Elimination Mechanism Research Articles

Selective industrial policy and overcapacity: Evidence from a quasi-natural experiment in China

Overcapacity has long been a "chronic problem affecting China's economic development." Why is China's overcapacity intractable? This study takes the "Revitalization Plans of Ten Industries" (the RPTI) issued by the Chinese government in 2009 as a quasi-natural experiment. It deploys the data of China's A-share listed enterprises and the difference-in-differences (DID) model to investigate the impact of the selective industrial policy on enterprise overcapacity. The results show that the policy has a significant and persistently negative effect on the capacity utilization rate of the treated group. Mechanistic studies reveal that policy-induced overcapacity is caused by increasing government subsidies for the treated group, depressing corporate investment efficiency, and growing capital misallocation. After considering zombie enterprises, we find that the policy mainly leads to the overcapacity of regular enterprises but has no negative impact on zombie enterprises, and zombie enterprises crowd out the capacity utilization rate of regular enterprises in the same industry. This study indicates that a selective industrial policy that prevents the market elimination mechanism from functioning is an underlying cause of overcapacity in China. The study's findings reveal why the administrative de-capacity policies enacted by the Chinese government have failed to eliminate backward capacity but rather created a new overcapacity issue.

Parameter extraction of photovoltaic model based on butterfly optimization algorithm with chaos learning strategy

Accurate extraction of photovoltaic (PV) model parameter is significant for the evaluation of PV system. Most of existing methods for extracting the PV parameters suffer from low extraction accuracy and are prone to fall into local optima. To solve these shortcomings, a chaos learning butterfly optimization algorithm (CLBOA) is proposed. First, the introduced Cauchy mutation increases the perturbation to butterflies and helps the algorithm to jump out of local optima. Second, a chaos learning strategy is proposed to lead the butterflies within the population to learn from the optimal individual using the improved Tent chaos mapping, which strengthens the learning exchange of the population, enhances the ability of global exploration and local exploitation, and optimizes the convergence speed and accuracy. Third, the three worst individual positions were randomly initialized using a terminal elimination mechanism to increase population diversity. After evaluating CLBOA through the CEC2022 benchmark suite, it was used to extract parameters for a total of 12 PV cell and module models for 5 materials. Compared with 9 well-known algorithms, CLBOA performs excellent in terms of convergence performance, parameters extraction accuracy, running time and improvement index. Finally, CLBOA was applied on YL PV power station model of Guizhou Power Grid in China under different irradiations and temperatures. The I-V and P-V curves reconstructed by CLBOA are highly fit to the synthesized curves, and the individual absolute error (IAE) and relative error (RE) also confirm the satisfactory accuracy of parameters extraction. Comprehensive analysis shows that CLBOA can extract parameters better than the comparison algorithms, demonstrating its strong potential for PV parameters extraction.

Physiological responses and removal mechanisms of ciprofloxacin in freshwater microalgae

Antibiotics, such as ciprofloxacin (CIP), are frequently detected in various environmental compartments, posing significant risks to ecosystems and human health. In this study, the physiological responses and elimination mechanisms of CIP in Chlorella sorokiniana and Scenedesmus dimorphus were determined. The exposure CIP had a minimal impact on the growth of microalgae, with maximum inhibit efficiency (IR) of 5.14% and 22.74 for C. sorokiniana and S. dimorphus, respectively. Notably, the photorespiration in S. dimorphus were enhanced. Both microalgae exhibited efficient CIP removal, predominantly through bioaccumulation and biodegradation processes. Intermediates involved in photolysis and biodegradation were analyzed through Liquid Chromatography High Resolution Mass Spectrometer (HPLC-MS/MS), providing insights into degradation pathways of CIP. Upregulation of key enzymes, such as dioxygenase, oxygenase and cytochrome P450, indicated their involvement in the biodegradation of CIP. These findings enhance our understanding of the physiological responses, removal mechanisms, and pathways of CIP in microalgae, facilitating the advancement of microalgae-based wastewater treatment approaches, particularly in antibiotic-contaminated environments.

