Multivalent Cu-Doped ZnO Nanoparticles with Full Solar Spectrum Absorbance and Enhanced Photoactivity
Bibliographic record
Abstract
Full solar spectrum absorbers are widely pursued for applications related to photocatalysis and photovoltaics. Here we report multivalent Cu-doped ZnO nanoparticles which exhibit full solar spectrum absorbance and high photoactivity. Metathesis-based, green-chemical approaches with synthesis yield of ∼100% are used. Cu incorporation in ZnO results in an increase of average solar spectrum absorbance from a mere 0.4% to 34%. On the other hand, (Zn, Cu)O composites result in materials with up to 64% average solar spectrum absorbance. Doped systems operate well under both visible and UV illumination. The nanomaterials prepared are characterized by using X-ray powder diffraction (XRD), scanning electron microscopy (SEM), Brunauer–Emmett–Teller (BET) surface area analysis, and X-ray photoelectron spectroscopy (XPS). Photocatalysts explored have particle sizes ≥50 nm. This is deliberately done in order to avoid the nanotoxic size regime of ZnO. Despite the large particle size and low specific surface area (<20 m 2 ·g –1 ), the best catalyst reported here compare favorably with recent reports on ZnO based systems. Using X-photoelectron spectroscopy and synthesis–property correlations, we infer that the presence of multivalent Cu (most likely in the form of Cu 1+δ O) on ZnO surface is responsible for the observed photoactivity enhancement.
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) | 0.000 | 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".