Catalytic Intermolecular Deoxygenative Coupling of Carbonyl Compounds with Alkynes by a Cp*Mo(II)-Catalyst.

Carbonyl is highly accessible and acts as an essential functional group in chemical synthesis. However, the direct catalytic deoxygenative functionalization of carbonyl compounds via a putative metal carbene intermediate is a formidable challenge due to the requirement of a high activation energy for the cleavage of strong C═O double bonds. Here, we report a class of bench stable and readily available Cp*Mo(II)-complexes as efficient deoxygenation catalysts that could catalyze the direct intermolecular deoxygenative coupling of carbonyl compounds with alkynes. Enabled by this powerful Cp*Mo(II)-catalyst, various valuable heteroarenes (10 different classes) were obtained in generally good yields and remarkable chemo- and regioselectivities. Mechanistic studies suggested that this reaction might proceed via a sequence of C═O double bonds cleavage, carbene-alkyne metathesis, cyclization, and aromatization processes. This strategy not only provided a general catalytic platform for the rapid preparation of heteroarenes but also opened a new window for the applications of Cp*Mo(II)-catalysts in organic synthesis.

Optimal design of groundwater pollution monitoring network based on a back-propagation neural network surrogate model and grey wolf optimizer algorithm under uncertainty.

In the optimal design of groundwater pollution monitoring network (GPMN), the uncertainty of the simulation model always affects the reliability of the monitoring network design when applying simulation-optimization methods. To address this issue, in the present study, we focused on the uncertainty of the pollution source intensity and hydraulic conductivity. In particular, we utilized simulation-optimization and Monte Carlo methods to determine the optimal layout scheme for monitoring wells under these uncertainty conditions. However, there is often a substantial computational load incurred due to multiple calls to the simulation model. Hence, we employed a back-propagation neural network (BPNN) to develop a surrogate model, which could substantially reduce the computational load. We considered the dynamic pollution plume migration process in the optimal design of the GPMN. Consequently, we formulated a long-term GPMN optimization model under uncertainty conditions with the aim of maximizing the pollution monitoring accuracy for each yearly period. The spatial moment method was used to measure the approximation degree between the pollution plume interpolated for the monitoring network and the actual plume, which could effectively evaluate the superior monitoring accuracy. Traditional methods are easily trapped in local optima when solving the optimization model. To overcome this limitation, we used the grey wolf optimizer (GWO) algorithm. The GWO algorithm has been found to be effective in avoiding local optima and in exploring the search space more effectively, especially when dealing with complex optimization problems. A hypothetical example was designed for evaluating the effectiveness of our method. The results indicated that the BPNN surrogate model could effectively fit the input-output relationship from the simulation model, as well as significantly reduce the computational load. The GWO algorithm effectively solved the optimization model and improved the solution accuracy. The pollution plume distribution in each monitoring yearly period could be accurately characterized by the optimized monitoring network. Thus, combining the simulation-optimization method with the Monte Carlo method effectively addressed the optimal monitoring network design problem under uncertainty.

Optimal layout design of groundwater pollution monitoring network using parameter iterative updating strategy-based ant colony optimization algorithm.

The scientific layout design of the groundwater pollution monitoring network (GPMN) can provide high quality groundwater monitoring data, which is essential for the timely detection and remediation of groundwater pollution. The simulation optimization approach was effective in obtaining the optimal design of the GPMN. The ant colony optimization (ACO) algorithm is an effective method for solving optimization models. However, the parameters used in the conventional ACO algorithm are empirically adopted with fixed values, which may affect the global searchability and convergence speed. Therefore, a parameter-iterative updating strategy-based ant colony optimization (PIUSACO) algorithm was proposed to solve this problem. For the GPMN optimal design problem, a simulation-optimization framework using PIUSACO algorithm was applied in a municipal waste landfill in BaiCheng city in China. Moreover, to reduce the computational load of the design process while considering the uncertainty of aquifer parameters and pollution sources, a genetic algorithm-support vector regression (GA-SVR) method was proposed to develop the surrogate model for the numerical model. The results showed that the layout scheme obtained using the PIUSACO algorithm had a significantly higher detection rate than ACO algorithm and random layout schemes, indicating that the designed layout scheme based on the PIUSACO algorithm can detect the groundwater pollution occurrence timely. The comparison of the iteration processes of the PIUSACO and conventional ACO algorithms shows that the global searching ability is improved and the convergence speed is accelerated significantly using the iteration updating strategy of crucial parameters. This study demonstrates the feasibility of the PIUSACO algorithm for the optimal layout design of the GPMN for the timely detection of groundwater pollution.

