Enhancement in the Rate of CO<sub>2</sub> Photoreduction Using Divalent Strontium Cation (Sr<sup>2+</sup>) Doped TiO<sub>2</sub> Nanotube Arrays
Bibliographic record
Abstract
The sunlight-assisted conversion of CO2 into portable value-added products has the potential to address both the energy and environmental challenges faced by the world. Therefore, we currently witness intense research activity and substantial progress in the field of CO2 photoreduction. Anatase TiO2 nanotube arrays (TNTAs) formed by electrochemical anodization constitute a well studied semiconductor photocatalyst with excellent optoelectronic properties and photocatalytic behavior [1, 2]. Yet, many aspects related to structure, surface composition, adsorbates, reaction intermediates, rate of reaction, multi-step electron transfer steps, etc. remain poorly understood even for TiO2. This led to our interest in forming surface coatings and bulk nanocomposites of TNTAs with metal oxides of Sr, Mg, Ca, Zn etc, which provide basic sites on the surface of the catalyst for the chemisorption of CO2 molecules. The higher basicity of hydroxyl groups associated with alkaline earth metals generate carbonate and bicarbonate adsorbates which enhance the reactivity of CO2 [3]. Divalent metal cations are also known to influence and modify the crystallographic texture of TiO2 during the annealing treatment step at elevated temperature [4].Here we performed divalent strontium cation (Sr2+) doping of anodically grown TNTAs using two different techniques – an electrochemical cathodic treatment in Sr2+-containing electrolytes and a wet impregnation technique using an aqueoussolution of strontium salts. The X-ray diffraction pattern and Raman spectra indicate the formation of Sr compounds at the TNTA surface. Sr-doping results in modification of the crystallographic texture of anatase phase TNTAs, increased lattice strain, reduced crystal size, and phonon confinement. Subtle changes are observed in the infrared spectra of the CO2 adsorbed on the Sr-doped TNTAs specifying a larger prevalence of monodentate and bidentate carbonate species on the surface. This attributes to the higher alkalinity of surface hydroxyls bound to Sr in comparison to the Ti-bound hydroxyls. In addition to linearly adsorbed CO2, a smaller population of bent CO2 molecules on the surface is observed, which suggests an enhanced stabilization of the carbon dioxide anion radical on the Sr-doped TNTA surfaces. Finally, under AM1.5G one sun illumination, the cathodized TNTAs produce 25 µmolg-1hr-1 whilethe wet impregnated Sr-doped TNTAs produce 17 µmolg-1hr-1 of CO which are nearly 3 and 2 times the amount of CO generated by bare TNTAs (7.7 µmolg-1hr-1) respectively. REFERENCES: Vahidzadeh, Ehsan, et al. "Asymmetric multipole plasmon-mediated catalysis shifts the product selectivity of CO2 photoreduction toward C2+ products." ACS Applied Materials & Interfaces13.6 (2021): 7248-7258. Zeng, Sheng, et al. "Optical control of selectivity of high rate CO2 photoreduction via interband-or hot electron Z-scheme reaction pathways in Au-TiO2 plasmonic photonic crystal photocatalyst." Applied Catalysis B: Environmental267 (2020): 118644. Kwon, Stephanie, et al. "Alkaline-earth metal-oxide overlayers on TiO 2: application toward CO 2 photoreduction." Catalysis Science & Technology6.21 (2016): 7885-7895. Kisslinger, Ryan, et al. "Preferentially oriented TiO2 nanotube arrays on non-native substrates and their improved performance as electron transporting layer in halide perovskite solar cells." Nanotechnology30.20 (2019): 204003.
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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 | 0.001 | 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".