Stepped‐Current‐Controlled Evolution of Cu(OH) <sub>2</sub> /CuO Tandem Nanostructure for Efficient Photothermal Conversion
Bibliographic record
Abstract
Abstract Photothermal conversion is an important compensation to the current energy system, which is capable of converting the sunlight into thermal energy. Copper serves as an excellent heat conductor but exhibits limited absorption over the solar spectrum, even with oxide coatings upon annealing. Herein, vertically aligned copper oxides are tailored on the copper surfaces by electrochemical anodization. The current transient renders a unique stepped profile, which corresponds to the structure evolution from a double‐layered stack, i.e., the Cu(OH) 2 nanowires sitting atop the CuO nanosheets, to a single‐layered CuO nanosheets. The as‐anodized CuO nanosheets impart strong light absorption in the range of 200–1200 nm. Under one sun illumination, the water rises up to 102.6 °C in 20 min inside copper tubes with the CuO coatings, as compared to the 60.6 °C without the coatings. Under frozen conditions, the CuO nanosheets also result in a rapid de‐icing process in just 700 s, in obvious contrast to the 1200 s for pristine copper. This is attributed to the high photothermal conversion efficiency of 73.6% for the CuO coatings, being more than doubled with respect to the copper. The photothermal coatings may find important applications in seawater desalination, evaporation‐induced electricity generation, hydrogen evolution reaction, etc.
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".