified. Nonparametric methods, such as machine learning or nonparametric double robust approaches, are robust to model misspecification, but the efficiency of nonparametric methods is low. METHOD: In the study, we proposed an improved MR method combining parametric and nonparametric models based on the previous MR method (Han, JASA 109(507):1159-73, 2014) to improve the robustness to model misspecification and the efficiency. We performed comprehensive simulations to evaluate the performance of the proposed method. RESULTS: Our simulation study showed that the MR estimators with only outcome regression (OR) models, where one of the models was a nonparametric model, were the most recommended because of the robustness to model misspecification and the lowest root mean square error (RMSE) when including a correct parametric OR model. And the performance of the recommended estimators was comparative, even if all parametric models were misspecified. As an application, the proposed method was used to estimate the effect of social activity on depression levels in the China Health and Retirement Longitudinal Study dataset. CONCLUSIONS: The proposed estimator with nonparametric and parametric models is more robust to model misspecification.

Covariate balance-related propensity score weighting in estimating overall hazard ratio with distributed survival data.

BACKGROUND: When data is distributed across multiple sites, sharing information at the individual level among sites may be difficult. In these multi-site studies, propensity score model can be fitted with data within each site or data from all sites when using inverse probability-weighted Cox regression to estimate overall hazard ratio. However, when there is unknown heterogeneity of covariates in different sites, either approach may lead to potential bias or reduced efficiency. In this study, we proposed a method to estimate propensity score based on covariate balance-related criterion and estimate the overall hazard ratio while overcoming data sharing constraints across sites. METHODS: The proposed propensity score was generated by choosing between global and local propensity score based on covariate balance-related criterion, combining the global propensity score fitted in the entire population and the local propensity score fitted within each site. We used this proposed propensity score to estimate overall hazard ratio of distributed survival data with multiple sites, while requiring only the summary-level information across sites. We conducted simulation studies to evaluate the performance of the proposed method. Besides, we applied the proposed method to real-world data to examine the effect of radiation therapy on time to death among breast cancer patients. RESULTS: The simulation studies showed that the proposed method improved the performance in estimating overall hazard ratio comparing with global and local propensity score method, regardless of the number of sites and sample size in each site. Similar results were observed under both homogeneous and heterogeneous settings. Besides, the proposed method yielded identical results to the pooled individual-level data analysis. The real-world data analysis indicated that the proposed method was more likely to find a significant effect of radiation therapy on mortality compared to the global propensity score method and local propensity score method. CONCLUSIONS: The proposed covariate balance-related propensity score in multi-site distributed survival data outperformed the global propensity score estimated using data from the entire population or the local propensity score estimated within each site in estimating the overall hazard ratio. The proposed approach can be performed without individual-level data transfer between sites and would yield the same results as the corresponding pooled individual-level data analysis.

A Heteroleptic Gold Hydride Nanocluster for Efficient and Selective Electrocatalytic Reduction of CO2 to CO.

It has been a long-standing challenge to create and identify the active sites of heterogeneous catalysts, because it is difficult to precisely control the interfacial chemistry at the molecular level. Here we report the synthesis and catalysis of a heteroleptic gold trihydride nanocluster, [Au22H3(dppe)3(PPh3)8]3+ [dppe = 1,2-bis(diphenylphosphino)ethane, PPh3 = triphenylphosphine]. The Au22H3 core consists of two Au11 units bonded via six uncoordinated Au sites. The three H atoms bridge the six uncoordinated Au atoms and are found to play a key role in catalyzing electrochemical reduction of CO2 to CO with a 92.7% Faradaic efficiency (FE) at -0.6 V (vs RHE) and high reaction activity (134 A/gAu mass activity). The CO current density and FECO remained nearly constant for the CO2 reduction reaction for more than 10 h, indicating remarkable stability of the Au22H3 catalyst. The Au22H3 catalytic performance is among the best Au-based catalysts reported thus far for electrochemical reduction of CO2. Density functional theory (DFT) calculations suggest that the hydride coordinated Au sites are the active centers, which facilitate the formation of the key *COOH intermediate.

