Fabrication of Titania Nanotube Arrays in Viscous Electrolytes
Bibliographic record
Abstract
This study probes the dependence of titania nanotube arrays nanoarchitecture on different synthesis parameters in viscous electrolytes. Titania nanotube arrays were synthesized in glycerol, ethylene glycol and carboxymethylcellulose as base materials. The effects of anodization voltage and time, as well as chemical composition and pH of the electrolyte bath were studied. Nanotube arrays with an inner diameter ranging from 16 to 91 nm, and wall thickness ranging from 7 to 29 nm were fabricated in a glycerol-water electrolyte. Water content of 5 wt% or higher was found to be essential for nanotubes fabrication in glycerol electrolyte. Diameter and length were influenced by varying water content above 5 wt%. Nanotube length was found to be time dependent at high pH values. A pH value of 6 was favorable for fabrication of highly ordered and continuous nanotube arrays with length up to 900 nm. Using modified ethylene glycol (containing 2 wt% and 0.5 wt% NH4F) instead of glycerol, resulted in nanotube length up to 430 nm after 1.5 hr anodization time. With a minor modification of electrochemical anodization cell, we successfully fabricated double-sided titania nanotube arrays layers with a total thickness of 9.5 microm for the first time. Nantube arrays were successfully fabricated in 2 wt% sodium carboxy mythylcellulose aqueous electrolyte (CMC electrolyte). These nanotube arrays had an inner diameter of 42 nm similar to those fabricated in 2 wt% urea-ethylene glycol electrolyte but their length was 450 nm.
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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".