Review of machine learning-based surrogate models of groundwater contaminant modeling.

Heavy computational load inhibits the application of groundwater contaminant numerical model to groundwater pollution source identification, remediation design, and uncertainty analysis, since a large number of model runs are required for these applications. Machine learning-based surrogate models are an effective approach to enhance the efficiency of the numerical models, and have recently attracted considerable attention in the field of groundwater contaminant modeling. Here, we review 120 research articles on machine learning-based surrogate models for groundwater contaminant modeling that were published between 1994 and 2022. We outline the state of the art method, identify the most significant research challenges, and suggest potential future directions. The six major applications of machine learning-based surrogate models are groundwater pollution source identification, groundwater remediation design, coastal aquifer management, uncertainty analysis of groundwater, groundwater monitoring network design, and groundwater transport parameters inversion. Together, these account for more than 90% of the studies we review. Latin hypercube sampling (LHS) is the most widely used sampling method, and artificial neural networks (ANNs) and Kriging are the two most widely used methods for constructing surrogate model. No method is universally superior, the advantages and disadvantages of different methods, as well as the applicability of these methods for different application purposes of groundwater contaminant modeling were analyzed. Some recommendations on the method selection for various application fields are given based on the reviews and experiences. Based on our review of the state-of-the-art, we suggest several future research directions to enhance the feasibility of the machine learning-based surrogate models of groundwater contaminant modeling: the alleviation of the curse of dimensionality, enhancing transferability, practical applications for real case studies, multi-source dada fusion, and real-time monitoring and prediction.

Stereoselective intermolecular radical cascade reactions of tryptophans or ɤ-alkenyl-α-amino acids with acrylamides via photoredox catalysis.

The radical cascade reaction is considered as one of the most powerful methods to build molecular complexity. However, highly stereoselective intermolecular radical cascade reactions that can produce complex cyclic compounds bearing multiple stereocenters via visible-light-induced photocatalysis have been challenging yet desirable. Herein we report a facile and efficient synthesis of multi-substituted trans-fused hexahydrocarbazoles via a stereoselective intermolecular radical cascade reaction of readily available tryptophans and acrylamides enabled by visible-light-induced photoredox catalysis. The trans-fused hexahydrocarbazoles with up to five stereocenters including two quaternary ones can be accessed in up to 82% yield, >20/1 diastereoselectivity, and 96% ee. Interestingly, the tetrahydrocarbazoles are favorably formed when the reaction is performed under air. Moreover, by simply switching the starting material from tryptophans to ɤ-alkenyl substituted α-amino acids, this protocol can be further applied to the stereoselective syntheses of 1,3,5-trisubstituted cyclohexanes which are otherwise challenging to access. Preliminary mechanistic studies suggest that the reaction goes through radical addition cascade and radical-polar crossover processes.

Machine learning-based optimal design of groundwater pollution monitoring network.

It is an important task of environmental management to design groundwater pollution monitoring network (GPMN) to find out the occurrence of pollution events and carry out remediation in time. However, there are many uncertain factors in the process of designing GPMN, which affect the GPMN design result. In the process of applying the Monte Carlo method for uncertainty analysis, groundwater numerical simulation model may be utilized thousands of times, which results in a huge computational load. In order to overcome this disadvantage, a machine learning (ML)-based surrogate model is constructed with Kriging method, to replace the computational simulation model under uncertainty of pollution sources and parameters. The 0-1 integer programming optimization model is constructed to maximally cover serious polluted area to detect the occurrence of groundwater pollution in time. The optimal design framework of GPMN based on proposed ML algorithm was applied in a domestic landfill in Baicheng City, China. The results showed that the ML-based surrogate model has a great fitness with the groundwater solute transport simulation model. The optimal results of GPMN indicated that monitoring wells should be mainly placed at the downstream of the leachate equalization basin. If more wells are allowed to be placed, part of wells could be placed at the downstream of the landfill. Moreover, the area where the pollution plumes of landfill site meet that of leachate equalization basin should be set as the key monitoring objective. Verification and comparison showed that the pollutant detection rate of the optimal layout scheme is far higher than random layout schemes, which proves the reliability of the ML-based optimal design scheme of GPMN.

Molybdenum-Catalyzed Deoxygenative Cyclopropanation of 1,2-Dicarbonyl or Monocarbonyl Compounds.

