Grain-Boundary-Rich Ceria Metallene Nanozyme with Abundant Metal Site Pairs Boosts Phosphatase-like Activity.

Natural phosphatases featuring paired metal sites inspire various advanced nanozymes with phosphatase-like activity as alternatives in practical applications. Numerous efforts to create point defects show limited metal site pairs, further resulting in insufficient activity. However, it remains a grand challenge to accurately engineer abundant metal site pairs in nanozymes. Herein, we report a grain-boundary-rich ceria metallene nanozyme (GB-CeO2) with phosphatase-like activity. Grain boundaries acting as the line or interfacial defects can effectively increase the content of Ce4+/Ce3+ site pairs to 72.28%, achieving a 49.28-fold enhancement in activity. Furthermore, abundant grain boundaries optimize the band structure to assist the photoelectron transfer under irradiation, which further increases the content of metal site pairs to 88.96% and finally realizes a 114.39-fold enhanced activity over that of CeO2 without irradiation. Given the different inhibition effects of pesticides on catalysts with and without irradiation, GB-CeO2 was successfully applied to recognize mixed toxic pesticides.

Predicting the catalytic activities of transition metal (Cr, Fe, Co, Ni) complexes towards ethylene polymerization by machine learning.

The study aims to execute machine learning (ML) method for building an intelligent prediction system for catalytic activities of a relatively big dataset of 1056 transition metal complex precatalysts in ethylene polymerization. Among 14 different algorithms, the CatBoost ensemble model provides the best prediction with the correlation coefficient (R2 ) values of 0.999 for training set and 0.834 for external test set. The interpretation of the obtained model indicates that the catalytic activity is highly correlated with number of atom, conjugated degree in the ligand framework, and charge distributions. Correspondingly, 10 novel complexes are designed and predicted with higher catalytic activities. This work shows the potential application of the ML method as a high-precision tool for designing advanced catalysts for ethylene polymerization.

Improving Interface Matching in MOF-on-MOF S-Scheme Heterojunction through π-π Conjugation for Boosting Photoelectric Response.

Accelerating the migration of interfacial carriers in a heterojunction is of paramount importance for driving high-performance photoelectric responses. However, the inferior contact area and large resistance at the interface limit the eventual photoelectric performance. Herein, we fabricated an S-scheme heterojunction involving a 2D/2D dual-metalloporphyrin metal-organic framework with metal-center-regulated CuTCPP(Cu)/CuTCPP(Fe) through electrostatic self-assembly. The ultrathin nanosheet-like architectures reduce the carrier migration distance, while the similar porphyrin backbones promote reasonable interface matching through π-π conjugation, thereby inhibiting the recombination of photogenerated carriers. Furthermore, the metal-center-regulated S-scheme band alignments create a giant built-in electric field, which provides a huge driving force for efficient carrier separation and migration. Coupling with the biomimetic catalytic activity of CuTCPP(Fe), the resultant heterojunction was utilized to construct photoelectrochemical uric acid biosensors. This work provides a general strategy to enhance photoelectric responses by engineering the interfacial structure of heterojunctions.

Catalytic Activity of 2-Imino-1,10-phenthrolyl Fe/Co Complexes via Linear Machine Learning.

In anticipation of the correlations between catalyst structures and their properties, the catalytic activities of 2-imino-1,10-phenanthrolyl iron and cobalt metal complexes are quantitatively investigated via linear machine learning (ML) algorithms. Comparatively, the Ridge Regression (RR) model has captured more robust predictive performance compared with other linear algorithms, with a correlation coefficient value of R2= 0.952 and a cross-validation value of Q2= 0.871. It shows that different algorithms select distinct types of descriptors, depending on the importance of descriptors. Through the interpretation of the RR model, the catalytic activity is potentially related to the steric effect of substituents and negative charged groups. This study refines descriptor selection for accurate modeling, providing insights into the variation principle of catalytic activity.

Anisotropic Dual S-Scheme Heterojunctions Mimic Natural Photosynthetic System for Boosting Photoelectric Response.

The design of heterojunctions that mimic natural photosynthetic systems holds great promise for enhancing photoelectric response. However, the limited interfacial space charge layer (SCL) often fails to provide sufficient driving force for the directional migration of inner charge carriers. Drawing inspiration from the electron transport chain (ETC) in natural photosynthesis system, we developed a novel anisotropic dual S-scheme heterojunction artificial photosynthetic system composed of Bi2O3-BiOBr-AgI for the first time, with Bi2O3 and AgI selectively distributed along the bicrystal facets of BiOBr. Compared to traditional semiconductors, the anisotropic carrier migration in BiOBr overcomes the recombination resulting from thermodynamic diffusion, thereby establishing a potential ETC for the directional migration of inner charge carriers. Importantly, this pioneering bioinspired design overcomes the limitations imposed by the limited distribution of SCL in heterojunctions, resulting in a remarkable 55-fold enhancement in photoelectric performance. Leveraging the etching of thiols on Ag-based materials, this dual S-scheme heterojunction is further employed in the construction of photoelectrochemical sensors for the detection of acetylcholinesterase and organophosphorus pesticides.

Superhydrophobic 3D-Assembled Metallic Nanoparticles for Trace Chemical Enrichment in SERS Sensing.

