Hot Hole Utilization in Au-TiO2 and Au-C3N4-TiO2 Core-Shell Heterojunctions for High Performance Photoelectrochemical Water Splitting
Bibliographic record
Abstract
Gold nanoparticles (Au NPs) coated with TiO2 shells exhibit strong localized surface plasmon resonance (LSPR) peaks at ~550-600 nm. The Au plasmons decay in femtoseconds through both interband and intraband damping processes to produce hot electron-hole pairs. These hot carriers have excess energy at room temperature which can be utilized to generate electricity or drive a chemical reaction. However, the hot carriers experience ultrafast recombination and thermal relaxation in hundreds of femtoseconds to a few picoseconds due to electron-electron scattering and collisions with phonons. A Schottky barrier heterojunction between Au and a n-type semiconductor such as TiO2 is an ideal method to separate the hot carrier pairs. In Au-TiO2 Schottky junctions, hot electrons are injected into TiO2 through thermionic emission and/or field emission before recombination and thermal equilibration, at timescales of roughly 250 fs. However the harvesting of residual hot holes in Au remains problematic partly because of the difficulty in forming Schottky junctions between Au and p-type semiconductors. The harvesting of hot holes is particularly important considering that hot holes in Au are on average, more energetic than hot electrons. In this work, we use an innovative photoanode architecture to harvest hot holes. Our photoanode architecture consists of Au NPs coated with a thin layer of amorphous TiO2 and deposited on transparent conductive oxide (TCO)-coated glass substrates. Hot electrons are extracted from the Au into the TCO contact through the TiO2 shell. Hot holes tunnel through the TiO2 to reach the alkaline electrolyte whether they oxidize hydroxyl ions and dissolved oxygen species to generate oxygen. Another novelty is the use of a layer of graphitic carbon nitride (g-C3N4) quantum dots to pump the plasmon through exciton-to-plasmon energy transfer and increase the production of hot carriers. g-C3N4 works to enhance the plasmonic light harvesting due to the excellent overlap between the emission of g-C3N4 and the LSPR absorption band of Au. Photocurrent densities as high as 3.7 mA/cm2 were achieved under AM1.5G one sun illumination with concomitant high Faradaic efficiencies. Photoelectrochemical action spectra collected using filtered AM1.5G illumination and monochromatic LEDs revealed the prominent role of visible photons in water-splitting performed using Au-C3N4-TiO2 core-shell ternary heterojunctions. A key goal moving forward is to replace the noble metal (Au) with transition metal nitride plasmonic absorbers while sustaining the high level of photoelectrochemical performance.
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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".