Incorporating TiO2 nanotubes with a peptide of D-amino K122-4 (D) for enhanced mechanical and photocatalytic properties
Bibliographic record
Abstract
Titanium dioxide (TiO2) nanotubes are promising for a wide variety of potential applications in energy, biomedical and environmental sectors. However, their low mechanical strength and wide band gap limit their widespread technological use. This article reports our recent efforts to increase the mechanical strength of TiO2 nanotubes with lowered band gap by immobilizing a peptide of D-amino K122-4 (D) onto the nanotubes. Topographies and chemical compositions of the peptide-coated and uncoated TiO2 nanotubular arrays were characterized by scanning electron microscopy and X-ray photoelectron spectroscopy (XPS). Properties of the peptide-coated and uncoated TiO2 nanotubular arrays, including hardness, elastic modulus, electron work function and photocurrent, were evaluated using micromechanical probe, Kelvin Probe and electrochemical system. Effect of the peptide on surface conductivity was also investigated through current mapping and I-V curve analysis with conductive atomic force microscopy. It is demonstrated that the peptide coating simultaneously enhances the mechanical strength, photocatalytic and electrical properties of TiO2 nanotubes.
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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) | 0.000 | 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".