Chiral iron(III)-salen-catalyzed oxidation of hydrocarbons

Biswas, Achintesh Narayan; Das, Purak; Kandar, Sujit Kumar; Agarwala, Arunava; Bandyopadhyay, Debkumar; Bandyopadhyay, Pinaki

doi:10.1016/j.catcom.2008.11.023

Cited by 9 publications

(5 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Thus, conversions obtained are modest but the formation of cyclohexene oxide without serious competition from the production of cyclohexenol and cyclohexenone is noteworthy as it is known that cyclohexene is prone to allylic oxidation. This result is in contrast with the chiral iron(III)-salen catalyzed oxidation [30] of cyclohexene, where allylic oxidation dominates over epoxidation with much lower selectivity.…”

Section: Resultscontrasting

confidence: 77%

Chiral Mn(III) salen catalyzed oxidation of hydrocarbons

Biswas

Das

Kandar

et al. 2010

Transition Met Chem

Self Cite

View full text Add to dashboard Cite

Four chiral manganese(III)-salen complexes (1-4) were employed as catalysts in the oxidation of hydrocarbons at room temperature using pentafluoroiodosylbenzene as terminal oxidant. The reactions were carried out in acetonitrile and dichloromethane. Norbornene has been selectively oxidized to exo-epoxynorborane in 85% yield. At room temperature, oxygenation of cyclohexane up to 14% in acetonitrile medium has also been achieved.

show abstract

Section: Resultscontrasting

confidence: 77%

Chiral Mn(III) salen catalyzed oxidation of hydrocarbons

Biswas

Das

Kandar

et al. 2010

Transition Met Chem

Self Cite

View full text Add to dashboard Cite

show abstract

“…Higher yields of up to 23% can be achieved using PhIO as the oxidant and catalysts bearing chiral salen ligands of type 42a-42d ( Figure 9). 115 Similarly to iron porphyrin complexes this increase in reactivity was attributed to the introduction of electron withdrawing substituents. However, both examples exhibit a lack of selectivity regarding the formation of alcohols and ketones as indicated by low A/K ratios.…”

Section: Iron Nonheme Complexes Bearing C- O-and S-donor Ligandsmentioning

confidence: 99%

Molecular iron complexes as catalysts for selective C–H bond oxygenation reactions

2015

View full text Add to dashboard Cite

The selective oxygenation of C-H bonds is a promising and interesting task for both academia and chemical industry. Inspired by the efficiency and selectivity of naturally occurring enzymes, iron heme and nonheme complexes have proven to be valuable candidates for the development of environmentally friendly catalysts as alternative to traditional systems. This feature article summarises developments of the last decade regarding oxygenation reactions of aliphatic and aromatic substrates with various oxidants catalysed by molecular iron complexes. With a special focus on the catalytic performance of the systems, both the potential and the limitations as well as mechanistic aspects are discussed in some detail.

show abstract

“…The authors compare the performance of various CNN architectures, including AlexNet, VGGNet, and ResNet, in terms of accuracy and sensitivity. They discuss the strengths and limitations of each model and provide insights into the potential of deep-learning approaches for diabetic retinopathy recognition [2] [10].…”

Section: Existing Methodsmentioning

confidence: 99%

Performance Comparison of CNN and DNN Algorithms for Automation of Diabetic Retinopathy Disease

Sankara Babu,

Mandapati,

Mandapati

et al. 2023

E3S Web Conf.

View full text Add to dashboard Cite

Automation of medical image analysis helps medical practitioners to ensure early detection of certain diseases. Diabetic Retinopathy (DR) is a widespread condition of diabetes mellitus and a main global cause of vision impairment. The manual diagnosis of diabetic retinopathy by ophthalmologists requires a significant amount of time, causing inconvenience and discomfort for patients. However, the use of automated technology makes it possible to quickly identify diabetic retinopathy, permitting the continuation of therapy without interruption and averting future ocular damage. This paper presents a comprehensive comparative analysis of six Convolutional Neural Networks and Deep Neural Networks based machine learning models, including simple CNN, VGG16, DenseNet121, ResNet50, InceptionV3, and EfficientNetB3, for the recognition of diabetic retinopathy using fundus photographs. The accuracy of various models is evaluated using the Cohen Kappa metric. The results of this study add a contribution to the research on the application of machine learning models for diagnosing diabetic retinopathy.

show abstract

Chiral iron(III)-salen-catalyzed oxidation of hydrocarbons

Cited by 9 publications

References 24 publications

Chiral Mn(III) salen catalyzed oxidation of hydrocarbons

Chiral Mn(III) salen catalyzed oxidation of hydrocarbons

Molecular iron complexes as catalysts for selective C–H bond oxygenation reactions

Performance Comparison of CNN and DNN Algorithms for Automation of Diabetic Retinopathy Disease

Contact Info

Product

Resources

About