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Record W3165736275 · doi:10.3390/molecules26113131

Solar Driven Photocatalytic Activity of Porphyrin Sensitized TiO2: Experimental and Computational Studies

2021· article· en· W3165736275 on OpenAlexafffund
S.O. Otieno, Anabel E. Lanterna, John Mack, Solomon Derese, Edith Amuhaya, Tebello Nyokong, J. C. Scaiano

Bibliographic record

VenueMolecules · 2021
Typearticle
Languageen
FieldEnergy
TopicAdvanced Photocatalysis Techniques
Canadian institutionsUniversity of Ottawa
FundersDepartment of Science and Technology, Ministry of Science and Technology, IndiaNational Research FoundationInternational Development Research Centre
KeywordsPorphyrinPhotocatalysisDensity functional theoryMaterials scienceSemiconductorVisible spectrumBand gapPhotochemistryOptoelectronicsSolar energyNanotechnologyChemical engineeringChemistryCatalysisComputational chemistryOrganic chemistryElectrical engineering

Abstract

fetched live from OpenAlex

The absence of a secure long-term sustainable energy supply is recognized as a major worldwide technological challenge. The generation of H2 through photocatalysis is an environmentally friendly alternative that can help solve the energy problem. Thus, the development of semiconductor materials that can absorb solar light is an attractive approach. TiO2 has a wide bandgap that suffers from no activity in the visible spectrum, limiting its use of solar radiation. In this research, the semiconductor absorption profile was extended into the visible region of the solar spectrum by preparing porphyrin-TiO2 (P-TiO2) composites of meso-tetra(4-bromophenyl)porphyrin (PP1) and meso-tetra(5-bromo-2-thienyl)porphyrin (PP2) and their In(III), Zn(II) and Ga(III) metal complexes. Density functional theory (DFT) and time-dependent density functional theory (TD-DFT) calculations were performed on the porphyrins to gain insight into their electron injection capability. The results demonstrate that P-TiO2 systems merit further in-depth study for applications that require efficient photocatalytic H2 generation.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.012
Threshold uncertainty score0.604

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.021
GPT teacher head0.308
Teacher spread0.287 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations15
Published2021
Admission routes2
Has abstractyes

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