Growth of Thiobacillus ferrooxidans on various substrates

1969 ◽  
Vol 15 (1) ◽  
pp. 135-138 ◽  
Author(s):  
C. J. M. McGoran ◽  
D. W. Duncan ◽  
C. C. Walden
Keyword(s):  
Ferrous Iron ◽  
Generation Time ◽  
Bacterial Population ◽  
Thiobacillus Ferrooxidans ◽  
Growth Curves ◽  
Insoluble Material ◽  
Growth System ◽  
Ph Range

When Thiobacillus ferrooxidans was grown on ferrous iron and chalcopyrite (CuFeS2) in excess of 96% of the bacterial population was associated with the insoluble material. When sulfur was the substrate 77% of the bacteria were so associated. This necessitated consideration of the complete growth system to obtain accurate growth curves. By using total bacterial nitrogen as the measure of growth, it was shown that T. ferrooxidans had a minimum generation time of 6.5 to 10 hours on a ferrous iron substrate, 7 to 8 days on a sulfur substrate, and 14 to 17 hours on a chalcopyrite substrate. The pH range for growth was dependent on the substrate used.

Effects of the oxygen transfer rate on ferrous iron oxidation by Thiobacillus ferrooxidans

1998 ◽  
Vol 23 (7-8) ◽  
pp. 427-431 ◽  
Author(s):  
D.S Savić ◽  
V.B Veljković ◽  
M.L Lazić ◽  
M.M Vrvić ◽  
J.I Vučetić
Keyword(s):  
Oxygen Transfer ◽  
Ferrous Iron ◽  
Transfer Rate ◽  
Thiobacillus Ferrooxidans ◽  
Iron Oxidation ◽  
Oxygen Transfer Rate ◽  
Ferrous Iron Oxidation

scholarly journals Oxidation of Ferrous Iron and Elemental Sulfur by Thiobacillus ferrooxidans

1988 ◽  
Vol 54 (7) ◽  
pp. 1694-1699 ◽  
Author(s):  
Romilio T. Espejo ◽  
Blanca Escobar ◽  
Eugenia Jedlicki ◽  
Paulina Uribe ◽  
Ricardo Badilla-Ohlbaum
Keyword(s):  
Ferrous Iron ◽  
Elemental Sulfur ◽  
Thiobacillus Ferrooxidans

A New Operation of Carbohydrate-Containing Wastewater Treatment in an Anaerobic Fluidized Bed System

1992 ◽  
Vol 26 (9-11) ◽  
pp. 2453-2456 ◽  
Author(s):  
A. Matsumoto ◽  
M. Sakamoto ◽  
T. Noike
Keyword(s):  
Anaerobic Digestion ◽  
Fluidized Bed ◽  
Ph Value ◽  
Type Systems ◽  
Growth Type ◽  
Attached Growth ◽  
Optimum Ph ◽  
Effects Of Ph ◽  
Growth System ◽  
Ph Range

The effects of pH on the characteristics of conversion of starch to methane in anaerobic digestion were investigated by using a laboratory-scale anaerobic fluidized bed reactor over the pH range of 5.8-7.0. Furthermore, a new operation method was proposed to treat wastewater containing starch effectively. At pH 6.2, starch was decomposed well and methane was also formed well. The phenomenon showed methanogenesis in an attached growth system developed more tolerance for lower pH than in suspended growth type systems. After all, pH 6.2 was the optimum pH value for conversion of starch tomethane in our experiment. From these results, it is proposed that the more effective anaerobic digestion is made possible by the operation at low pH (for example 6.2) which accelerates acidogenesis and doesn't inhibit methanogenesis in attached growth systems.

Bacterial Normalized Binary Fission Growth Model

2021 ◽  
Author(s):  
John P Keady
Keyword(s):  
Growth Model ◽  
Bacterial Growth ◽  
Bacterial Population ◽  
Size Range ◽  
Control Sample ◽  
Growth Curves ◽  
Growth Cycle ◽  
Binary Fission ◽  
Bacterial Proliferation

Mathematical models have traditionally been used to facilitate the interpretation of bacterial growth curves in order to more accurately understand and identify variations in bacterial proliferation. Here, a binary fission growth model was developed to normalize starting bacterial levels, allowing for the identification of changes in bacterial growth and the separation of a bacterial population as it correlates to size. This normalized binary fission model (NBF) relies on a multi-bin growth mode, where each bin is associated with a size range during a growth cycle. The proposed NBF model allows for a determination of the percentage of treated bacteria eradicated compared to a control sample, either generally across all bacterial binary fission sizes or specific to a size range or bin. Comparisons between the NBF model and experimental observations demonstrates that bacterial growth curves, and the ratio of sample growth to a control, can be used to both determine and normalize initial variations in bacterial size, and quantity, among test samples, as well as identify final nutrient levels and the percentage of bacteria affected by treatment.

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