Exploring the Antibacterial Properties of Lignin‐coated Magnetic Nanoparticles Synthesized in a One‐pot Process<sup>†</sup>
Bibliographic record
Abstract
Given the current grave problems with antibiotic resistance, the discovery of novel, unconventional antibacterial drugs is not just important but also urgent. In this contribution, we report on the synthesis and testing of several composite nanomaterials that may find applications as therapeutic drugs or surface disinfectants. These materials are based on magnetic nanostructures coated with lignin, for example, lignin@FeCo. The magnetic properties of these nanocomposites facilitate removal or localization, whereas the lignin shell provides biocompatibility. These nanomaterials are mild antibacterials in the absence of light, but when illuminated become powerful antibacterial agents with typically ≥6 log units of bacterial reduction in 1-5 min of irradiation. These materials are strongly absorbing, including in the very useful NIR biological window, which we illustrate using 810 nm LED irradiation. We also show that in the short time required for antibacterial action, thermal changes are very small (≤5°C). Further, biocompatibility tests using fibroblasts show very limited cell damage and no enhanced adverse effect during 810 nm NIR illumination. As a surface coating for the active material, lignin provides a "trojan horse" strategy to facilitate the antibacterial action.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| 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) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".