Formation of ROS-Generating Nitrogen-Species in Bacteria-Derived Carbon Quantum Dots
Bibliographic record
Abstract
Bacteria are rapidly emerging as an intriguing, natural carbon source for the synthesis of carbon quantum dots (CQDs) [1,2]. Bacteria-derived CQDs have been proposed for microbial live/dead differentiation, environmental pollutant detection and infectious biofilm control. Anti-biofilm properties of bacteria-derived CQDs are due to the generation of reactiveoxygen-species (ROS) [3], but the specific nitrogen-species responsible for ROS-generation by bacteria-derived CQDs are unknown. Equally, the chemical components of source-bacteria yielding optimal ROS-generation by bacteria-derived CQDs are unknown. To address these open questions, CQDs were prepared by hydrothermal-carbonization of different strains of bacteria. Formation of low-yield CQDs (diameter 2-3 nm) was confirmed using UV-vis absorption and fluorescence emission spectroscopy. Amide bands characteristic of proteins in Fourier Transform InfraRed (FTIR) spectra of source-bacteria remained visible upon hydrothermal-carbonization in bacteria-derived CQDs as minor bands. X-ray Photoelectron Spectroscopy (XPS) indicated an N1s photo-electron binding energy peak at 399.5 eV in source-bacteria due to amines that were converted upon carbonization into pyrrolic (400.5 eV) and graphitic (401.8 eV) nitrogen in bacteria-derived CQDs. Considering the occurrence of amines in proteinaceous amide bonds, the combination of FTIR and XPS results demonstrates that bacterial proteins are converted into pyrrolic and graphitic nitrogen-species upon hydrothermal-carbonization, as confirmed by relations between the occurrence of amine nitrogen in source-bacteria with pyrrolic and graphitic nitrogen in CQDs. Relations between ROS-generation with the occurrence of pyrrolic and graphitic nitrogen-species, identified these nitrogen-species in bacteria-derived CQDs as being responsible for enhanced ROS-generation and accompanying antibiofilm activity. These findings enable selection of source-bacteria with optimized ROS-generation and anti-biofilm activity upon carbonization based on their protein content.
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How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".