Designing 1D Plasmonic Ag/CuWO <sub>4</sub> Nanocomposite for Enhancing Visible-Light Photoelectrochemical Performance
Bibliographic record
Abstract
We report the synthesis and characterization of CuWO 4 , its functionalization with plasmonic Ag nanostructures and its photoelectrochemical properties. First, a solution-phase polyvinylpyrrolidone (PVP)-assisted approach was used to prepare shape-controlled plasmonic Ag (nanoparticles (NPs) and nanowires (NWs)) via heterogeneous nucleation. The growth process and morphological tuning of the as-synthesized Ag nanostructures were investigated experimentally. Molecular dynamics (MD) simulations were used to understand the underlying principles that govern nanowire growth by analyzing the interaction energies between crystal surfaces and PVP as well as the atom density profile. Significant enhancements of the photocurrent (45% and 140%, respectively) at the thermodynamic potential for oxygen evolution (0.62 V vs Ag/AgCl) were obtained for Ag NP/CuWO 4 (0.11 mA cm −2 ) and Ag NW/CuWO 4 (0.18 mA cm −2 ) photoanodes, respectively, compared to pristine CuWO 4 photoanode. Moreover, the incorporation of Ag NWs significantly enhances the incident photon to current conversion efficiency (IPCE) across the 350–550 nm spectral range, revealing a maximum around 10%. The obtained improvement is attributed to improved light harvesting by Ag-induced surface plasmon resonance (SPR) effects with a dual peak absorption, together with more effective charge carrier transfer/separation. Therefore, incorporation of the as-prepared plasmonic nanostructures with CuWO 4 causes a considerable improvement of the photoelectrochemical activity for energy conversion/storage applications.
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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) | 0.000 | 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".