Interaction of Carboxyalkylated Cellulose Nanocrystals and Antibiotics
Bibliographic record
Abstract
Although antibiotics are beneficial for treating infections, their release into the environment has raised global concerns. In this work, the interactions of cellulose nanocrystal (CNC) derivatives with sulfamethoxazole (SMX), ciprofloxacin (CIP), and doxycycline (DOX) antibiotics were studied fundamentally. CNC was carboxyalkylated to bear different carbon chain lengths but similar negative charges on its surface. The highest level of adsorption of DOX on the carboxypantadecanated CNC (i.e., carboxyalkylated CNC with more carbon spacer, PCNC) occurred at pH 6.0, which was due to the electrostatic and π interactions along with hydrogen bonding. The contact angle and quartz crystal microbalance (QCM) adsorption analyses revealed a faster interaction and adsorption of DOX than other antibiotics on PCNC. The results also depicted the diffusion of DOX into the porous structure of CNC derivatives, especially that of PCNC. Also, a more compact adsorbed layer of DOX was formed on PCNC than on other CNC derivatives. Carboxyalkylation was observed to slightly reduce the surface area of CNC, while the antibiotic adsorption drastically increased the surface area of CNC due to their adsorption on the surface. XPS analysis revealed that carboxyalkylation significantly enhanced the C-C/C-H bond, while antibiotic adsorption on PCNC enhanced C-N/C-O and C-C/C-H bonds in antibiotic-loaded CNC samples. Overall, carboxyalkylated CNC was observed to have an outstanding affinity for capturing antibiotics, especially DOX, which could pave the way for the use of CNC in such applications that surface/antibiotic interactions were essential.
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.
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".