Protein S-palmitoylation enhances profibrotic signaling in response to cadmium

Cadmium (Cd) is a naturally occurring, toxic environmental metal found in foods. Humans do not have an efficient mechanism for Cd elimination; thus, Cd burden in humans increases with age. Cell and mouse studies show that Cd burden from low environmental levels of exposure impacts lung cell metabolism, proliferation signaling and cell growth as part of disease-promoting profibrotic responses in the lungs. Prior integrative analysis of metabolomics and transcriptomics identified the zDHHC11 transcript as a central functional hub in response to Cd dose. zDHHC11 encodes a protein S-palmitoyltransferase, but no evidence is available for effects of Cd on protein S-palmitoylation. In the present research, we studied palmitoylation changes in response to Cd and found increased protein S-palmitoylation in human lung fibroblasts that was inhibited by 2-bromopalmitate (2-BP), an irreversible palmitoyltransferase inhibitor. Mass spectrometry-based proteomics showed palmitoylation of proteins involved in divalent metal transport and in fibrotic signaling. Mechanistic studies showed that 2-BP inhibited palmitoylation of divalent metal ion transporter ZIP14 and also inhibited cellular Cd uptake. Transcription analyses showed that Cd stimulated transforming growth factor (TGF)-β1 and β3 expression within 8 h and lung fibrotic markers α-smooth muscle actin, matrix metalloproteinase-2, and collagen 1α1 gene expression and that these effects were blocked by 2-BP. Because 2-BP also blocked palmitoylation of proteins controlled by TGFβ1, these results show that palmitoylation impacts Cd-dependent fibrotic signaling both by enhancing cellular Cd accumulation and by supporting post-translational processing of TGFβ1-dependent proteins.

Improving the stability of multiwalled carbon nanotube/silicone rubber composites strain sensors by nanosilica

AbstractConductive polymer composites use elastomers as a matrix for strain sensors. However, elastic polymer composites usually exhibit shoulder peak effects in their resistance response signals, limiting their applicability as strain sensors. In this study, we modified multiwalled carbon nanotubes (MWCNTs)/methyl vinyl silicone rubber (VMQ) by incorporating nanosilica (NSD) to eliminate the shoulder peak effect. The results showed that the addition of a suitable amount of NSD can improve the reconstruction of the conductive network of composite material and eliminated the shoulder peak effect of the composite material in the resistive response signal. The mechanism of eliminating the shoulder peak effect was elucidated, while the mechanical properties of the composite material were improved, which endowed the composite material with excellent strain sensing properties, including sensitivity (GF = 127) and response time (270 ms), and maintained excellent stability and recovery even in 15,000 cycles. These findings highlight the potential of our strain sensor for the real‐time strain monitoring of large‐deformation isolation bearings.Highlights NSD eliminates shoulder peak effect in MWCNT/VMQ composites. Explained the shoulder peak effect elimination mechanism. NSD enhanced mechanical properties of MWCNT/VMQ composites. MWCNT‐NSD20/VMQ composites have stable resistive strain response properties.

Electrochemical Ruthenium‐Catalysed Directed C−H Functionalization of Arenes with Boron Reagents

AbstractOrtho‐directed electrochemical C−H functionalization of aromatics with boron‐based coupling partners has been achieved under ruthenium catalysis through an oxidatively induced reductive elimination mechanism. Our method provides a single set of conditions delivering C−H arylation, alkenylation and methylation, yielding ortho‐functionalized products with good functional group tolerance, including examples of late‐stage functionalization in synthetically useful yields. By harnessing electricity as a ‘green’ oxidant, this method circumvents the need for stoichiometric quantities of chemical oxidants, enhancing our sustainability metrics.

Novel non-radical elimination mechanism of phosphorus flame retardant parent compound in Triton X-100 enhanced solar activated peroxydisulfate process: Key role of energy transfer from singlet oxygen to Triton X-100

The elimination mechanism of emerging contaminants by triplet excited state organic compounds generated from non-radical pathway was rarely concerned in advanced oxidation process (AOP). This study proposed Triton X-100 (TX-100, a nonionic surfactant) enhanced solar activated persulfate process (Solar/PDS/TX-100 process) as a novel method to rapidly eliminate 9,10-Dihydro-9-oxa-10-phosphaphenanthrene-10-oxide (DOPO, an organic phosphorus flame retardant parent compound), which mainly depended on 3TX-100* as a new reactive intermediate, instead of reactive oxygen species and SO4•−. The results of DOPO degradation and probe compounds experiments, EPR spectra as well as triplet excited state compound scavenge experiments demonstrated that the observed degradation rate constant (kobs-DOPO) was 3.70 × 10-3 s−1 in Solar/PDS/TX-100 process, 4.86 times higher than that in Solar/PDS process where kobs-DOPO was 7.61 × 10-4 s−1, and 3TX-100* was the dominant reactive intermediate leading to the enhancement of DOPO degradation where the second order rate constant of 3TX-100* vs. DOPO was 6.98 × 109 M−1s−1. Singlet oxygen was the energy precursor to trigger the transformation from TX-100 to 3TX-100*, which promoted the yield of 3TX-100*. The degradation pathways and products toxicity assessment demonstrated that Solar/PDS/TX-100 process had better performance on DOPO detoxification than Solar/PDS process.

