Cathodically Inserted, Widely Dispersed Sr<sup>2+</sup> Surface Dopants Produce a Threefold Improvement in the CO<sub>2</sub> Photoreduction Activity of TiO<sub>2</sub> Nanotube Arrays
Bibliographic record
Abstract
Strontium surface doping of anodic TiO 2 nanotube arrays (TNTAs) is performed using an electrochemical cathodization method in an Sr 2+ -containing electrolyte. The doped strontium does not result in either phase-segregated Sr, SrO, or SrTiO 3 . Instead, Sr-doping results in modification of the crystallographic texture of anatase phase TNTAs, reduced crystal size, and increased lattice strain. Subtle changes are observed in the Fourier-transform infrared spectra (FTIR) of the carbon dioxide (CO 2 ) adsorbed on the Sr-cathodized TNTA (Sr-C-TNTA), indicating a larger prevalence of monodentate carbonate and bidentate bicarbonate species on the surface. This is attributed to the higher alkalinity of surface hydroxyls bound to Sr in comparison to that of the Ti-bound hydroxyls. In addition to linearly adsorbed CO 2, a population of bridging bidentate carbonate adsorbate is observed, suggesting an enhanced stabilization of the CO 2 anion radical on the Sr-C-TNTA surfaces. Under AM1.5G 1 sun illumination, the Sr-C-TNTAs produce 25.0 μmol g –1 hr –1 carbon monoxide (CO), a greater than threefold improvement over the amount of CO generated by bare TNTAs (7.7 μmol g –1 hr –1 ). The spectroscopic characterization data are consistent with high-entropy surface-doping, creating relatively isolated and thermally stable Sr atoms on the surface of TiO 2 . These results highlight the potential of electrochemical cathodization in achieving high-entropy surface-doped semiconductors and single-atom catalysts.
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".