Effect of Electrolyte Conductivity on Controlled Electrochemical Synthesis of Zinc Oxide Nanotubes and Nanorods
Bibliographic record
Abstract
A one-step, catalyst- and seed-layer-free growth process is used to control the morphology of ZnO nanotubes and nanorods by modifying the electrolyte conductivity in an amperometric electrodeposition technique. This method does not require the use of O 2 bubbling or any etching step. ZnO nanotubes with high surface areas are found to form in less conductive electrolytes with monovalent anions (Cl –, NO 3 –, ClO 4 – ), and nanorods with smaller surface areas are produced in more conductive electrolytes with divalent anions (SO 4 2–, C 2 O 4 2– ), all mixed with ZnCl 2 at 80 °C. Our conductance measurements of the electrolytes confirm the important effect of the supporting electrolyte on controlling the observed morphologies and further suggest that ion diffusion in the electrolyte plays a key role in the growth mechanism of ZnO nanotubes and nanorods. In particular, ion diffusion in a more conducting electrolyte supported by divalent anions facilitates growth in the [0001] and [10–11] directions, with preferential growth in the [0001] direction therefore favoring one-dimensional or nanorod growth. On the other hand, in a less conducting electrolyte supported by monovalent anions, ion diffusion is sufficiently slow, which facilitates growth in the [0001] and [10–11] directions but with a higher contribution in the [10–11] direction due to termination of the (0001) plane by anion adsorption, leading to growth of the perimeter walls of the nanotubes. Furthermore, we demonstrate that the as-prepared ZnO nanotubes can be used as an effective photoanode material in a typical dye-sensitized solar cell application.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".