Back

Illuminating the Potential: Light Harvesting and Semiconducting Properties of Bacterial Microcompartment Shell Proteins

Bedi, S.; Rose, S. M.; Sinha, S.

2024-12-20 bioengineering
10.1101/2024.12.16.628711 bioRxiv
Show abstract

Biomaterials with self-assembly, genetic tunability, self-healing, and biocompatibility hold promise for next-generation electronics. This study explores disc-like shell proteins from bacterial microcompartments as photovoltaic materials. These self-assembling proteins exhibit semiconducting properties, including a low work function and non-linear I-V behavior. Notably, under UV light, they generate significant photocurrent without external voltage, demonstrating efficient electron transfer. Electron conduction occurs via tunneling, enabled by distinct electron-rich surface regions. High responsivity and quantum efficiency outperform prior protein-based systems, emphasizing their potential in photocurrent and light-harvesting applications. Genetic mutations revealed a proton-coupled electron transfer (PCET) mechanism, where tyrosine residues strategically positioned near proton abstraction sites enhance photocurrent generation. This study highlights the innovative capabilities of shell proteins for energy-efficient devices and demonstrates how genetic engineering can further optimize biomaterial performance, paving the way for sustainable electronic applications.

Matching journals

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

1
Nano Letters
● 71 papers in training set
Top 0.1%
9.3%
Similar papers in this journal
2
Journal of the American Chemical Society
● 217 papers in training set
Top 0.4%
8.5%
Similar papers in this journal
3
Angewandte Chemie International Edition
○ 93 papers in training set
Top 0.2%
6.0%
Similar papers in this journal
4
Nature Communications
○ 5641 papers in training set
Top 28%
5.3%
Similar papers in this journal
5
ACS Nano
● 113 papers in training set
Top 0.4%
5.0%
Similar papers in this journal
6
Small
○ 78 papers in training set
Top 0.2%
4.7%
7
Advanced Functional Materials
○ 46 papers in training set
Top 0.3%
4.7%
8
ACS Applied Materials & Interfaces
● 39 papers in training set
Top 0.2%
3.9%
9
Advanced Science
○ 286 papers in training set
Top 3%
3.1%
50% of probability mass above
10
Nature Nanotechnology
○ 32 papers in training set
Top 0.2%
3.1%
11
ChemBioChem
○ 55 papers in training set
Top 0.4%
3.1%
12
ACS Applied Bio Materials
● 24 papers in training set
Top 0.2%
2.6%
13
Advanced Materials
○ 56 papers in training set
Top 0.5%
2.3%
14
Chemical Communications
● 25 papers in training set
Top 0.2%
2.3%
15
Science Advances
● 1243 papers in training set
Top 16%
2.3%
16
Nature Chemistry
○ 42 papers in training set
Top 0.4%
1.9%
17
Nanoscale
● 42 papers in training set
Top 0.3%
1.8%
18
ACS Sensors
● 49 papers in training set
Top 0.4%
1.7%
19
ACS Central Science
● 71 papers in training set
Top 0.8%
1.7%
20
Biomacromolecules
● 29 papers in training set
Top 0.3%
1.4%
21
ACS Synthetic Biology
● 287 papers in training set
Top 2%
1.4%
22
Scientific Reports
○ 3612 papers in training set
Top 59%
1.4%
23
Biosensors and Bioelectronics
○ 57 papers in training set
Top 0.5%
1.4%
24
ACS Omega
● 105 papers in training set
Top 2%
1.3%
25
Nanoscale Advances
● 15 papers in training set
Top 0.3%
1.1%
26
Communications Biology
○ 993 papers in training set
Top 24%
1.1%
27
Chemistry – A European Journal
○ 14 papers in training set
Top 0.2%
1.1%
28
Advanced Healthcare Materials
○ 85 papers in training set
Top 1%
1.1%
29
Langmuir
● 36 papers in training set
Top 0.5%
1.1%
30
Advanced Biology
○ 29 papers in training set
Top 0.7%
1.0%

"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.