Multiply robust subgroup analysis based on a single-index threshold linear marginal model for longitudinal data with dropouts.

Identifying subpopulations that may be sensitive to the specific treatment is an important step toward precision medicine. On the other hand, longitudinal data with dropouts is common in medical research, and subgroup analysis for this data type is still limited. In this paper, we consider a single-index threshold linear marginal model, which can be used simultaneously to identify subgroups with differential treatment effects based on linear combination of the selected biomarkers, estimate the treatment effects in different subgroups based on regression coefficients, and test the significance of the difference in treatment effects based on treatment-subgroup interaction. The regression parameters are estimated by solving a penalized smoothed generalized estimating equation and the selection bias caused by missingness is corrected by a multiply robust weighting matrix, which allows multiple missingness models to be taken account into estimation. The proposed estimator remains consistent when any model for missingness is correctly specified. Under regularity conditions, the asymptotic normality of the estimator is established. Simulation studies confirm the desirable finite-sample performance of the proposed method. As an application, we analyze the data from a clinical trial on pancreatic cancer.

Estimation of average treatment effect based on a multi-index propensity score.

BACKGROUND: Estimating the average effect of a treatment, exposure, or intervention on health outcomes is a primary aim of many medical studies. However, unbalanced covariates between groups can lead to confounding bias when using observational data to estimate the average treatment effect (ATE). In this study, we proposed an estimator to correct confounding bias and provide multiple protection for estimation consistency. METHODS: With reference to the kernel function-based double-index propensity score (Ker.DiPS) estimator, we proposed the artificial neural network-based multi-index propensity score (ANN.MiPS) estimator. The ANN.MiPS estimator employed the artificial neural network to estimate the MiPS that combines the information from multiple candidate models for propensity score and outcome regression. A Monte Carlo simulation study was designed to evaluate the performance of the proposed ANN.MiPS estimator. Furthermore, we applied our estimator to real data to discuss its practicability. RESULTS: The simulation study showed the bias of the ANN.MiPS estimators is very small and the standard error is similar if any one of the candidate models is correctly specified under all evaluated sample sizes, treatment rates, and covariate types. Compared to the kernel function-based estimator, the ANN.MiPS estimator usually yields smaller standard error when the correct model is incorporated in the estimator. The empirical study indicated the point estimation for ATE and its bootstrap standard error of the ANN.MiPS estimator is stable under different model specifications. CONCLUSIONS: The proposed estimator extended the combination of information from two models to multiple models and achieved multiply robust estimation for ATE. Extra efficiency was gained by our estimator compared to the kernel-based estimator. The proposed estimator provided a novel approach for estimating the causal effects in observational studies.

Developing an immune signature for triple-negative breast cancer to predict prognosis and immune checkpoint inhibitor response.

Aim: We aimed to develop a new signature based on immune-related genes to predict prognosis and response to immune checkpoint inhibitors in patients with triple-negative breast cancer (TNBC). Materials & methods: Single-sample gene set enrichment was used to develop an immune-based prognostic signature (IPRS) for TNBC patients. We conducted multivariate Cox analysis to evaluate the prognosis value of the IPRS. Result: An IPRS based on 66 prognostic genes was developed. Multivariate Cox analysis indicated that the IPRS was an independent factor for prognosis. PD-1, PD-L1, PD-L2 and CTLA4 gene expression was higher in the low-risk group, suggesting IPRS could predict the response to immune checkpoint inhibitors. Conclusion: The IPRS might be a reliable signature to predict TNBC patients' prognosis and response to immune checkpoint inhibitors, but needs prospective validation.

Nickel-Platinum Nanoparticles as Peroxidase Mimics with a Record High Catalytic Efficiency.