Learning by selective plasmid loss for intracellular synthetic classifiers

We propose a learning mechanism for intracellular synthetic genetic classifiers based on the selective elimination (curing) of plasmids bearing parts of the classifier circuit. Our focus is on a two-input, two-plasmid classifier scheme designed to solve a simple proof-of-concept learning problem. The problem is formulated in terms of Boolean variables, and the learning process boils down to selecting the classification rule from three options, given a set of training examples. We begin with a Boolean description of the classifier circuit, demonstrating how it implements the required learning algorithm. We then transition to a continuous steady-state model and establish conditions on its parameters to ensure that the learning process and the classifier output correspond to the Boolean description, at least approximately. The approach to intracellular classifier learning presented here essentially relies on two key prerequisites: (i) compatibility among the plasmids constituting the classifier, such that they have independent or weakly interacting copy number control systems, and (ii) conditional elimination mechanism in each plasmid triggered by a signal from the gene network. The feasibility of this approach is supported by recent experimental findings on engineering compatible pairs and triplets of plasmids and controlled selective plasmid curing. While learning by plasmid loss has certain limitations in universality, we anticipate that it provides greater persistence of a trained classifier to internal and external fluctuations and to degradation over time, as compared to alternative intracellular learning mechanisms outlined in the literature, such as based on gene network dynamics or on variable copy numbers of plasmids sharing a common copy number control system.

Low dose cadmium exposure regulates miR-381–ANO1 interaction in airway epithelial cells

Chronic obstructive pulmonary disease (COPD) is the 3rd leading cause of death worldwide. Cigarette smoke which has approximately 2–3 µg of Cadmium (Cd) per cigarette contributes to the environmental exposure and development and severity of COPD. With the lack of a cadmium elimination mechanism in humans, the contribution of cadmium induced stress to lung epithelial cells remains unclear. Studies on cadmium responsive miRNAs suggest regulation of target genes with an emphasis on the critical role of miRNA–mRNA interaction for cellular homeostasis. Mir-381, the target miRNA in this study is negatively regulated by cadmium in airway epithelial cells. miR-381 is reported to also regulate ANO1 (Anoctamin 1) expression negatively and in this study low dose cadmium exposure to airway epithelial cells was observed to upregulate ANO1 mRNA expression via mir-381 inhibition. ANO1 which is a Ca2+-activated chloride channel has multiple effects on cellular functions such as proliferation, mucus hypersecretion and fibroblast differentiation in inflamed airways in chronic respiratory diseases. In vitro studies with cadmium at a high concentration range of 100–500 µM is reported to activate chloride channel, ANO1. The secretory epithelial cells are regulated by chloride channels like CFTR, ANO1 and SLC26A9. We examined “ever” smokers with COPD (n = 13) lung tissue sections compared to “never” smoker without COPD (n = 9). We found that “ever” smokers with COPD had higher ANO1 expression. Using mir-381 mimic to inhibit ANO1, we demonstrate here that ANO1 expression is significantly (p < 0.001) downregulated in COPD derived airway epithelial cells exposed to cadmium. Exposure to environmental cadmium contributes significantly to ANO1 expression.

Molecular and cellular mechanisms of developmental synapse elimination in the cerebellum: Involvement of autism spectrum disorder-related genes.

Neural circuits are initially created with excessive synapse formation until around birth and undergo massive reorganization until they mature. During postnatal development, necessary synapses strengthen and remain, whereas unnecessary ones are weakened and eventually eliminated. These events, collectively called "synapse elimination" or "synapse pruning", are thought to be fundamental for creating functionally mature neural circuits in adult animals. In the cerebellum of neonatal rodents, Purkinje cells (PCs) receive synaptic inputs from multiple climbing fibers (CFs). Then, inputs from a single CF are strengthened and those from the other CFs are eliminated, and most PCs become innervated by single CFs by the end of the third postnatal week. These events are regarded as a representative model of synapse elimination. This review examines the molecular and cellular mechanisms of CF synapse elimination in the developing cerebellum and argues how autism spectrum disorder (ASD)-related genes are involved in CF synapse development. We introduce recent studies to update our knowledge, incorporate new data into the known scheme, and discuss the remaining issues and future directions.

