Preparation of Antibacterial Softwood via Chemical Attachment of Quaternary Ammonium Compounds Using Supercritical CO<sub>2</sub>
Bibliographic record
Abstract
Conversion of inexpensive and abundant softwood into useful construction materials is of significant economic and environmental importance. The currently available methods for wood treatment are not sufficiently effective to overcome the drawbacks of softwood including warping and biodeterioration. In the present research, a novel process was developed to address these challenges by chemically attaching antibacterial quaternary ammonium compounds (QACs) to hemlock using supercritical carbon dioxide (scCO 2 ). Nine QACs containing at least one hydroxyl group were synthesized and characterized by 1D and 2D nuclear magnetic resonance spectroscopy (NMR), attenuated total reflectance Fourier transform infrared spectroscopy (ATR-FTIR), and high resolution mass spectroscopy (HRMS). The antibacterial activity of these QACs were screened against the representative bacteria Escherichia coli, revealing that the antibacterial activity is dependent on the QAC molecular structure. Two QACs that demonstrated strong antibacterial activity were selected and chemically attached to hemlock by using hexamethylene diisocyanate (HDI) as a linker via a carbamate/urethane linkage in scCO 2 . The chemically modified hemlock demonstrated exceptional antibacterial activity and improved dimensional stability, suggesting potential application of this route in the conversion of softwood into advanced durable decking and fencing materials.
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".