Nonlithographic Formation of Ta<sub>2</sub>O<sub>5</sub> Nanodimple Arrays Using Electrochemical Anodization and Their Use in Plasmonic Photocatalysis for Enhancement of Local Field and Catalytic Activity
Bibliographic record
Abstract
We demonstrate the formation of Ta 2 O 5 nanodimple arrays on technologically relevant non-native substrates through a simple anodization and annealing process. The anodizing voltage determines the pore diameter (25–60 nm), pore depth (2–9 nm), and rate of anodization (1–2 nm/s of Ta consumed). The formation of Ta dimples after delamination of Ta 2 O 5 nanotubes occurs within a range of voltages from 7 to 40 V. The conversion of dimples from Ta into Ta 2 O 5 changes the morphology of the nanodimples but does not impact dimple ordering. Electron energy loss spectroscopy indicated an electronic band gap of 4.5 eV and a bulk plasmon band with a maximum of 21.5 eV. Gold nanoparticles (Au NPs) were coated on Ta 2 O 5 nanodimple arrays by annealing sputtered Au thin films on Ta nanodimple arrays to simultaneously form Au NPs and convert Ta to Ta 2 O 5 . Au NPs produced this way showed a localized surface plasmon resonance maximum at 2.08 eV, red-shifted by ∼0.3 eV from the value in air or on SiO 2 substrates. Lumerical simulations suggest a partial embedding of the Au NPs to explain this magnitude of the red shift. The resulting plasmonic heterojunctions exhibited a significantly higher ensemble-averaged local field enhancement than Au NPs on quartz substrates and demonstrated much higher catalytic activity for the plasmon-driven photo-oxidation of p -aminothiophenol to p, p ′-dimercaptoazobenzene.
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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".