On the mechanism of carboxylate elimination from carbohydrate monoester-derived radicals.

A computational study of the mechanism of hydrogen atom transfer-induced carboxylate elimination from monoacylated 1,2-diol groups in pyranosides is presented. A comprehensive analysis of the 1,2-migration, elimination and fragmentation pathways reveals that concerted elimination via a 7-membered, hydrogen-bonded transition state is favored. Relative rates of elimination inferred from an intramolecular competition experiment are consistent with the trends obtained from the calculations.

Neutrofilné extracelulárne pasce a ich vplyv v patogenéze vybraných ochorení

Neutrophil extracellular traps (NETs) are net-like extracellular structures extruded by activated neutrophils capable of trapping many pathogens, including Gram-positive and Gram-negative bacteria, fungi, protozoa, and viruses. NETs are composed of DNA strands, histones, and antimicrobial proteins derived from neutrophil granules and nuclei. Pathogens are immobilized in them and exposed to a local, high and lethal concentration of effector proteins. Excessive release of NETs or violation of their elimination mechanisms is associated with several disease states. Toxic products of NETs (histones, granule proteins) induce cell apoptosis, promote tissue damage and inflammation. Components of NETs can serve as autoantigens and initiate reactions important in the pathogenesis of several autoimmune diseases. The presence of an increased amount of circulating NETs can influence the progression of cancer disease, the formation of metastases and the failure of treatment. NETs also play an important role in coagulation and blood clot formation.

A Novel Tightly Coupled Solution for SINS/Polarized Navigation System/Odometer Integration Using Polarized and Installed Angle Errors Model

Precise and reliable autonomous navigation in a GPS-denied environment is critical to unmanned systems. The idea of combining SINS, the polarized navigation system (PNS), and the odometer (OD) inspired by desert ants has been proven to be effective for autonomous navigation. However, there are two major challenges for polarization navigation nowadays: inaccurate modeling and obtaining reliable heading information when some sensor channels are blocked. Aiming at these two problems, a tightly-coupled solution for SINS/PNS is proposed in this paper. To obtain a refined integrated navigation system model, the installation errors between the inertial units and PNS, and polarization angle calculation errors are analyzed and modeled for SINS/PNS. Then, to quickly gain the accurate state estimation, an improved iterative unscented filtering method is devised. In particular, the sigma-point updating step with the conditional distribution of high-dimensional Gaussian distribution random variables is developed, which employs partial states to sample in each iteration to reduce the calculation burden. Finally, a detection and elimination mechanism for the abnormal light channels is provided to enhance the reliability of the integration in the presence of a light blockage. The optical channels for navigation are chosen using this mechanism depending on the difference between the predicted and measured incident light intensities. The results in both simulations and outdoor experiments show that the proposed method provides a higher heading estimation accuracy than the traditional SINS/PNS navigation method. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This article is motivated by the inaccurate modeling and obtaining reliable heading in the presence of light occlusion for the polarization navigation. Various integrated navigation algorithms based on polarized skylight have been widely developed. However, the complex environment and inaccurate modeling limit the application of the polarization navigation. This article gives a novel accurate modeling method and reliable navigation algorithm. Tightly-coupled model with installation errors and the polarization angle error aims to improve the accuracy of the polarization navigation model. The improved iterative unscented Kalman filter is utilized to quickly obtain accurate state estimation. And the light detection and elimination mechanism is used to select normal light channels to navigate in the presence of light blockage. This navigation method can also extend the application of the polarization navigation.

