Synthesis and Characterization of Metal- and Non-Metal-Doped Titania Nanotubes for Solar Hydrogen Generation and Methanol Electrooxidation
Bibliographic record
Abstract
Functional nanostructured TiO2 materials with unique electrical, chemical and catalytic properties are being increasingly used in many applications, including lithium-ion batteries, gas sensors, hydrogen generation, electrocatalysis and photocatalysis. A number of different pathways and processes have been developed and reported to synthesize titania nanotubes (TNTs), nanorodes, and nanospheres. The most common methods include sol-gel, hydrothermal treatment, electrophoretic deposition and electrochemical anodization. The latter technique has been utilized by many researchers to fabricate highly ordered titania nanotubes and, subsequently, doped with both metals and non-metals. The last step—inclusion of metals and non-metals into the TNT matrix—is critical for solar hydrogen generation since pure titaina is insufficient under visible light due to its wide band gap, which is around 3.2 eV. Furthermore, metal oxides such as titanium dioxide suffer from lower surface area and electrical conductivity compared with carbon-based materials for use in direct methanol fuel cells. Accordingly, they are often combined with carbon-based materials and/or transition metals to form oxide-carbon hybrid structures. Highly ordered titania nanotubes (Figure 1) were fabricated employing a simple electrochemical anodization technique in a conventional two-electrode cell with titanium foil and graphite as working and counter electrodes, respectively. Metals, including nickel and platinum, were electrodeposited on TNT or TNT-C materials using a pulsed current electrodeposition technique [1]. All samples were then characterized by scanning electron microscopy, X-ray powder diffractometry, cyclic voltammetry, and chronoamperometry. Structural examination of the samples confirmed the presence of rutile and anatase nanocrystalline TNTs after annealing at different temperatures and durations. The presence of nanoparticles, including platinum and nickel, were also confirmed. For methanol electrooxidation, the activity and durability of the hybrid catalyst layers were determined in a solution containing one molar sulfuric acid and one molar methanol. The hybrid catalyst layer showed a better performance towards methanol oxidation compared with state-of-the-art carbon-supported platinum layers. For solar hydrogen generation, the efficiency of the photoanodes was found to increase as the amount of dopant increased to 3.0%. Further increase in dopant, however, resulted in a decrease in efficiency, possibly due to a higher rate of electron-hole recombination. Reference 1. S. Karimi & F.R. Foulkes, Electrochemistry Communications 19 (2012) 17-20
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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 | 0.001 | 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".