Back

Non-photosynthetic bacteria produce photocurrent mediated by NADH.

Shlosberg, Y.; Limwongyut, J.; Moreland, A. S.; Bazan, G. C.

2023-01-19 bioengineering
10.1101/2023.01.16.524302 bioRxiv
Show abstract

In recent years, the concern from the global climate change has driven an urgent need to develop clean energy technologies that do not involve combustion process that emit carbon into the atmosphere. A promising concept is microbial fuel cells that utilize bacteria as electron donors in a bio-electrochemical cell performing a direct electron transfer via conductive protein complexes or by secretion of redox active metabolites such as quinone or phenazine derivatives. In the case of photosynthetic bacteria (cyanobacteria) electrons can also be extracted from the photosynthetic pathway mediated mostly by NADH and NADPH. In this work, we show for the first time that the intact non-photosynthetic bacteria Escherichia coli can produce photocurrent that is enhanced upon addition of an exogenous electron mediator. Furthermore, we apply 2D-fluorescence measurement to show that NADH is released from the bacterial cells, which may apply as a native electron mediator in microbial fuel cells.

Matching journals

The top 9 journals account for 50% of the predicted probability mass.

1
Scientific Reports
○ 3612 papers in training set
Top 2%
12.9%
Similar papers in this journal
2
Biosensors and Bioelectronics
○ 57 papers in training set
Top 0.1%
12.1%
Similar papers in this journal
3
Frontiers in Bioengineering and Biotechnology
○ 98 papers in training set
Top 0.1%
8.0%
Similar papers in this journal
4
Applied and Environmental Microbiology
● 339 papers in training set
Top 2%
3.3%
Similar papers in this journal
5
RSC Advances
● 22 papers in training set
Top 0.1%
3.3%
Similar papers in this journal
6
Advanced Biology
○ 29 papers in training set
Top 0.1%
3.3%
7
PLOS ONE
● 5266 papers in training set
Top 35%
3.3%
8
Chemical Engineering Journal
○ 11 papers in training set
Top 0.1%
2.7%
9
Analytical Chemistry
● 218 papers in training set
Top 1%
2.5%
50% of probability mass above
10
Algal Research
○ 21 papers in training set
Top 0.1%
2.2%
11
International Journal of Molecular Sciences
○ 494 papers in training set
Top 7%
1.8%
12
Applied Microbiology and Biotechnology
○ 32 papers in training set
Top 0.4%
1.8%
13
ACS Applied Bio Materials
● 24 papers in training set
Top 0.4%
1.8%
14
Water Research
○ 79 papers in training set
Top 0.6%
1.8%
15
iScience
○ 1154 papers in training set
Top 22%
1.5%
16
ChemBioChem
○ 55 papers in training set
Top 0.8%
1.4%
17
Nature Communications
○ 5641 papers in training set
Top 48%
1.4%
18
Analytical and Bioanalytical Chemistry
○ 18 papers in training set
Top 0.2%
1.1%
19
Microbiology Spectrum
● 469 papers in training set
Top 8%
1.1%
20
Colloids and Surfaces B: Biointerfaces
○ 10 papers in training set
Top 0.2%
1.1%
21
Biochimica et Biophysica Acta (BBA) - Bioenergetics
○ 18 papers in training set
Top 0.2%
1.1%
22
Lab on a Chip
● 96 papers in training set
Top 0.9%
1.0%
23
Chemistry – A European Journal
○ 14 papers in training set
Top 0.2%
1.0%
24
The Journal of Physical Chemistry Letters
● 63 papers in training set
Top 0.7%
1.0%
25
ACS Synthetic Biology
● 287 papers in training set
Top 2%
0.9%
26
Communications Biology
○ 993 papers in training set
Top 29%
0.9%
27
Biotechnology and Bioengineering
○ 53 papers in training set
Top 0.9%
0.9%
28
Langmuir
● 36 papers in training set
Top 0.5%
0.9%
29
ACS Omega
● 105 papers in training set
Top 3%
0.9%
30
Heliyon
○ 152 papers in training set
Top 8%
0.9%

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.