While nanoscale mimics of peroxidase have been extensively developed over the past decade or so, their catalytic efficiency as a key parameter has not been substantially improved in recent years. Herein, we report a class of highly efficient peroxidase mimic-nickel-platinum nanoparticles (Ni-Pt NPs) that consist of nickel-rich cores and platinum-rich shells. The Ni-Pt NPs exhibit a record high catalytic efficiency with a catalytic constant (Kcat) as high as 4.5 × 107 s-1, which is ∼46- and 104-fold greater than the Kcat values of conventional Pt nanoparticles and natural peroxidases, respectively. Density functional theory calculations reveal that the unique surface structure of Ni-Pt NPs weakens the adsorption of key intermediates during catalysis, which boosts the catalytic efficiency. The Ni-Pt NPs were applied to an immunoassay of a carcinoembryonic antigen that achieved an ultralow detection limit of 1.1 pg/mL, hundreds of times lower than that of the conventional enzyme-based assay.

Stabilizing Hard Magnetic SmCo5 Nanoparticles by N-Doped Graphitic Carbon Layer.

We report a chemical method to synthesize size-controllable SmCo5 nanoparticles (NPs) and to stabilize the NPs against air oxidation by coating a layer of N-doped graphitic carbon (NGC). First 10 nm CoO and 5 nm Sm2O3 NPs were synthesized and aggregated in reverse micelles of oleylamine to form SmCo-oxide NPs with a controlled size (110, 150, or 200 nm). The SmCo-O NPs were then coated with polydopamine and thermally annealed to form SmCo-O/NGC NPs, which were further embedded in CaO matrix and reduced with Ca at 850 °C to give SmCo5/NGC NPs of 80, 120, or 180 nm, respectively. The 10 nm NGC coating efficiently stabilized the SmCo5 NPs against air oxidation at room temperature or at 100 °C. The magnetization value of the 180 nm SmCo5/NGC NPs was stabilized at 86.1 emu/g 5 days after air exposure at room temperature and dropped only 1.7% 48 h after air exposure at 100 °C. The stable SmCo5/NGC NPs were aligned magnetically in an epoxy resin, showing a square-like hysteresis behavior with their Hc reaching 51.1 kOe at 150 K and 21.9 kOe at 330 K and their Mr stabilized at around 84.8 emu/g. Our study demonstrates a new strategy for synthesizing and stabilizing SmCo5 NPs for high-performance nanomagnet applications in a broad temperature range.

Anisotropic Strain Tuning of L10 Ternary Nanoparticles for Oxygen Reduction.

Tuning the performance of nanoparticle (NP) catalysts by controlling the NP surface strain has evolved as an important strategy to optimize NP catalysis in many energy conversion reactions. Here, we present our new study on using an eigenforce model to predict and experiments to verify the strain-induced catalysis enhancement of the oxygen reduction reaction (ORR) in the presence of L10-CoMPt NPs (M = Mn, Fe, Ni, Cu, Ni). The eigenforce model allowed us to predict anisotropic (that is, two-dimensional) strain levels on distorted Pt(111) surfaces. Experimentally, by preparing a series of 5 nm L10-CoMPt NPs, we could push the ORR catalytic activity of these NPs toward the optimum region of the theoretical two-dimensional volcano plot predicted for L10-CoMPt. The best ORR catalyst in the alloy NP series we studied is L10-CoNiPt, which has a mass activity of 3.1 A/mgPt and a specific activity of 9.3 mA/cm2 at room temperature with only 15.9% loss of mass activity after 30 000 cycles at 60 °C in 0.1 M HClO4.

CuPd Nanoparticles as a Robust Catalyst for Electrochemical Allylic Alkylation.

An efficient CuPd nanoparticle (NP) catalyst (3 nm CuPd NPs deposited on carbon support) is designed for catalyzing electrochemical allylic alkylation in water/isopropanol (1:1 v/v) and 0.2 m KHCO3 solution at room temperature. The Pd catalysis was Pd/Cu composition-dependent, and CuPd NPs with a Pd/Cu ratio close to one are the most efficient catalyst for the selective cross-coupling of alkyl halides and allylic halides to form C-C hydrocarbons with product yields reaching up to 99 %. This NP-catalyzed electrochemical allylic alkylation expands the synthetic scope of cross-coupling reactions and can be further extended to other organic reaction systems for developing green chemistry electrosynthesis methods.

