scholarly journals Problems in organic chemistry: A functional group approach (Daniels, Ralph; Bauer, Ludwig)

1964 ◽  
Vol 41 (8) ◽  
pp. 464
Author(s):  
Frank L. Lambert
Author(s):  
Jie Jack Li ◽  
Chris Limberakis ◽  
Derek A. Pflum

Searching for reaction in organic synthesis has been made much easier in the current age of computer databases. However, the dilemma now is which procedure one selects among the ocean of choices. Especially for novices in the laboratory, it becomes a daunting task to decide what reaction conditions to experiment with first in order to have the best chance of success. This collection intends to serve as an "older and wiser lab-mate" one could have by compiling many of the most commonly used experimental procedures in organic synthesis. With chapters that cover such topics as functional group manipulations, oxidation, reduction, and carbon-carbon bond formation, Modern Organic Synthesis in the Laboratory will be useful for both graduate students and professors in organic chemistry and medicinal chemists in the pharmaceutical and agrochemical industries.


Molecules ◽  
2021 ◽  
Vol 26 (2) ◽  
pp. 249
Author(s):  
Raquel G. Soengas ◽  
Humberto Rodríguez-Solla

The 1,3-butadiene motif is widely found in many natural products and drug candidates with relevant biological activities. Moreover, dienes are important targets for synthetic chemists, due to their ability to give access to a wide range of functional group transformations, including a broad range of C-C bond-forming processes. Therefore, the stereoselective preparation of dienes have attracted much attention over the past decades, and the search for new synthetic protocols continues unabated. The aim of this review is to give an overview of the diverse methodologies that have emerged in the last decade, with a focus on the synthetic processes that meet the requirements of efficiency and sustainability of modern organic chemistry.


2019 ◽  
Vol 99 ◽  
pp. 130-139 ◽  
Author(s):  
Chao Wang ◽  
Zhongguan Jiang ◽  
Lizhi Zhou ◽  
Bingguo Dai ◽  
Zhuoyan Song

1997 ◽  
Vol 101 (31) ◽  
pp. 5978-5995 ◽  
Author(s):  
Christopher L. Claypool ◽  
Francesco Faglioni ◽  
William A. Goddard ◽  
Harry B. Gray ◽  
Nathan S. Lewis ◽  
...  

2003 ◽  
Vol 43 (3) ◽  
pp. 1051-1057 ◽  
Author(s):  
Bo Kyeng Hou ◽  
Lawrence P. Wackett ◽  
Lynda B. M. Ellis

2020 ◽  
Author(s):  
Katherine M. Smith ◽  
Skyler Kern ◽  
Peter E. Hamlington ◽  
Marco Zavatarelli ◽  
Nadia Pinardi ◽  
...  

Abstract. We present a newly developed reduced-order biogeochemical flux model that is complex and flexible enough to capture open-ocean ecosystem dynamics, but reduced enough to incorporate into highly resolved numerical simulations with limited additional computational cost. The reduced-order model, which is derived from the full 56 state variable Biogeochemical Flux Model (BFM56; Vichi et al. (2007)), follows a biological and chemical functional group approach and allows for the development of critical non-Redfield nutrient ratios. Matter is expressed in units of carbon, nitrogen, and phosphate, following techniques used in more complex models. To reduce the overall computational cost and to focus on open-ocean conditions, the reduced model eliminates certain processes, such as benthic, silicate, and iron influences, and parameterizes others, such as the bacterial loop. The model explicitly tracks 17 state variables, divided into phytoplankton, zooplankton, dissolved organic matter, particulate organic matter, and nutrient groups. It is correspondingly called the Biogeochemical Flux Model 17 (BFM17). After providing a detailed description of BFM17, we couple it with the one-dimensional Princeton Ocean Model (POM) for validation using observational data from the Sargasso Sea. Results show good agreement with the observational data and with corresponding results from BFM56, including the ability to capture the subsurface chlorophyll maximum and bloom intensity. In comparison to previous reduced-order models of similar size, BFM17 provides improved correlations between model output and field data, indicating that significant improvements in the reproduction of in situ data can be achieved with a low number of variables, while maintaining the functional group approach.


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