Analysis of resources applied to rationalize elimination mechanisms

This study aimed to analyze second-semester organic chemistry students’ problem-solving strategies, specifically focusing on the resources activated while solving problems on E2, E1, and E1cB elimination reactions. Using the theoretical framework by Elby and Hammer, we defined a resource as a unit of information used in the problem-solving process. The resources activated to solve elimination reaction problems were probed using a mixed-methods approach using survey assessments and think-aloud interviews. The data were analyzed quantitatively and qualitatively following a validated set of scoring criteria. The results align with existing findings that students focus on surface-level structural information and use resources that have been repetitively emphasized over multiple semesters. Resources related to acid–base chemistry were activated more often than reaction-specific resources, such as conformational analyses or carbocation rearrangements. Although acid–base resources aid students in successfully analyzing reaction mechanisms, additional resources must be activated to rationalize specific mechanisms and to explain the products formed. This calls for instructors to provide formative and summative assessments that evaluate the many resources required to elucidate elimination reaction mechanisms and product stereochemistry.

Interaction of α‐Cembrenediol with Human Serum Albumin Based on Spectroscopic and Computational Analyses

α‐Cembrenediol exhibits a wide range of biological activities, including antibacterial, antitumor, and neuroprotective effects. However, knowledge of the absorption, transport, and release of α‐cembrenediol in drug metabolism within the body is currently limited. Therefore, we aimed to gain a comprehensive understanding of the in vivo transport, distribution, and elimination mechanisms of α‐cembrenediol and investigate the interaction between α‐cembrenediol and HSA. To this end, we utilized various methods including UV absorption spectroscopy, steady‐state fluorescence analysis, circular dichroism measurements, molecular docking studies, and molecular dynamics simulations. The results of the UV and fluorescence spectra clearly demonstrated that HSA interacts with α‐cembrenediol. Specifically, the fluorescence spectra results showed that at a temperature of 310 K, the fluorescence quenching constant (KSV) and binding constant (Kb) between HSA and α‐cembrenediol were determined to be 1.28 × 103 Lmol−1 and 218.27 Lmol−1, respectively. As the temperature was decreased from 310 K to 293 K, both KSV and Kb values increased, indicating the presence of a static quenching mechanism throughout the interaction process. Moreover, the results indicated the presence of a singular, specific binding site for α‐cembrenediol on HSA, as evidenced by an approximate count of one binding site at all three temperatures. Additionally, this binding process occurred spontaneously (ΔG &lt; 0), with van der Waals interactions and hydrogen bonding as the primary driving forces (ΔH = −9.40 kJmol−1 and ΔS = −14.50 Jmol−1·K−1). The binding of α‐cembrenediol to Sudlow site I of HSA was confirmed through molecular docking, a combination of fluorescence probe substitution, and molecular dynamics simulation experiments. Moreover, the results demonstrated that α‐cembrenediol binding led to alterations in the structural conformation of HSA, as confirmed by three‐dimensional fluorescence, synchronous fluorescence, and circular dichroism spectra. This study offers crucial insights into the interaction between α‐cembrenediol and HSA, contributing to an improved understanding of the compound’s pharmacokinetic properties.

Application of Artificial Intelligence in Computer Neural Network Algorithm Technology in the Age of Big Data

Abstract The arrival of the big data era makes the amount of data explosive growth, which puts forward new challenges and demands for computer network technology, and the integration of big data and network technology has become an important trend. This paper uses the optimization strategy and the elimination mechanism of the genetic algorithm to optimize the inertia weight and particle position speed updating mechanism of the particle swarm algorithm and combines the searching method of the Tennessee whisker algorithm with the sharing mechanism of the particle swarm algorithm to achieve the optimal data searching ability. Finally, the improved artificial intelligence algorithm and MapReduce are combined to improve the performance of the computer neural network algorithm in big data processing. The average data redundancy rate of this paper’s algorithm for big data processing is only 1.18%, and the resource integration checking rate always exceeds 85%, according to simulation experiments. In addition, the algorithm also shows good performance in practical applications, and it can achieve accurate classification of big data labels in big data label classification tasks while maintaining a low energy overhead. Meanwhile, it can accurately recognize electronic medical record data in large medical databases. Big data processing can benefit greatly from the proposed neural network algorithm in this paper.

