Anodic Cu<sub>2</sub>S and CuS nanorod and nanowall arrays: preparation, properties and application in CO<sub>2</sub> photoreduction
Bibliographic record
Abstract
Copper sulfide semiconductors made from earth-abundant elements have an optical absorption edge at ca. 1.2 eV, nearly ideal for solar energy harvesting. We report the growth and formation mechanism of vertically oriented arrays of copper sulfide nanostructures formed by electrochemical anodization. Key parameters that affect the morphology and phase of the nanostructures are type and strength of electrolyte, anodization voltage and duration. Cu₂S and CuS nanostructures were obtained on both copper foil and copper-coated flexible Kapton substrates, and depending on the anodization parameters, consisted of vertically oriented arrays of nanowalls, nanoleafs or rods with branched nanodendrites. The anodization parameters also controlled the phase and stoichiometry of the nanostructures. p-type conduction for Cu₂S nanostructures and n-type conduction for CuS nanostructures were revealed by admittance spectroscopy and Mott Schottky analysis. We also observed a weak, but nevertheless promising and previously unnoticed, photocatalytic action in copper sulfide nanorod and platelet arrays for the sunlight-driven conversion of CO₂ into CH₄. Under irradiation by AM 1.5G simulated sunlight at room temperature, a CH₄ production rate as high as 38 μmol m(-2) h(-1) was obtained using the copper sulfide nanostructure arrays as stand-alone photocatalysts for CO₂ photoreduction.
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 | 0.000 |
| 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".