Harvesting Lost Photons: Plasmon and Upconversion Enhanced Broadband Photocatalytic Activity in Core@Shell Microspheres Based on Lanthanide‐Doped NaYF<sub>4</sub>, TiO<sub>2</sub>, and Au
Bibliographic record
Abstract
Efficiently harvesting solar energy for photocatalysis remains very challenging. Rational design of architectures by combining nanocomponents of radically different properties, for example, plasmonic, upconversion, and photocatalytic properties, offers a promising route to improve solar energy utilization. Herein, the synthesis of novel, plasmonic Au nanoparticle decorated NaYF4:Yb3+, Er3+, Tm3+‐core@porous‐TiO2‐shell microspheres is reported. They exhibit high surface area, good stability, broadband absorption from ultraviolet to near infrared, and excellent photocatalytic activity, significantly better than the benchmark P25 TiO2. The enhanced activity is attributed to synergistic effects from nanocomponents arranged into the nanostructured architecture in such a way that favors the efficient charge/energy transfer among nanocomponents and largely reduced charge recombination. Optical and energy‐transfer properties are modeled theoretically to support our interpretations of catalytic mechanisms. In addition to yielding novel materials and interesting properties, the current work provides physical insights that can contribute to the future development of plasmon‐enhanced broadband catalysts.
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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) | 0.001 | 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".