Combating Fuel Biocontamination: Tailored Antimicrobial Peptides and an Innovative Delivery Strategy
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Microbial invasion and subsequent fuel biocontamination have long posed significant challenges, leading to a significant infrastructural damage. The lack of systematic data on the correlation between environmental parameters and microbial growth has hampered the development of targeted solutions to date. To address this challenge, this study reports a targeted strategy to inactivate and control the proliferation of commonly identified fuel-contaminating microbial clusters through the development of synthetic peptides that can be delivered directly to fuel samples. From a library of short peptides which was designed based on the indolicidin template peptides, three unique sequences were found to have good broad-spectrum activity toward a range of microbes such as Bacillus, Sphingomonas, and Hormoconis, with P17, showing the highest killing potential. The structural analyses of the peptides based on circular dichroism spectroscopy revealed the helical propensity of the peptides in SDS micelles and a random flexible structure in solution. The peptides showed stability under biological conditions and minimal cytotoxicity against mammalian cells. This study presents an innovative method to effectively address fuel biocontamination using short peptides coupled with a potentially scalable protocol to administer the peptides to fuel samples.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it