The transition-metal-catalyzed cyclopropanation of alkenes by the decomposition of diazo compounds is a powerful and straightforward strategy to produce cyclopropanes, but is tempered by the potentially explosive nature of diazo substrates. Herein we report the Mo-catalyzed regiospecific deoxygenative cyclopropanation of readily available and bench-stable 1,2-dicarbonyl compounds, in which one of the two carbonyl groups acts as a carbene equivalent upon deoxygenation and engages in the subsequent cyclopropanation process. The use of a commercially available Mo catalyst afforded an array of valuable cyclopropanes with exclusive regioselectivity in up to 90 % yield. The synthetic utility of this method was further demonstrated by gram-scale syntheses, late-stage functionalization, and the cyclopropanation of a simple monocarbonyl compound. Preliminary mechanistic studies suggest that phosphine (or silane) acts as both a mild reductant and a good oxygen acceptor that efficiently regenerates the catalytically active Mo catalyst through reduction of the Mo-oxo complexes.

Optimal design of groundwater pollution monitoring network based on the SVR surrogate model under uncertainty.

The simulation-optimization method is widely used in the design of the groundwater pollution monitoring network (GPMN). The uncertainty of the simulation model will significantly affect the design results of GPMN. When the Monte Carlo method is used to consider the influence of model uncertainty on the optimization results, the simulation model needs to be invoked many times, which will cause a huge amount of calculation. To reduce the calculation load, the study proposed to use the support vector regression (SVR) method to construct the surrogate model to couple the simulation model and the optimization model in the optimal design of GPMN. The optimization goal is to maximize the accuracy of the spatial description of pollution plume in each monitoring period. The study also considered the dynamic changes in the migration and morphological of pollution plumes in the optimization of GPMN. Finally, the West Shechang coal gangue pile in Fushun of China was used as a case study to verify the effectiveness of the above method. The results demonstrate that the SVR surrogate model can fit the input-output relationship of the simulation model to a high degree with less computation. The optimized monitoring network can reveal essential and comprehensive information about pollution plumes. The study provides a stable and reliable method for the design of GPMN.

Application of ensemble surrogates and adaptive sequential sampling to optimal groundwater remediation design at DNAPLs-contaminated sites.

In this study, we aimed to develop an optimal groundwater remediation design for sites contaminated by dense non-aqueous phase liquids by using an ensemble of surrogates and adaptive sequential sampling. Compared with previous approaches, our proposed method has the following advantages: (1) a surrogate surfactant-enhanced aquifer remediation simulation model is constructed using a Gaussian process; (2) the accuracy of the surrogate model is improved by constructing ensemble surrogates using five different surrogate modelling techniques, i.e., polynomial response surface, radial basis function, Kriging, support vector regression, and Gaussian process; (3) we conducted comparisons and analyses based on 31 surrogate models derived from different combinations of the five surrogate modelling techniques; and (4) the reliability of the optimal solution was improved by implementing adaptive sequential sampling. The two proposed methods were applied to a hypothetical perchloroethylene-contaminated site in order to demonstrate their performance. The results showed that the best surrogate model integrated all five of the surrogate modelling methods, with an R2 value of 0.9913 and a root mean squared error of 0.0159, thereby demonstrating the advantage of using ensemble surrogates. In addition, the reliability of the optimization model solution was improved by adaptive sequential sampling, which avoided false solutions.

Chance-constrained multi-objective optimization of groundwater remediation design at DNAPLs-contaminated sites using a multi-algorithm genetically adaptive method.

In this paper, a multi-algorithm genetically adaptive multi-objective (AMALGAM) method is proposed as a multi-objective optimization solver. It was implemented in the multi-objective optimization of a groundwater remediation design at sites contaminated by dense non-aqueous phase liquids. In this study, there were two objectives: minimization of the total remediation cost, and minimization of the remediation time. A non-dominated sorting genetic algorithm II (NSGA-II) was adopted to compare with the proposed method. For efficiency, the time-consuming surfactant-enhanced aquifer remediation simulation model was replaced by a surrogate model constructed by a multi-gene genetic programming (MGGP) technique. Similarly, two other surrogate modeling methods-support vector regression (SVR) and Kriging (KRG)-were employed to make comparisons with MGGP. In addition, the surrogate-modeling uncertainty was incorporated in the optimization model by chance-constrained programming (CCP). The results showed that, for the problem considered in this study, (1) the solutions obtained by AMALGAM incurred less remediation cost and required less time than those of NSGA-II, indicating that AMALGAM outperformed NSGA-II. It was additionally shown that (2) the MGGP surrogate model was more accurate than SVR and KRG; and (3) the remediation cost and time increased with the confidence level, which can enable decision makers to make a suitable choice by considering the given budget, remediation time, and reliability.