Silver‐doped carbon fibers at low loading capacity that display high antibacterial properties
Bibliographic record
Abstract
Abstract BACKGROUND Renewable resources have been considered for producing many advanced materials. In this respect, silver‐doped carbon‐based materials with a low loading capacity and high antibacterial activity can also be generated via environmentally friendly routes and from renewable resources. RESULTS Silver‐doped carbon fibers were prepared from wood and cotton fibers via complexation and reduction reactions for depositing silver on the fibers and subsequently via carbonization of the loaded fibers. Silver loading before carbonization enhanced the stability of silver on the carbonized fibers, decreased silver loss (from 30 to 12%) and lowered the silver loading (5.66%) needed to achieve excellent antibacterial activity. However, wood‐based silver‐doped carbon fibers performed better than cotton‐based carbon fibers with respect to loss of antibacterial activity (12% at a silver loading of 5.66% versus 24% at a silver loading of 7.97% in 800 h respectively). These results are attributed to more uniformity in the distribution of silver nanoparticles in the porous structure of wood fibers than in cotton fibers. CONCLUSION The wood‐based carbon fibers exhibited better antibacterial activity and stronger affinity for maintaining silver and lower required silver loadings, and the sequence of silver loading and carbonization was crucial in developing silver‐loaded carbon fibers with good antibacterial performance. © 2019 Society of Chemical Industry
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".