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Record W4387187480 · doi:10.1149/11205.0041ecst

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

2023· article· en· W4387187480 on OpenAlexaff
Damini Vrushabendrakumar, Kazi M. Alam, Navneet Kumar, Narendra Chaulagain, Harshitha Rajashekhar, Saralyn Riddell, Karthik Shankar

Bibliographic record

VenueECS Transactions · 2023
Typearticle
Languageen
FieldEnergy
TopicAdvanced Photocatalysis Techniques
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsDivalentDopingStrontiumMaterials scienceElectrochemistryAtom (system on chip)Inorganic chemistryCharacterization (materials science)CobaltCatalysisAnalytical Chemistry (journal)NanotechnologyChemical engineeringPhysical chemistryChemistryOptoelectronicsElectrode

Abstract

fetched live from OpenAlex

We explored the selective surface doping of anodically formed TiO 2 nanotube arrays (TNTAs) with divalent Sr 2+ cations using an electrochemical cathodization process in order to modify the basicity of the surface and modify the interactions of the surface with vapor phase CO 2 . The resulting Sr-doped TNTAs showed a 3-fold increase in CO 2 photoreduction activity. Characterization using a broad suite of bulk- and surface-sensitive characterization techniques revealed the doped Sr atoms to be widely dispersed on the surface. Our results show a way forward to achieve single atom catalysts using cathodic doping of TiO 2 .

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.198
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.023
GPT teacher head0.277
Teacher spread0.254 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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