Dynamic Feature-Aware Visual Inertial Odometry in Dynamic Environments

The majority of current Simultaneous Localization and Mapping (SLAM) systems operate under the assumption that the surroundings of an autonomous mobile robot are static. This assumption is impractical in dynamic urban office settings and undermines the effectiveness of current SLAM algorithms. We propose an enhanced visual-inertial odometry system that can accurately locate the mobile robot position in real-time in semi-dynamic environments while showing superior performance. To ensure real-time performance, the front end of the algorithm combines ORB features with optical flow matching. To address dynamic feature mismatches in temporarily stationary environments, we design an outlier elimination mechanism based on feature reprojection and Random Sample Consensus (RANSAC) principles. During the feature tracking process before calculating the current robot pose, outliers are processed and eliminated based on the small-scale motion assumption between frames, mitigating the impact of dynamic and temporarily stationary objects and thus enhancing the robustness of the algorithm. The effectiveness and accuracy of the algorithm are validated through quantitative comparisons with state-of-the-art algorithms on the EuRoC dataset.

Transcriptional profiling of human peripheral blood mononuclear cells in household contacts of pulmonary tuberculosis patients provides insights into mechanisms of Mycobacterium tuberculosis control and elimination

ABSTRACT Household contacts (HHCs) of patients with active tuberculosis (ATB) are at higher risk of Mycobacterium tuberculosis (M. tuberculosis) infection. However, the immune factors responsible for different defense responses in HHCs are unknown. Hence, we aimed to evaluate transcriptome signatures in human peripheral blood mononuclear cells (PBMCs) of HHCs to aid risk stratification. We recruited 112 HHCs of ATB patients and followed them for 6 years. Among the HHCs, only 2 developed ATB, while the remaining HHCs were classified into three groups: (1) HHC-1 group (n = 23): HHCs with consistently positive T-SPOT.TB test, negative chest radiograph, and no clinical symptoms or evidence of ATB during the 6-year follow-up period; (2) HHC-2 group (n = 15): HHCs with an initial positive T-SPOT result that later became negative without evidence of ATB; (3) HHC-3 group (n = 14): HHCs with a consistently negative T-SPOT.TB test and no clinical or radiological evidence of ATB. HHC-2 and HHC-3 were combined as HHC-23 group for analysis. RNA sequencing (RNA-seq) in PBMCs, with and without purified protein derivative (PPD) stimulation, identified significant differences in gene signatures between HHC-1 and HHC-23. Gene ontology analysis revealed functions related to bacterial pathogens, leukocyte chemotaxis, and inflammatory and cytokine responses. Modules associated with clinical features in the HHC-23 group were linked to the IL-17 signaling pathway, ferroptosis, complement and coagulation cascades, and the TNF signaling pathway. Validation using real-time PCR confirmed key genes like ATG-7, CXCL-3, and TNFRSF1B associated with infection outcomes in HHCs. Our research enhances understanding of disease mechanisms in HHCs. HHCs with persistent latent tuberculosis infection (HHC-1) showed significantly different gene expression compared to HHCs with no M. tuberculosis infection (HHC-23). These findings can help identify HHCs at risk of developing ATB and guide targeted public health interventions.

Dual barrier system against xenomitochondrial contamination in mouse embryos

Heteroplasmic mammalian embryos between genetically distant species fail to develop to term, preventing transmission of xenomitochondrial DNA to progeny. However, there is no direct evidence indicating the mechanisms by which species specificity of the mitochondrial genome is ensured during mammalian development. Here, we have uncovered a two-step strategy underlying the prevention of xenomitochondrial DNA transmission in mouse embryos harboring bovine mitochondria (mtB-M embryos). First, mtB-M embryos showed metabolic disorder by transient increase of reactive oxygen species at the 4-cell stage, resulting in repressed development. Second, trophoblasts of mtB-M embryos led to implantation failure. Therefore, we tested cell aggregation with tetraploid embryos to compensate for the placentation of mtB-M embryos. The 14 mtB-M embryos harboring bovine mtDNAs developed to term at embryonic day 19.5. Taken together, our results show that contamination of bovine mtDNA is prohibited by embryonic lethality due to metabolic disruption and failure of placentation, suggesting these represent xenomitochondrial elimination mechanisms in mammalian embryos.

Nazarov reaction triggered by active lithium salts in cyclopentyl methyl ether

Abstract In this study, we developed a lithium-activated Nazarov reaction of highly activated divinyl ketones in the presence of mixed lithium salts by combining LiClO4 with either LiOMe or LiI in cyclopentyl methyl ether (CPME). This reaction produces LiOPh in the reaction mixture, exhibiting aspects of a Nazarov reaction in the presence of base. The additives LiOMe and LiI not only facilitated the dissolution of LiClO4 aggregates but also promoted the addition–elimination mechanism, thereby accelerating the reaction and preventing side reactions.