Parsimonious Methodology for Synthesis of Silver and Copper Functionalized Cellulose
Bibliographic record
Abstract
Abstract Metal nanomaterials, such as silver and copper, are often incorporated into commercial textiles to take advantage of their antibacterial and antiviral properties. In this study eight different methods were employed to synthesize silver, copper, and silver/copper functionalized cotton batting textiles. Using silver and copper nitrate as precursors, different reagents were used to initiate/catalyze the deposition of metal, including: (1) no additive, (2) sodium bicarbonate, (3) green tea, (4) sodium hydroxide, (5) ammonia, (6,7) sodium hydroxide/ammonia at a 1:2 and 1:4 ratio, and (8) sodium borohydride. The use of sodium bicarbonate as a reagent to reduce silver onto cotton has not been used previously in literature and was compared to established methods. All synthesis methods were performed at 80°C for one hour following textile addition to the solutions. The products were characterized by X-ray fluorescence (XRF) analysis for quantitative determination of the metal content and X-ray absorption near edge structure (XANES) analysis for silver and copper speciation on the textile. Scanning electron microscopy (SEM) with energy dispersive X-ray (EDX) and size distribution inductively coupled plasma mass spectrometry (ICP-MS) were used to further characterize the products of the sodium bicarbonate, sodium hydroxide, and sodium borohydride synthesis methods following ashing of the textile. For the silver treatment methods (1 mM Ag+), sodium bicarbonate and sodium hydroxide resulted in the highest amounts of silver on the textile (8900 mg Ag/kg textile and 7600 mg Ag/kg textile) and for copper treatment (1 mM Cu+) the sodium hydroxide and sodium hydroxide/ammonium hydroxide resulted in the highest amounts of copper on the textile (3800 mg Ag/kg textile and 2500 mg Ag/kg textile). Formation of copper oxide was dependent on the pH of the solution, with 4 mM ammonia and other high pH solutions resulting in majority of the copper on the textile existing as copper oxide, with smaller amounts of ionic-bound copper.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".