Synergistic Effect of Plasmonic Gold Nanoparticles Decorated Carbon Nanotubes in Quantum Dots/TiO<sub>2</sub> for Optoelectronic Devices
Bibliographic record
Abstract
Abstract Here, a facile approach to enhance the performance of solar‐driven photoelectrochemical (PEC) water splitting is described by means of the synergistic effects of a hybrid network of plasmonic Au nanoparticles (NPs) decorated on multiwalled carbon nanotubes (CNTs). The device based on TiO2–Au:CNTs hybrid network sensitized with colloidal CdSe/(CdSexS1−x)5/(CdS)1 core/alloyed shell quantum dots (QDs) yields a saturated photocurrent density of 16.10 ± 0.10 mA cm−2 [at 1.0 V vs reversible hydrogen electrode (RHE)] under 1 sun illumination (AM 1.5G, 100 mW cm−2), which is ≈26% higher than the control device. The in‐depth mechanism behind this significant improvement is revealed through a combined experimental and theoretical analysis for QDs/TiO2–Au:CNTs hybrid network and demonstrates the multifaceted impact of plasmonic Au NPs and CNTs: i) hot‐electron injection from Au NPs into CNTs and TiO2; ii) near‐field enhancement of the QDs absorption and carrier generation/separation processes by the plasmonic Au NPs; iii) enhanced photoinjected electron transport due to the highly directional pathways offered by CNTs. These results provide fundamental insights on the properties of QDs/TiO2–Au:CNTs hybrid network, and highlights the possibility to improve the performance of other solar technologies.
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".