The performance of surface-enhanced Raman spectroscopy (SERS) is determined by the interaction between highly diluted analytes and boosted localized electromagnetic fields in nanovolumes. Although superhydrophobic surfaces are developed for analyte enrichment, i.e., to concentrate and transfer analytes toward a specific position, it is still challenging to realize reproducible, uniform, and sensitive superhydrophobic SERS substrates over large scales, representing a major barrier for practical sensing applications. To overcome this challenge, a superhydrophobic SERS chip that combines 3D-assembled gold nanoparticles on nanoporous substrates is proposed, for a strong localized field, with superhydrophobic surface treatment for analyte enrichment. Intriguingly, by concentrating droplets in the volume of 40 µL, the sensitivity of 1 nm is demonstrated using 1,2-bis(4-pyridyl)-ethylene molecules. In addition, this unique chip demonstrates a relative standard deviation (RSD) of 2.2% in chip-to-chip reproducibility for detection of fentanyl at 1 µg mL-1 concentration, revealing its potential for quantitative sensing of chemicals and drugs. Furthermore, the trace analysis of fentanyl and fentanyl-heroin mixture in human saliva is realized after a simple pretreatment process. This superhydrophobic chip paves the way toward on-site and real-time drug sensing to tackle many societal issues like drug abuse and the opioid crisis.

Catalytic Performance of Cobalt(II) Polyethylene Catalysts with Sterically Hindered Dibenzopyranyl Substituents Studied by Experimental and MLR Methods.

Given the great importance of cobalt catalysts supported by benchmark bis(imino)pyridine in the (oligo)polymerization, a series of dibenzopyran-incorporated symmetrical 2,6-bis(imino) pyridyl cobalt complexes (Co1-Co5) are designed and prepared using a one-pot template approach. The structures of the resulting complexes are well characterized by a number of techniques. After activation with either methylaluminoxane (MAO) or modified MAO (MMAO), the complexes Co1-Co4 are highly active for ethylene polymerization with a maximum activity of up to 7.36 × 106 g (PE) mol-1 (Co) h-1 and produced highly linear polyethylene with narrow molecular weight distributions, while Co5 is completely inactive under the standard conditions. Particularly, complex Co3 affords polyethylene with high molecular weights of 85.02 and 79.85 kg mol-1 in the presence of MAO and MMAO, respectively. The 1H and 13C NMR spectroscopy revealed the existence of vinyl end groups in the resulting polyethylene, highlighting the predominant involvement of the ß-H elimination reaction in the chain-termination process. To investigate the mechanism underlying the variation of catalytic activities as a function of substituents, multiple linear regression (MLR) analysis was performed, showing the key role of open cone angle (θ) and effective net charge (Q) on catalytic activity.

The Quantitative Influence of Coordinated Halogen Atoms on the Catalytic Performance of Bisiminoacenaphthylnickel Complexes in Ethylene Polymerization.

In experiments, nickel bromine complexes usually show a better catalytic performance in ethylene polymerization compared to their nickel chlorine analogues. Therefore, the present modeling study has been performed to investigate the effect of coordinated halogen atoms on the catalytic performances of two bisiminoacenaphthyl nickel systems, namely, Ni-Br and Ni-Cl. By using the multiple linear regression analysis (MLRA), the catalytic activity can be well predicted by the descriptors of effective net charge (Qeff ) and bite angle (ß), with correlation coefficient R2 values over 0.91. Meanwhile, the molecular weights of polyethylene are predicted by the descriptors of Qeff and open cone angle (θ). The calculated contributions of each descriptor show that the electronic effect is the predominant factor in Ni-Br system, while the steric effect becomes the dominant factor in Ni-Cl system. The different determined effect is expected to the main reason for the different catalytic performance between two Ni systems.

Prediction of catalytic activities of bis(imino)pyridine metal complexes by machine learning.

This work demonstrates the potential of machine learning (ML) method to predict catalytic activity of transition metal complex precatalyst toward ethylene polymerization. For this purpose, 294 complexes and 15 molecular descriptors were selected to build the artificial neural network (ANN) model. The catalytic activity can be well predicted by the obtained ANN model, which was further validated by external complexes. Boruta algorithm was employed to explicitly decipher the importance of descriptors, illustrating the conjugated bond structure, and bulky substitutions are favorable for catalytic activity. The present work indicates that ML could give useful guidance for the new design of homogenous polyolefin catalyst.

Catalytic performance of bis(imino)pyridine Fe/Co complexes toward ethylene polymerization by 2D-/3D-QSPR modeling.

The two-dimensional and three-dimensional quantitative structure-property relationship (2D- and 3D-QSPR) approaches are applied to investigate the catalytic performance for a total data set of 55 bis(imino)pryridine iron and cobalt complexes, including the catalytic activity, molecular weight, and melting temperature of the product. The obtained models for the catalytic performance of interest exhibit good results by both 2D- and 3D-QSPR modeling, meanwhile higher predictive and validation powers observed in the 3D type. The modeling results indicate that the bulky substituents on ortho-position of the singular side phenyl ring and positive charge on para-position of the phenyl ring within the ligand are favorable to catalytic activity, while unfavorable to the molecular weight of product. Based on the obtained QSPR models, four new complexes are designed and predicted with good catalytic activity and very high molecular weight, which are in good agreement with our recent experimental report. © 2019 Wiley Periodicals, Inc.