Ternary CoPtAu Nanoparticles as a General Catalyst for Highly Efficient Electro-oxidation of Liquid Fuels.

Efficient electro-oxidation of formic acid, methanol, and ethanol is challenging owing to the multiple chemical reaction steps required to accomplish full oxidation to CO2 . Herein, a ternary CoPtAu nanoparticle catalyst system is reported in which Co and Pt form an intermetallic L10 -structure and Au segregates on the surface to alloy with Pt. The L10 -structure stabilizes Co and significantly enhances the catalysis of the PtAu surface towards electro-oxidation of ethanol, methanol, and formic acid, with mass activities of 1.55 A/mgPt , 1.49 A/mgPt , and 11.97 A/mgPt , respectively in 0.1 m HClO4 . The L10 -CoPtAu catalyst is also stable, with negligible degradation in mass activities and no obvious Co/Pt/Au composition changes after 10 000 potential cycles. The in situ surface-enhanced infrared absorption spectroscopy study indicates that the ternary catalyst activates the C-C bond more efficiently for ethanol oxidation.

Unraveling Surface Plasmon Decay in Core-Shell Nanostructures toward Broadband Light-Driven Catalytic Organic Synthesis.

Harnessing surface plasmon of metal nanostructures to promote catalytic organic synthesis holds great promise in solar-to-chemical energy conversion. High conversion efficiency relies not only on broadening the absorption spectrum but on coupling the harvested energy into chemical reactions. Such coupling undergoes hot-electron transfer and photothermal conversion during the decay of surface plasmon; however, the two plasmonic effects are unfortunately entangled, making their individual roles still under debate. Here, we report that in a model system of bimetallic Au-Pd core-shell nanostructures the two effects can be disentangled through tailoring the shell thickness at atomic-level precision. As demonstrated by our ultrafast absorption spectroscopy characterizations, the achieved tunability of the two effects in a model reaction of Pd-catalyzed organic hydrogenation offers a knob for enhancing energy coupling. In addition, the two intrinsic plasmonic modes at 400-700 and 700-1000 nm in the bar-shaped nanostructures allow for utilizing photons to a large extent in full solar spectrum. This work establishes a paradigmatic guidance toward designing plasmonic-catalytic nanomaterials for enhanced solar-to-chemical energy conversion.

Sibling Death in Childhood and Early Adulthood and Risk of Early-Onset Cardiovascular Disease.

Importance: Sibling death is a highly traumatic event, but empirical evidence on the association of sibling death in childhood and early adulthood with subsequent risk of incident cardiovascular disease (CVD) remains limited. Objective: To evaluate the association between sibling death in the early decades of life and subsequent risk of incident early-onset CVD. Design, Setting, and Participants: This population-based cohort study included 2 098 659 individuals born in Denmark from 1978 to 2018. Follow-up started at age 1 year or the date of the first sibling's birth, whichever occurred later, and it ended at the first diagnosis of CVD, the date of death, emigration, or December 31, 2018, whichever came first. Data analyses were conducted from November 1, 2021, through January 10, 2022. Exposures: The death of a sibling. Main Outcomes and Measures: The outcome was early-onset CVD. Cox models were used to estimate hazard ratios (HRs) with 95% CIs. Results: This study included 2 098 659 individuals (1 076 669 [51.30%] male; median [IQR] age at death of sibling, 11.48 [4.68-21.32] years). During the median (IQR) follow-up of 17.52 (8.85-26.05) years, 1286 and 76 862 individuals in the bereaved and nonbereaved groups, respectively, were diagnosed with CVD. Sibling death in childhood and early adulthood was associated with a 17% increased risk of overall CVD (HR, 1.17; 95% CI, 1.10-1.23; cumulative incidence in bereaved individuals, 1.96% [1.61%-2.34%]; cumulative incidence in nonbereaved individuals at age 41 years, 1.35% [1.34%-1.37%]; cumulative incidence difference: 0.61% [95% CI, 0.24%-0.98%]). Increased risks were also observed for most type-specific CVDs, in particular for myocardial infarction (HR, 1.66; 95% CI, 1.12-2.46), ischemic heart disease (HR, 1.52; 95% CI, 1.22-1.90), and heart failure (HR, 1.50; 95% CI, 1.00-2.26). The association was observed whether the sibling died due to CVD (HR, 2.54; 95% CI, 2.04-3.17) or non-CVD (HR, 1.13; 95% CI, 1.06-1.19) causes. The increased risk of CVD was more pronounced for individuals who lost a twin or younger sibling (HR, 1.25; 95% CI, 1.15-1.36) than an elder sibling (HR, 1.11; 95% CI, 1.03-1.20). Conclusions and Relevance: In this cohort study of the Danish population, sibling death in childhood and early adulthood was associated with increased risks of overall and most type-specific early-onset CVDs, with the strength of associations varying by cause of death and age difference between sibling pairs. The findings highlight the need for extra attention and support to the bereaved siblings to reduce CVD risk later in life.

Spectroscopic visualization of reversible hydrogen spillover between palladium and metal-organic frameworks toward catalytic semihydrogenation.

Hydrogen spillover widely occurs in a variety of hydrogen-involved chemical and physical processes. Recently, metal-organic frameworks have been extensively explored for their integration with noble metals toward various hydrogen-related applications, however, the hydrogen spillover in metal/MOF composite structures remains largely elusive given the challenges of collecting direct evidence due to system complexity. Here we show an elaborate strategy of modular signal amplification to decouple the behavior of hydrogen spillover in each functional regime, enabling spectroscopic visualization for interfacial dynamic processes. Remarkably, we successfully depict a full picture for dynamic replenishment of surface hydrogen atoms under interfacial hydrogen spillover by quick-scanning extended X-ray absorption fine structure, in situ surface-enhanced Raman spectroscopy and ab initio molecular dynamics calculation. With interfacial hydrogen spillover, Pd/ZIF-8 catalyst shows unique alkyne semihydrogenation activity and selectivity for alkynes molecules. The methodology demonstrated in this study also provides a basis for further exploration of interfacial species migration.

Why surface hydrophobicity promotes CO2 electroreduction: a case study of hydrophobic polymer N-heterocyclic carbenes.

We report the use of polymer N-heterocyclic carbenes (NHCs) to control the microenvironment surrounding metal nanocatalysts, thereby enhancing their catalytic performance in CO2 electroreduction. Three polymer NHC ligands were designed with different hydrophobicity: hydrophilic poly(ethylene oxide) (PEO-NHC), hydrophobic polystyrene (PS-NHC), and amphiphilic block copolymer (BCP) (PEO-b-PS-NHC). All three polymer NHCs exhibited enhanced reactivity of gold nanoparticles (AuNPs) during CO2 electroreduction by suppressing proton reduction. Notably, the incorporation of hydrophobic PS segments in both PS-NHC and PEO-b-PS-NHC led to a twofold increase in the partial current density for CO formation, as compared to the hydrophilic PEO-NHC. While polymer ligands did not hinder ion diffusion, their hydrophobicity altered the localized hydrogen bonding structures of water. This was confirmed experimentally and theoretically through attenuated total reflectance surface-enhanced infrared absorption spectroscopy and molecular dynamics simulation, demonstrating improved CO2 diffusion and subsequent reduction in the presence of hydrophobic polymers. Furthermore, NHCs exhibited reasonable stability under reductive conditions, preserving the structural integrity of AuNPs, unlike thiol-ended polymers. The combination of NHC binding motifs with hydrophobic polymers provides valuable insights into controlling the microenvironment of metal nanocatalysts, offering a bioinspired strategy for the design of artificial metalloenzymes.